Sector Interviews – Discussion Points
38551
Africa Region
WORKING PAPER SERIES NO. 100
The Impact of Morbidity and Mortality on Municipal Human Resources and Service Delivery
An Analysis of 3 African Cities
Zara Sarzin
Water and Urban, East and Southern Africa (AFTU1)
September 2006
The Impact of Morbidity and Mortality on Municipal Human Resources and Service Delivery: An Analysis of 3 African Cities
Africa Region
WORKING PAPER SERIES NO. 100
SEPTEMBER 2006
Abstract
The Africa Region Working Paper Series expedites dissemination of applied research and policy studies with potential for improving economic performance and social conditions in Sub-Saharan Africa. The series publishes papers at preliminary stages to stimulate timely discussions within the Region and among client countries, donors, and the policy research community. The editorial board for the series consists of representatives from professional families appointed by the Region’s Sector Directors. For additional information, please contact Momar Gueye, (82220), Email: mgueye@ or visit the Web Site: .
The findings, interpretations, and conclusions in this paper are those of the authors. They do not necessarily represent the views of the World Bank, its Executive Directors, or the countries that they represent and should not be attributed to them.
Table of Contents
1 Table of Contents 3
2 Acknowledgements 4
3 Abbreviations and Acronyms 5
4 Executive Summary 6
5 Introduction 8
5.1 Purpose and Rationale for the Study 8
5.2 Scope 11
6 Municipal Profiles 12
6.1 High Level Comparison of the Three Municipalities 13
6.2 Kampala City Council (KCC) 14
6.3 Ilala Municipal Council (IMC) 16
6.4 City Council of Nairobi (CCN) 18
7 Burden of Disease and Staff Deaths 21
7.1 Leading Causes of Morbidity and Mortality 21
7.2 Mortality of Municipal Workers 26
8 Data and Methodology 30
8.1 Data Collection 30
8.2 Incidence versus Prevalence Approach 31
8.3 Analysis of the Municipal Workforce 32
8.4 Estimating the Cost of a New HIV Infection 32
8.5 Modelling the Demographic Impact of HIV/AIDS in the Workplace 36
8.6 Estimating Aggregate Costs dues to HIV/AIDS 37
8.7 Illustrating the Impact of HIV/AIDS Prevention and Treatment Programmes 37
8.8 Estimating the Cost of Morbidity 38
8.9 Illustrating the Impact of Malaria Prevention Programmes 39
8.10 Full Assumption Set 39
8.11 Questioning the Theoretical Framework 39
9 Results 41
9.1 Cost of a New HIV Infection 41
9.2 Aggregate Impact of HIV/AIDS in the Workplace 43
9.3 Cost of Morbidity 44
9.4 Impact of Workplace Interventions 45
10 Conclusions and Recommendations 52
10.1 Accessing National Level Funds for Workplace Health Interventions 57
10.2 Dissemination of Findings and Areas for Further Research 58
11 References 59
12 Annex A: Acknowledgements 62
13 Annex B: Human Resource Data 65
14 Annex C: Interview Questions 75
15 Annex D: Literature Review 78
16 Annex E: Full Assumptions Set 83
17 Annex F: Sensitivity Analysis 89
Authors’Affiliation and Sponsorship
Zara Sarzin, Senior Consultant at The World Bank zsarzin@
Acknowledgements
| |
|This study would not have been possible without the valuable assistance and generous contribution of the following people: |
| |
|Jaime Biderman, Sector Manger, Water and Urban, East and Southern Africa, World Bank; |
|Geoffrey Katsoleh, Assistant Town Clerk (Legal), City Council of Nairobi; |
|David Kiggundu Tamale, Programme Coordinator, Kampala City Council; |
|Jane Njuguna, Principal Administrative Officer, City Council of Nairobi; |
|Agnes Nyoni, AMICAALL Tanzanian Programme; |
|Kate Kuper, Senior Urban Specialist, AFTU1, World Bank; and |
|Nina Schuler, Deutsche Gesellschaft für Technische Zusammenarbeit (GTZ). |
| |
|There were many other people who generously contributed their time and expertise and to whom the author is extremely grateful. Their names are listed |
|in Annex A. |
Abbreviations and Acronyms
|AAA |Analytical and Advisory Activities |
|AIDS |Acquired Immune-Deficiency Syndrome |
|AMMP |Adult Morbidity and Mortality Project |
|CAO |Chief Administrative Officer |
|CCN |City Council of Nairobi |
|CPG |Commuted Pension Gratuity |
|HIV |Human Immunodeficiency Virus |
|IEC |Information, Education and Communication |
|IMC |Ilala Municipal Council |
|ITN |Insecticide Treated Net |
|KCC |Kampala City Council |
|KSH |Kenyan Shillings |
|LAPF |Local Authorities Provident Fund |
|MAP |Multi-Country HIV/AIDS Program, Multi-sectoral Aids Program |
|MDGs |Millennium Development Goals |
|NHIF |National Health Insurance Fund (Tanzania), National Hospital Insurance Fund (Kenya) |
|NSSF |National Social Security Fund |
|OVC |Orphans and Vulnerable Children |
|TB |Tuberculosis |
|TSH |Tanzanian Shillings |
|UGSH |Ugandan Shillings |
|VCT |Voluntary Counselling and Testing |
Executive Summary
Municipalities are affected by morbidity and mortality (particularly as a result of the HIV/AIDS epidemic) in three ways. Firstly, morbidity and mortality raise the direct and indirect costs of labour to the municipality, which consequently undermines the quality and efficiency of municipal service provision. It also significantly lowers the returns on any investments in capacity building. Secondly, the HIV/AIDS epidemic expands the demand for municipal services, particularly health and social welfare services. And thirdly, disease impacts on the affordability of services and local revenue collection. These problems are compounded by the fact that municipalities often lack the capacity to adequately assess the impact of morbidity and mortality on municipal functioning, and then design and implement effective interventions.
This study focuses on the financial impact of morbidity and mortality in the municipal workplace. The central purpose of the study is to develop a simple methodology and modelling tool that municipalities can use to evaluate the impact of morbidity and mortality on municipal human resources and to estimate the costs and benefits of workplace prevention and treatment programmes. Research was conducted in three East African municipalities: Kampala City Council, Ilala Municipal Council (Dar es Salaam) and the City Council of Nairobi.
While this paper focuses on three large municipalities, the work is relevant for any local government authority and the methodology can equally be applied to any local service delivery organization or public sector entity.
There were significant challenges in collecting data for the purposes of the study, and so many of the assumptions and parameters in the model have been estimated based on the limited data that were available. The emphasis is on establishing a methodology that will allow results to be refined iteratively over time as data collection and collation improves. The preliminary results from the model will help to reinforce the commitment of municipal managers to address the impact of disease in the workplace and will also be a useful tool to advocate for local and national resources. The study also highlights the serious need to improve human resource information systems for overall municipal management purposes.
In Kenya, Uganda and Tanzania the national disease burden is mostly due to communicable and potentially preventable disease, particularly HIV/AIDS, malaria and TB. This pattern of disease is repeated at the city and municipal levels, where HIV/AIDS, TB and malaria account for a significant share of morbidity and mortality in all three site cities.
Mortality rates among municipal staff are extremely troubling. In the City Council of Nairobi, there are around 190 to 200 employee deaths reported every year (approximately four deaths every week), equivalent to a crude death rate of 1 to 1.5 percent. In Ilala Municipal Council there are 40 to 50 reported deaths every year (equivalent to a crude death rate of 1 to 1.5 percent). And, in Kampala City Council, there are 35 to 40 reported deaths every year. It is likely that the true number of employee deaths is higher due to underreporting.
The analysis shows that the cost of HIV/AIDS in the workplace is considerable, even using the conservative base case assumptions of the model:
• the present value cost of a new HIV/AIDS infection is roughly twice the annual salary of an employee; and
• the annual cost of HIV/AIDS in the workplace is between one and two percent of the municipal wage bill.
Disease also undermines the capacity of the municipality to deliver services through increased absenteeism, lower productivity, and the loss of experienced and knowledgeable staff. As a result, the quality, quantity and timeliness of municipal services are compromised. This becomes even more serious as cities undertake institutional reform programs to improve the productivity of workers who might then fall ill.
There are three main strategies that the municipality can employ to manage the impact of morbidity and mortality on municipal human resources and service delivery: (1) investing in prevention activities including Information, Education and Communication (IEC), and the promotion and distribution of condoms in the workplace; (2) investing in the treatment and care of sick employees; and (3) investing in broadening the skills of employees to facilitate re-allocation of responsibilities and establishing career development and succession plans.
The preliminary results demonstrate that beyond ethical and moral imperatives to plan and implement programmes to mitigate the impact of disease in the workplace, there is a strong financial imperative to act now.
Analysis shows that workplace prevention and treatment programmes are in most cases profitable investments. Making some simple assumptions about illness and death in the workplace, the analysis demonstrates that in most cases, the municipalities will achieve positive returns on investments in prevention and treatment of HIV/AIDS in the workplace. In cities where malaria accounts for a large proportion of regular absenteeism, it may also make sense for municipalities to build prevention activities (in particular the provision of Insecticide Treated Bed Nets) into their workplace health programme. These workplace interventions can also have other non-financial benefits including strengthened staff morale, improved labour relations and skills retention, while buying time for drug prices to fall and for advances in medical research. The ethical and moral imperatives to act are also high.
There may also be opportunities for municipalities to access funds from national programmes for HIV/AIDS, TB and malaria interventions, particularly in view of the high returns on investments in workplace programs for the municipalities. The analysis is expected to be a useful tool to strengthen and shape requests for both local and national funding.
Introduction
Anecdotal evidence from mayors, city officials and communities suggests that morbidity and mortality (particularly as a result of HIV/AIDS and malaria) are having a significant impact on municipal service delivery and are undermining the development gains that have been made in cities and towns of East and Southern Africa.
Morbidity and Mortality raise the cost of labour to the municipality and consequently undermines service delivery. Morbidity and mortality in general, and the HIV/AIDS epidemic in particular, increase the direct costs borne by the municipality through increased absenteeism, lower productivity, higher health care costs, funeral expenses, pension payments, and recruiting and training of replacement workers. There are also indirect and less measurable costs to the municipality that may include service delivery failures or disruptions, loss of institutional memory, experience and skills, breakdown in morale, disruption of established teams, diversion of management time, and deteriorating labour relations.[1]
HIV/AIDS also expands the demand for municipal services, particularly health and social welfare services. As a result of the epidemic, local authorities face increasing demands for new and expanded services. These might include demands for expanded health and social welfare services, home and community based care, cemetery space, and services targeted at orphans and street families.
In addition, HIV/AIDS has an impact on local revenue collection and the affordability of services. If the spread of the disease is left unchecked, the epidemic has the potential to affect the local economy and the growth rate of cities and towns. Households tend to suffer from multiple infections and productive labour and household income is diverted to the care of sick household members, and the payment of medical expenses and funeral costs. This contraction in household income and shift in household expenditure away from savings and consumption towards medical expenses, affects households’ ability to pay local rates, user fees and taxes. This also impacts negatively on the local business environment, contributing further to lower local revenue collection from market fees, business licenses and taxes.
Local authorities often lack capacity to develop and implement effective HIV/AIDS interventions. In the last few years, there has been increasing attention paid to decentralised responses to HIV/AIDS and the contribution that local governments can make in engaging, mobilising and supporting communities, identifying priorities, and coordinating local responses. National resources, including the World Bank’s Multi-Country AIDS Programme (MAP), are increasingly being allocated to the local level. However, a significant challenge continues to be the absorptive capacity of local governments and the quality and effectiveness of local interventions. In many cases, local decision makers lack the information, systems and capacity to understand the full extent of the problem and then design, implement and monitor effective interventions.
1 Purpose and Rationale for the Study
The purpose of the study is to develop a simple methodology and modelling tool to evaluate the impact of morbidity and mortality on the human resources of three municipalities in East Africa. It is intended that the model will be adapted for use by other local governments and local decision makers to plan cost effective and high impact interventions within their workplaces. The study draws on the methodology of similar studies conducted in the private sector (particularly in South Africa).[2]
The rationale for this study is consistent with the Urban Sector’s broader strategic contribution to the Millennium Development Goals[3]. There are several important areas where core urban activities have the potential to contribute significantly to public health outcomes. Some of these linkages include: (1) the contribution of adequate water and sanitation services to urban health outcomes; (2) the impact of drainage on the incidence of malaria in cities and towns; (3) the urban character of the HIV/AIDS epidemic and the concentration of high risk and vulnerable groups in urban areas, including street families and orphans and vulnerable children; (4) the decentralised nature of service delivery in East and Southern African, which means that local authorities are often responsible for providing primary health care services and community outreach at a local level; (5) the contribution of slum upgrading, improvements in housing and transport, and adequate waste management systems to public heath and safety.
The study provides valuable information to the participating municipalities beyond its immediate scope.
The research will help participating municipalities to identify gaps in human resource records and processing and to devise strategies for improving data collection, collation and analysis over time. There is very little information collected and collated in municipalities to allow municipal managers to conduct any meaningful analysis of the impact of disease on municipal human resources and service delivery. This dearth of data is symptomatic of broader deficiencies in municipal human resource management systems that impact on the full range of municipal functions, since collecting and analysing human resource data are critical to improving efficiency and effectiveness. In the process of gathering data for the study, weaknesses in municipal systems and processes for managing data became evident. There are significant gaps in data collection and even where relevant data are collected, it is rarely in an electronic format or collated across departments. Municipalities often lack the information technology and skills to overcome these challenges.
The study establishes a methodology that will allow results to be refined iteratively over time as data collection and collation improves. The study makes use of available data to estimate assumptions and parameters in the model. The model is a simplification and yields illustrative results that allow for basic comparisons to be made across various courses of action. The data limitations require caution in using the model for more rigorous impact assessments or as a tool to predict the future implications of particular interventions. A valuable output of the study is the methodology—the results will improve iteratively over time as better systems for human resource data and processing are established.
The preliminary results from the model will help to reinforce the commitment of municipal managers to address the impact of disease in the workplace and will also be a useful tool to advocate for resources. The preliminary results of the study demonstrate that beyond ethical and moral imperatives to plan and implement programmes to mitigate the impact of disease in the workplace, there is a strong financial imperative to act now. In most cases it is cost-effective for municipalities to allocate resources for prevention and treatment programmes for staff. There may also be opportunities for municipalities to access funds from national programmes for HIV/AIDS, TB and malaria interventions. If this is the case, the analysis and preliminary results from the study will be a useful tool to strengthen and shape requests for both local and national funding.
The model can help municipalities make informed decisions about allocating resources across different types of interventions. The model will help municipalities to target resources to the most cost effective interventions. For example, it may be more cost-effective for municipalities to allocate resources to prevention rather than treatment programmes, or to design interventions that can be tailored to the needs of a particular category of employee.
The model will assist the municipality with forward planning and budgeting. All of the municipalities that participated in the study highlighted the challenges of planning and budgeting for staff recruitment, training, medical costs and funeral expenses. The model can be used by the municipalities (and even by individual departments) to generate rough estimates of future costs that can assist municipal planners to formulate their budgets forecasts.
The study will facilitate comparisons to be made and enable experiences to be shared across the three municipalities and also across departments within each of the municipalities. There is little consistency in how each municipality (or even how each department within a given municipality) approaches the challenges of ill-health in the workplace, particularly HIV/AIDS. This often manifests itself in uncoordinated attempts at data collection and analysis, and ad hoc policies and programmes. While this poses obvious and significant challenges for municipal managers, it also creates some opportunities for lessons to be learned, diverse experiences to be shared and best practice to emerge.
The benefits of this work can be extended by refining the methodology and model and applying it in other municipalities.
The intention is to adapt the methodology and model for use by other local authorities. There is scope to expand the Handbook and Training CD-ROM for Local Government Responses for HIV/AIDS[4] to include a simple modelling tool to estimate the cost of ill-health in the workplace and to assist municipalities with resources allocation decisions. The model could be made available on a CDROM with a manual that highlights the key inputs to the model and which suggests possible approaches for collecting data and formulating relevant assumptions.
In addition, the study can inform the design and contribute to the effectiveness of Urban, Health and HIV/AIDS operations.
The study can inform the design of urban operations and contribute to the sustainability of urban investments. Disease in general and HIV/AIDS in particular, have the potential to undermine urban investments to strengthen municipal management, municipal finance, local service delivery and local economic development. There is therefore a strong argument for urban projects to include activities to mitigate against this risk. For example, by supporting improvements in human resources management systems, supporting the development of HIV workplace policies and programmes, and by integrating relevant material (e.g. on HIV/AIDS) into capacity building for municipal planners and managers. Specifically, there are opportunities to address some of these issues within two World Bank Urban projects currently being prepared in Kampala (Kampala Institutional and Infrastructure Development Project) and Nairobi (Kenya Municipal Program).
The research might also highlight specific issues of relevance to Health and HIV/AIDS programmes. Each of the three municipalities have either devolved, deconcentrated or delegated responsibilities for primary health care and community outreach within their localities. Illness, absenteeism and deaths of municipal workers undermine the effectiveness of service delivery in these areas. In addition, the municipalities are strategically placed to influence public health outcomes in urban environments through their other activities (e.g. waste management, water and sanitation etc) and in this way, they can extend the reach and/or deepen the impact of national Health and HIV/AIDS programmes. The study may inform the design of institutional arrangements and funding modalities to strengthen municipal responses and enhance coordination at the local level.
2 Scope
The study focuses on the direct costs of morbidity and mortality on the human resources of the three participating municipalities and examines the cost impact of hypothetical prevention and treatment programmes. A brief qualitative assessment of the impact on service delivery is also included. This is indicative only and a more quantitative assessment of the impact on service delivery would be an important area for future work. Furthermore, the study does not examine the impact of morbidity and mortality on the demand for services or the impact of disease on local government revenues.
The study considers the costs and benefits from the perspective of the municipality. It does not seek to estimate or measure the broader social cost of morbidity and mortality.
Box 1: A visit to a Council Owned Clinic
Let’s call this council owned clinic, “Clinic X” to protect the privacy of the health staff that work there. Clinic X was one of several health facilities visited in Ilala, Kampala and Nairobi municipalities to collect data on absenteeism, illness and death of health workers.
Clinic X has 18 staff members including nurses, a midwife, nursing officers, a dental officer, a laboratory worker, medical clinical officers, nursing assistants, support staff, a messenger and guards.
Two staff members died of AIDS in recent years, including a member of the support staff and a midwife. The midwife’s condition deteriorated rapidly in the three years before she died. She was absent for approximately 9 months in the year before she died, and 6 months in each of the previous two years. When she was able to come to work, she was only able to fulfil 30 to 50 percent of her duties. Her replacement does not have the same level of training and can only carry out 70 percent of the job’s original requirements.
Of the current 18 staff members, six are known to be HIV-positive including the midwife, nursing officer, two of the three nursing assistants, a medical clinical officer and a member of the support staff. The actual number of HIV-positive employees might be higher as many staff members are reluctant to be tested or disclose their HIV status. The families of three of these employees are also known to be affected. Several of the HIV-positive employees are already quite sick, and are absent for substantial amounts of time (for example the support staff member was absent for a whole year in 2004, and the medical clinical officer is only at work 50 to 60 percent of the time). The interviewees indicated that there were other clinics in the municipality where the situation is worse.
Staff are also regularly absent from work due to illnesses other than HIV/AIDS (particularly malaria, but also respiratory tract infections, aerial diseases and diarrhoeal diseases), to look after sick family members, to attend funerals, or because they can’t afford transport costs. Absenteeism was estimated as follows:
• Malaria: every month, one staff member is absent for two to three days (or up to five days).
• To look after sick family members: every month, one staff member is absent for two days.
• Funerals: every two months, one person is absent for two days.
• Failure to afford transport costs: every month, one person takes one day.
• Other: every month, one staff member is absent two days.
All absenteeism is in addition to annual leave and salaries continue to be paid.
Interviewees highlighted several consequences for health service delivery including: increased workload for other staff members; reduction in the comprehensiveness and quality of care; cutback in outreach services; and reduced time for one-on-one counselling.
Municipal Profiles
Three African municipalities were invited to participate in the study—the City Council of Nairobi (CCN) in Kenya, Kampala City Council (KCC) in Uganda, and Ilala Municipal Council (IMC) in Dar es Salaam, Tanzania. The municipalities were selected on the basis of several criteria including: (1) the existence of recently completed, active or proposed Bank operations in the site cities particularly in the urban sector, but also in health and HIV/AIDS (See Table 6.1 below); (2) the availability of a basic data set to support the study; and (3) interest on the part of the municipality to participate in the study.
Table 6.1: Relevant World Bank Urban, Health and HIV/AIDS Projects and Programmes
| |Uganda |Tanzania |Kenya |
|Urban |Second Local Government Development Project |Local Government Support Project (FY04) |Kenya Municipal Project (Proposed FY07) |
| |(FY03) | | |
| |Uganda Local Service Delivery Project | | |
| |(Proposed FY07) | | |
| |Kampala Institutional and Infrastructure | | |
| |Development Project (Proposed FY06) | | |
|HIV/AIDS |HIV/AIDS Control Project (FY01) |Multi-Sectoral AIDS Project |HIV/AIDS Disaster Response Project (FY00) |
| | | |Total War on HIV/AIDS (TOWA) Project (FY05) |
|Health | |Second Health Sector Development Project |Decentralized Reproductive Health & HIV/AIDS |
| | |(FY03) |Project (FY00) |
| | | |Health Sector Reform Project (Proposed FY09) |
1 High Level Comparison of the Three Municipalities
Table 6.2: Comparison of Three Municipal Organisations
| |Kampala City Council |Ilala Municipal Council |City Council of Nairobi |
|Population |1.2 million |637,573 |2.5 to 3 million |
| | | |2.14 million (1999 census) |
|Area |169 square kilometres |210 square kilometres |569 square kilometres[5] |
|Population density |7,378 people per square km |3,036 people per square km |4,394 to 5,272 |
|Households |310,000 |148,386 |651,861 (1999 census) |
|Percentage of urban population |39.6 percent[6] |8 percent |approximately one third |
| | |(31 percent for Dar es Salaam) | |
|Estimate of daytime population |2 million | |3.5 million |
|Population growth rate |3.9 percent |4.3 percent (Dar es Salaam, 1998-2002 ) |approximately 7 percent |
|Contribution to National GDP |over 50 percent | |over 50 percent |
|Wage earners |approximately 50 percent of the population| |approximately 950,000 |
| |is economically active | | |
|Residents below the poverty |15 percent[7] | |880,000 people |
|line | | | |
|Percentage in informal |approximately a third | |60 percent (in 6 percent of residential |
|settlements | | |land) |
|Administrative structure |5 divisions |3 divisions |8 divisions (Nairobi Province) |
| |99 parishes |22 wards |49 locations |
| |998 villages |65 sub-wards |110 sub-locations |
|Local revenues as a percentage |37 percent |40 percent |91 percent |
|of all sources of funds | | | |
|Salaries and allowances as a |27 percent |approximately 50 percent |76 percent |
|percentage of municipal |(35 percent excluding external/donor | | |
|expenditure |funds) | | |
|Number of employees |1,252 (one third are support staff earning|1,298 (30% are support staff earning |13,345 employees: 77% support staff |
| |around $100 pm; 45% are semi-skilled |around $75 pm; 50% semi-skilled earning |earning around $160 pm |
| |earning around $150 pm) |around $120 pm) |Teachers are not employees of the |
| |1,538 teachers |2,487 primary teachers |municipality |
2 Kampala City Council (KCC)
Kampala is the capital city of Uganda and its largest urban centre with a population of 1.2 million residents (accounting for 40 percent of Uganda’s urban population) and a day-time population that is estimated at well over 2 million people. According to the 2002 population census, the city’s annual population growth rate is 3.9 percent (well above the national average of 3.3 percent). Kampala is the commercial and industrial hub of Uganda and generates over 50 percent of national Gross Domestic Product. [8],[9],[10]
For administrative purposes, Kampala City Council is divided into five divisions namely: Central, Kawempe, Makindye, Nakawa and Rubaga Divisions.[11] District Divisions are further sub-divided into 99 parishes and 998 villages.
Table 6.3: Population and Density by KCC Division
|Division |Area (square kms) |Population 2002 |Density (popn/km2) |Population Distribution |
| | | | |(%) |
|Central |14.6 |90,392 |6,191 |8 |
|Kawempe |31.5 |268,659 |8,529 |22 |
|Makindye |40.6 |301,090 |7,416 |25 |
|Nakawa |42.5 |246,298 |5,795 |20 |
|Rubaga |33.8 |302,105 |8,938 |25 |
|District |169 |1,208,544 |7,378 |100 |
|National | |24.4 million |124 | |
The Council’s budget is financed from government transfers (conditional and unconditional grants), donor contributions and own source revenue. Local revenues, which account for approximately 40 percent of Council funds, are generated mainly from property taxes, ground rent, market fees, licences and vehicle parking fees.
Table 6.4: KCC Sources of Revenue[12]
|Projected Revenue Source (UGSH) |Actual |Revised Budget 2004/05 |Share of Total |
| |2003/04 | | |
|Local Revenue |21,537,603,900 |28,639,094,928[13] |37% |
|Unconditional Grants |1,612,248,634 |2,245,500,000 |3% |
|Delegated Funds |8,748,189,688 |10,100,567,000 |13% |
|Conditional Grants |5,882,542,582 |6,312,456,000 |8% |
|Development Grants |6,349,501,000 |7,103,924,000 |9% |
|External Funds |15,747,060,238 |22,029,431,893 |29% |
|Gross Revenue |59,877,146,042 |76,430,973,821 |100% |
Salaries and wages account for 27 percent of the Kampala district budget, as can be seen in Table 6.4 below. Excluding external funds, employee costs account for 35 percent of the municipal budget.
Table 6.5: District Expenditure[14]
|District Expenditure (UGSH Billion) |Actual 2003/04 |Share of Total |
|Capital Expenditure |22.86 |33% |
|Delegated Salaries (Teachers) |10.85[15] |16% |
|Employee Cost |5.01 |7% |
|Debt/Payables |4.14 |6% |
|Council Expenditure (Allowances) |2.68 |4% |
|Non-wage |23.16 |34% |
|Total |68.70 |100% |
Kampala City Council is structured around its service delivery responsibilities in the areas of water and sanitation, education, health, roads and transport and solid waste management. These are summarised in Table 6.6 below.
Table 6.6: KCC Service Delivery Responsibilities[16]
|Municipal Departments |Service Delivery Responsibilities |
|Corporate Services (Management Support |Administration services, human resource management, legal services, law enforcement, information technology, public |
|Services, Town Clerk Administration, |relations, economic planning, office of the Clerk to council and statutory bodies. |
|Council, Committees, Commissions) | |
|Finance |Preparation of budgets, maintenance of accounts, expenditure control, mobilisation and management of revenues, |
| |ensuring financial transparency and accountability in the Council’s transactions. |
|Internal Audit |A statutory body responsible for financial auditing and internal performance auditing. |
|Health and Environment |Development of public health policies and plans, preparation of health, education and immunisation programmes, |
| |management and supervision of nurses and midwives at council clinics, formulating clear rules and regulations for the|
| |divisions and ensuring their implementation; ensuring provision and rational and transparent use of essential drugs, |
| |equipment and supplies. |
|Education and Sports |Management and administrative support to pre-primary, primary and post- primary schools in Kampala city. |
|Works and Physical Planning (Technical |Management of engineering contracts, management and maintenance of roads (including street lighting and traffic |
|Services and Works) |management), drainage systems, KCC vehicles, plant and buildings, land management and surveys, cadastral records and |
| |land records, approval of business plans and occupancy licensing, physical planning and development control. |
|Gender and Community Services |Community welfare, vulnerable groups, youth and child affairs, enforcement of regulations for agriculture, livestock |
| |and fisheries extension services, preservation and promotion of cultural sites and monuments, facilitation of |
| |positive industrial relations, enhancement of income-generating activities for women. |
|Law Enforcement |Ensuring public compliance with city laws and regulations, development control and demolition of illegal structures, |
| |prosecution of tax defaulters. |
There are 1,252 employees of the Council (as at June 2005, excluding teachers). One third of employees are support staff earning on average UGSH 177,000 per month (around USD 100 per month). A further 45 percent of employees are semi-skilled earning UGSH 244,000 per month (around USD 150 per month). The following table shows the distribution of staff across departments by salary grade, where U1 is the highest grade and U8 is the lowest.
Table 6.7: KCC Employees by Department and Salary Scale (Excluding Teachers)[17]
|Department |U1 |U2 |U3 |U4 |
|Kariakoo |8 |- |- |20 |
|Ilala |6 |- |- |23 |
|Ukonga |8 |9 |37 |22 |
|Total |22 |9 |37 |65 |
According to the 2002 Population Census, IMC had a population of 637,573 (in 148,386 households), 90 percent of whom reside in urban settings.
In 2000, a total of 27,500 businesses were registered in Dar es Salaam region, of which 43 percent were registered in Ilala district. It is estimated that there are also approximately 150,000 small traders in the district.
Over half of the Council’s budget is financed from Central Government transfers account. Own source revenues (principally from property taxes, municipal service levy, trade licences and billboards) account for forty percent of the budget.
Table 6.9: IMC Source of Revenue
|Source of Funds |Budget 2004/05 (TSH) |Percentage of Total |
|Own Source Revenues |7,450,000,000 |40% |
|Grant to Substitute Revenue |322,369,464 |2% |
|Community Contributions |256,340,000 |1% |
|Donors |1,550,000,000 |8% |
|Central Government Grants |9,077,137,588 |49% |
|Total |18,655,847,052 |100% |
The annual personal emoluments paid to Ilala Municipal Council employees are in the order of TSH 8.5 billion per annum, representing approximately 50 percent of the municipal budget.
Table 6.10: IMC Expenditure
|Expenditure |FY 2004/05 (TSH) |Percentage of Total |
|Recurrent Expenditure |11,587,442,902 |63% |
|Development Plan |6,560,768,000 |35% |
|Statutory Contributions |360,614,000 |2% |
|Total |18,508,824,898 |100% |
IMC is organised into nine departments structured around its service delivery responsibilities and reporting to the Municipal Director (see Table 6.11 below).
Table 6.11: IMC Departments and Service Delivery Responsibilities
|Department |Service Delivery Responsibilities |
|Administration and Personnel |Human resource and administrative services to other departments. |
|Planning and Coordination |Urban planning, including physical planning, surveys and mapping, land management, valuation and architecture. |
| |Economic planning, including community development, development planning and investment policy, statistics and |
| |research and information technology. |
| |Agricultural and livestock services, including horticulture and crop inspection, agriculture and irrigation extension|
| |services, and livestock development. |
| |Natural resources and environment, including forestry, parks and gardens, fisheries, wildlife and beekeeping. |
|Education |Pre-primary, primary, secondary, vocational and adult education and MEMKWA (special education programme for those |
| |without primary education).[18] |
|Treasury |Revenue collection, management of accounting systems, preparation of budget estimates, preparation of annual accounts|
| |and financial reports to Council. |
|Health |Provision, coordination and supervision of health services within the Municipality. |
|Works |Road construction, maintenance and associated drainage. |
|Rural Development |Coordination of economic, social and community services in rural areas of the municipality, and coordination of |
| |conservation activities and land use in rural areas. |
|Waste Management |Collection, transportation and disposal of refuse and liquid waste, including street cleaning. |
|Trade and Informal Sector |Markets, micro-enterprises and informal sector development services. |
Table 6.12 summarises the number of municipal employees by department and salary scale. There are 1,298 employees of the Council (excluding teachers). Approximately 30 percent of these are support staff earning on average TSH 81,000 per month (around USD 75 per month). A further 50 percent are semi-skilled workers earning on average TSH 127,000 per month (around USD 120 per month). There are also 2,437 primary teachers employed by the council earning in the order of TSH 150,000 per month.
Table 6.12: IMC Number of Employees by Department and Salary Scale
|Department |Managers |Supervisors and |Semi-Skilled |Support Staff |Total |
| | |Professionals | | | |
|Administration |4 |7 |125 |156 |292 |
|Education |1 |4 |30 |15 |50 |
|Teachers | | | | |2,487 |
|Finance |1 |7 |76 |1 |85 |
|Health |10 |72 |349 |127 |558 |
|Planning & Coordination |21 |49 |62 |39 |171 |
|Rural Development |1 |6 |12 | |19 |
|Trade and Information |1 |6 |12 |1 |20 |
|Waste Management | |2 |4 |57 |63 |
|Works |2 |10 |27 |1 |40 |
|Total |41 |163 |697 |397 |3,856 |
3 City Council of Nairobi (CCN)
Nairobi is the capital city of Kenya, covering an area of 684 square kilometres. According to the 1999 Population Census, the city’s population was 2.14 million in 1999 and was projected to grow to 2.87 million by 2005. The day-time population of the city is estimated at 3.5 million people, representing more than 10 percent of the national population and a third of Kenya’s urban population.
Nairobi is Kenya’s industrial and commercial centre, contributing over 50 percent of the national Gross Domestic Product. There is also a growing informal economy in the city, which is thought to sustain about 950,000 wage earners.
Half of Nairobi’s residents live below the poverty line, while approximately 60 percent of Nairobi’s residents live in informal settlements covering 6 percent of the city’s residential land.
Approximately 90 percent of the City Council’s budget is financed from local revenues, including property rates (41 percent in 2003/04), permits (17 percent) and parking fees (8 percent). External sources of funds are less than 10 percent of the budget and include LATF transfers from the central government and an allocation from the Road Maintenance Fund.
Table 6.13: CCN Source of Revenue
|Source of Funds |FY 2003/04 (KSH) |Percentage of Total |
|Local Revenues | | |
|Rates |1,273,016,805 |41% |
|Permits |513,904,102 |17% |
|Parking Fees |253,671,092 |8% |
|Other |758,761,129 |25% |
|Total Local Revenues |2,799,353,128 |91% |
|LATF |93,859,780 |3% |
|Road Maintenance |196,707,154 |6% |
|Other External Funds |597,150 |0% |
|Total |3,090,517,212 |100% |
Records of departmental expenditure in 2003/04 indicate that 76 percent of the municipal budget is spent on salaries and wages.
Table 6.14: CCN Departmental Expenditure
|Expenditure |FY 2003/04 (KSH) |Percentage of Total |
|Personnel |3,146,996,209 |76% |
|Operation |543,317,561 |13% |
|Maintenance |447,616,153 |11% |
|Capital Projects |313,304 |0% |
|Total |4,138,243,227 |100% |
CCN is organised into several departments, summarised in Table 6.15 below.
Table 6.15: CCN Departments and Service Delivery Responsibilities
|Department |Service Delivery Responsibilities |
|City Planning |City planning, building control, land surveys, approval of building plans and control, subdivision of surveyed land, change of land |
| |usage, amalgamation of plots, advertising and billboards, enforcement and by-laws, and architecture. |
|Social Services and |Community development, street families, sports, shelter (housing), recreation and welfare, markets (collection of cess, maintenance of|
|Housing |markets). |
|Environment |General cleanliness of the city and its environs, beautification of the city, collection and removal of garbage, transportation and |
| |disposal of garbage, road sweeping, drain clearing, gulley emptying. |
|Town Clerk |Legal cases for the Council, valuation of Council properties, procurement, general cleanliness of city hall and annex, control |
| |incoming and outgoing calls and mail, minutes and printing for Council. |
|Engineering |Road construction and maintenance, construction and maintenance of primary schools, repairs of buildings including rental housing, |
| |street lighting within the city, construction and maintenance of clinics, fire fighting services, surveying, architects. |
|City Treasurer |Revenue collection, payment to various departments, procurement for Council, maintenance of stores, budgeting and accounting. |
|Public Health |Curative and preventative services, dental services, ambulance services, city mortuary, maternity services, nutrition services. |
|Education |Pre-primary and nursery education, cleaning of schools. |
|City Inspectorate |Enforcing by-laws, prosecuting offenders, investigation of crimes, guard Council property. |
|Housing Dev. |Management of the Site and Service Schemes in Nairobi. |
CCN considers itself to be overstaffed in the lower staff grades and understaffed in the senior and technical cadres. Support staff account for 77 percent of the workforce and earn on average KSH 13,000 per month including pension contributions (around USD 160 per month). Approximately 60 percent of employees live in informal settlements. A summary of staff numbers by department and salary scale is included in Table 6.16 below, based on data from the June 2005 payroll. Teachers working in CNN owned schools are not employees of the municipality.
Table 6.16: CCN Number of Employees by Department and Salary Scale (June 2005 Payroll)
|Department |Managers (Grades |Supervisors and |Semi-Skilled (Grades|Support Staff |Total |
| |1–5) |Professionals(Grades| |(Grades | |
| | | |10–13) |14–19) | |
| | |6–9) | | | |
|City Education[19] |6 |22 |342 |1350 |1720 |
|City Engineer |6 |63 |333 |1,046 |1,448 |
|City Inspectorate |6 |47 |183 |2,741 |2,977 |
|City Planning |9 |36 |157 |204 |406 |
|City Treasurer |7 |62 |338 |454 |861 |
|Dept. of Environment |2 |8 |292 |2,285 |2,587 |
|Housing Development |4 |17 |41 |105 |167 |
|Public Health |14 |280 |500 |826 |1,620 |
|Social Services |2 |28 |110 |997 |1,137 |
|Town Clerk |9 |58 |98 |266 |431 |
|Total |65 |621 |2,394 |10,274 |13,354 |
|Percentage of employees |0.5% |4.7% |18% |77% |100% |
Burden of Disease and Staff Deaths
1 Leading Causes of Morbidity and Mortality
This section describes the leading causes of morbidity and mortality in each of the site cities, within the context of national burden of disease profiles. More detailed information is provided on HIV/AIDS, tuberculosis (TB) and malaria, which are the dominant causes of ill health and death in East Africa.
In Kenya, Uganda and Tanzania the national disease burden is mostly due to communicable and potentially preventable disease, particularly HIV/AIDS, malaria and TB.
• In Tanzania, results from the Adult Morbidity and Mortality Project (AMMP) suggest that infectious diseases, particularly AIDS, malaria, TB and gastroenteritis, account for the majority of premature deaths among Tanzanians aged 15 to 59 years.[20]
• In Uganda, the 1995 Burden of Disease study attributes over 60 percent of life years lost from premature death to five groups of preventable conditions, including malaria (15.4 percent), acute lower respiratory tract infections (10.5 percent) and AIDS (9.1 percent). The Annual Health Sector Performance Report for the 2003/2004 financial year notes that HIV/AIDS, TB and malaria are the most significant causes of morbidity and mortality. Nationally, malaria is the leading cause of death, killing 400 Ugandans daily, mostly pregnant women and children under five.[21]
• Communicable diseases such as malaria, acute respiratory infections and diarrhoeal diseases are the leading causes of morbidity and mortality in Kenya. The most significant causes of morbidity in Kenya in order of significance are: (1) malaria (accounting for 30 percent of outpatient cases, of which 19 percent are admitted); (2) acute respiratory infections (accounting for up to 25 percent of outpatient attendance); and (3) diarrhoeal diseases.
This pattern of disease is repeated at the city and municipal levels. HIV/AIDS, TB and malaria account for the dominant share of morbidity and mortality in all three site cities (see Figures 7.1 to 7.5 below).
• AMMP data for the Dar es Salaam sentinel surveillance site showed that the leading cause of death for adults aged 15-59 years was HIV/AIDS/TB (50.3 percent for men and 61.9 percent for women) and Acute Febrile Illness[22] (18.5 percent for men and 14.8 percent for women).[23] HIV/AIDS/TB accounted for 31 percent of Years Life Lost for the 15-59 age group, while Acute Febrile Illness accounted for 9.8 percent of Years Life Lost for the same age group.[24]
• Data from health facilities in Kampala district indicate that malaria is the dominant cause of morbidity.
• Data from City Council of Nairobi health clinics suggest that respiratory diseases and malaria are the leading causes of morbidity, while HIV/AIDS and related opportunistic infections (including TB, pneumonia and meningitis) are the most significant causes of death.[25]
|Figure 7.1: Dar es Salaam Leading Causes of Mortality in Adult Males[26] |Figure 7.2: Dar es Salaam Leading Causes of Mortality in Adult Women[27] |
| | |
|[pic] |[pic] |
|Figure 7.3: Kampala District Health Facilities | |
|Leading Causes of Morbidity[28] | |
| | |
|[pic] | |
|Figure 7.4: Nairobi Health Facilities |Figure 7.5: Nairobi Health Facilities |
|Leading Causes of Morbidity in 2004[29] |Leading Causes of Mortality in 2004[30] |
| | |
|[pic] |[pic] |
HIV/AIDS
From the first reported cases of AIDS (1983 in Tanzania, 1984 in Kenya, and 1985 in Uganda) the disease spread rapidly and prevalence rose sharply. AIDS is now the leading cause of death and the major cause of declining life expectancy in Kenya and Tanzania, and a significant cause of mortality in Uganda. All three countries have severe generalised epidemics, with heterosexual contact accounting for the large majority of new infections.
• In Tanzania, 140,000 people were estimated to be living with HIV/AIDS in 1985 (1.3 percent prevalence). This number grew to 900,000 by 1990 (7.2 percent prevalence) to approximately 1,820,000 in 2003. [31]
• Cumulatively, in Uganda, over 2 million people have been infected and more than 900,000 have died from HIV/AIDS since the start of the epidemic. A comprehensive HIV/AIDS response in the last decade has resulted in prevalence rates falling from about 18 percent in 1992 to between 2.8 percent and 6.6 percent in 2003. However, preliminary results from the 2004–2005 Uganda HIV/AIDS Sero-Behavioural Survey indicate that 7 percent of adults (slightly over 800 000 people) are infected with HIV. [32]
• Since the first reported AIDS case in Kenya, over 1.5 million people have died from the disease and more than 1.2 million children younger than 15 years (3.7 percent of the total population) have been orphaned through the death of their mother. Due to HIV/AIDS, life expectancy in Kenya has dropped from 60 years in 1993 to about 47 years in 2005. Kenya had an estimated adult HIV prevalence rate of 7.5 percent in 2004 and 1.6 million Kenyans are thought to be living with HIV/AIDS. At least 180 000 people die from the disease each year.[33]
In all three countries, there is a gender bias to the epidemic, with higher incidence and prevalence rates in women.
• Data from TACAIDS suggest that 7 percent of Tanzanian adults aged 15–49 are infected with HIV, and that the prevalence among women is higher (7.7 percent) than men (6.3 percent). Data from blood donors in 2002 suggest an overall prevalence of 9.7 percent, with women having a higher prevalence (12.3 percent) than men (9.1 percent). In Dar es Salaam, women were more likely to be infected (12.2 percent) than men (9.4 percent).[34]
• Women in Uganda are disproportionately at a higher risk of HIV infection than men. Preliminary results from the 2004-2005 Uganda HIV/AIDS Sero-Behavioural Survey suggests that 7.9 percent of women are HIV-positive, compared to 6 percent of men.
• In Kenya, a 2003 HIV serosurvey of adults suggested national prevalence rates of 8.7 percent in women and 4.6 percent in men.
There are also significant differences in prevalence rates between urban and rural areas, and strong regional variations.
• In Tanzania, rural prevalence is estimated at roughly half the urban rate. For both sexes, urban residents have a significantly higher risk of HIV infection (10.9 percent) than rural residents (5.3 percent). Regions with the highest HIV prevalence are Mbeya (14 percent), Iringa (13 percent) and Dar es Salaam (10.9 percent). [35]
• In Uganda, there is higher prevalence in urban areas (10.7 percent) relative to rural areas (6.4 percent). [36]
• In Kenya, average urban prevalence (10 percent) is nearly twice the rate in rural areas (5–6 percent).[37]
The impact of HIV/AIDS on economic activity is particularly severe because the disease is most prevalent in adults during the most productive years of their lives.
• In Uganda, eighty percent of reported AIDS cases occur among 15–45 year olds. This age group constitutes the largest and most productive portion of the labour force. [38]
• In Kenya, prevalence data suggest that the majority of non-paediatric infections occur in young women aged 15–24 years, and young men under 30.
Table 7.1: HIV/AIDS Indicators
| |Kenya |Uganda |Tanzania |
|Adult Prevalence (15–49 years) |7.5 %[39] |4.1 %[41] |8.8 %[43] |
| | |(2.8 % – 6.6 %) |(6.4 %–11.9 %) |
| |6.7%[40] |7%[42] | |
|Percentage Urban Population[44] |33 % |14 % |32 % |
|Urban Prevalence[45] |10% |10.7% |10.9% |
|Rural Prevalence[46] |5.6% |6.4% |5.3% |
|Estimated number of people living with HIV/AIDS (0-49 years) |1.6 million |0.35 to 0.88 million |1.2 to 2.3 million |
|[47] | | | |
|Reported number of people needing antiretroviral therapy in |233,831 |114, 000 |263,000 |
|2004[48] | | | |
|Reported number of people receiving antiretroviral therapy |38, 000 |63,896 |8,300 |
|(15-49 years) [49] | | | |
Tuberculosis (TB)
Closely associated with the HIV epidemic is the rising incidence of TB in all three countries. HIV undermines the immune system and increases the likelihood of acquiring a new TB infection. HIV positive people are also more likely to progress from latent to active TB and suffer a relapse after treatment. TB in turn accelerates the progression of the HIV infection and is the leading cause of AIDS deaths.[50]
• Tanzania's incidence of TB rose sharply from 11,753 cases in 1983 to 64,665 notified cases in 2003 (of which 92.4 percent were new cases). Nearly two thirds of notified cases in 2003 were males (62 percent) and the most affected age group for both sexes was 25–34 years (34 percent).[51]
• In Uganda, over 60 percent of all new TB cases are associated with HIV/AIDS.[52] A study conducted among a paediatric cohort in Uganda revealed that 18 percent of HIV infected infants developed TB compared with 1.4 percent of those that were not HIV positive, and the rate of treatment success was 31 percent and 83 percent respectively.[53] On average a TB patient loses 4 months of work per year[54].
• In Kenya, numbers of reported TB cases have increased sevenfold from 1990 to 2003, and the significant cause of the increasing burden of TB is attributed to the HIV/AIDS epidemic. The 2003 National Leprosy and TB Programme Annual report for 2003 notes that HIV is the most significant risk factor for reactivation of latent TB and HIV-infected TB patients are also at an increased risk of recurrent TB. There has been a gradual increase over the last decade in the proportion of cases in women aged between 15 and 34, and men aged between 25 and 34. These are the categories with high HIV prevalence rates.[55]
Table 7.2: WHO TB Indicators (2005)
| |Tanzania |Uganda |Kenya |
|Global rank (by est. number of cases) |14 |16 |10 |
|Incidence (all cases/100 000 pop/year) |371 |411 |610 |
|Incidence (new ss+/100 000 pop/year) |157 |179 |262 |
|Prevalence (all cases/100 000 pop) |524 |652 |884 |
|TB mortality (all cases/100 000 pop/year) |86 |96 |133 |
|TB cases HIV+ (adults 15-49 y, percent) |36 |21 |29 |
|New cases multi-drug resistant (percent) |1.2 |0.5 |0 |
Malaria
Malaria is a significant cause of morbidity and mortality in all three countries, however, at a city level only Kampala and Dar es Salaam have significant incidence due to their tropical climates and large annual rainfalls.
• In Tanzania, malaria is the number one cause of inpatient and outpatient cases.[56] Dar es Salaam has endemic and perennial malaria, with transmission occurring during the entire year.
• Malaria is the leading cause of morbidity and mortality in Uganda. Malaria accounted for 52 percent of all outpatient attendance in 2003, 30 percent of inpatient admissions and 9–14 percent of inpatient deaths. The significance of malaria is evident in Kampala where it accounts for the largest number of cases reported at district health facilities.
• Nairobi has a relatively low incidence of malaria due to its urban setting and high altitude. However, malaria is a growing phenomenon in informal settlements such as Kibera, which lack adequate drainage systems.[57]
The majority of deaths due to malaria occur in children under five years of age, and pregnant women (whose immunity is temporarily impaired). Malaria is rarely a cause of death in productive aged adults (especially in urban areas), and so when mortality is attributed to malaria it is likely that the true cause of death is HIV/AIDS. Malaria is however a significant cause of morbidity in productive aged adults even in urban areas.
2 Mortality of Municipal Workers
This section summarises the available data on deaths of municipal workers in each of the three site cities. It is likely that there is significant underreporting of deaths due to weaknesses in record management and human resource management systems. None of the municipalities systematically records the cause of death or collects death certificates when staff members die. Even where the death certificates are kept in the individual’s personnel file, they rarely attribute AIDS as the cause of death due to stigma and shame.
City Council of Nairobi
Data on employee deaths at CCN were drawn from several sources including: (1) Payroll (employees deleted from the payroll in 2004); (2) minutes of Council meetings noting staff obituaries (2001–2004); and (3) interviews with Chief Administrative Officers (CAOs) in each of the departments (2001–2004). There were significant discrepancies across different sources of data.
Figure 7.6 summarises the number of staff deaths provided by the CAOs (supplemented with numbers from the minute books for some departments and years where data were missing). The data suggest that approximately 190 to 200 employees have died in each of the last four years, or approximately four staff members every week (however the numbers of employee deaths are probably underreported).
Figure 7.6: City Council of Nairobi Employee Deaths (2001–2004)
[pic]
Table 7.3: City Council of Nairobi Crude Death Rates
| |2001 |2002 |2003 |2004 |
|Number of employee deaths |189 |190 |197 |198 |
|Number of employees (middle of year) | 19,472 | 18,375 |17,528 | 14,159 |
|Crude death rate |1.0% |1.0% |1.1% |1.4% |
Data extracted from the minute books include job designation (job title) for about 60 percent of staff obituaries. Of these, about 60 percent are subordinate staff, watchmen, cleaners, caterers and labourers.
Ilala Municipal Council
Figure 7.7 shows the number of employee deaths over the last five and a half years across municipal departments. The data are drawn from several sources including: (1) a handwritten register of staff deaths (115 deaths from 2000 to June 2005); (2) detailed data from individual personnel files for a subset of staff deaths (80 deaths, including 13 deaths not contained in the original handwritten register); and (3) detailed data on teachers’ deaths provided by the Education Department (63 deaths over the period).
Figure 7.7: Ilala Municipal Council Employee Deaths (2000-2004)
[pic]
Detailed data for teachers include cause of death and suggests that 60 percent of teachers died from AIDS. The underlying cause of the majority of deaths attributed to TB and malaria is also likely to be HIV/AIDS, which suggests that, in reality, more than 80 percent of teachers died from AIDS.
Figure 7.8: Ilala Teacher Deaths by Cause (2002-2005)
[pic]
Detailed data collected from personnel files (for a subset of employee deaths) and from the Education Department suggest an average age at death of 43 years.
Kampala City Council
Data on staff deaths were provided by the Personnel Department (handwritten register of staff deaths) and the Senior Personnel Officer for Teachers (data compiled electronically).
Table 7.9: Kampala City Council Staff Deaths (1998 – June 2005)
|Year |Teachers |All Other Staff |
|1998 |Not recorded |28 |
|1999 |Not recorded |36 |
|2000 |1 |Not recorded |
|2001 |10 |25 |
|2002 |24 |19 |
|2003 |16 |11 |
|2004 |14 |20 |
|Jun-05 |4 |Not recorded |
Deaths of health workers accounted for approximately 30 percent of staff deaths (excluding teachers).
In most cases the data for teachers indicate suspected cause of death. Of those staff records with an indicative cause of death, approximately 75 percent of deaths are recorded as and attributed to “long illness” (a euphemism for AIDS).
Data and Methodology
This section describes the approach used to estimate the direct costs of morbidity and mortality in the municipal workplace. It is worth emphasising that the primary purpose of this work was to develop a methodology and modelling tool that could be adapted and refined over time as municipal systems for collecting, collating and analysing data improve. There were significant challenges in collecting data for the purposes of the study, and so many of the assumptions and parameters in the model have been estimated based on the limited data that were available.
1 Data Collection
The challenges of collecting data for the purposes of the study were enormous.
Firstly, there is little robust data on the health status of municipal employees that would indicate HIV prevalence and incidence rates, causes of absenteeism and illness in the workplace, and causes of death. In costing exercises carried out in the private sector on the impact of HIV/AIDS in the workplace, a Voluntary Counselling and Testing (VCT) programme is often included as part of the data collection exercise. These prevalence studies have demonstrated that HIV infection rates can vary by age, sex, race, employment status (temporary or permanent staff), occupational class, salary grade and living arrangements (including whether or not the employee lives in informal housing areas, and whether or not they rent or own their own home). It was not possible to estimate or disaggregate prevalence rates for municipal employees according to any of these variables, and a uniform rate was therefore applied to all employees with reference to national or urban prevalence rates. Workplace prevalence studies have also demonstrated that there is substantial variability in HIV prevalence rates across different firms and results often vary significantly from national or regional averages. In future, the municipalities might choose to conduct anonymous VCT programmes in the workplace to collect accurate and detailed data on prevalence rates.
Finding credible data on incidence is even more problematic, since national health reports typically do not include estimates of incidence rates or track incidence over time. There is academic literature that describes a process for estimating incidence rates based on a time series of prevalence rates, however this calculation was considered well beyond the scope of the study.
It was also difficult to accurately determine the causes of illness or death in the workplace. The causes of illness or absenteeism are not typically known by the municipality and, while some employees might attend staff or council clinics for treatment, these health facilities do not usually differentiate between municipal employees and their other patients. In the case of death, the municipality does not always collect a death certificate or record the cause of death. Even where a death certificate is available, it rarely attributes AIDS as the cause of death.
For the purposes of this study, the most relevant sources of data were used, including reported cases of diagnoses from council-owned clinics and national or regional health statistics on major diseases. These sources of data often use different measures of the severity of the disease to determine “leading causes” including number of reported cases, years life lost, or disability-adjusted life years lost.
Secondly, municipal information and human resource management systems are very weak. They are typically characterised by manual processes for data collection, collation, storage and analysis. There is little consistency in processes for data management across departments or divisions within the municipalities. Data in individual personnel files are often unwieldy (in the form of detailed letters and forms) and often much of this data are missing. Very little data are available on absenteeism, illness, medical claims and cause of death. It also appears that the integrity of the available data is sometimes questionable. For example, there were often significant variances found when comparing different sources of data, suggesting one or more data source is either incomplete or wrong.
In most cases, the study relied on whatever electronic data were available, supplemented by information collected during interviews with municipal officers. The Human Resource Departments typically provided data on salary structures and allowances, employee benefits (including health benefits, funeral expenses, pension arrangements), staff deaths, and average recruitment and training costs. Electronic staff lists were provided by the Payroll or Human Resource Departments. Annex B contains a summary of this information.
Interviews with heads of department or chief administrative officers, a small sample of head teachers and health facility administrators were relied on to estimate some of the parameters in the model. Respondents were asked: (a) to provide details on individual staff deaths, including cause of death and the absenteeism and productivity in the two years prior to death; (b) to estimate or provide information on the average number of days of absenteeism per employee per year and attribute these to different causes; and (c) to discuss the impact of absenteeism and death on service delivery. The full set of interview questions is included in Annex C.
Finally, there was little information available on the cost and impact of different workplace interventions. The emphasis of this study was therefore on developing a methodology and model for evaluating the cost impact of hypothetical prevention and treatment programmes. The cost and impact of alternate workplace strategies are parameters in the model that can be modified by the user.
A literature review was also carried out to provide further information for the purposes of setting assumptions in the model. See Annex D for a summary of the assumptions used in similar studies.[58]
2 Incidence versus Prevalence Approach
The study explores the impact of both morbidity and mortality on municipal human resources and service delivery. The model applies an incidence approach for HIV/AIDS and opportunistic infections ultimately leading to death (assuming that AIDS accounts for the majority of staff deaths) and applies a prevalence approach for other causes of morbidity.
Since the most significant cause of mortality of municipal workers is AIDS, the analysis focuses on the costs incurred by the municipality over the lifetime of an HIV-positive employee. When an employee becomes infected with HIV, the municipality becomes liable for a future stream of costs that can span more than a decade. Most of these costs only begin to emerge five or more years following infection.
The cost of a new HIV infection can be estimated by calculating the present value of the future stream of costs incurred by the municipality over the lifetime of an HIV-positive employee. It is instructive to look at the costs in this way (applying an incidence approach) because it is then possible to evaluate the costs and benefits of prevention and treatment programmes. For example, every new HIV infection prevented will result in a cost saving equal to the present value of future costs that have been avoided. Similarly, treatment of HIV-positive employees results in cost savings (improved productivity, lower absenteeism, extended productive time in the workplace) represented by a lower net present value.
In contrast, the costs associated with morbidity or illnesses that do not typically lead to death (such as malaria) tend to occur regularly over short timeframes. Here an incidence approach is problematic because some illnesses only have a duration of a few days or a few weeks. The cost of illness was therefore calculated based on the average number of days that an employee is likely to fall ill each year. The cost is determined on an annual basis rather than calculating a net present value.
3 Analysis of the Municipal Workforce
The demographics of the employees of each municipality were analysed using electronic records provided by the three municipalities. This analysis was limited by the scope of the data fields contained in these records, for example some municipalities do not keep electronic data on employee sex or date of birth.
Costs associated with morbidity and mortality typically vary according to salary level and staff seniority. For example: the cost of a day of absenteeism is greater for senior and highly paid staff; senior staff typically have better health-care benefits and larger pension entitlements; and the more senior a staff member the more difficult and expensive it is likely to be to recruit and train a replacement.
For these reasons, employees were categorised into groups based on salary scales. Sufficient flexibility has been built into the model to allow the user to select their own categories, for example, categories could be chosen to reflect other variables such as municipal department, job designation, age or sex, if this data are available.
The number of employees and average salary costs were determined for each employee category. The average salary costs include any regular allowances paid by the municipality through the payroll system together with any employer contributions to pension and health insurance plans.
4 Estimating the Cost of a New HIV Infection
To estimate the cost of a new HIV infection, it was necessary to identify and quantify the costs incurred by the municipality over the lifetime of an HIV positive employee and after their death, and to determine the timeframe associated with these costs.
Due to the long latency of the disease, the municipality might not incur any additional costs for more than five years following infection. When symptoms begin to appear and the employee’s condition deteriorates, the municipality might become liable for additional health-care costs, absenteeism increases, productivity falls and managers’ time is diverted. On death, the municipality typically meets the cost of funeral expenses and several employees might take time off work to attend the funeral. A terminal benefit is usually paid from the pension fund, which may represent an additional cost to the municipality. Costs are also incurred to recruit and train a replacement and it might take several months before the replacement staff member becomes fully productive (see Figure 8.1).
Figure 8.1: Costs incurred over the lifetime of an HIV+ Employee[59]
[pic]
Table 8.1 describes the methodology for estimating the different cost incurred by the municipality in respect of an HIV-positive employee.
Table 8.1: Methodology for Estimating the Various Costs incurred over the lifetime of an HIV+ Employee
|Cost Element |Methodology |
|Increased absenteeism in the years |Data constraints limited the extent to which the level of absenteeism could be estimated empirically. None of the |
|immediately prior to death |municipalities collects or records leave information electronically. In addition, the municipalities and council |
| |clinics do not typically gather, record or collate data on employee visits to council clinics and, even where this|
| |information exists in some format (e.g. in the form of inpatient or outpatient registers at staff clinics), data |
| |are not recorded electronically. |
| |Increased absenteeism in the years immediately prior to death was therefore estimated based on interviews |
| |conducted with heads of department and supervisors in each of the municipalities. Interviewees were asked to list |
| |the staff members that died or retired due to ill health in the previous three to five years.[60] Where possible, |
| |respondents were asked to indicate the diagnosed or suspected cause of death. |
| |The amount of unpaid sick leave in the years immediately prior to death (over and above annual and administrative |
| |leave) was provided for individual employees (where this information was known) or estimated for all staff deaths |
| |in the department. In almost all cases, additional absenteeism was experienced only in the last two years prior to|
| |death. |
| |A day of absenteeism was valued at the average daily wage rate (including regular allowances, and employer |
| |contributions to pension and health insurance plans, paid through the payroll system). |
|Lost productivity in the years |The impact on productivity in the years immediately prior to death was estimated based on interviews conducted |
|immediately prior to death |with heads of department and supervisors in each of the municipalities. In almost all cases, respondents indicated|
| |that productivity was affected only in the two years prior to death. |
| |“Lost productivity” was valued using a percentage of the average daily wage rate (including regular allowances, |
| |and employer contributions to pension and health insurance plans, paid through the payroll system). |
|Health care costs borne by the |The three municipalities have different health-care policies and schemes. |
|municipality |Ilala Council employees and their families are covered by the National Health Insurance Fund (NHIF) and the rate |
| |of employer contributions is stipulated in the NHIF Act. There is no additional cost to IMC when an employee seeks|
| |HIV/AIDS related medical care, except that in the long run employer contributions might be increased to compensate|
| |for higher claim rates. |
| |Kampala City Council does not cover any out-of-pocket medical costs incurred by employees, although employees are |
| |entitled to free medical care at approved public facilities. The public and social costs of this care were not |
| |modelled. |
| |Nairobi City Council employees have access to free medical care at government and council hospitals and clinics, |
| |and the compulsory National Hospital Insurance Fund (NHIF) covers a portion of inpatient medical costs. Any |
| |out-of-pocket expenses incurred by employees are reimbursed by Council at the rate of 100 percent for expenses |
| |incurred at public facilities, and 50 percent for expenses incurred at private facilities. The process of getting |
| |medical claims reimbursed is tedious and long, and so many employees abandon their claims. |
| |Approximate health-care costs reimbursed by the municipality in the two years prior to death are typically low, |
| |and were roughly estimated based on interviews with chief administrative officers in each of the departments and |
| |HR. |
|Funeral expenses covered by the |Calculated based on formulae provided by the human resources departments in each of the municipalities. |
|municipality | |
|Terminal benefits from pension fund |The cost is zero for defined contribution schemes. For defined benefit schemes, this should be equal to the |
| |difference between the present value of pension payments due on death and the present value of pension payments |
| |due on normal retirement age.[61] |
| |This is a complex calculation owing to the fact that some of the municipalities have multiple schemes (IMC and CCN|
| |each have three different pension schemes) covering different categories of employees, and with different formulae|
| |and conditions for death and retirement benefits. The calculation also depends on assumptions of average age at |
| |death, average number of years of service (at death and at normal retirement), percentage of the workforce covered|
| |by each defined benefit scheme, and rates and formulae for commutation of pension benefits. The estimation of |
| |these assumptions would rely on questionable data. When initial calculations were made, the results were quite low|
| |and so it was decided to exclude this cost item from the analysis. |
|Recruitment costs |This was estimated based on interviews with the human resources departments at IMC and CCN, and with the Kampala |
| |District Service Commission. |
|Cost of training a replacement |Estimates based on interviews conducted with the human resources department, heads of department and supervisors |
| |in each of the municipalities. |
|Productivity loss before a replacement|The productivity loss before a replacement worker is fully trained was estimated based on interviews conducted |
|worker is fully trained |with heads of department and supervisors in each of the municipalities. |
|Additional supervision time |Estimates based on interviews conducted with heads of department and supervisors in each of the municipalities. |
There are many indirect costs to the municipality that are difficult to measure or model and so have been excluded from the analysis. These include: (1) future increases in employer contributions to medical and pension schemes resulting from HIV/AIDS; (2) cost of service failures or disruptions due to absenteeism, lower productivity, missing skills, and vacant positions; (3) accidents due to sick or inexperienced employees; (4) loss of institutional memory, experience and skills; (5) breakdown in morale and disruption of established teams; (6) diversion of senior managers’ time; and (7) deteriorating labour relations.[62]
A survival period of ten years was assumed from infection to death in the absence of treatment.[63] The municipality was assumed to incur costs in the two years prior to death (years 9 and 10 of the projection) and in the year following death (year 11 of the projection). The present value of the future stream of costs was calculated for each category of employees.
In theory, the applied discount rate should reflect the opportunity cost of capital to the municipality.[64] Estimating the opportunity cost of capital for a municipality is not a straightforward exercise. It depends on the mix of different sources of municipal funds, including own source revenues, central government transfers, grants from donors, and commercial rate loans, each of which has its own opportunity cost of capital. For simplicity, discount rates of three percent and ten percent were modelled to establish reasonable book-ends for the results. This choice is consistent with the typical range of discount rates used for calculating the net present value of health investments.[65] Given that central government transfers account for the major share of municipal budgets in the three cities, and that a significant part of the national budgets are donor funded at very favourable rates of interest, it is likely that the appropriate discount rate is towards the lower end of the modelled range. For this reason, the main body of the paper presents the results at a discount rate of three percent (the results at a discount rate of 10 percent are included in Appendix F).
5 Modelling the Demographic Impact of HIV/AIDS in the Workplace
The assumption for HIV prevalence in each of the three municipalities was set subjectively with reference to: (1) the national, urban and city HIV prevalence rates; and (2) the estimated number of staff deaths due to HIV/AIDS over the last 12 months. A uniform prevalence rate was applied to all employees of the municipality and no attempt was made to vary the assumption by age, sex or category of employee.[66]
Studies in the private sector have shown that unskilled and skilled workers are two to three times more likely to be infected than supervisors and managers.[67]
Incidence rates were estimated based on available statistics and modelled as a uniform rate across all years of the projection.
To keep the model as simple as possible, a deterministic approach was taken and a “linear disease progression” assumed. For example, using a life expectancy of ten years in the absence of treatment, it was assumed that of the HIV-positive employees at the start of the projection, one tenth will die in each of the first ten years of the projection. Each new cohort of HIV infections will progress through each stage of the disease at the same time and at a uniform rate (implying that everyone in the cohort will die after ten years).
In all three municipalities, cases of retirement on medical grounds are rare, and employees typically stay on the payroll until they die. Therefore the model only estimates deaths due to AIDS and ignores retirements on medical grounds.
The model takes into account normal attrition (labour turnover) rates and allows for different worker replacement rates. Normal attrition is not assumed to reduce the number of HIV-positive employees for two reasons. Firstly, replacement workers may also be infected and, secondly, most HIV-positive employees wish to maintain access to medical care and benefits and are therefore unlikely to resign voluntarily.[68]
6 Estimating Aggregate Costs dues to HIV/AIDS
Aggregate costs for the municipality as a whole were obtained by overlaying the demographic analysis with the cost estimates for individual HIV positive employees. Aggregate costs are expressed as a percentage of the annual wage bills of the municipalities.
7 Illustrating the Impact of HIV/AIDS Prevention and Treatment Programmes
HIV/AIDS prevention and treatment programmes can be viewed as investments, the benefits of which are the costs avoided when an employee does not fall ill.[69] The cost incurred in making the investment needs to weighed against the benefits that accrue from that investment.
The model considers two strategies for mitigating the impact of HIV/AIDS in the workplace: (1) a hypothetical prevention programme[70]; and (2) a treatment programme for HIV-positive employees. Sufficient flexibility has been built into the model to allow the user to vary the cost of prevention and treatment programmes, or to vary the assumptions about the impact that these programmes are likely to have on incidence rates and longevity.
Ultimately, the results will depend on whether or not treatment costs are borne by the municipality, and this, in turn, will be driven by national and municipal policy. There are several different scenarios including: (1) national policy to provide free treatment; (2) cost sharing between the central government and the municipality; (3) cost sharing between the municipality and the individual seeking treatment; or (4) cost sharing between the central government and the individual. Municipalities might be able to seek out free coverage for the treatment.
Table 8.2: Costs and Benefits of HIV/AIDS Workplace Interventions
| |Prevention Programme |Treatment Programme |
|Description |Information, Education and Communication (IEC), |Antiretroviral treatment for HIV positive employees. |
| |provision of condoms etc. | |
|Costs |Modelled simply as a fixed amount per employee in the |The cost of a first-line drug regimen for all |
| |workplace. |HIV-positive employees. |
|Benefits |New HIV incidence rate falls (new infections are |Life expectancy of HIV-positive employees is extended |
| |avoided). |(productive time in the workplace is extended and the |
| | |costs associated with death are deferred). |
| | |Absenteeism is reduced and productivity improves. |
| | |Medical expenses as a result of the treatment of |
| | |opportunistic infections falls. |
|Net benefit |The present value of the avoided costs less the cost of|The present value of the avoided costs less the cost of|
| |the prevention programme. |the prevention programme. |
There are several non-financial benefits of workplace interventions that are not taken into account in the model. Firstly, by deferring the incidence of future costs (for example, the costs associated with the treatment of serious opportunistic infections), additional benefits might accrue as drug prices fall, new treatments are developed and public infrastructure to treat HIV/AIDS expands. Secondly, the municipality also buys itself time to expand and deepen its response and to implement its strategies. Thirdly, workplace programmes can reduce the impact of HIV/AIDS on employee morale, improve institutional memory and improve labour relations.[71]
8 Estimating the Cost of Morbidity
A prevalence approach was applied to estimate the annual costs of illness.[72] The cost to the municipality includes absenteeism, lower productivity (when a sick employee comes to work), additional health-care costs, leave for care givers, and leave to attend funerals.
Interviews conducted with heads of department and supervisors in each of the municipalities were used to estimate the following assumptions:
• The average number of days each year that an employee is absent due to ill health
• The average number of days each year that an employee is less than fully productive on the job due to ill health, and their average rate of productivity during this time
• The average number of days each year that an employee is absent to care for sick family members
• The average number of days each year that an employee is absent to attend funerals.
Average annual per-person medical expenses were estimated with reference to the municipalities’ medical policies, health-care schemes, and actual expenses from municipal budgets.
9 Illustrating the Impact of Malaria Prevention Programmes
Although there are several major diseases that result in employee illness and absenteeism, it was decided to look specifically at the portion of annual costs that could be attributed to malaria, and then to model the impact of a malaria prevention strategy. Malaria is a dominant cause of illness in Kampala and in Dar es Salaam (although to a lesser degree), resulting in significant worker absenteeism. In addition, malaria can be prevented through sensitisation and the provision of Insecticide Treated Nets (ITNs), possibly through workplace programmes.
The cost of illness attributable to malaria was estimated for each category of employee based on data from the Adult Mortality and Morbidity Project (Ilala Municipality) and from municipal health facilities in Nairobi and Kampala.
The impact of providing ITNs to employees as part of a malaria prevention programme was modelled. The model relies on assumptions for the percentage of the workforce covered, average cost per employee covered (e.g. for insecticide treated bed nets[73]), number of years before nets require re-treatment or replacement, and percentage reduction in costs (absenteeism, lost productivity, leave for care givers, and medical care) attributable to malaria.
10 Full Assumption Set
Annex E contains the full set of assumptions used to calculate the preliminary set of results presented in this paper.
11 Questioning the Theoretical Framework
Is it appropriate to apply a cost– benefit approach in a public sector context?
The study draws on an approach and methodology that has been applied widely in the private sector (particularly in South Africa) to demonstrate the cost effectiveness of workplace HIV/AIDS programmes. This body of work estimates the long-run cost of HIV/AIDS to business, often expressed as a “long-run AIDS tax”, or the increase in wage or operating costs as a result of the epidemic.[74] Several studies take the analysis further to estimate the costs and benefits of workplace HIV/AIDS prevention, care and treatment programmes. These studies demonstrate that there is typically a strong business case or financial imperative for employer-sponsored workplace programmes. For example, a study conducted by Sydney Rosen and others on the impact of HIV/AIDS on six South African businesses demonstrated that for all six firms, it made financial sense to provide free anti-retrovirals to all levels of employees in the workplace. Or expressed in another way, the firms earned a positive return on their investments in treatment programmes.
Although used extensively in the private sector, a cost-benefit approach has not been widely applied in the public sector.[75] Many would argue that public sector organisations do not perceive costs and benefits in the same way and that without a profit motive there are much weaker incentives to maximise the cost effectiveness of resource allocation decisions. In the particular case of local governments, it might be argued that the short term agendas of local politicians distort the incentives for investments that yield benefits accruing over a longer timeframe. These are valid concerns.
Nevertheless, there are several compelling arguments for applying a cost–benefit approach in a public sector context and particularly for local governments. Firstly, there is a strong normative dimension to this type of work. Local governments should be making the most cost-effective decisions in order to maximise improvements in service delivery in resource scarce settings. One possible impediment to actually making decisions on this basis might be a lack of information on the financial impact of various strategies versus the financial consequences of the status quo scenario. Demonstrating the magnitude of potential financial benefits may also create incentives for changing traditional ways of thinking. Secondly, there are significant resources being made available for HIV/AIDS, TB and malaria as part of national programmes. Local governments can make use of a cost–benefit analysis to shape and strengthen proposals for national funds. There have been challenges in articulating demand for national funds and the model might help provide the basis for a detailed and well-reasoned proposal from the local government to the National AIDS Council or equivalent body.
What if municipal employees are not actually productive?
The model estimates the costs incurred by the municipality when an employee is ill. Absenteeism and “lost productivity” account for the most significant portion of total costs. The cost of absenteeism and reduced productivity (when a sick employee comes to work) is calculated using the average daily wage rate. This implicitly assumes that municipal employees have a “neutral” product or that the value of the services that they produce is equal to their cost of labour. Alternate assumptions would be that the municipal employee has a positive product (adds more value than what he or she is paid) or at the other extreme, has a negative product (is rent seeking and extracts more value that what he or she is paid). If either of these scenarios is true, then the model either underestimates or overestimates the financial cost of illness in the workplace. A factor has been included in the model to facilitate some sensitivity analysis of this assumption.
It is important to note that when this study refers to absenteeism or “lost productivity”, it is measuring the wage value of days of work lost. No attempt has been made to explicitly measure the productivity of municipal workers, which is beyond the scope of this study but would be an interesting area for further research.
Another point to bear in mind is that many of the municipalities with which the Bank is working have or will implement institutional reforms. These reforms often include retrenchment of surplus labour and increasing the productivity of labour. Even where labour is not considered to have a neutral or negative product, there is an opportunity cost to that daily wage. Many municipalities are realizing that unproductive employees compromise their ability to allocate resources efficiently in order to maximise service delivery. As municipalities are increasingly at the frontline of service delivery in Africa, and institutional and financial reforms are seen as a necessary requirement for this, the need to restructure the organisation, promote culture change and improve productivity is increasing. The World Bank is supporting an increasing number of programs in large cities where the focus is on institutional and financial restructuring as the basis for any infrastructure investments. Institutional audits and associated restructuring of functions and establishment of reliable human resource management systems are core activities in this process. Having comprehensive human resource data and understanding the risks of morbidity and mortality are critical factors in improving the organisational behaviour of municipalities.
In addition, the World Bank continues to invest significant resources in capacity building activities for local governments. Morbidity and Mortality, particularly as a result of HIV/AIDS works to undermine the returns on these investments. It is also worth noting that the costs of HIV/AIDS are higher for senior (and more productive) staff, and so as municipalities implement institutional reforms and restructuring strategies, there will be an increasing proportion of higher cadre staff. Therefore costs as a percentage of the wage bill will rise and the negative impact of HIV/AIDS in the workplace will become more pronounced.
Results
1 Cost of a New HIV Infection
Figure 9.1 shows the present value cost of a new HIV infection for different categories of employees in each of the three municipalities (in US dollars). Figures 9.2 to 9.4 present the results graphically in local currency. The results show that the cost of a new infection rises with seniority and salary levels in all of the municipalities.
The analysis produces similar results across the three municipalities. To a large degree this reflects the similarity of the HIV/AIDS epidemic in Tanzania, Kenya and Uganda and comparable urban prevalence rates. The analysis and modelling is likely to yield quite different findings in either low prevalence (e.g. West Africa) or high prevalence settings (e.g. South Africa).
At a discount rate of 3 percent, the cost of a new infection is roughly double the annual salary for CCN and KCC employees, and 1.5 times annual salary for IMC employees.[76]
Figure 9.1: Present value cost of a new HIV infection across municipalities (USD)
| |Nairobi City Council |Kampala City Council |Ilala Municipal Council |
| | Present Value Cost of |Average Salary |PV Cost as a % of | Present Value Cost of |Average Salary |
| |New Infection at 3% | |Average Salary |New Infection at 3% | |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |54,989,659 |57,393,941 |59,918,437 |62,569,158 |65,352,415 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.2% |2.3% |2.4% |2.5% |2.7% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |126,652,593 |132,014,051 |137,643,581 |143,554,588 |149,761,146 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.6% |1.5% |1.5% |1.5% |1.5% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |78,105,056 |80,043,693 |82,040,490 |84,097,191 |86,215,593 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.2% |1.2% |1.2% |1.2% |1.1% |
Table 9.1 is calculated on the assumptions outlined in Annex E. These base case assumptions were set conservatively and in many cases the estimates of HIV/AIDS related costs were modified downwards to ensure that results were not overstated. A sensitivity analysis was conducted to test the impact of varying key assumptions and the results are documented in Annex F. Table 9.6 summarises the results of an alternate scenario with the following modified assumptions: (1) prevalence rates revised to WHO estimates of urban prevalence; (2) life expectancy in the absence of treatment reduced from 10 years to 8 years; (3) absenteeism increased by a further two additional months in each of the two years prior to death; and (4) productivity reduced by a further 20 percent in each of the two years prior to death.
Table 9.6: Impact of relaxing assumptions of HIV/AIDS related costs
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |73,762,370 |77,058,214 |80,518,851 |84,152,520 |87,967,872 |
|Wage Bill |2,517,012,961 |2,496,213,867 |2,474,741,442 |2,452,561,105 |2,429,636,544 |
|Cost % Wages |2.9% |3.1% |3.3% |3.4% |3.6% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |238,133,138 |248,596,526 |259,583,082 |271,118,967 |283,231,646 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.9% |2.9% |2.9% |2.9% |2.9% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |159,845,481 |164,028,456 |168,336,920 |172,774,638 |177,345,488 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |2.4% |2.4% |2.4% |2.4% |2.4% |
2 Cost of Morbidity
Figures 9.7 to 9.9 show the annual aggregate cost of illness in each of the municipalities (in the first year of the projection). The cost includes: (1) additional paid leave due to illness; (2) productivity lost when employees are sick at work; (3) leave to attend the funerals; (4) leave to care givers; and (5) medical expenses to the extent that these are covered by the municipalities. The results show that the cost of illness in the workplace is approximately one percent of total wages.
Figure 9.7: City Council of Nairobi (KSH)
[pic]
Figure 9.8: Kampala City Council (UGSH)
[pic]
Figure 9.9: Ilala Municipal Council (TSH)
[pic]
3 Impact of Workplace Interventions
Providing Free ARVs to HIV-positive employees
Figures 9.10 to 9.14 illustrate the economic consequences of treating an individual employee at a discount rate of three percent (results at a discount rate of 10 percent are included in Annex F). It assumes that treatment begins on average in the sixth year following infection, and results in a four-year extension to life expectancy. In addition, in the two years prior to death, it is assumed that absenteeism and productivity losses are reduced.
For the City Council of Nairobi, the results show that it is worthwhile providing treatment for all categories of employees even if the City Council bears 100 percent of the cost.
For Kampala City Council, the results at a discount rate of 10 percent suggest that it makes economic sense to treat all categories of employees, assuming the municipality meets the full cost of treatment. At a discount rate of 3 percent, the cost of treatment outweighs the benefit of treatment for support staff and teachers. If however, the municipality only pays 50 percent of the cost of treatment, the benefits of treatment outweighs the cost of treatment for all categories of employees (see Table 9.3).
For employees of Ilala Municipal Council, the results suggest that the financial benefits of providing treatment to HIV positive employees only outweigh the costs for managers and supervisors. If the municipality pays 50 percent of the costs of treatment, it makes financial sense to treat all employees (at a discount rate of 10 percent).
Table 9.3: Summary of Results
|Discount Rate |City Council of Nairobi |Kampala City Council |Ilala Municipal Council |
|3% |Positive return to providing treatment for all|Positive return to providing treatment for all|Positive return to providing treatment for |
| |employees |employees except support staff and teachers. |managers and supervisors. |
| | |Positive return to providing treatment for all|Positive return to providing treatment for |
| | |employees if municipality meets 50% of |almost all employees if municipality meets 50%|
| | |treatment costs. |of treatment costs. |
|10% |Positive return to providing treatment for all|Positive return to providing treatment for all|Positive return to providing treatment for |
| |employees |employees |managers and supervisors. |
| | | |Positive return to providing treatment for all|
| | | |employees if municipality meets 50% of |
| | | |treatment costs. |
If the municipality does not bear the full cost of the treatment programme or the cost of antiretroviral drugs falls, then the financial imperatives to provide treatment are even stronger.
Figure 9.10: City Council of Nairobi (KSH)
[pic]
Figure 9.11: Kampala City Council (UGSH)
[pic]
Figure 9.12: Kampala City Council (UGSH): Municipality only bears 50% of the cost of ARVs
[pic]
Figure 9.13: Ilala Municipal Council (TSH)
[pic]
Figure 9.14: Ilala Municipal Council (TSH): Municipality only bears 50% of the cost of ARVs
[pic]
The results presented above are premised on the assumption that a workplace treatment programme would cover first-line drug regimen costs only. The question arises whether or not a local government would be able to implement such a policy (and the desirability of such a policy) given the public health risks associated with drug-resistant viral strains. While the prices of first-line drug prices have fallen significantly, second-line treatment costs are still very expensive, and can be in the order of seven to 28 times the cost of first-line drugs.
If ten percent of employees require second-line treatment at a cost of seven times the first-line treatment costs, then the cost of treatment exceeds the financial benefit of treatment for additional categories of employees (see table 9.4 below). For more detailed results refer to the sensitivity analysis in Annex F.
Table 9.4: Summary of Results: ten percent of HIV-positive employees require second-line drugs (at a cost of seven times first-line drug costs)
|Discount Rate |City Council of Nairobi |Kampala City Council |Ilala Municipal Council |
|3% |Positive return to providing treatment for all|Positive return to providing treatment for |Positive return to providing treatment for |
| |employees |managers and supervisors. |managers only. |
|10% |Positive return to providing treatment for all|Positive or approximately break-even return to|Positive return to providing treatment for |
| |employees |providing treatment for all employees |managers only. |
Impact of a HIV/AIDS Prevention Programme
Tables 9.5 to 9.7 below, summarise the results of a cost benefit analysis for a workplace prevention programme. Assuming the programme costs US$ 10 per person and that the impact of the programme is a 50 percent reduction in HIV incidence rates[80], then the financial benefits of the programme far outweigh the financial costs of the programme in all three municipalities.
Table 9.5: City Council of Nairobi (KSH)
| |Number of |Cost of Prevention|Infections |Benefit of Prevention Programme (PV |"Break Even" Infections|
| |Employees |Programme (US$ 10 |Averted |Cost of Infections Averted) |to Avert |
| | |per person) | | | |
| |2006 |KSH |50% |@ 3% |@ 10% |@ 3% |@ 10% |
|1. Managers (Grades 1-5) |65 |52,647 |0 |633,992 |348,164 |0 |0 |
|2. Supervisors, Professionals (Grades 6-9) |621 |502,985 |4 |3,137,437 |1,721,403 |1 |1 |
|3. Semi-Skilled (Grades 10-13) |2394 |1,939,044 |48 |26,238,071 |14,385,606 |4 |6 |
|4. Support Staff (Grades 14-19) |10274 |8,321,529 |69 |24,577,418 |13,456,712 |23 |42 |
|Total |13354 |10,816,206 |121 |54,586,918 |29,911,885 | | |
Table 9.6: Kampala City Council (UGSH)
| |Number of |Cost of Prevention|Infections |Benefit of Prevention Programme (PV |"Break Even" Infections|
| |Employees |Programme (US$ 10 |Averted |Cost of Infections Averted) |to Avert |
| | |per person) | | | |
| |2006 |UGSH |50% |@ 3% |@ 10% |@ 3% |@ 10% |
|1. Managers (U1,U2) |34 |596,207 |0 |5,967,881 |3,220,569 |0 |0 |
|2. Supervisors, Professionals (U3-U4) |127 |2,227,008 |1 |6,904,780 |3,725,247 |0 |0 |
|3. Semi-Skilled (U5, U6, U7) |693 |12,152,098 |9 |58,960,639 |31,891,620 |2 |4 |
|4. Support Staff (U8) |397 |6,961,591 |2 |8,313,104 |4,511,621 |2 |3 |
|5. Teachers (All Grades) |1538 |26,969,591 |7 |29,822,133 |16,180,611 |6 |12 |
|Total |2789 |48,906,494 |19 |109,968,537 |59,529,667 | | |
Table 9.7: Ilala Municipal Council (TSH)
| |Number of |Cost of Prevention|Infections |Benefit of Prevention Programme (PV |"Break Even" Infections|
| |Employees |Programme (US$ 10 |Averted |Cost of Infections Averted) |to Avert |
| | |per person) | | | |
| |2006 |TSH |50% |@ 3% |@ 10% |@ 3% |@ 10% |
|1. Managers |41 |442,800 |0 |1,452,156 |788,390 |0 |0 |
|2. Supervisors and Professionals |171 |1,846,800 |1 |3,664,967 |1,987,103 |1 |1 |
|3. Semi-Skilled |765 |8,262,000 |16 |30,324,204 |16,406,967 |4 |8 |
|4. Support Staff |442 |4,773,600 |3 |4,070,559 |2,202,947 |4 |7 |
|5. Teachers (All Grades) |2437 |26,319,600 |17 |36,609,205 |19,820,546 |12 |22 |
|Total |3856 |41,644,800 |37 |76,121,092 |41,205,954 | | |
Impact of a Malaria Prevention Programme (Provision of Subsidised Insecticide Treated Bed Nets to Employees)—KCC and IMC Only
Since malaria is not a significant cause of morbidity and mortality in Nairobi, the impact of a malaria prevention programme was only modelled for Kampala City Council and Ilala Municipal Council.
Making some very simple assumptions about the share of absenteeism attributable to malaria, a simple calculation of the costs and benefits of providing Insecticide Treated Nets (ITNs) to employees was carried out. It was assumed that ITNs cost US$ 5 each and last three years, and result in a 50 percent reduction in absenteeism.
For Kampala City Council, the benefits of providing ITNs exceed the costs, even where the municipality meets 100 percent of the costs of providing the nets.
In the case of Ilala Municipal Council, the net benefit of providing ITNs to employees is only positive under a cost sharing arrangement, where the municipality meets approximately half of the costs of providing the nets.
Table 9.8: Kampala City Council (UGSH) —100% cost of ITNs borne by municipality
| | |Present Value at 3% |Present Value at 10%|2006 |2007 |2008 |
|Cost of Prevention Programme | | | | | |
| |Insecticide Treated Bed Nets |24,453,247 |24,453,247 |24,453,247 | | |
|Benefits of malaria Programme |28,273,966 |26,490,550 |9,243,999 |9,706,199 |10,191,508 |
|Net Benefits |3,820,719 |2,037,303 |(15,209,248) |9,706,199 |10,191,508 |
Table 9.9: Ilala Municipal Council (TSH)—100% cost of ITNs borne by municipality
| | |Present Value at 3% |Present Value at 10%|2006 |2007 |2008 |
|Cost of Prevention Programme | | | | | |
| |Insecticide Treated Bed Nets |20,822,400 |20,822,400 |20,822,400 | | |
|Benefits of Malaria Programme |12,720,540 |11,928,222 |4,240,180 |4,367,385 |4,498,407 |
|Net Benefits |(8,101,860) |(8,894,178) |(16,582,220) |4,367,385 |4,498,407 |
Table 9.9: Ilala Municipal Council (TSH)—50% cost of ITNs borne by municipality
| | |Present Value at 3% |Present Value at 10%|2006 |2007 |2008 |
|Cost of Prevention Programme | | | | | |
| |Insecticide Treated Bed Nets |10,411,200 |10,411,200 |10,411,200 | | |
|Benefits of Malaria Programme |12,720,540 |11,928,222 |4,240,180 |4,367,385 |4,498,407 |
|Net Benefits |2,309,340 |1,517,022 |(6,171,020) |4,367,385 |4,498,407 |
Conclusions and Recommendations
Morbidity and mortality contribute directly and indirectly to the municipal wage bill, which is a large slice of the municipal budget in most cities. This cost of morbidity and mortality in the municipal workplace (expressed as a percentage of the municipal wage bill) could instead be directed to more productive municipal activities. The preceding analysis has shown that:
• the present value cost of a new HIV/AIDS infection is roughly twice the annual salary of an employee; and
• the annual cost of HIV/AIDS in the workplace is between one and two percent of the municipal wage bill.
Disease also undermines the capacity of the municipality to deliver services through increased absenteeism, lower productivity, and the loss of experienced and knowledgeable staff. Municipal employees highlighted several ways in which service delivery is compromised by illness and death in the workplace, including:
• Failure to meet targets and deadlines (delays in expected outcomes).
• Poor quality and low quantity of service delivered.
• Work is re-allocated across remaining employees, however the person taking on the work might not be qualified or willing and so the quality of the service declines.
• In the Education department, teacher absenteeism coupled with a shortage of replacements means that schedules are disrupted, children are left without schooling, and the quality of instruction falls. This problem is particularly severe in disadvantaged schools where teachers are forced to look after more than one classroom. Illness or death of inspectors, planning and management staff in the municipal education departments also compromises learning outcomes.
• In the Inspectorate/Law Enforcement department, sickly staff cannot be deployed or are put on light duties, and as a result service delivery is compromised.
• In health facilities, illness of staff results in a reduction in the comprehensiveness and quality of care, a cutback in outreach services, and reduced time for one-on-one counselling. It also results in increased workload for health workers in facilities that are already understaffed.
There are three high level strategies that the municipality can employ to manage the impact of morbidity and mortality on municipal human resources and service delivery:
1. investing in prevention activities including Information, Education and Communication (IEC), and the promotion and distribution of condoms in the workplace;
2. investing in the treatment and care of sick employees; and
3. investing in broadening the skills of employees to facilitate re-allocation of responsibilities and establishing career development and succession plans.
The analysis has shown that workplace prevention and treatment programmes are in most cases profitable investments. Making some simple assumptions about illness and death in the workplace, the analysis demonstrates that in most cases, the municipalities will achieve positive returns on investments in prevention and treatment of HIV/AIDS in the workplace. In addition, in cities where malaria accounts for a large proportion of regular absenteeism, it may make sense for municipalities to build prevention activities (in particular Insecticide Treated Bed Nets) into their workplace health programme. These investments may have other non-financial benefits including skills retention, improving morale in the workplace, improving labour relations, buying time for advances in medical research and falling costs of drugs, and demonstrating local government leadership. The ethical and moral imperatives to act are also high.[81]
It is important that municipalities engage with National AIDS Councils and Ministries of Health as they develop their workplace health policies and programmes. In particular, the municipalities need to understand the national health and HIV/AIDS strategies, the Government’s policy on providing anti-retrovirals to public sector employees, and mechanisms by which the municipalities can access national funds for HIV/AIDS, TB and malaria (see Section 10.1 below for accessing national level funds).
The following table details the challenges highlighted by the participating municipalities and the corresponding recommendations for: (1) individuals (leadership and municipal managers); (2) institutional entities such as council owned clinics; and (3) municipal policies and practices (in the areas of human resource management, budgeting and planning). This has important implications for municipal managers and urban programmes supporting municipal reforms.
Table 10.1: Mitigating the impact of Disease in the Workplace
|Leadership |Challenges |
| |Workplace health issues are not always viewed as a priority for municipal management; uneven management commitment to workplace |
| |health issues. |
| |Management does not have the skills or time to guide municipal HIV/AIDS responses. |
| |It is unclear who is responsible for workplace health issues and employee welfare; responsibility is fragmented across the |
| |public health department, community services department, human resources department and the district HIV/AIDS committee and/or |
| |focal point (where they exist). |
| |Recommendations |
| |Leaders need to make and demonstrate a commitment to addressing workplace health issues (e.g. participate in workplace |
| |programmes) |
| |Conduct sensitisation of councillors and management on the impact of morbidity and mortality on the supply and demand for |
| |services, and ensure that this is included as part of the orientation of senior managers. |
| |Management must clearly establish responsibilities for workplace health issues and employee welfare. |
| |Council and municipal management should prioritise the resourcing and delivery of workplace health programmes. |
| |Workplace health issues should be placed on the agenda of senior management meetings. |
| |Municipal managers should engage with employee unions and staff associations on workplace health issues. |
|Council Owned Clinics |Challenges |
| |Staff clinics (where they exist) are poorly equipped, understaffed and are often without drugs. |
| |No clear referral system for staff seeking support, care and treatment for HIV/AIDS. |
| |Manual record keeping in staff clinics (both inpatients and outpatients registers) hinders the collation and analysis of data. |
| |Recommendations |
| |Information on the usage of council owned medical facilities by staff should be collected and recorded. |
| |Record keeping in staff clinics should be computerised. |
| |Ensure that staff clinics are adequately equipped, staffed and supplied with drugs. |
| |Establish a referral system for staff seeking support, care and treatment for HIV/AIDS. |
| |Ensure adequate protective gear is available for health workers. |
|Budgeting and Planning |Challenges |
| |Lack of information and modelling tools to adequately plan for illness and deaths of municipal employees, staffing implications |
| |and associated costs. |
| |Failure to take into account the direct and indirect costs of workplace health issues in the municipal budget. |
| |Lack of budget provision for workplace health interventions. [82] |
| |Recommendations |
| |Adequate and realistic provisions should be made in council budgets for direct and indirect costs that arise from health issues |
| |in the workplace. |
| |Budget line items should be devised to enable the costs associated with workplace issues to be tracked at a meaningful level of |
| |detail over time. |
| |Adequate provision should be made for the cost of workplace health programmes. |
|Human Resources Management |Challenges |
| |Human resource management systems are characterised by manual processes and record keeping, hindering (among other things) |
| |monitoring and evaluation of the impact of disease on municipal human resources and service delivery. |
| |Only a subset of personnel information is maintained in electronic format (typically for payroll purposes). |
| |Data in individual personnel files are difficult to access, mine or analyse; human resource data are often missing in individual|
| |personnel files. |
| |Electronic data are also difficult to analyse because or missing data, errors and irregularities. |
| |No data systematically collected or collated on absenteeism, medical claims and deaths; level of productivity of municipal |
| |employees is unknown. |
| |Recommendations |
| |Leave data should be collected and collated electronically on a regular (monthly) basis. Details should include the type of |
| |leave (including sick leave, leave to care for sick family members, funeral attendance) and the nature of the illness in the |
| |case of long term absenteeism. In some municipalities this information exists at the departmental level (or school level) but |
| |the information is not collated or analysed in a meaningful way. |
| |Establish systems to monitor and analyse absenteeism and declining productivity over time. |
| |Collect details of medical claims paid and record these electronically. |
| |Systematically collect and record data on staff terminations, including the type of termination (death, medical retirement, |
| |retirement, abscondment etc.) and the cause of death or disability. Death certificates should be collected by the municipality. |
| |Human resource management systems and processes should be reviewed to determine how best to simplify procedures and computerise |
| |personnel records. |
| |Basic human resource data should be held at headquarters for staff employed at divisional level (KCC), including the number of |
| |contract staff. |
| |Implement measures to ensure that confidentiality of personal information is maintained. |
| |Identify critical municipal functions that are vulnerable to absenteeism and skills shortages. |
| |Devise strategies to mitigate against the impact of illness and death on critical municipal function by investing in broadening |
| |the skills of employees to facilitate re-allocation of responsibilities; and establish career development plans and succession |
| |plans. |
| |Include HIV prevention and workplace health issues in staff orientation programmes. |
|Workplace policies and |Challenges |
|programmes |Municipal HIV/AIDS Committees/Teams are not functioning effectively and are not adequately addressing workplace health issues. |
| |Municipalities have not drafted and approved workplace policy for HIV/AIDS.[84] Slow progress on a workplace policy is an |
|Note: a workplace HIV/AIDS |obstacle to the development and implementation of a workplace programme. |
|policy should be part of a |Recommendations |
|broader, integrated strategy to|Establish a workplace sub-team in the Municipal or District HIV/AIDS Committee to specifically manage workplace HIV/AIDS issues.|
|address HIV/AIDS in the |The team should include representation from the staff association and/or trade union and human resources department. Establish |
|locality, including a range of |the group’s terms of reference and to whom the group is accountable to. |
|external responses.[83] |Consider conducting a prevalence study in the workplace to establish a baseline prevalence rate and to determine how the |
| |prevalence pattern varies by sex, age, salary scale, occupational class and other variables. It may be desirable to conduct |
| |surveys regularly to monitor prevalence over time. A Knowledge Attitudes and Practices (KAP) study may also be a worthwhile |
| |preparatory activity to refine and target interventions to increase awareness and reduce stigma in the workplace. |
| |Develop and implement a workplace policy for HIV/AIDS. |
| |Invest in prevention programmes to reduce incidence of disease including Information, Education and Communication (IEC) |
| |activities, and promotion and distribution of condoms. |
| |Ensure employees have access to VCT services. |
| |Invest in programmes to increase openness and reduce stigma in the workplace. |
| |Establish Employee Assistance Programmes including counselling, support and advice to infected and affected employees. |
| |Improve access to treatment, care and support for employees and their families affected by HIV/AIDS and other major diseases. |
| |Assess capacity to develop and implement workplace health programmes and policies (and review on a regular basis). |
| |Conduct training and sensitization of managers and supervisors to ensure they can effectively manage HIV/AIDS related problems |
| |in the workplace. |
| |Explore options for mobilising external funds to support the implementation of workplace programmes. For Kampala City Council, a|
| |staff association could be formed to access funding under the Community-led HIV/AIDS Initiative (CHAI). |
|Donor supported programmes |Challenges |
| |HIV/AIDS has the potential to undermine investments to strengthen municipal management, municipal finance, local service |
| |delivery and local economic development. |
| |Recommendations |
| |Mainstream HIV/AIDS into urban projects to mitigate against this risk and contribute to the sustainability of urban investments.|
1 Accessing National Level Funds for Workplace Health Interventions
Local governments and municipalities might want to plan and implement specific activities in response to the analysis presented in this paper. It is important that workplace health issues, particularly as a result of HIV/AIDS and malaria, be addressed within the framework of a city HIV/AIDS strategy and malaria control programme, where they exist.
There is a spectrum of interventions that can be planned and implemented in the workplace, many at little or no cost to the municipality but with a significant impact on morbidity and mortality of municipal employees. Other types of interventions require financial resources, and there are several possible sources of funding for these. Firstly, the municipality might allocate its own funds for workplace health interventions, or there might already be an allocation in the municipal budget for HIV/AIDS related activities (possibly by way of a central government transfer). Secondly, the municipality or local government can identify and leverage local resources by developing partnerships and coordination mechanisms with civil society and non governmental organisations. And thirdly, local government authorities can apply for national level funds from national HIV/AIDS, TB and malaria programmes typically through their line ministry and/or National Aids Council. It is therefore very important that national level counterparts are identified and involved very early in the planning process to ensure effective coordination and efficient use of funds.
In many cases, it will be appropriate for the local government or municipality to seek national level funding through their line ministry, since national HIV/AIDS programmes (including the World Bank's Multi-Sector AIDS Programme) typically allocate funds to support activities in sector ministries. These may encompass or complement activities in individual local governments, particularly those activities designed to address workplace health issues or the mainstreaming of HIV/AIDS into local government activities. Funds to enhance capacity and implement programmes in the local government sector are usually provided on the basis of annual work plans and budgets submitted by the Ministry of Local Government to the National AIDS Council or Secretariat. In some countries, local governments and municipalities (particularly the larger ones) might be eligible for funding directly from national programmes outside of line ministry plans.[85]
Local governments and municipalities might also be able to access funds through decentralised public sector agencies with delegated responsibility for project implementation. Typically, National AIDS Councils or Secretariats have established HIV/AIDS committees at lower levels of government to coordinate and facilitate community level subprojects. Sub-national HIV/AIDS Committees usually have the mandate to appraise, approve and supervise small-scale community based subprojects and to facilitate coordination with civil society organisations and decentralised sector agencies at the local level. Funding for sub-national HIV/AIDS Committees do not typically extend to interventions in the municipal workplace, but they may allow for other relevant activities including capacity building activities for HIV/AIDS committee members in the areas of planning, procurement and financial management.[86]
2 Dissemination of Findings and Areas for Further Research
It is intended that one eventual output of this work is a simple modelling tool that can be used by local governments to assess the impact of morbidity and mortality on the municipal workplace, and to evaluate the costs and benefits of various workplace intervention scenarios. To deliver this output, a number of steps would need to be taken including:
• validation of assumptions, methodology and results with each of the municipalities who participated in the study, possibly through a series of small workshops;
• refinement of the model based on the outputs of these workshops;
• development of a manual to accompany the modelling tool; and
• production and dissemination of appropriate learning tools such as a CDROM.
There are also several areas that might benefit from further research and analysis, including the following:
• Assessment of the impact of morbidity and mortality (particularly HIV/AIDS) on the demand for municipal services. HIV/AIDS affects the demand for services provided by local authorities. As a result of the epidemic, local authorities will face increasing demands for new or expanded services. These might include demands for expanded health and social welfare services, home and community based care, cemetery space, and services targeted at orphans and street families.
• Analysis of the impact of morbidity and mortality on affordability of services and local revenue collection. Households tend to suffer from multiple infections and productive labour and household income is diverted to the care of sick household members, and to paying medical expenses and funeral costs. This contraction in household income and shift in household expenditure away from savings and consumption towards medical expenses, affects households’ ability to pay local rates, user fees and taxes. It also impacts negatively on the local business environment, contributing further to lower local revenue collection from market fees, business licenses and taxes.
• Comparative study using the same model in a selection of high and low HIV prevalence countries in Africa. The analysis presented in this paper yielded similar results for Kampala, Nairobi and Dar es Salaam, reflecting to a large degree the similarity of the HIV/AIDS epidemic in Tanzania, Kenya and Uganda and comparable urban prevalence rates. The analysis and modelling is likely to yield quite different findings in either low prevalence (e.g. West Africa) or high prevalence settings (e.g. South Africa).
References
Adult Morbidity and Mortality Project. 2004. The Policy Implication of Tanzania’s Mortality Burden. 4 vols.
City Council of Kampala. 2005. Annual Budget 2005-2006. June 2005.
City Council of Kampala. 2005. The First Kampala Citizens Report Card: Measuring Citizens’ Satisfaction with Key Public Services. April 2005.
Fox MP, Rosen S, MacLeod WB et al. 2004. “The impact of HIV/AIDS on labour productivity in Kenya” Tropical Medicine & International Health 9: 318–324.
Government of Kenya, Ministry of Health, National Leprosy and Tuberculosis Programme. 2003. Annual Report 2003.
Government of Kenya, Ministry of Health. 2001. AIDS in Kenya; background, Projections, Impact, Interventions, Policy. Sixth edition, 2001.
Government of Kenya. 2005. National HIV/AIDS Strategic Plan 2005/6 – 2009/10: A Call to Action. Final Draft, 11 March 2005.
Government of Tanzania, Ministry of Health, National Tuberculosis and Leprosy Programme. 2003. Annual Report 2003.
Government of Tanzania. 2002. Population and Housing Census,
Ilala Municipal Council and UNDP. 2004. Draft Ilala Municipal HIV/AIDS Profile. May 2004.
Kalembe, Isaac. 2005. “Malaria Kills 400 Ugandans Daily”, New Vision (Kampala), November 4, 2005.
Kenya National AIDS and STD Control Programme (NASCOP), Ministry of Health. 2004. Sentinel Surveillance of HIV and STDS in Kenya, 2004. April 2004.
Loewenson R. and A. Whiteside. 1997. Social and Economic Issues of HIV/AIDS in Southern Africa. Report prepared for SafAIDS, Harare, accessible at .
Morris, C.N., and E. J. Cheevers. 2000. “The direct costs of HIV/AIDS in a South African Sugar Mill.” AIDS Analysis Africa 10 (5).
Over, Mead et al. HIV/AIDS Treatment and Prevention in India: Modeling the Cost and Consequences. The World Bank, Human Development network, health, Nutrition and Population Series.
Republic of Uganda and Uganda Aids Commission. 2004. The Revised National Strategic Framework for HIV/AIDS Activities in Uganda 2003/04-2005/06: A guide for all HIV/AIDS Stakeholders.
Republic of Uganda, Ministry of Health, STD/AIDS Control Programme. 2003. HIV/AIDS Surveillance Report. June 2003.
Republic of Uganda, Ministry of Health. 1999. National Health Policy. September 1999.
Republic of Uganda, Ministry of Health. 2004. Health Policy Statement 2004/2005. June 2004
Republic of Uganda, Ministry of Health. 2005. HIV/AIDS Sero-Behavioural Survey 2004-05: Preliminary Report. June 2005.
Republic of Uganda, Ministry of Public Service. 2000. Baseline Survey of the Trends and Impact of HIV/AIDS on the Public Service in Uganda: Final Report.
Republic of Uganda, Sector HIV/AIDS Committee ACP Desk. 2003. Sector Workplan and Budget on HIV/AIDS Activities for Fiscal year 2003/04.
Republic of Uganda. 2004. Annual Health Sector Performance Report: Financial Year 2003/2004. October 2004.
Rosen Sydney et al. 2004. “The Cost of HIV/AIDS to Businesses in Southern Africa.” AIDS 18: 317-324.
Rosen, Sydney and Jonathan L. Simon. 2003. “Shifting the Burden of HIV/AIDS.” Bulletin of the World Health Organisation 2003, 81 (2).
Rosen, Sydney et al. 2000. “Care and Treatment to Extend the Working Lives of HIV-Positive Employees: Calculating the Benefits to Business.” South African Journal of Science, July 2000.
Rosen, Sydney, et al. 2000. "The Response of African Business to HIV/AIDS." HIV/AIDS in the Commonwealth 200/01: 1-11.
Schneider and Kelly. 2003. Modelling the Impact of HIV/AIDS to Employers of Financial Services Companies in South Africa. Actuarial Solutions.
Schneider, Martin. 2004. Natural History and Mortality in HIV-positive individuals living in resource-poor settings: A Literature Review.
SIAPAC. 2002. Impact Assessment of HIV/AIDS on the Municipalities of Ongwediva, Oshakati, Swakopmund, Walvis Bay and Windhoek: Volume 2: Ongwediva.
Stover, J. and Bollinger, L. 1999. The Economic Impact of AIDS, The Futures Group, Washington.
Stover. 2004. “Projecting the demographic consequences of adult HIV prevalence trends: the Spectrum Projection Package,” Sexually Transmitted Infections 2004.
Sydney Rosen, Jonathon Simon, William MacLeod, Matthew Fox, Donald M Thea, Jeffrey R Vincent. 2003. “AIDS Is Your Business.” Harvard Business Review, Vol. 81, No. 1, February 2003.
Tanzania Commission for AIDS (TACAIDS). 2005. Tanzania HIV/AIDS Indicator Survey 2003-04.
Tanzania Ministry of Health. 2001. Burden of Disease Profile, Dar es Salaam, Graphs of Selected Health and Demographic Indicators.
Tanzania National Health Insurance Fund. 2004. The Impact of HIV/AIDS Pandemic on the National health Insurance Fund’s Operations: Paper presented to TPHA Annual Scientific Conference and Annual General meeting on 8th-12th November 2004”, BOT Training Institute, Mwanza Tanzania.
Tanzania, Ministry of Health, National Aids Control Programme. 2003. Surveillance Report No. 18.
Thomas, E.P., et al. 2005. HIV Prevalence Study and Costing Analysis undertaken for the development of an HIV/AIDS Workplace Strategy for Buffalo City Municipality. Medical Research Council and South African Cities Network. Available at .
Turning Bureaucrats into Warriors: Preparing and Implementing Multi-Sector HIV-AIDS Programmes In Africa: A Generic Operations Manual (World Bank, 2004).
Uganda Bureau of Statistics (UBOS) and International Livestock Research Institute (ILRI). 2005. Where are the Poor? Mapping Patterns of Well-Being in Uganda: 1992 and 1999.
Uganda Bureau of Statistics (UBOS). 2002. 2002 Uganda Population and Housing Census: Main Report.
UNAIDS. 1998. HIV-related Opportunistic Diseases, UNAIDS Technical Update, October 1998.
World Bank. 2005. Kampala Institutional and Infrastructure Development Project (KIIDP) Project Concept Note.
World Bank. 2004. Turning Bureaucrats into Warriors: Preparing and Implementing Multi-Sector HIV-AIDS Programmes In Africa: A Generic Operations Manual.
World Health Organisation (WHO). 2005. Global Tuberculosis Control: Surveillance, Planning, Financing, accessible at .
World Health Organisation (WHO). 2005. Summary Country Profile for HIV/AIDS Treatment Scale Up: Uganda, accessible at .
World Health Organisation (WHO). 2005. Summary Country Profile for HIV/AIDS Treatment Scale Up: Kenya, accessible at .
World Health Organisation (WHO). 2005. Summary Country Profile for HIV/AIDS Treatment Scale Up: United Republic of Tanzania, accessible at .
World Health Organisation (WHO). 2005. The World Health Report 2005, accessible at .
World Health Organisation (WHO). WHO Country Cooperation Strategy: Uganda (2002-2005), accessible at .
World Health Organisation (WHO). WHO Country Cooperation Strategy: Kenya (2002-2005), accessible at .
World Health Organisation (WHO). WHO Country Cooperation Strategy: United Republic of Tanzania (2002-2005), accessible at .
Annex A: Acknowledgements
Dar es Salaam
|John Lubuva |Municipal Director, Ilala Municipal Council |
|Baraka Mujungu |Human Resources Director, Ilala Municipal Council |
|Deo Mboya |Resource Officer, Ilala Municipal Council |
|Patrick Fulla |ICT, Ilala Municipal Council |
|Daniel ? |Municipal Economist, Ilala Municipal Council |
|Elina Nnachibona |HIV/AIDS Coordinator (Community) |
|Lilian Mnzava |Ilala Municipal Council DACC |
|Dr. Judith Kahama |Municipal Medical Officer, Ilala Municipal Council |
|Agness Lutende |Acting DACC |
|Benson Nallya |Ilala Municipal Council Health Services |
|Fillo Hyaera |Ilala Municipal Council MMHC |
|Sister Gerwalda Mumba |Ilala Municipal Council AKC |
|Dr. Chalamilla |Ilala Health Services |
|Wema Kajigili |Education Department, Ilala Municipal Council |
|Hyacinta Mwambashi |Deputy Headmistress, Olympio Primary School |
|Mr. Kashato |Deputy Headmaster, Forodhani Secondary School |
|Mr. Lyimo |Deputy Headmaster, Benjamin William Mkapa Secondary School |
|Hamisi Omary |Head Teacher, Kinyerezi Primary School |
|Juliana Mrema |Assistant Head Teacher , Kinyerezi Primary School |
|Martin Kitilla |National Coordinator, AMICAALL Tanzanian Programme |
|Dr. Yusuf Hemed |Adult Morbidity and Mortality Project (AMMP) |
|David Whiting |Adult Morbidity and Mortality Project (AMMP) |
|Mrs. Rustica Tembele |Director, District and Community Response, TACAIDS |
|Dr. R.O. Swai |Programme Manager, National AIDS Control Programme |
|Mr. Josibert Rubona |Health Information and Research Section, Ministry of Health |
|Dr Lwilla |TB and Leprosy Programme, MOH |
|Dr. Deo Mtasiwa |Medical Officer, City Council of Dar es Salaam |
|Benson Atung |Acting Country Manager, World Bank |
|Emmanuel G. Malangalila |Senior Health Specialist, AFTH1, World Bank |
|Julie Mclaughlin |Lead Health Specialist, AFTH1, World Bank |
|Dr. Subilaga Kasesela-Kaganda |AMREF Tanzania |
|Elias Baruti |LAPF |
|Dr. Aifena Mramba |Quality Control Officer, Tanzania National Health Insurance Fund |
Kampala
|Mrs. Ruth Mubiru |Personnel Director, Kampala City Council |
|Annette Rwemereza |Training Manager, Kampala City Council |
|Jane Makayu |Coordinators Office, Education Department, Kampala City Council |
|Florence Barigye |Coordinators Office, Education Department, Kampala City Council |
|Dr. Mina Nakawuka |District HIV/AIDS Focal Point, Kampala City Council |
|Dr. Livingstone Makanga |Central Division Medical Officer, Kampala City Council |
|Anne Galiwngo |District Education Officer, Kampala City Council |
|David Mubiru |Head Teacher |
|Jane Semugoma |Head Teacher |
|Beatrice Turyasingura |Head Teacher |
|John Wasswa Kasule |ITC, Kampala City Council |
|Angela Semambo |Kampala District Service Commission |
|Prossy Ssebina |Finance Director, , Kampala City Council |
|Dr. John Mugisa |AMICAALL Uganda |
|Sister Helen Oluka |Senior Nursing Officer, Kampala City Council |
|Dr. Lukoye |Zonal TB/Leprosy Supervisor, Kampala |
|Sister Jenniffer Businge |Nursing Officer, Kiswa Clinic |
|Apollo Soita |Clinical Officer, Nagulu and Kiswa Clinics |
|Robert Kakungulu |KCC Senior Staff Clinic |
|Sister Mbowa |KCC Senior Staff Clinic |
|Stepehn Higobero |Principal Assistant Town Clerk, Kampala Central Division |
|John w. Higenyi |Senior Assistant Town Clerk, Kampala Central Division |
|Ronald Ssebanenya |Division Engineer, Kampala Central Division |
|Dr. Makanga |Division Medical Officer, Kampala Central Division |
|Chris Byekwaso |Division finance Officer, Kampala Central Division |
|Caroline Bankusha |Head Welfare, Kampala Central Division |
|Henry Ssebaggala |Division Planner, Kampala Central Division |
|Kariisa Merab |Division Education Officer, Kampala Central Division |
|City Law Officer |Vincent Katungi, Kampala Central Division |
|Dr. Peter Okwero |Lead Health Specialist, World Bank Uganda Country Office |
|Mr. JJ Sonko |Ministry of Local Government |
|Dr. Dickson Opul |Executive Director, Uganda Business Coalition on HIV/AIDS |
|Moses Doka |Resource Centre, Ministry of Health |
|Hebert Mulira |Database Manager, Ministry of Health |
|Joyce Kalumba |Uganda Aids Commission Resource Centre |
|Rosemary Kabugo |District Desk Officer, Uganda Aids Commission Resource Centre |
Nairobi
|Dr. Daniel M. Nguku |Medical Officer of Health, City Council of Nairobi |
|Dorkas Sogomo |Director Human Resources Department, City Council of Nairobi |
|Benter Ogot |Computer Manager, City Council of Nairobi |
|Pater Kavithi |City Planning Department, City Council of Nairobi |
|Joyce Somoni |Social Services and Housing Department, City Council of Nairobi |
|Joyce Boyani |Environment Department, City Council of Nairobi |
|Virginia Maleche |Environment Department, City Council of Nairobi |
|Linnet Abdallah |TC Department, City Council of Nairobi |
|Patrick Luvisia |TC Department, City Council of Nairobi |
|Jennifer Nyaga |Engineering Department, City Council of Nairobi |
|Emmah Karachu |Engineering Department, City Council of Nairobi |
|Henry Momanyi |CAO City Treasurer, City Council of Nairobi |
|Martin Sahenyi |Public Health Department, City Council of Nairobi |
|Linet Nyachieo |Education Department, City Council of Nairobi |
|Michael Mwangi |Education Department, City Council of Nairobi |
|Lilian Shikanda |City Inspectorate Department, City Council of Nairobi |
|Alex Macharia |Housing Development Department, City Council of Nairobi |
|Joseph Mwaniki |Paymaster, City Council of Nairobi |
|Kent Muksya |Secretary Kenya Local Government Workers Union |
|Lydia Elizeba Myawanga |Secretary Kenya Local Government Workers Union |
|Head Teacher |Nairobi Primary School |
|Head Teacher |Kibera Primary School |
|Mr. Augustine M.R. Odipo |Secretary General, Association of Local Government Authorities of Kenya |
|Mr. Hamisi Mbogo |Deputy Secretary General, Association of Local Government Authorities of Kenya |
|Margaret M. Jobita |National AIDS Technical Adviser, AMICAALL Kenya |
|Father Julius Muranga |Municipality of Nairobi AIDS Secretariat (MONAS) |
|Timothy Musyoka |Environmental Networks in Cities (NICE) NGO, Municipality of Nairobi AIDS Secretariat (MONAS) |
|Agnes Manjera |Municipality of Nairobi AIDS Secretariat (MONAS) |
|Peter Mutua |Municipality of Nairobi AIDS Secretariat (MONAS) |
Washington DC
|Barjor Mehta |Snr. Urban Specialist, WBI, World Bank |
|Bert Voetberg |Lead Health Specialist, AFTHV, World Bank |
|Isabel Rocha Pimenta |WBIHD, World Bank |
|Jaime Biderman |Sector Manager, AFTU1, World Bank |
|Jean Jacques de St.Antoine |AFTH1, World Bank |
|Kate Kuper |Snr. Urban Specialist, AFTU1, World Bank |
|Lance Morrell |Lead Operations Officer, AFTU1, World Bank |
|Matthew Glasser |Snr. Urban Development Specialist, AFTU1, World Bank |
|Mead Over |Lead Economist, Health, DECRG, World Bank |
|Mike Mills |Lead Economist, AFTH1, World Bank |
|Nina Schuler |TUDUR, World Bank |
|Solomon Alemu |Snr. Sanitary Engineer, AFTU1, World Bank |
|Susan Stout |OPCS, World Bank |
|Aijaz Ahmad |AFTU1, World Bank |
Annex B: Human Resource Data
Allowances
| |Ilala Municipal Council |Kampala City Council[87] |Nairobi City Council |
|Housing |n/a |UGSH per month |KSH per month |
| | |Applicable to: |Salary Scale |
| | | | |
| | |500,000 |100,000 |
| | |U1-U2 |1-2 |
| | | | |
| | |200,000 |80,000 |
| | |Surveyors and Medical Officers |3-4 |
| | | | |
| | |155,000 |60,000 |
| | |U3-U5 |5-6 |
| | | | |
| | |60,000 |50,000 |
| | |U6-U7 |7-8 |
| | | | |
| | |50,000 |40,000 |
| | |Support Staff |9-10 |
| | | | |
| | | |30,000 |
| | | |11-13 |
| | | | |
| | | |24,000 |
| | | |14-15 |
| | | | |
| | | |20,000 |
| | | |16-17 |
| | | | |
| | | |15,000 |
| | | |18-19 |
| | | | |
|Lunch |n/a |3,000 per day for all staff. |n/a |
|Transport |n/a |2,000 per working day for all staff except|n/a |
| | |those entitled to mileage allowance, | |
| | |provided with Council vehicles or on | |
| | |contract terms. | |
|Mileage |n/a |Provided at prevailing rates applicable in|n/a |
| | |Central Government. | |
|Representation/ Responsibility |n/a |UGSH per month |n/a |
| | |Applicable to: | |
| | | | |
| | |168,300 | |
| | |Heads of Department | |
| | | | |
| | |168,300 | |
| | |Deputies | |
| | | | |
| | |85,000 | |
| | |U2 | |
| | | | |
| | |60,000 | |
| | |Sen. Personal Secretaries | |
| | | | |
| | |40,000 | |
| | |U3 | |
| | | | |
| | |40,000 | |
| | |Clerks to Council | |
| | | | |
| | |40,000 | |
| | |Personnel Officers | |
| | | | |
| | |40,000 | |
| | |Nursing Officers in Charge of Units | |
| | | | |
| | |30,000 | |
| | |Registry Staff | |
| | | | |
| | |16,700 | |
| | |Committee Clerks | |
| | | | |
|Public Relations |n/a |500,000 | |
| | |U1 | |
| | | | |
| | |300,000 | |
| | |U2 | |
| | | | |
| | |300,000 | |
| | |Personal Secretaries | |
| | | | |
|Dirty and Heavy Work Bonus |n/a |1,300 per month for cesspool, cemetery |1,000 per month for dirty work |
| | |attendant, ambulance driver, nursing aids.|1,500 per month for heavy work |
| | | |2,000 per month for dirty and heavy work |
| | | |combined. |
|Proficiency Driving Bonus |n/a |n/a |2,000 per month after 6 months continuous |
| | | |service. |
|Annual Leave Travel Allowance |Travel allowances depend on distance | |Job Grade |
| |travelled and range from TSH 10,000 | |% Basic Salary |
| |(Morogoro, Pwani, Dar es Salaam) to TSH | | |
| |80,000 (Kagera na Mara) | |1-10 |
| | | |5% |
| | | | |
| | | |11-20 |
| | | |6% |
| | | | |
|Subsistence Allowances | |Safari Day Allowance, Night Allowance, On | |
| | |Call Allowance, Acting Allowance, Duty | |
| | |Allowance, Tools Allowance and | |
| | |resettlement Allowance. | |
Leave Policy
| |Ilala Municipal Council |Kampala City Council |Nairobi City Council |
|Annual Leave |28 days (45 days for teachers) |24-36 days per annum depending on salary |30-36 days per annum depending on salary |
| | |grade. |grade. |
| | |U2+ 36 days |1-14 36 days |
| | |U8 – U3 30 days |15-17 32 days |
| | |Less than U8 24 days |18-20 30 days |
|Sick Leave |An employee is entitled to six months of |An employee is entitled to three months of|An employee is entitled to six months of |
| |sick leave at full pay and six months at |sick leave at full pay and six months at |leave on full salary followed by three |
| |half pay, after which they are retired on |half pay, after which they are retired on |months on half salary. |
| |medical grounds. |medical grounds. | |
| |In practice, few employees are retired on |In practice, few employees are retired on | |
| |medical grounds and employees continue to |medical grounds and employees continue to | |
| |receive full pay until death. |receive full pay until death. | |
| | |The same sick leave arrangements apply to | |
| | |teachers. | |
|Compassionate Leave | |Employees are entitled to compassionate |Up to 30 days (may be offset against paid |
| | |leave to attend funerals and care for a |leave accruing in the future). |
| | |sick spouse or sick children. | |
|Other | |Study leave |Study leave |
| | |Leave without pay |Leave without pay (up to 50 days) |
| | | |Maternity leave |
|Paid Public Holidays | | |11 days |
Human Resource Records
| |Ilala Municipal Council |Kampala City Council |Nairobi City Council |
|Data on absenteeism |A manual daily attendance register is kept|Employees are supposed to submit medical |Each section keeps a manual, daily |
| |by the head of department, head of section|Form 25 to their managers to get approval |attendance register (muster roll). Each |
| |or registry assistant. This is usually in |for sick leave. These forms are supposed |month these are submitted to the CAO, who |
| |the form of a list of names and signatures|to be kept on individual files. |in turn forwards these to the paymaster to|
| |of staff in attendance on each day. |In practice, only the most severe cases of|effect deductions for unpaid leave. There |
| | |absenteeism are reported and recorded in |is no collation or analysis of sick leave.|
| | |personnel files (only about 1% of | |
| | |employees submit MF25 forms). |In theory, an officer who is absent due to|
| | |Schools keep manual, daily attendance |illness (or seeks medical attention that |
| | |registers for teachers. |for which he may be entitled to benefits) |
| | | |is required to provide a medical |
| | | |certificate. |
Medical Policy
| |Ilala Municipal Council |Kampala City Council |Nairobi City Council |
|Medical Policy |Ilala employees and their families are |Pensionable or full-time KCC employees and|Free medical services are provided at most|
| |covered by the National Health Insurance |their families are entitled to free |government and municipal health |
| |Fund (NHIF). |medical care and drugs if they attend |facilities. |
| | |approved public health facilities. |The council refunds 100 percent of any |
| | |Where a Government Medical Officer |medical costs incurred by employees at |
| | |prescribes a drug that is not on the MoH’s|government health facilities, and 50 |
| | |list and no substitute is available, the |percent of any medical costs incurred at |
| | |patient may be required to pay for the |private health facilities. |
| | |drug and request reimbursement later. |This covers both inpatient and outpatient |
| | |An employee is entitled to free medical |health services, but excludes maternity |
| | |treatment in Government hospitals but a |and dental expenses. Council may make a |
| | |daily accommodation allowance is payable. |contribution towards surgical or medical |
| | |If employees seek medical care at private |appliances. |
| | |health facilities, they are responsible |The policy is extended to the spouse of |
| | |for meeting all medical expenses. |the employee and children less than 18 |
| | |Teachers do not have access to free |years of age. |
| | |medical care. |Employees submit medical receipts to |
| | | |Medical Officer of Health and Human |
| | | |Resources to get out of pocket expenses |
| | | |reimbursed by Council. |
| | | |When medical claims are reimbursed, |
| | | |records are kept in individual medical |
| | | |files showing the cost of treatment, and |
| | | |the facility where the expenses were |
| | | |incurred). |
|Existence of a Health Insurance|The National Health Insurance Fund. |No Health Insurance Scheme. |The National Hospital Insurance Fund |
|Scheme | | |(NHIF) is a compulsory scheme. Deductions |
| | | |are made monthly from employee salaries. |
| | | |The scheme covers a portion of inpatient |
| | | |medical costs. |
|Membership of the Scheme |All civil servants, their spouses and | |All Kenyan residents over 18 years of age |
| |their dependants (less than 18 years of | |with an income over a prescribed minimum |
| |age and not exceeding four in number) are | |amount. Spouses and dependents are covered|
| |members of the scheme, following three | |by the scheme. |
| |months of contributions by the principal | | |
| |member. | | |
|Contributions |The contribution rate is six percent of | |Contributions (based on salary level) are |
| |employees’ monthly salary shared equally | |deducted by the employer from the member’s|
| |by employers and employees. The | |salary. |
| |municipality makes a monthly contribution | | |
| |to the Fund equal to three per cent of | | |
| |employee salaries. | | |
|Benefits |The inpatients hospital care fee (up to a | |NHIF will provide in-patient cover of up |
| |certain limit) and outpatients care (drugs| |to KSH 360, 000 per year each for the |
| |and medicines listed on a specific list). | |contributor, spouse and children below the|
| |The fund covers treatment for | |age of 18 years. NHIF pays between KSH 400|
| |opportunistic infections. Antiretroviral | |and KSH 2000 per day for up to 180 days. |
| |therapy is paid for by the Government | | |
| |under special programmes. | | |
|Are employer contributions |The benefits package offered by the fund | | |
|capped? Will they increase as |depends on the amount of money collected | | |
|the costs of the HIV/AIDS |from contributions. The majority of | | |
|epidemic rise?[88] |members are low contributors (less than | | |
| |10,000 TSH per month), but have the | | |
| |highest utilisation rate. The number of | | |
| |beneficiaries’ visits are increasing | | |
| |rapidly which in the long run will have a | | |
| |negative impact on the fund’s activities. | | |
| |Most of the increase can be attributed to | | |
| |higher inpatient cases, most of which are | | |
| |due to HIV/AIDS. Projections show that | | |
| |only with a 20 percent contribution rate, | | |
| |the Fund could afford to offer the | | |
| |comprehensive benefit package to its | | |
| |members without jeopardising the Fund’s | | |
| |viability and sustainability. [89] | | |
|Where do employees seek medical| |Public facilities tend to run out of drugs|Employees seek medical care at city |
|care? | |and so employees often seek medical |council facilities, government facilities |
| | |treatment at private health facilities. |and private facilities. |
| | |[90] |Staff Clinic for the exclusive use of |
| | | |Council Employees. |
Retirement Schemes
Ilala Municipal Council Pension Schemes
| |Local Authorities Provident Fund[91] |Public Service Pensions Scheme[92] |National Social Security Fund (NSSF)[93] |
|Legislation |LAPF is a Provident Fund Scheme |The Political Service Retirement Benefits |National Social Security Act of 1997 |
| |established by LAPF Act No.6, of 2000. |Act, 1999. | |
|Membership |All public servants employed by Local |Teachers |Operational Staff |
| |Government Authorities. | | |
|Type of Scheme |Defined Contribution. |Defined Benefit |Defined Benefit |
|Contributions |The Act provides that, employers and |The Act provides that, employers and | |
| |employees are each required to contribute |employees are each required to contribute | |
| |15 percent and 5 percent of the member’s |15 percent and 5 percent of the member’s | |
| |monthly gross salary respectively. In |monthly gross salary respectively. | |
| |addition, both the employer and employee | | |
| |may decide to pay supplementary | | |
| |contributions. | | |
|Categories of Benefits |The LAPF Act stipulates two types of |The following benefits are stipulated in |The Act provides for the following |
| |benefits namely: |the Act: |benefits: |
| |Normal retirement benefits |Retirement from age 55 to 60 years. |Normal retirement pension at age 60 |
| |Withdrawal benefits on termination of |Death gratuity |Early retirement from age 55 |
| |service (including termination on medical |Sickness Benefit |Invalidity pension |
| |grounds and death) |Funeral benefits |Survivors pension |
| | |Invalidity benefit |Funeral grants |
| | |Marriage and maternity benefits. |Maternity benefit |
| | |Withdrawal because of emigration or |Employment injury benefit; and |
| | |unemployment. |Health insurance benefit. |
|Normal retirement benefit |The accumulated value of contributions at |More than 15 years of service: |More than 15 years of contributions: |
| |the declared rate of interest over the |(a) commuted pension gratuity equal to |The monthly retirement pension is 30% of |
| |relevant period. |15.5 times half the specified amount plus |the average monthly earnings of the |
| | |a reduced annual pension equal to half the|retired insured person supplemented by |
| | |specified amount; or |1.5% of average monthly earnings for every|
| | |(b) a pension at the annual rate of |12 months of pension insurance (over 180 |
| | |1/540th of pensionable emoluments for each|months) to a maximum of 67.5% of the |
| | |complete month of his pensionable service.|average earnings. |
| | | | |
| | |Highest pensionable emoluments over the | |
| | |last 12 months are used for the purposes | |
| | |of calculating the pension. | |
| | |Less than 15 years of service: | |
| | |Gratuity equal to 5 times the annual | |
| | |pension amount (that would be paid if | |
| | |there was no qualifying period). | |
|Death benefit |The accumulated value of contributions at |The Death Gratuity is an amount equal to |Survivor benefit is an amount equal to 24 |
| |the declared rate of interest over the |the greater of the member’s annual |times the monthly amount of the retirement|
| |relevant period. |pensionable emoluments or the commuted |or invalidity pension. |
| | |pension gratuity. |A funeral grant is paid to a family who |
| | |Funeral benefits are granted in accordance|incurred expenses for the burial of a |
| | |with the Schedule to the Act (?) |deceased insured person. |
Kampala City Council Staff Pension Scheme
|Legislation |Article 254 of the Constitution of the Republic of Uganda. |
| |Pensions Act (Cap 281) and Pensions Regulations. |
| |Municipalities and Public Authorities Provident Fund. |
|Type of Scheme |Defined Benefit Scheme |
|Contributions |Since 1994, the pensions’ scheme has been non contributory for employees and Council.[94] |
|Conditions on which a pension |A pension is payable on retirement under the following circumstances: abolition of office, compulsory retirement (retrenchment),|
|is payable |medical retirement, removal from office (with 10 years of pensionable service), on reaching 45 years of age (with 10 years of |
| |pensionable service), on mandatory retirement at 65 years, and on marriage (female officer with 5 years of service). |
|Benefits payable under the |Commuted Pension Gratuity (CPG) which is an advance payment of pension to the pensioner equal to 1/3 of 15 years pension on |
|scheme |retirement. |
| |Monthly pension. |
| |Contract gratuity for officers serving on a contract. |
| |Death gratuity. |
| |Short service gratuity. |
| |Marriage gratuity. |
| |Survivors’ benefit. |
|Normal Retirement Benefit |Pensionable Staff |
| |Pensioners receive a lump sum called the Commuted Pension Gratuity (CPG) and/or a monthly pension until death. Pensioners have |
| |the option to receive their entire pension as an annuity or to commute 1/3rd of their pension for a 15-year period and receive |
| |it as a lump sum at retirement. This is the option that is preferred by most pensioners. |
| |CPG = [Length of service in months x annual salary on retirement x 1/500] x 1/3 x 15 |
| |Monthly Pension = [Length of service in months x annual salary on retirement x 1/500] x 2/3 x 1/12 |
| |If a pensioner dies within 15 years of retirement, the spouse and children are entitled to receive benefits up to a period of 15|
| |years from retirement date (Survivor’s Benefit). |
| |Contract Staff |
| |Gratuity % x Annual Salary |
| |The rate of payment is stipulated in the terms of the contract agreement. |
|Benefits payable on Death |Death Gratuity |
|(Death Gratuity, Survivors’ |Pensionable employees (confirmed in their appointment) receive a death gratuity equal to the greater of the CPG and 3 x annual |
|Benefit) |salary at time of death. |
| |Survivor’s Benefits |
| |Payable when a pensioner dies before the expiry of fifteen years after the date of his or her retirement. Payment is made to the|
| |surviving spouse and children for the remaining period up to 15 years from the date of retirement. Or, in the case of a serving |
| |public officer, for a period of 15 years following death. |
Nairobi City Council Pension Schemes
Three retirement schemes: (1) Provident Fund, which provides for lump sum benefit on death, and an option of a lump sum benefit or pension on retirement; (2) Superannuation Fund, which provides for an option of a pension or lump sum benefit on retirement; and (3) NSSF.
| |National Social Security Fund (NSSF) |Provident Fund |Kenya Local Government Officers’ |
| | | |Superannuation Fund |
|Legislation |National Social Security Fund Act Chapter |Provident Fund Act Chapter 191 |The Kenya Local Government (Pensions) |
| |258 | |Regulations 1963 |
|Type of Scheme |Defined Contribution. |Defined Contribution. Government employees|Defined Benefit Fund. |
| | |only. | |
|Contributions |Both employees (deducted from wages) and |Both employees (deducted from wages) and |Employee contributions deducted from wages|
| |the Council make contributions to the |the Council make contributions to the |equal to 12 percent of salary plus housing|
| |fund. |fund. |allowance. |
| |Maximum of Sh200 per month. |Contributions linked to salary (excluding |Employer contributions equal to 15 percent|
| | |allowances). |of salary plus housing allowance. |
|Benefits |Age benefit payable at 55 or above on |Benefits payable on medical retirement, |Age benefit payable at 55 or above on |
| |retirement. |abolishment of office, resignation with |retirement, after ten years of continuous |
| |Survivor’s benefit payable to dependent |ten or more years of continuous service, |service. |
| |relatives on the death of the member. |at 55 or above on retirement, and on early|Early retirement benefit payable from age |
| |Invalidity benefit payable if a member if |retirement from age 50. |50, after ten years of continuous service.|
| |permanently disabled. |Amount of the benefit is the total | |
| |Withdrawal benefit payable from age 50. |standard contributions paid in respect of |Benefit payable if a member if permanently|
| |[Early retirement benefit] |the member together with the declared |disabled or ill, after 10 years of |
| |Emigration grant payable if a member |interest (bonus) on those contributions |continuous service. |
| |emigrates from Kenya with no intention to |(not less than 3 percent per annum), |Pension calculated at the rate of 1/480 |
| |return. |together with any gratuities payable. |times the number of months in continuous |
| |Amount of the benefit is the total |Benefits may either be taken as a lump sum|employment times the average annual salary|
| |standard contributions paid in respect of |or pension (seven-eighths of the total |over the previous three years. |
| |the member together with the declared |amount). |Pension may be commuted (two thirds lump |
| |interest on those contributions (not less | |sum equal to 20 times the reduction in the|
| |than 2.5 percent per annum). | |annual pension) |
| | | |On death, if the member has more than 10 |
| | | |but less than 20 years of continuous |
| | | |service (and Rules VI or VII do not |
| | | |apply), the pension payable is 20 percent |
| | | |of the average annual salary over the last|
| | | |3 years. With more than 20 years of |
| | | |service, the member is entitled to 50 |
| | | |percent of the pension payable on |
| | | |retirement. [Average length of service is |
| | | |approximately 15 years] |
Bereavement Policy
| |Ilala Municipal Council |Kampala City Council |Nairobi City Council |
|Who is covered |Applies to staff members only. |Applies to a council employee, spouse and |Employees and their immediate family |
| | |biological children below the age of 18 |members. Immediate family members only get|
| | |(up to 4 children). |transport expenses). |
|Expenses Covered |
|Coffin |TSH 80,000 |USH 60,000 (all staff) |KSH 10,000 (employees only) |
|Body Treatment |Nil |USH 40,000 (all staff) |Nil |
|Grave |Nil |USH 100,000 (all staff except teachers) |Nil |
|Transport |150 x km x tonnage (avg 500km, 3 tonnes). |USH 1,500 x number of kms to the burial |Maximum KSH 50,000 Minimum KSH 20,000 |
| | |place. Official/physical transport |Average KSH 30,000 (employees and nuclear |
| | |provided where possible (vehicle). |family members). |
|Average Funeral Expenses |Average cost = 80,000 + (150 x 500 x 3) = |USH 450,000 |KSH 40,000 |
| |TSH 305,000 | | |
Recruitment Costs
| |Ilala Municipal Council |Kampala City Council |Nairobi City Council |
|Source |Estimates from Human Resource Manager, |Estimates from KCC District Service | |
| |Heads of Department and Section Heads. |Commission, and Heads of Department | |
| | |(Central Division) | |
|Recruitment costs (including |Approximately TSH 100,000 per employee for|Estimates of cost per employee: 3,500,000 |None. Recruitment freeze for lower staff |
|time of non-recruiting costs) |all staff categories (including teachers) |for managers, 1,500,000 for supervisors |cadres. Senior staff recruited by Public |
| |but excluding support staff. |and professionals, 500,000 for semi |Service Commission. |
| | |skilled staff and 70,000 for teachers. | |
|Average time a position is left|Approximately 5 months | | |
|open. | | | |
Training Costs
| |Ilala Municipal Council |Kampala City Council |Nairobi City Council |
|Induction Training | |1 week duration. | |
| | |A 10 person course typically costs | |
| | |500,000. | |
|Description of Training | |Three types of training:[95] | |
|Programme | |1. Career and skill development (long | |
| | |courses, e.g. post graduate diplomas) | |
| | |2. LGDP generic modules with consultants | |
| | |sourced through LGTB (workshops and | |
| | |seminars) | |
| | |3. Discretionary (study tours, other) | |
| | |The training programme is funded by a | |
| | |Capacity Building Grant, which amounted to| |
| | |UGSH 679,915,000 in 2004/05. | |
| | |KCC also has a local budget for Career and| |
| | |Generic courses (which is sourced from | |
| | |local revenues and doesn’t have to go | |
| | |through the LGTB procurement process), | |
| | |which amounted to UGSH 4,245,445 in | |
| | |2004/05. | |
|Beneficiaries of Training | |Top Managers (Councillors, Heads of | |
| | |Departments, Town Clerk, Deputy Town | |
| | |Clerk): 60% (workshops and seminars) | |
| | |Middle Managers (Section Heads): 20% | |
| | |(workshops and seminars). | |
| | |Senior managers: 19% (career courses). | |
| | |Supporting staff: 1% (some workshops). | |
| | |Civil society | |
|Training Records | |Training records are added to HR files. | |
| | |Individual writes a report which also goes| |
| | |on file. | |
Annex C: Interview Questions
Department and Section Heads – Discussion Points
General Information
1. What department or section are you responsible for?
2. What is your team responsible for? (How do the team’s activities relate to service delivery?)
a. How many staff do you supervise? In what staff functions? Are they full time or temporary staff?
b. Which job functions are most critical for service delivery?
Deaths or Retirements due to Ill Health
3. How many staff members have left employment over the last 3-5 years? How many of these died? How many of these have retired due to ill health?
4. For those staff that have died or retired due to ill health:
a. In what year did they die or retire due to ill health? What was their job position? Sex? Age at death?
b. What was the cause of death or ill health?
c. How much time were these staff members absent from work in each of the 2-3 years before they died? Was this leave taken in addition to their annual leave?
d. What was their % productivity when they were at work in each of the 2-3 years before they died?
e. When they died (or retired due to ill health), how long did their position remain vacant?
f. How many days did the replacement employee attend training courses? How many days of on-the job training did they receive (requiring the time of an on-the-job trainer)?
g. How long before they were fully productive (in months)? What was their level of productivity before they were fully productive?
h. In the last year of their illness and after their death, how many days of your time were taken up responding to the situation (re-allocation of responsibilities, recruitment, training etc)?
Ideally the following table should be completed for each death (or retirement due to ill health):
| | | | |
|Base year for projections |2006 |2006 |2006 |
|Currency for monetary variables |KSH |UGSH |TSH |
|Discount rates |3% and 10% |3% and 10% |3% and 10% |
|Exchange rate to USD[101] |0.01235 |0.00057 |0.00093 |
|Expense inflation rate |3.5% |3.5% |3.5% |
Categories of Municipal Employees
City Council of Nairobi
|Description of Categories (Salary Scales) |Average Salary (KSH) |Number of Staff |Replacement Rate |
|Managers (Grades 1-5) |63,391 |65 |100% |
|Supervisors and Professionals (Grades 6-9) |31,254 |621 |100% |
|Semi-Skilled (Grades 10-13) |21,692 |2,394 |0%[102] |
|Support Staff (Grades 14-19) |13,071 |10,274 |0%[103] |
Kampala City Council
|Description of Categories (Salary Scales) |Average Salary (USH) |Number of Staff |Replacement Rate |
|Managers (U8) |2,268,905 |34 |100% |
|Supervisors and Professionals (U3-U4) |605,175 |127 |100% |
|Semi-Skilled (U5, U6, U7) |276,133 |693 |100% |
|Support Staff (U8) |192,828 |397 |100% |
|Teachers (All Grades) |166,842 |1,538 |100% |
Ilala Municipal Council
|Description of Categories (Salary Scales) |Average Salary (TSH) |Number of Staff |Replacement Rate |
|Managers (TGSL-F) |413,752 |41 |100% |
|Supervisors and Professionals (TGSF-E) |232,894 |171 |100% |
|Semi-Skilled (TGSD-A) |126,606 |765 |100% |
|Support Staff (TGOS) |81,399 |442 |100% |
|Teachers (All Grades) |150,000 |2,437 |100% |
Other Assumptions about the Municipal Workforce
| |Kenya |Uganda |Tanzania |
|Natural attrition (labour turnover) rate |5% |5% |5% |
|Planned increases in the size of the municipal workforce |0% |0% |0% |
|Number of working days (used to calculate the average daily |218 |219 |219 |
|wage rate) | | | |
|Expected annual rate of salary increases |5% |5% |3% |
|Productivity adjustment |1 |1 |1 |
HIV Prevalence Rates
| |Kenya |Uganda |Tanzania |
|Modelled Municipal Prevalence Rate[104] |12% |9% |9% |
|Determined with reference to: |
|Annual number of staff deaths (2004) |198 |34 |46 |
|Percentage of staff deaths attributed to AIDS (approx) |75% |75% |75% |
|Adult Prevalence (15-49 years) |7.5%[105] |4.1%[107] |8.8%[109] |
| | |(2.8% – 6.6%) |(6.4% – 11.9%) |
| |6.7%[106] |7%[108] | |
|Percentage Urban Population[110] |33 % |14 % |32 % |
|Urban Prevalence Rate |10%[111] |10.7%[112] |10.9%[113] |
|Rural Prevalence Rate |5.6%[114] |6.4%[115] |5.3% |
|City Prevalence Rates (by sex, age) | |Women in Kampala: 12.5%[116] |Women in Dar es Salaam: |
| | |Men in Kampala: 5.2%[117] |12.2%[118] |
| | | |Men in Dar es Salaam: 9.4%[119]|
| | | |Blood donors (Ilala 2003): |
| | | |6.9%[120]; Blood donors (Dar |
| | | |2003): Men 9.5%, Women |
| | | |14.8%[121]; Blood donors (Dar |
| | | |2003): Age 5-24: 8.4%, Age |
| | | |25-34: 11.1%, Age 35+: |
| | | |9.7%[122] |
HIV Incidence Rates
| |Kenya |Uganda |Tanzania |
|Modelled Incidence Rate |1.5% |1% |1.5% |
|Determined with reference to: | | | |
|Adult HIV Incidence Rate |US Military Study in Kericho, |US Military Study in Rakai and |2005 Estimate: 1.5%[124] |
| |Kenya estimated incidence at |Kiyonga estimated incidence at|2006 Estimate: 1.49%[125] |
| |2.2%[123] |1.3% | |
HIV/AIDS Disease Progression without Treatment[126],[127]
| |Without Treatment |
|Infection to the appearance of symptoms |5 years |
|Appearance of symptoms to AIDS |4 years |
|AIDS to Death |1 year |
|Total |10 years |
Cost Incurred Over the Lifetime of an HIV Positive Employee
| |CCN |KCC |IMC |
|Additional paid leave in the year prior to death |3 months |5 months |5 months |
|Productivity in the year prior to death |25% |50% |50% |
|Additional paid leave in the second last year prior to death |2 months |0.5 months |0.5 months |
|Productivity in the second last year prior to death |40% |80% |80% |
|Annual medical care costs in each of the two years prior to death |3,500 |Nil |Nil |
|Percentage of employees covered by a Defined Benefit Pension Scheme |45% |100% |100% |
|Death and Pension benefits paid on death (exceeding the present value of normal retirement |Not modelled |Not modelled[128] |Not modelled |
|benefits) expressed as a multiple of annual salary | | | |
|Average funeral expenses |KSH |UGSH |TSH |
| |40,000 |450,000[129] |250,000 |
|Average number of colleagues who attend funeral (assumed to be in the same employee category) |8 |8 |8 |
|Average number of days to attend funeral of colleague |2 days |2 days |2 days |
|Vacancy before a position is filled (months) |Nil |3 months |5 months |
|Average recruitment cost |Nil[130] |Varied estimates |TSH 100,000 per |
| | |across categories of|vacant post |
| | |employees. |(excluding support |
| | |UGSH 3,500,000 for |staff) |
| | |U1-2, UGSH 1,500,000| |
| | |for U3-4, UGSH | |
| | |500,000 for U5-7, | |
| | |UGSH 70,000 for | |
| | |teachers. Nil for | |
| | |support staff. | |
|Number of days training |Nil[131] |5 days |5 days |
|Average training cost |Nil[132] |UGSH 50,000[133] |TSH 50,000 |
|Months until fully productive |2 months |3 months |1 month |
|Productivity until fully productive |50% |50% |90% |
|Supervision time (months) in each of the two years before death (assume average salary of |0.25 |0.25 |0.25 |
|category 1 employees) | | | |
Morbidity
| |CCN |KCC |IMC |
|Absenteeism due to illness (days) |11 |12 |12 |
|Days at work when sick |11 |12 |12 |
|Productivity when at work sick |35% |50% |50% |
|Share of absenteeism attributable to malaria |12% |33%[134] |18.5% |
|Leave to attend funerals of family and friends (days) |5 |5 |5 |
|Leave to care givers (days) |3 |3 |3 |
|Annual medical expenses reimbursed by the municipality |150 |0 |0 |
Cost and Impact of HIV/AIDS Prevention Programme
| |CCN |KCC |IMC |
|Per annum cost of prevention |KSH 810 (USD 10) |UGSH 17,535 (USD 10) |TSH 10,800 (USD 10) |
|programme (per employee) [135] | | | |
|Percentage reduction in |50% |50% |50% |
|incidence rates | | | |
Cost of Antiretroviral Therapy for HIV Positive Employees[136]
| |CCN |KCC |IMC |
|Modelled |KSH 6,480 |UGSH 315,639 |TSH 264,600 |
|Determined with reference to: |
|Adult regimen |Comprises a generic form of the fixed-dose|The first-line regimen is zidovudine (or |Stavudine + lamivudine + nevirapine; |
| |combination stavudine + lamivudine + |stavudine) + lamivudine + nevirapine (or |stavudine + lamivudine + efavirenz; |
| |nevirapine. [137] |efavirenz). [138] |zidovudine + lamivudine + efavirenz; and |
| | | |zidovudine + lamivudine + nevirapine. |
| | | |[139] |
|Cost per person per year |The cost of the first-line drug regimen in|In July 2004, the average cost of the |As at April 2004, the average cost of the |
| |subsidized public settings is US$ 80 per |first-line regimen was US$ 180 per person |first-line drug regimen for adults was US$|
| |person per year. [140] |per year, including the cost of drugs, |245 per person per year and is expected to|
| | |laboratory tests and training. [141] |continue to fall rapidly. [142] |
|Percentage of treatment costs |100% |100% |100% |
|incurred by municipality | | | |
|Year treatment commences[143] |6 |6 |6 |
Impact of Treatment on HIV/AIDS Disease Progression[144]
| |Without Treatment |Unstructured |Structured |
| | |Antiretroviral Therapy |Antiretroviral Therapy |
|Infection to the appearance of symptoms |5 years |5 years |5 years |
|Appearance of symptoms to the failure of treatment |n/a |1-3 years |5 years |
|Appearance of symptoms (no treatment) or failure of treatment (with |4 years |3 years |3 years |
|treatment) to AIDS | | | |
|AIDS to Death |1 year[145] |1 year |1 year |
|Total |10 years |10-12 years |14 years |
Impact on Costs Incurred by the Municipality over the Lifetime of an HIV Positive Employee
| |CCN |KCC |IMC |
|Death and pension benefits paid on death (multiple of salary) |Not modelled |Not modelled |Not modelled |
|Percentage reduction in absenteeism in 2 years prior to death |50% |50% |50% |
|Productivity in year before death (%) |+ 20% |+ 20% |+ 20% |
|Productivity in second last year before death (%) |+ 20% |+ 20% |+ 20% |
|Reduction in medical costs |50% |50% |50% |
Cost and Treatment of Malaria Prevention Programme
There are three possible types of bed nets that could be distributed as part of a work place programme: (1) untreated nets; (2) ITN bundled nets (untreated nets with one treatment dose) and (3): Long lasting insecticide treated nets (LLIN) that are factory treated and may not require treatment during its lifespan.[146]
| |CCN |KCC |IMC |
|Cost of pre-treated ITNs[147] |USD 5 |USD 5 |USD 5 |
|Number of years after which nets require replacement |3 years |3 years |3 years |
|Impact on costs due to employee sickness: reduction in incidence |50% |50% |50% |
Annex F: Sensitivity Analysis
Results at a Discount Rate of 10 Percent
As noted in the main body of the study, the applied discount rate should reflect the opportunity cost of capital to the municipality. Estimating the opportunity cost of capital for a municipality is not a straightforward exercise and depends on the mix of different sources of municipal funds, including own source revenues, central government transfers, grants from donors, and commercial rate loans. For simplicity, discount rates of three percent and ten percent were modelled to establish reasonable book-ends for the results. This choice is consistent with the typical range of discount rates used for calculating the net present value of health investments.[148] Given that central government transfers account for the major share of municipal budgets in the three cities, and that a significant part of the national budgets are donor funded at very favourable rates of interest, it is likely that the appropriate discount rate is towards the lower end of the modelled range. For this reason, the main body of the paper presents the results at a discount rate of three percent and the results at a discount rate of 10 percent are included in this Appendix.
Cost of a New HIV Infection (10 Percent Discount Rate)
At a discount rate of 10 percent, the cost of a new infection is roughly equal to the annual salary for CCN and KCC employees and approximately two thirds of the annual salary for IMC employees.
Figure F.1: City Council of Nairobi (KSH)
[pic]
Figure F.2: Kampala City Council (UGSH)
[pic]
Figure F.3: Ilala Municipal Council (TSH)
[pic]
Impact of Workplace Interventions (10 Percent Discount Rate)
Figure F.4: City Council of Nairobi (KSH)
[pic]
Figure F.5: Kampala City Council (UGSH)
[pic]
Figure F.6: Ilala Municipal Council (TSH)
[pic]
Figure F.7: Ilala Municipal Council (TSH): Municipality only bears 50% of the cost of ARVs
[pic]
Sensitivity Analysis
The sensitivity analysis looks at the degree to which the results change when key assumptions are varied.
The table below outlines the scenarios that were modelled. For simplicity, only the projected annual cost of HIV/AIDS in the workplace (as a percentage of the wage bill) is presented for each scenario.
|Scenario |Description |
|Base case (conservative |See Annex E for the full assumption set. |
|estimates of HIV/AIDS related | |
|cost) | |
|Sensitivity 1: Demographic |Revise prevalence rates to WHO estimates of urban prevalence (whereas the base case estimates the municipal prevalence based on |
|Assumptions |the historical numbers of employee deaths). The urban prevalence rate is slightly lower than the base case for Nairobi (10 |
| |percent), and slightly higher for Kampala (10.7 percent) and Dar es Salaam (10.9 percent) |
| |In addition, reduce the life expectancy in the absence of treatment from 10 years to 8 years. |
| |In addition, reduce the life expectancy in the absence of treatment by a further one year to 7 years. |
|Sensitivity 2: Municipal |Modify numbers of CCN staff downwards (to 6,000) so that proportions in each employee category are roughly equivalent with those|
|Restructuring (City Council of |for IMC and KCC. |
|Nairobi) | |
|Sensitivity 3: Productivity of |Value of the product of a municipal employee is twice the salary of that employee (base case assumes a municipal employee’s |
|a Municipal Employee |product is equal to what the employee is paid). |
| |Value of the product of a municipal employee is 1.5 times the salary of that employee (base case assumes a municipal employee’s |
| |product is equal to what the employee is paid). |
| |Value of the product of a municipal employee is 0.5 times the salary of that employee (base case assumes a municipal employee’s |
| |product is equal to what the employee is paid). |
|Sensitivity 4: Absenteeism and |Increase absenteeism by one month in each of the two years prior to death. |
|productivity in two years prior|Increase absenteeism by a further month (total of two additional months) in each of the two years prior to death. |
|to AIDS death |In addition, reduce productivity by 10 percent in each of the two years prior to death. |
| |In addition, reduce productivity by a further 10 percent (total reduction of 20 percent) in each of the two years prior to |
| |death. |
|Sensitivity 5: Replacement |Increase recruitment costs by 50% (still nil for City Council of Nairobi) |
|worker costs (does not affect |In addition, increase training costs by 50% (still nil for City Council of Nairobi) |
|the first year of the |In addition, increase months until fully productive to six |
|projection) |In addition, reduce productivity in the first six months to 50 percent (only affects IMC) |
|“High” Case (relax some |Revised prevalence rates to WHO estimates of urban prevalence (whereas the base case estimates the municipal prevalence based on|
|assumptions) |the historical numbers of employee deaths). The urban prevalence rate is slightly lower than the base case for Nairobi (10 |
| |percent), and slightly higher for Kampala (10.7 percent) and Dar es Salaam (10.9 percent) |
| |In addition, reduce the life expectancy in the absence of treatment from 10 years to 8 years. |
| |In addition, increase absenteeism by two additional months in each of the two years prior to death. |
| |In addition, reduce productivity by 20 percent in each of the two years prior to death. |
|Second-line drug regimen costs |Ten percent of employees who receive ARVs require second-line drugs at a cost of seven times the first-line drug costs. |
Base Case
The base case uses the assumption set that is detailed in Annex E. Many of the assumptions for HIV/AIDS related costs were estimated conservatively and adjusted downwards.
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs | 54,989,659 | 57,393,941 | 59,918,437 | 62,569,158 | 65,352,415 |
|Wage Bill | 2,517,012,961 | 2,499,733,461 | 2,481,947,811 | 2,463,629,809 | 2,444,751,946 |
|Cost % Wages |2.2% |2.3% |2.4% |2.5% |2.7% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs | 126,652,593 | 132,014,051 | 137,643,581 | 143,554,588 | 149,761,146 |
|Wage Bill | 8,142,188,649 | 8,549,298,082 | 8,976,762,986 | 9,425,601,135 | 9,896,881,192 |
|Cost % Wages |1.6% |1.5% |1.5% |1.5% |1.5% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs | 78,105,056 | 80,043,693 | 82,040,490 | 84,097,191 | 86,215,593 |
|Wage Bill | 6,662,048,433 | 6,861,909,885 | 7,067,767,182 | 7,279,800,198 | 7,498,194,203 |
|Cost % Wages |1.2% |1.2% |1.2% |1.2% |1.1% |
Sensitivity 1: Vary Demographic Assumptions
Sensitivity 1a: Urban Prevalence Rates
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |49,990,599 |52,176,310 |54,471,307 |56,881,053 |59,411,287 |
|Wage Bill |2,517,012,961 |2,502,079,856 |2,486,752,056 |2,471,008,946 |2,454,828,881 |
|Cost % Wages |2.0% |2.1% |2.2% |2.3% |2.4% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |150,575,860 |156,950,038 |163,642,924 |170,670,455 |178,049,362 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.8% |1.8% |1.8% |1.8% |1.8% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |94,593,901 |96,941,806 |99,360,149 |101,851,042 |104,416,662 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.4% |1.4% |1.4% |1.4% |1.4% |
Sensitivity 1b: Eight year life expectancy in the absence of treatment
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |62,488,249 |65,220,388 |68,089,133 |71,101,316 |74,264,108 |
|Wage Bill |2,517,012,961 |2,496,213,867 |2,474,741,442 |2,452,561,105 |2,429,636,544 |
|Cost % Wages |2.5% |2.6% |2.8% |2.9% |3.1% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |188,219,826 |196,187,547 |204,553,655 |213,338,068 |222,561,702 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.3% |2.3% |2.3% |2.3% |2.2% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |118,242,376 |121,177,258 |124,200,186 |127,313,803 |130,520,828 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.8% |1.8% |1.8% |1.7% |1.7% |
Sensitivity 1c: Seven year life expectancy in the absence of treatment
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |71,415,142 |74,537,586 |77,816,152 |81,258,647 |84,873,267 |
|Wage Bill |2,517,012,961 |2,492,023,874 |2,466,162,433 |2,439,384,076 |2,411,642,017 |
|Cost % Wages |2.8% |3.0% |3.2% |3.3% |3.5% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |215,108,372 |224,214,340 |233,775,606 |243,814,935 |254,356,231 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.6% |2.6% |2.6% |2.6% |2.6% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |135,134,144 |138,488,295 |141,943,070 |145,501,489 |149,166,660 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |2.0% |2.0% |2.0% |2.0% |2.0% |
Sensitivity 2: Municipal Restructuring (City Council of Nairobi)
Sensitivity 2a: Number of employees in lower cadres falls
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |69,437,552 |72,555,999 |75,830,368 |79,268,455 |82,878,448 |
|Wage Bill |1,468,888,256 |1,448,892,799 |1,428,131,169 |1,406,564,473 |1,384,151,875 |
|Cost % Wages |4.7% |5.0% |5.3% |5.6% |6.0% |
Sensitivity 3: Productivity of Municipal Employees
Sensitivity 3a: Productive output of municipal employee is twice salary
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |103,075,300 |107,883,864 |112,932,857 |118,234,299 |123,800,813 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |4.1% |4.3% |4.6% |4.8% |5.1% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |233,741,685 |244,457,597 |255,709,305 |267,523,599 |279,928,607 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.9% |2.9% |2.8% |2.8% |2.8% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |142,611,712 |146,485,549 |150,475,602 |154,585,356 |158,818,402 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |2.1% |2.1% |2.1% |2.1% |2.1% |
Sensitivity 3b: Productive output of municipal employee is 1.5 times salary
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |79,032,480 |82,638,903 |86,425,647 |90,401,728 |94,576,614 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |3.1% |3.3% |3.5% |3.7% |3.9% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |180,197,139 |188,235,824 |196,676,443 |205,539,093 |214,844,876 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.2% |2.2% |2.2% |2.2% |2.2% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |110,358,384 |113,264,621 |116,258,046 |119,341,273 |122,516,998 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.7% |1.7% |1.6% |1.6% |1.6% |
Sensitivity 3c: Productive output of municipal employee is 0.5 times salary
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |30,946,839 |32,148,980 |33,411,228 |34,736,588 |36,128,217 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |1.2% |1.3% |1.3% |1.4% |1.5% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |73,108,047 |75,792,277 |78,610,719 |81,570,083 |84,677,415 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |0.9% |0.9% |0.9% |0.9% |0.9% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |45,851,728 |46,822,765 |47,822,934 |48,853,108 |49,914,188 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |0.7% |0.7% |0.7% |0.7% |0.7% |
Sensitivity 4: Absenteeism and productivity in two years prior to AIDS death
Sensitivity 4a: Additional one month of absenteeism in each of the two years prior to death
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |56,489,379 |58,968,647 |61,571,879 |64,305,272 |67,175,335 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.2% |2.4% |2.5% |2.6% |2.7% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |134,591,227 |140,349,616 |146,395,925 |152,744,549 |159,410,605 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.7% |1.6% |1.6% |1.6% |1.6% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |84,600,553 |86,734,055 |88,931,563 |91,194,996 |93,526,332 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.3% |1.3% |1.3% |1.3% |1.2% |
Sensitivity 4b: Additional two months of absenteeism in each of the two years prior to death
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |57,989,100 |60,543,354 |63,225,320 |66,041,386 |68,998,254 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.3% |2.4% |2.5% |2.7% |2.8% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |142,529,861 |148,685,182 |155,148,269 |161,934,510 |169,060,064 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.8% |1.7% |1.7% |1.7% |1.7% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |91,096,050 |93,424,418 |95,822,636 |98,292,801 |100,837,071 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.4% |1.4% |1.4% |1.4% |1.3% |
Sensitivity 4c: Reduce productivity by 10 percent in each of the two years prior to death
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |61,449,992 |64,177,291 |67,040,955 |70,047,802 |73,204,991 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.4% |2.6% |2.7% |2.8% |3.0% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |151,384,491 |157,982,543 |164,910,499 |172,184,852 |179,822,922 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.9% |1.8% |1.8% |1.8% |1.8% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |98,341,028 |100,886,745 |103,508,833 |106,209,584 |108,991,357 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.5% |1.5% |1.5% |1.5% |1.5% |
Sensitivity 4d: Reduce productivity by a further 10 percent (20 percent in total) in each of the two years prior to death
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |64,910,885 |67,811,229 |70,856,589 |74,054,218 |77,411,728 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.6% |2.7% |2.9% |3.0% |3.2% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |160,239,121 |167,279,905 |174,672,728 |182,435,193 |190,585,780 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.0% |2.0% |1.9% |1.9% |1.9% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |105,586,005 |108,349,072 |111,195,030 |114,126,367 |117,145,644 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.6% |1.6% |1.6% |1.6% |1.6% |
Sensitivity 5: Replacement worker costs
Sensitivity 5a: Increase recruitment costs by 50%
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |54,989,659 |57,393,941 |59,918,437 |62,569,158 |65,352,415 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.2% |2.3% |2.4% |2.5% |2.7% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |130,089,063 |135,450,521 |141,080,051 |146,991,058 |153,197,616 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.6% |1.6% |1.6% |1.6% |1.5% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |79,641,356 |81,579,993 |83,576,790 |85,633,491 |87,751,893 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.2% |1.2% |1.2% |1.2% |1.2% |
Sensitivity 5b: Increase training costs by 50%
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |54,989,659 |57,393,941 |59,918,437 |62,569,158 |65,352,415 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.2% |2.3% |2.4% |2.5% |2.7% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |130,716,588 |136,078,046 |141,707,576 |147,618,583 |153,825,141 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.6% |1.6% |1.6% |1.6% |1.6% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |80,508,956 |82,447,593 |84,444,390 |86,501,091 |88,619,493 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.2% |1.2% |1.2% |1.2% |1.2% |
Sensitivity 5c: Increase months until fully productive to six
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |59,604,183 |62,239,191 |65,005,950 |67,911,046 |70,961,398 |
|Wage Bill |2,517,012,961 |2,499,733,461 |2,481,947,811 |2,463,629,809 |2,444,751,946 |
|Cost % Wages |2.4% |2.5% |2.6% |2.8% |2.9% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |139,876,550 |145,696,006 |151,806,434 |158,222,384 |164,959,132 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |1.7% |1.7% |1.7% |1.7% |1.7% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |83,007,224 |85,020,810 |87,094,803 |89,231,016 |91,431,315 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.2% |1.2% |1.2% |1.2% |1.2% |
Sensitivity 5d: Reduce productivity in the first six months to 50 percent (only affects IMC)
| |2006 |2007 |2008 |2009 |2010 |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |94,998,911 |97,372,247 |99,816,784 |102,334,656 |104,928,065 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |1.4% |1.4% |1.4% |1.4% |1.4% |
“High” Case
| |2006 |2007 |2008 |2009 |2010 |
|City Council of Nairobi | | | | | |
|HIV/AIDS Related Costs |73,762,370 |77,058,214 |80,518,851 |84,152,520 |87,967,872 |
|Wage Bill |2,517,012,961 |2,496,213,867 |2,474,741,442 |2,452,561,105 |2,429,636,544 |
|Cost % Wages |2.9% |3.1% |3.3% |3.4% |3.6% |
|Kampala City Council | | | | | |
|HIV/AIDS Related Costs |238,133,138 |248,596,526 |259,583,082 |271,118,967 |283,231,646 |
|Wage Bill |8,142,188,649 |8,549,298,082 |8,976,762,986 |9,425,601,135 |9,896,881,192 |
|Cost % Wages |2.9% |2.9% |2.9% |2.9% |2.9% |
|Ilala Municipal Council | | | | | |
|HIV/AIDS Related Costs |159,845,481 |164,028,456 |168,336,920 |172,774,638 |177,345,488 |
|Wage Bill |6,662,048,433 |6,861,909,885 |7,067,767,182 |7,279,800,198 |7,498,194,203 |
|Cost % Wages |2.4% |2.4% |2.4% |2.4% |2.4% |
Sensitivity: Second-line drug regimen costs
Figures F.8 to F.13 below show the present value cost of a new HIV infection with and without treatment, assuming that ten percent of HIV-positive employees require second-line drugs at a cost of seven times the first-line drug costs.
Figure F.8: City Council of Nairobi (KSH)
[pic]
Figure F.9: City Council of Nairobi (KSH)
[pic]
Figure F.10: Kampala City Council (UGSH)
[pic]
Figure F.11: Kampala City Council (UGSH)
[pic]
Figure F.12: Ilala Municipal Council (TSH)
[pic]
Figure F.12: Ilala Municipal Council (TSH)
[pic]
|Africa Region Working Paper Series |
|Series # |Title |Date |Author |
|ARWPS 1 |Progress in Public Expenditure Management in Africa: Evidence|January 1999 |C. Kostopoulos |
| |from World Bank Surveys | | |
|ARWPS 2 |Toward Inclusive and Sustainable Development in the |March 1999 |Markus Kostner |
| |Democratic Republic of the Congo | | |
|ARWPS 3 |Business Taxation in a Low-Revenue Economy: A Study on Uganda|June 1999 |Ritva Reinikka |
| |in Comparison with Neighboring Countries | |Duanjie Chen |
|ARWPS 4 |Pensions and Social Security in Sub-Saharan Africa: Issues |October 1999 |Luca Barbone |
| |and Options | |Luis-A. Sanchez B. |
|ARWPS 5 |Forest Taxes, Government Revenues and the Sustainable |January 2000 |Luca Barbone |
| |Exploitation of Tropical Forests | |Juan Zalduendo |
|ARWPS 6 |The Cost of Doing Business: Firms’ Experience with Corruption|June 2000 |Jacob Svensson |
| |in Uganda | | |
|ARWPS 7 |On the Recent Trade Performance of Sub-Saharan African |August 2000 |Francis Ng and Alexander J. Yeats |
| |Countries: Cause for Hope or More of the Same | | |
|ARWPS 8 |Foreign Direct Investment in Africa: Old Tales and New |November 2000 |Miria Pigato |
| |Evidence | | |
|ARWPS 9 |The Macro Implications of HIV/AIDS in South Africa: A |November 2000 |Channing Arndt |
| |Preliminary Assessment | |Jeffrey D. Lewis |
|ARWPS 10 |Revisiting Growth and Convergence: Is Africa Catching Up? |December 2000 |C. G. Tsangarides |
|ARWPS 11 |Spending on Safety Nets for the Poor: How Much, for |January 2001 |William J. Smith |
| |How Many? The Case of Malawi | | |
|ARWPS 12 |Tourism in Africa |February 2001 |Iain T. Christie |
| | | |D. E. Crompton |
|ARWPS 13 |Conflict Diamonds |February 2001 |Louis Goreux |
|ARWPS 14 |Reform and Opportunity: The Changing Role and Patterns of |March 2001 |Jeffrey D. Lewis |
| |Trade in South Africa and SADC | | |
|ARWPS 15 |The Foreign Direct Investment Environment in Africa |March 2001 |Miria Pigato |
|ARWPS 16 |Choice of Exchange Rate Regimes for Developing Countries |April 2001 |Fahrettin Yagci |
|ARWPS 18 |Rural Infrastructure in Africa: Policy Directions |June 2001 |Robert Fishbein |
|ARWPS 19 |Changes in Poverty in Madagascar: 1993-1999 |July 2001 |S. Paternostro |
| | | |J. Razafindravonona David Stifel |
|ARWPS 20 |Information and Communication Technology, Poverty, and |August 2001 |Miria Pigato |
| |Development in sub-Saharan Africa and South Asia | | |
|ARWPS 21 |Handling Hierarchy in Decentralized Settings: Governance |September 2001 |Navin Girishankar A. Alemayehu |
| |Underpinnings of School Performance in Tikur Inchini, West | |Yusuf Ahmad |
| |Shewa Zone, Oromia Region | | |
|ARWPS 22 |Child Malnutrition in Ethiopia: Can Maternal Knowledge |October 2001 |Luc Christiaensen |
| |Augment The Role of Income? | |Harold Alderman |
|ARWPS 23 |Child Soldiers: Preventing, Demobilizing and Reintegrating |November 2001 |Beth Verhey |
|ARWPS 24 |The Budget and Medium-Term Expenditure Framework in Uganda |December 2001 |David L. Bevan |
|ARWPS 25 |Design and Implementation of Financial Management Systems: An|January 2002 |Guenter Heidenhof H. Grandvoinnet |
| |African Perspective | |Daryoush Kianpour B. Rezaian |
|ARWPS 26 |What Can Africa Expect From Its Traditional Exports? |February 2002 |Francis Ng |
| | | |Alexander Yeats |
|ARWPS 27 |Free Trade Agreements and the SADC Economies |February 2002 |Jeffrey D. Lewis |
| | | |Sherman Robinson |
| | | |Karen Thierfelder |
|ARWPS 28 |Medium Term Expenditure Frameworks: From Concept to Practice.|February 2002 |P. Le Houerou Robert Taliercio |
| |Preliminary Lessons from Africa | | |
|ARWPS 29 |The Changing Distribution of Public Education Expenditure in |February 2002 |Samer Al-Samarrai |
| |Malawi | |Hassan Zaman |
|ARWPS 30 |Post-Conflict Recovery in Africa: An Agenda for the Africa |April 2002 |Serge Michailof |
| |Region | |Markus Kostner |
| | | |Xavier Devictor |
|ARWPS 31 |Efficiency of Public Expenditure Distribution and Beyond: A |May 2002 |Xiao Ye |
| |report on Ghana’s 2000 Public Expenditure Tracking Survey in | |S. Canagaraja |
| |the Sectors of Primary Health and Education | | |
|ARWPS 33 |Addressing Gender Issues in Demobilization and Reintegration |August 2002 |N. de Watteville |
| |Programs | | |
|ARWPS 34 |Putting Welfare on the Map in Madagascar |August 2002 |Johan A. Mistiaen |
| | | |Berk Soler |
| | | |T. Razafimanantena |
| | | |J. Razafindravonona |
|ARWPS 35 |A Review of the Rural Firewood Market Strategy in West Africa|August 2002 |Gerald Foley |
| | | |Paul Kerkhof |
| | | |Djibrilla Madougou |
|ARWPS 36 |Patterns of Governance in Africa |September 2002 |Brian D. Levy |
|ARWPS 37 |Obstacles and Opportunities for Senegal’s International |September 2002 |Stephen Golub |
| |Competitiveness: Case Studies of the Peanut Oil, Fishing and| |Ahmadou Aly Mbaye |
| |Textile Industries | | |
|ARWPS 38 |A Macroeconomic Framework for Poverty Reduction Strategy |October 2002 |S. Devarajan |
| |Papers : With an Application to Zambia | |Delfin S. Go |
|ARWPS 39 |The Impact of Cash Budgets on Poverty Reduction in Zambia: A|November 2002 |Hinh T. Dinh |
| |Case Study of the Conflict between Well Intentioned | |Abebe Adugna |
| |Macroeconomic Policy and Service Delivery to the Poor | |Bernard Myers |
|ARWPS 40 |Decentralization in Africa: A Stocktaking Survey |November 2002 |Stephen N. Ndegwa |
|ARWPS 41 |An Industry Level Analysis of Manufacturing Productivity in |December 2002 |Professor A. Mbaye |
| |Senegal | | |
|ARWPS 42 |Tanzania’s Cotton Sector: Constraints and Challenges in a |December 2002 |John Baffes |
| |Global Environment | | |
|ARWPS 43 |Analyzing Financial and Private Sector Linkages in Africa |January 2003 |Abayomi Alawode |
| | | | |
|ARWPS 44 |Modernizing Africa’s Agro-Food System: Analytical Framework |February 2003 |Steven Jaffee |
| |and Implications for Operations | |Ron Kopicki |
| | | |Patrick Labaste |
| | | |Iain Christie |
|ARWPS 45 |Public Expenditure Performance in Rwanda |March 2003 |Hippolyte Fofack |
| | | |C. Obidegwu |
| | | |Robert Ngong |
|ARWPS 46 |Senegal Tourism Sector Study |March 2003 |Elizabeth Crompton |
| | | |Iain T. Christie |
|ARWPS 47 |Reforming the Cotton Sector in SSA |March 2003 |Louis Goreux |
| | | |John Macrae |
|ARWPS 48 |HIV/AIDS, Human Capital, and Economic Growth Prospects for |April 2003 |Channing Arndt |
| |Mozambique | | |
|ARWPS 49 |Rural and Micro Finance Regulation in Ghana: Implications for|June 2003 |William F. Steel |
| |Development and Performance of the Industry | |David O. Andah |
|ARWPS 50 |Microfinance Regulation in Benin: Implications of the PARMEC |June 2003 |K. Ouattara |
| |LAW for Development and Performance of the Industry | | |
|ARWPS 51 |Microfinance Regulation in Tanzania: Implications for |June 2003 |Bikki Randhawa |
| |Development and Performance of the Industry | |Joselito Gallardo |
|ARWPS 52 |Regional Integration in Central Africa: Key Issues |June 2003 |Ali Zafar |
| | | |Keiko Kubota |
|ARWPS 53 |Evaluating Banking Supervision in Africa |June 2003 |Abayomi Alawode |
|ARWPS 54 |Microfinance Institutions’ Response in Conflict Environments:|June 2003 |Marilyn S. Manalo |
| |Eritrea- Savings and Micro Credit Program; West Bank and Gaza| | |
| |– Palestine for Credit and Development; Haiti – Micro Credit | | |
| |National, S.A. | | |
|AWPS 55 |Malawi’s Tobacco Sector: Standing on One Strong leg is Better|June 2003 |Steven Jaffee |
| |than on None | | |
|AWPS 56 |Tanzania’s Coffee Sector: Constraints and Challenges in a |June 2003 |John Baffes |
| |Global Environment | | |
|AWPS 57 |The New Southern AfricanCustoms Union Agreement |June 2003 |Robert Kirk |
| | | |Matthew Stern |
|AWPS 58a |How Far Did Africa’s First Generation Trade Reforms Go? An |June 2003 |Lawrence Hinkle |
| |Intermediate Methodology for Comparative Analysis of Trade | |A. Herrou-Aragon |
| |Policies | |Keiko Kubota |
|AWPS 58b |How Far Did Africa’s First Generation Trade Reforms Go? An |June 2003 |Lawrence Hinkle |
| |Intermediate Methodology for Comparative Analysis of Trade | |A. Herrou-Aragon |
| |Policies | |Keiko Kubota |
|AWPS 59 |Rwanda: The Search for Post-Conflict Socio-Economic Change, |October 2003 |C. Obidegwu |
| |1995-2001 | | |
|AWPS 60 |Linking Farmers to Markets: Exporting Malian Mangoes to |October 2003 |Morgane Danielou |
| |Europe | |Patrick Labaste |
| | | |J-M. Voisard |
|AWPS 61 |Evolution of Poverty and Welfare in Ghana in the 1990s: |October 2003 |S. Canagarajah |
| |Achievements and Challenges | |Claus C. Pörtner |
| | | | |
|AWPS 62 |Reforming The Cotton Sector in Sub-Saharan Africa: SECOND |November 2003 |Louis Goreux |
| |EDITION | | |
|AWPS 63 (E) |Republic of Madagascar: Tourism Sector Study |November 2003 |Iain T. Christie |
| | | |D. E. Crompton |
|AWPS 63 (F) |République de Madagascar: Etude du Secteur Tourisme |November 2003 |Iain T. Christie |
| | | |D. E. Crompton |
|AWPS 64 |Migrant Labor Remittances in Africa: Reducing Obstacles to |Novembre 2003 |Cerstin Sander |
| |Development Contributions | |Samuel M. Maimbo |
|AWPS 65 |Government Revenues and Expenditures in Guinea-Bissau: |January 2004 |Francisco G. Carneiro |
| |Casualty and Cointegration | |Joao R. Faria |
| | | |Boubacar S. Barry |
|AWPS 66 |How will we know Development Results when we see them? |June 2004 |Jody Zall Kusek |
| |Building a Results-Based Monitoring and Evaluation System to | |Ray C. Rist |
| |Give us the Answer | |Elizabeth M. White |
|AWPS 67 |An Analysis of the Trade Regime in Senegal (2001) and UEMOA’s|June 2004 |Alberto Herrou-Arago |
| |Common External Trade Policies | |Keiko Kubota |
|AWPS 68 |Bottom-Up Administrative Reform: Designing Indicators for a |June 2004 |Talib Esmail |
| |Local Governance Scorecard in Nigeria | |Nick Manning |
| | | |Jana Orac |
| | | |Galia Schechter |
|AWPS 69 |Tanzania’s Tea Sector: Constraints and Challenges |June 2004 |John Baffes |
|AWPS 70 |Tanzania’s Cashew Sector: Constraints and Challenges in a |June 2004 |Donald Mitchell |
| |Global Environment | | |
|AWPS 71 |An Analysis of Chile’s Trade Regime in 1998 and 2001: A Good |July 2004 |Francesca Castellani |
| |Practice Trade Policy Benchmark | |A. Herrou-Arago |
| | | |Lawrence E. Hinkle |
|AWPS 72 |Regional Trade Integration inEast Africa: Trade and Revenue |August 2004 |Lucio Castro |
| |Impacts of the Planned East African Community Customs Union | |Christiane Kraus |
| | | |Manuel de la Rocha |
|AWPS 73 |Post-Conflict Peace Building in Africa: The Challenges of |August 2004 |Chukwuma Obidegwu |
| |Socio-Economic Recovery and Development | | |
|AWPS 74 |An Analysis of the Trade Regime in Bolivia in2001: A Trade |August 2004 |Francesca Castellani |
| |Policy Benchmark for low Income Countries | |Alberto Herrou-Aragon |
| | | |Lawrence E. Hinkle |
|AWPS 75 |Remittances to Comoros- Volumes, Trends, Impact and |October 2004 |Vincent da Cruz |
| |Implications | |Wolfgang Fendler |
| | | |Adam Schwartzman |
|AWPS 76 |Salient Features of Trade Performance in Eastern and Southern|October 2004 |Fahrettin Yagci |
| |Africa | |Enrique Aldaz-Carroll |
|AWPS 77 | |November 2004 |Alan Gelb |
| |Implementing Performance-Based Aid in Africa | |Brian Ngo |
| | | |Xiao Ye |
|AWPS 78 |Poverty Reduction Strategy Papers: Do they matter for |December 2004 |Rene Bonnel |
| |children and Young people made vulnerable by HIV/AIDS? | |Miriam Temin |
| | | |Faith Tempest |
|AWPS 79 |Experience in Scaling up Support to Local Response in |December 2004 |Jean Delion |
| |Multi-Country Aids Programs (map) in Africa | |Pia Peeters |
| | | |Ann Klofkorn Bloome |
|AWPS 80 |What makes FDI work? A Panel Analysis of the Growth Effect of|February 2005 | |
| |FDI in Africa | |Kevin N. Lumbila |
|AWPS 81 |Earnings Differences between Men and Women in Rwanda |February 2005 |Kene Ezemenari |
| | | |Rui Wu |
|AWPS 82 |The Medium-Term Expenditure Framework | | |
| |The Challenge of Budget Integration in SSA countries |April 2005 |Chukwuma Obidegwu |
|AWPS 83 |Rules of Origin and SADC: The Case for change in the Mid Term| |Paul Brenton |
| |Review of the Trade Protocol |June 2005 |Frank Flatters |
| | | |Paul Kalenga |
|AWPS 84 | | |Chukwuemeka Anyamele |
| |Sexual Minorities, |July 2005 |Ronald Lwabaayi |
| |Violence and AIDS in Africa | |Tuu-Van Nguyen, and Hans Binswanger |
| | | | |
|AWPS 85 |Poverty Reducing Potential of Smallholder Agriculture in | | |
| |Zambia: |July 2005 |Paul B. Siegel |
| |Opportunities and Constraints | |Jeffrey Alwang |
|AWPS 86 |Infrastructure, Productivity and Urban Dynamics | | |
| |in Côte d’Ivoire |July 2005 |Zeljko Bogetic |
| |An empirical analysis and policy implications | |Issa Sanogo |
|AWPS 87 |Poverty in Mozambique: | |Louise Fox |
| |Unraveling Changes and Determinants |August 2005 |Elena Bardasi, |
| | | |Katleen Van den Broeck |
| | | | |
|AWPS 88 |Operational Challenges: |August 2005 |Nadeem Mohammad |
| |Community Home Based Care (CHBC) forPLWHA in Multi-Country | |Juliet Gikonyo |
| |HIV/AIDS Programs (MAP) forSub-Saharan Africa | | |
| | | | |
|AWPS 89 |Framework for Forest Resource Management in Sub-Saharan |August 2005 |Giuseppe Topa |
| |Africa | | |
| | | | |
|AWPS 90 |Kenya: Exports Prospects and Problems |September 2005 |Francis Ng |
| | | |Alexander Yeats |
| | | | |
|AWPS 91 |Uganda: How Good a Trade Policy Benchmark for |September 2005 |Lawrence E. Hinkle |
| |Sub-Saharan-Africa | |Albero Herrou Aragon |
| | | |Ranga Rajan Krishnamani |
| | | |Elke Kreuzwieser |
| | | | |
|AWPS 92 |Community Driven Development in South Africa, 1990-2004 |October 2005 |David Everatt Lulu Gwagwa |
| | | | |
|AWPS 93 |The Rise of Ghana’’s Pineapple Industry from Successful take |November 2005 |Morgane Danielou |
| |off to Sustainable Expansion | |Christophe Ravry |
| | | | |
|AWPS 94 |South Africa: Sources and Constraints of Long-Term Growth, |December 2005 |Johannes Fedderke |
| |1970-2000 | | |
| | | |Lawrence Edwards |
|AWPS 95 |South Africa’’s Export Performance: Determinants of Export |December 2005 |Phil Alves |
| |supply | | |
| | | | |
| |Industry Concentration in South African Manufacturing: Trends|December 2005 |Gábor Szalontai Johannes Fedderke |
|AWPS 96 |and Consequences, 1972-96 | | |
| | | | |
| |The Urban Transition in Sub-Saharan Africa: Implications for |December 2005 |Christine Kessides |
|AWPS 97 |Economic Growth | | |
| |and Poverty Reduction | | |
| | | | |
| |Measuring Intergovernmental Fiscal Performance in South |May 2006 |Navin Girishankar |
|AWPS 98 |Africa | |David DeGroot |
| |Issues in Municipal Grant Monitoring | |T.V. Pillay |
| | | | |
| |Improving Nutrition in Ethiopia | | |
|AWPS 99 |A Multi-sectoral challenge |July 2006 |Jesper Kuhl |
| | | |Luc Christiaensen |
| | | | |
| |The Impact of Morbidity and Mortality on Municipal Human | | |
|AWPS 100 |Resources and Service Delivery |September 2006 |Zara Sarzin |
-----------------------
[1] See Thomas (2005): 54.
[2] See studies by Thomas and Rosen listed in the references to this study.
[3] Particularly the goals of “Reduce Child Mortality”, “Improve Maternal Health”, “Combat HIV/AIDS, Malaria and Other Diseases” and “Ensure Environmental Sustainability” (including improving the lives of slum-dwellers).
[4] The handbook is available on the World Bank website at . Copies of the CD-ROM can be ordered by email from Urbanhelp@.
[5] Excluding 120 square kilometres of Nairobi Game Park and Jomo Kenyatta International Airport.
[6] UBOS (2002): 7.
[7] UBOS and ILRI (2005): 18.
[8] Uganda’s 2002 Population and Housing Census estimates that Kampala's population grew at a rate of 3.9% per annum between 1991 and 2002.
[9] The First Kampala Citizen’s Report Card (2005): 3.
[10] KIDDP, Project Concept Note.
[11] Each division has devolved responsibilities for service delivery and revenue collection mandated by the 1997 Local Government Act.
[12] City Council of Kampala Annual Budget (2005): 10, 12.
[13] Includes UGSH 3,686,358,260 of Graduated Tax.
[14] City Council of Kampala Annual Budget (2005): 10.
[15] Extract of teachers’ salaries provided by KCC included only UGSH 3 billion of salaries for primary teachers only.
[16] This summary reflects the organisational structure envisaged by the Institutional Restructuring Plan, which is currently being implemented.
[17] KCC Staff List (Restructure.xls).
[18] There are 20 nursery schools, 98 primary schools and 20 secondary schools in the Ilala district.
[19] Excludes primary and secondary teachers who are employees of the central government.
[20] AMMP (2004) Volume 2: 147.
[21] Kalembe, Isaac, “Malaria Kills 400 Ugandans Daily”, New Vision (Kampala), November 4, 2005.
[22] Mostly malaria.
[23] AMMP (2004) Volume 2: 96.
[24] AMMP (2004) Volume 2: 103, 167.
[25] UNAIDS (1998). Common opportunistic infections and diseases include: (a) bacterial diseases such as tuberculosis (TB), Mycobacterium avium complex disease (MAC), bacterial pneumonia and septicaemia; (b) protozoal diseases such as Pneumocystis carinii pneumonia (PCP), toxoplasmosis, microsporidiosis, cryptosporidiosis, isosporiasis and leishmaniasis; (c) fungal diseases such as candidiasis, cryptococcosis (cryptococcal meningitis) and penicilliosis; (d) viral diseases such as those caused by cytomegalovirus (CMV), herpes simplex and herpes zoster virus; (e) HIV-associated malignancies such as Kaposi sarcoma, lymphoma and squamous cell carcinoma.
[26] Source: AMMP (2002).
[27] Ibid.
[28] Source: Ministry of Health, Uganda.
[29] Source: Medical Officer of Health, City Council of Nairobi.
[30] Ibid.
[31] WHO (2005) Tanzania: 1. And TACAIDS (2005): 1.
[32] WHO (2005) Uganda: 1.
[33] WHO (2005) Kenya: 1; NASCOP (2004): 1; KNASAP 2005-2010 (2005): 15; WHO (2005) Kenya: 1
[34] TACAIDS (2005): 69; WHO (2005): 1.
[35] WHO (2005): 1; TACAIDS (2005): 75.
[36] WHO (2005) Uganda: 1.
[37] Government of Kenya (2005): 14.
[38] Republic of Uganda and Uganda AIDS Commission (2004): 7, 16.
[39] WHO (2005) Kenya: 1. Ministry of Health, 2004.
[40] UNAIDS/WHO Epidemiological Fact Sheet – Kenya 2004 Update: 2.
[41] WHO (2005) Uganda: 1. WHO/UNAIDS, 2003.
[42] WHO (2005) Uganda: 1. WHO/UNAIDS, 2003. 2004-2005 Uganda HIV/AIDS Sero-Behavioural Survey (UHSBS) for adults aged 15 to 59.
[43] WHO (2005) Tanzania: 1. WHO/UNAIDS, 2003.
[44] UNAIDS/WHO Global HIV/AIDS Online Database (2004).
[45] TACAIDS (2005): 75; WHO (2005) Uganda: 1; UNAIDS/WHO Epidemiological Fact Sheet – Kenya 2004 Update: 2.
[46] Ibid.
[47] WHO (2005) Tanzania: 1 (WHO/UNAIDS, 2003); WHO (2005) Uganda: 1 (WHO/UNAIDS, 2003); WHO (2005) Kenya: 1 (Ministry of Health, 2004).
[48] Ibid.
[49] Ibid.
[50] WHO suggests that without proper treatment, approximately 90 percent of HIV positive people die within months of contracting TB.
[51] Government of Tanzania, MOH (2003).
[52] WHO Country Cooperation Strategy Uganda (2002-2005): 6
[53] Republic of Uganda and Ugandan AIDS Commission (2004): 15.
[54] Republic of Uganda (2004): 36.
[55] Government of Kenya (2003): 11.
[56] WHO Tanzania Country Cooperation Strategy 2002-2005.
[57] In 1999, a Centre for Clinical Research scientist supported by USAMRU, Dr Robert Kimtai, found that more than 40% of children microscopically diagnosed as having malaria at major hospitals in Nairobi had no history of having left the city during the previous 3 months. Many, however, were living in Kibera.
[58] E.g.: Percent of deaths and medical retirements due to HIV/AIDS: 60% (Fox, 2004); Life expectancy in the absence of treatment: 7 years (Rosen, 2000); Cost and Impact of prevention programs: $10 annually per employee, cuts infection rates by 50% (Rosen, 2003); 80% of those on ART continue with it and survive into the following year (Stover, 2004); ARVs extend working life by 5 years (Rosen, 2003).
[59] Adapted from Thomas et al (2005): 44.
[60] Where names were provided, these were treated confidentially.
[61] Rosen (2004): 319.
[62] Find Rosen reference.
[63] Over (2004): 42.
[64] The opportunity cost of capital is the cost of forgoing the next best alternative use of funds.
[65] Over (2004): 7, 80. “The appropriate discount rate is open to debate. A ten percent rate is often used for government investment projects outside the health sector. For the health sector the cost-effectiveness guidelines developed for the United States by Gold and others (1996) recommend that a discount rate of three percent be applied to both health gains and costs. The World Bank’s 1993 World Development Report made the same recommendation for developing countries.”
[66] A recent prevalence study in Buffalo City Municipality in South Africa showed that infection levels were highest among temporary workers, younger women and men, semi-skilled workers, those living in informal housing and those renting their homes. Also, studies in the private sector have shown that unskilled and skilled workers are two to three times more likely to be infected than supervisors and managers (see Rosen, 2003).
[67] Rosen (2003).
[68] Rosen (2004): 320.
[69] Rosen (2003)
[70] There are a range of possible workplace prevention strategies including: (1) development and implementation of a workplace policy on HIV/AIDS; (2) providing Information, Education, and Communication (IEC) in the workplace, involving peer educators and municipal management; (3) condom promotion and distribution; (4) access to voluntary counselling and testing; and (4) access to diagnosis and treatment of sexually transmitted diseases (at council clinics or elsewhere). While several of these activities cost money, there are many activities that the municipality can carry out at little or no cost.
[71] Thomas (2005): 54.
[72] This is meant to capture the cost of illness other than that attributable to HIV/AIDS. However, it is not easy to separate HIV/AIDS and other causes, particularly since HIV/AIDS reduces immunity to a broad range of diseases.
[73] Uganda Annual Health Sector Performance Report (2004): 28. Use of Insecticide Treated Nets is one of the most cost-effective methods of malaria prevention in highly endemic areas.
[74] Rosen et al (2004): 321-322.
[75] There has been one such study conducted by Buffalo City Municipality in South Africa in collaboration with Sydney Rosen of Boston University.
[76] The choice of discount rate is discussed on page 36.
[77] Exchange rate: KSH 73.5 = 1USD (December 2005).
[78] Exchange rate: UGSH 1,822 = 1USD (December 2005).
[79] Exchange rate: TSH 1,157 = 1USD (December 2005).
[80] See Rosen (2003) “Aids is your Business”. The author notes that (in South Africa), HIV prevention programmes typically cost between $10 and $15 per employee per annum and achieved substantial reductions in new HIV infections. The author analyses the financial impact of a workplace prevention programme that costs $10 per employee per annum and which reduces new infection rates by 50 percent.
[81] Thomas (2005): 43.
[82] It is important to note that many interventions have minimal cost, and external funding can be accessed to implement programmes to address workplace health issues.
[83] For more information, refer to the “Local Government Responses to HIV/AIDS: A Handbook” available on the World Bank website at . Copies of the CD-ROM can be ordered by email from Urbanhelp@.
[84] CCN has a draft policy that has yet to be approved by Council.
[85] World Bank (2004): 53. 76.
[86] World Bank (2004): 66.
[87] City Council of Kampala, “Allowances”, 2 November 2001.
[88] BCM study assumed that premiums would not increase if HIV/AIDS-related illness caused an increase in medical claims, since BCM has capped it contributions to medical aid. See Thomas (2005): 46.
[89] Tanzania NHIF (2004): 2-3, 9, 11.
[90] Approximately 50 percent of KCC staff attend public facilities (Dr. Mina Nakawuka). The clinics that are reasonably well attended by staff include the KCC senior staff clinic, and the municipal clinics in Nakawa and Kawempe Divisions.
[91] The Local Authorities Provident Fund Act, 2000.
[92] The Political Service Retirement Benefits Act, 1999.
[93] National Social Security Act, 1997.
[94] Previously, the staff pensions’ scheme was contributory (5 percent employee contribution, 10 percent employer contribution, as a percentage of gross salary) and contributions were sent to the National Insurance Corporation (NIC). This system was wound up in 1994 and now employees remain in the scheme which is now non-contributory.
[95] Before LGDP there was no systematic training. LGDPII created a “training coordinator” position funded by KCC to coordinate capacity building under LGDPII.
[96] Uganda, Malamba et al, 1999
[97] ASSA, 2004
[98] An alternative scenario was modelled using a wage multiplier that also included the wage-weighted share of the company’s net operating profit.
[99] Morris and Cheevers 2000; Greener 1997
[100] Infection to symptoms: remains at 5 years
Symptoms to treatment failure: + 5 years
Treatment failure to AIDS: 3 years (from 4)
AIDS to death: remains at 5 years.
[101] IMF projected rate for 2006.
[102] The City Council of Nairobi have implemented an employment freeze for staff in grades 10-19.
[103] The City Council of Nairobi have implemented an employment freeze for staff in grades 10-19.
[104] Modelled HIV prevalence rates were estimated based on historical staff deaths attributed to AIDS and assumptions about average survival periods in the absence of treatment. National, urban and city prevalence rates were also taken into account. The model does not vary prevalence rates by employee category, age or sex. This functionality could be built into the model at a later stage.
[105] WHO (2005) Kenya: 1. Ministry of Health, 2004.
[106] UNAIDS/WHO Epidemiological Fact Sheet – Kenya 2004 Update: 2.
[107] WHO (2005) Uganda: 1. WHO/UNAIDS, 2003.
[108] WHO (2005) Uganda: 1. WHO/UNAIDS, 2003. 2004-2005 Uganda HIV/AIDS Sero-Behavioiural Survey (UHSBS) for adults aged 15 to 59.
[109] WHO (2005) Tanzania: 1. WHO/UNAIDS, 2003.
[110] UNAIDS/WHO Global HIV/AIDS Online Database (2004).
[111] UNAIDS/WHO Epidemiological Fact Sheet – Kenya 2004 Update: 2.
[112] WHO (2005) Uganda: 1.
[113] TACAIDS (2005): 75.
[114] UNAIDS/WHO Epidemiological Fact Sheet – Kenya 2004 Update: 2.
[115] WHO (2005) Uganda: 1.
[116] 2004-2005 Uganda HIV/AIDS Sero-behavioural Survey: Preliminary report: 19.
[117] Ibid.
[118] TACAIDS (2005): 69.
[119] Ibid.
[120] MOH NACP (2004): 17.
[121] MOH NACP (2004): 20.
[122] MOH NACP (2004): 23.
[123] Blood samples were obtained from subjects enrolled in the U.S. Military HIV Research Program’s HIV and Malaria Cohort Study in Kericho, Kenya. This study of 2,803 adult residents of a tea plantation in Kericho, Kenya is gathering data on the prevalence, incidence and risk factors for acquisition of HIV-1 infection as a preparatory step toward HIV-1 vaccine testing.
[124] NACP HIV/AIDS/STI Surveillance Report Number 18, p. 27.
[125] Ibid.
[126] Assumptions were drawn from Over (2004): 42. Assumed upper bound of additional life years from unstructured anti retroviral therapy.
[127] See a literature review by Schneider, Martin et al (2004) “Natural history and mortality in HIV-positive individuals living in resource-poor settings: A literature review”, June 2004. Factors associated with survival periods include: age (younger ages have a longer survival period); immunological and virological parameters (low CD4 lymphocytes and high viral load are associated with shorter survival period); and type of virus (HIV-2 is less virulent than HIV-1).
|Survival time from AIDS to death |11 (7-19) months in resource limited settings compared with 9.5 to 22 months in|
| |industrialised countries. |
|Survival time from ................
................
In order to avoid copyright disputes, this page is only a partial summary.
To fulfill the demand for quickly locating and searching documents.
It is intelligent file search solution for home and business.
Related download
- sector interviews discussion points
- please refer to anzfa s guide to applications and
- minutes of the directors of the
- profile of those who do not spay neuter in the capital region
- annual report nc state university
- maritime logistics logistics management of shipping and
- annexes infodev
- alaska accountability workbook
Related searches
- closed questions for interviews examples
- advantage of interviews in research
- advantages of interviews in research pdf
- benefits of interviews in research
- advantages of interviews in research
- use of interviews in research
- interviews research method
- advantages of interviews pdf
- disadvantages of interviews in research
- interviews advantages and disadvantages
- advantages of interviews in research methods
- types of interviews in research