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Examining the Wealth Trends in Kombewa, KenyaA Thesis Submitted in Partial Fulfillment of the Requirements of the Renée Crown University Honors Program at Syracuse University Alizée McLorgCandidate for Bachelor of Science Degree and Renée Crown University Honors Spring 2020Honors Thesis in Public HealthThesis Project Advisor: Dr. David Larsen, Associate Professor of Public HealthThesis Project Reader: Dr. Bhavneet Walia, Assistant Professor of Public HealthHonors Director: Dr. Danielle Smith, Director of Renée Crown Honors ProgramDate: 4/19/2019 AbstractThe primary purpose of this study is to understand wealth trends in a rural Kenyan community. Understanding wealth trends is important for understanding health outcomes, overall well-being and for informing economic and health policy. Using the Kombewa Health and Demographic Surveillance System, 20,370 households were assessed between 2011-2018. Data on household materials, assets, education and mortality were used. Three indices were developed to quantify wealth: principal component analysis, multiple correspondence analysis and the multidimensional poverty index. Wealth quintiles and levels of deprivation relating to socioeconomic status were then created and analyzed over time. The first two indices demonstrate an increase in wealth during the assessment period with the percentage of households in the wealthiest quintile increasing from 19% to 23%. The multidimensional poverty index, however, shows no change in socioeconomic status over time. Among other factors, lack of sanitation and improved water seems to be the main justification. Our results indicate that households are accumulating assets, but their increased accumulation is not translating to changes in living conditions known to improve health. Hence, while houses are getting wealthier, they are not necessarily getting healthier.Keywords: Health and Demographic Surveillance System (HDSS), Kenya, low and middle income countries (LMIC), multidimensional poverty index (MPI), multiple correspondence analysis (MCA), principal component analysis (PCA), socioeconomic status (SES), wealth indexExecutive SummaryMeasuring wealth is essential for understanding the success of a community and to predict health outcomes. Socioeconomic status (SES), defined by the American Psychological Association ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2020","3","26"]]},"author":[{"dropping-particle":"","family":"Association","given":"American Psychological","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2007"]]},"title":"Socioeconomic status","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(Association, 2007)","manualFormatting":"(APA, 2007)","plainTextFormattedCitation":"(Association, 2007)","previouslyFormattedCitation":"(Association, 2007)"},"properties":{"noteIndex":0},"schema":""}(APA, 2007) is the social standing of an individual or group. Examinations of SES often reveals inequities of access to resources and correlated health outcomes. Although in high income countries, income is a common measurement of SES, many low and middle income countries (LMIC) do not have data on household income. Therefore, several methods for measuring SES in LMIC were developed, including asset-based wealth indices. Using the Kombewa Health and Demographic Surveillance System, three wealth indices were created to examine wealth and SES trends. The Kombewa HDSS is situated in rural Western Kenya and has been collecting demographic data since 2011. Our analysis included households that had at least two available housing characteristic data points. A total of 20,370 households between 2011-2018 were included. Two wealth indices and one SES index were created using the HDSS: principal component analysis (PCA), multiple correspondence analysis (MCA) and multidimensional poverty index (MPI). PCA and MCA only used housing characteristic data and measured wealth while the MPI also incorporated education and mortality to measure multidimensional wealth or SES. After examining the change in quintiles over time, PCA and MCA indices demonstrated a slight increase in wealth. During the first time period, 22% of households were in the poorest quintile. In the last time period, only 16% of households were in the poorest quintile. PCA and MCA ranked most of the households in the same quintile with a correlation coefficient of 0.971. However, MPI results demonstrated that the SES of households did not change over time. About 70% of households were considered non-deprived for all time periods. Deprivations in education and mortality remained stagnant while access to sanitation and water decreased over time.Our results suggest that although asset accumulation and improved housing material has increased over time, sanitation and water decreased. The decrease in sanitation may be attributed to lack of coordination among organizations responsible for the water in Kombewa. Sanitation and water access are associated with health outcomes so while houses are getting wealthier, they many not necessarily be getting healthier. These changes must come from more comprehensive policy approaches because improved well-being and health of the community is co-dependent on the government and household decision-making. Table of ContentsContents TOC \o "1-3" \h \z \u Abstract PAGEREF _Toc37781651 \h 2Executive Summary PAGEREF _Toc37781652 \h 3Table of Contents PAGEREF _Toc37781653 \h 5Acknowledgements PAGEREF _Toc37781654 \h 6Chapter 1: Introduction PAGEREF _Toc37781655 \h 7Introduction PAGEREF _Toc37781656 \h 7Literature Review PAGEREF _Toc37781657 \h 8Creation of Wealth Indices: PAGEREF _Toc37781658 \h 9Principal Component Analysis (PCA): PAGEREF _Toc37781659 \h 11Multiple Correspondence Analysis (MCA): PAGEREF _Toc37781660 \h 11Multidimensional Poverty Index: PAGEREF _Toc37781661 \h 12Chapter 2: Methods PAGEREF _Toc37781662 \h 14Methods PAGEREF _Toc37781663 \h 14Study design PAGEREF _Toc37781664 \h 14Setting PAGEREF _Toc37781665 \h 14Participants PAGEREF _Toc37781666 \h 15Variables PAGEREF _Toc37781667 \h 15Data sources PAGEREF _Toc37781668 \h 15Statistical methods PAGEREF _Toc37781669 \h 16Bias PAGEREF _Toc37781670 \h 18Chapter 3: Results and Discussion PAGEREF _Toc37781671 \h 19Results PAGEREF _Toc37781672 \h 19PCA and MCA PAGEREF _Toc37781673 \h 19MPI PAGEREF _Toc37781674 \h 20Discussion PAGEREF _Toc37781675 \h 20Key results PAGEREF _Toc37781676 \h 20Interpretation PAGEREF _Toc37781677 \h 21Conclusion PAGEREF _Toc37781678 \h 22Reference List PAGEREF _Toc37781679 \h 23Appendices PAGEREF _Toc37781680 \h 26AcknowledgementsThis thesis would not be possible without the support of several people. First, thank you to Naomi Shanguhyia for supporting my project, for being excited with me and for connecting me with her friend Gladys, who made Kenya feel a bit more like home. Thank you to the entire HDSS team, Winnie Al-Abdneger, Ken Omolo and Peter Sifuna, for welcoming me to Kombewa and always taking time to explain. Their support and friendship made this thesis possible. Thank you to Dr. Andrea Shaw, for supervising and supporting my experience in Kenya and for her continued mentorship. Thank you to my reader, Dr. Bhavneet Walia, who helped contextualize this project and helped me better understand and develop an interest in health economics. Thank you to my parents, who supported my ambitious ideas, for listening to me and for always being there; I am beyond lucky to have them. Finally, thank you to my advisor, Dr. David Larsen, who helped me reflect on how to make this thesis meaningful at every step. His guidance helped me build genuine relationships, further develop my interest in global health and develop tangible research skills. His mentoring has been substantial to my academic and professional growth and I feel very fortunate to have had his support. Thank you all for your time and effort in helping me complete this project.A special thanks to the Renée Crown Honors Program for financially supporting this project through the Crown-Wise Fund.Chapter 1: Introduction IntroductionAn estimated 736 million people lived in extreme poverty in 2015, with over half of them living in sub-Saharan Africa ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1080/1364436012010051","ISBN":"0821348248","ISSN":"1364436X","abstract":"Focuses on the Eight Annual International Conference on Education, Spirituality and the Whole Child in Great Britain. Venue of the conference; Date; Theme of the conference related to spirituality.","author":[{"dropping-particle":"","family":"World Bank","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World Bank","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"Poverty Overview","type":"article"},"uris":[""]},{"id":"ITEM-2","itemData":{"URL":"","accessed":{"date-parts":[["2020","3","26"]]},"author":[{"dropping-particle":"","family":"United Nations","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"United Nations","id":"ITEM-2","issued":{"date-parts":[["2019"]]},"title":"Ending Poverty | United Nations","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(United Nations, 2019; World Bank, 2019)","plainTextFormattedCitation":"(United Nations, 2019; World Bank, 2019)","previouslyFormattedCitation":"(United Nations, 2019; World Bank, 2019)"},"properties":{"noteIndex":0},"schema":""}(United Nations, 2019; World Bank, 2019). Poverty decreases access to health care, decreases opportunity for educational attainment and increases food insecurity ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"HB Ferguson, S Bovaird, MP Mueller. The impact of poverty on educational outcomes for children. Paediatr Child Health 2007;12(8):701-706. Over the past decade, the unfortunate reality is that the income gap has widened between Canadian families. Educational outcomes are one of the key areas influenced by family incomes. Children from low-income families often start school already behind their peers who come from more affluent families, as shown in measures of school readiness. The incidence, depth, duration and timing of poverty all influence a child's educational attainment, along with community characteristics and social networks. However, both Canadian and international interventions have shown that the effects of poverty can be reduced using sustainable interventions. Paediatricians and family doctors have many opportunities to influence readiness for school and educational success in primary care settings. Les répercussions de la pauvreté sur l'éducation des enfants Depuis dix ans, l'écart des revenus s'est creusé entre les familles canadiennes, ce qui est une triste réalité. L'éducation est l'un des principaux domaines sur lesquels influe le revenu familial. Souvent, lorsqu'ils commencent l'école, les enfants de familles à faible revenu accusent déjà un retard par rapport à leurs camarades qui proviennent de familles plus aisées, tel que le démontrent les mesures de maturité scolaire. L'incidence, l'importance, la durée et le moment de la pauvreté ont tous une influence sur le rendement scolaire de l'enfant, de même que les caractéristiques de la communauté et les réseaux sociaux. Cependant, tant au Canada que sur la scène internationale, il est possible de réduire les effets de la pauvreté au moyen d'interventions soutenues. Les pédiatres et les médecins de familles ont de nombreuses occasions d'agir sur la maturité et la réussite scolaire dans le cadre des soins de premier recours.","author":[{"dropping-particle":"","family":"Ferguson","given":"H B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bovaird Mph","given":"S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mueller","given":"M P","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Paediatr Child Health","id":"ITEM-1","issued":{"date-parts":[["2007"]]},"title":"The impact of poverty on educational outcomes for children","type":"report","volume":"12"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1007/s11205-017-1589-1","ISSN":"15730921","abstract":"This paper discusses households’ food insecurity among low income, poor urban households in and around the City of Tshwane, South Africa’s capital city. Using systematic random sampling with sampling interval of three, primary data were collected from 900 selected households, though only data from 827 households were analyzed following a rigorous coherence tests. The survey was conducted in Attridgeville, Soshanguve, and Tembisa. In the process, the study employed the use of two-way analyses of variance to explain differences between actual and expected household food security perceptions and those of severe, moderate and mild food insecurity. A favourable (adverse) variance could be interpreted to imply that means for achieving household food security are lower (higher) than predicted or that food security is higher (lower) than expected given the same level of main determinants. The observed variance is partitioned into components attributable to different sources of variation. ANOVA provides a statistical test of whether or not the means of several groups experiencing favourable (adverse) variances are equal. The main findings are that variances in the population means of households’ experiences of food insecurity vary by income class of the head of household, engagement in formal or informal income sources and by categories of social grants received. Poorer households that depend largely on cash income for food purchases experience highest food security variances and those receiving state pension. As such, timely receipt of household income under conditions of unimpeded access to social grants will improve urban poor households’ food security. The level of educational attainment has a very strong impact on a household’s food security. Those with “no schooling” have the lowest level of food security. Experiencing high variances in access to child grants, and low incomes, younger female household heads experience the highest degree of variances in food security and should be particularly targeted in an effective food security policy plan. Negative food security variance among these categories of South Africans could be devastating.","author":[{"dropping-particle":"","family":"Akinboade","given":"Oludele Akinloye","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Adeyefa","given":"Segun Adeyemi","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Indicators Research","id":"ITEM-2","issue":"1","issued":{"date-parts":[["2018"]]},"page":"61-82","publisher":"Springer Netherlands","title":"An Analysis of Variance of Food Security by its Main Determinants Among the Urban Poor in the City of Tshwane, South Africa","type":"article-journal","volume":"137"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1196/annals.1425.011","ISSN":"17496632","abstract":"People in poor countries tend to have less access to health services than those in better-off countries, and within countries, the poor have less access to health services. This article documents disparities in access to health services in low- and middle-income countries (LMICs), using a framework incorporating quality, geographic accessibility, availability, financial accessibility, and acceptability of services. Whereas the poor in LMICs are consistently at a disadvantage in each of the dimensions of access and their determinants, this need not be the case. Many different approaches are shown to improve access to the poor, using targeted or universal approaches, engaging government, nongovernmental, or commercial organizations, and pursuing a wide variety of strategies to finance and organize services. Key ingredients of success include concerted efforts to reach the poor, engaging communities and disadvantaged people, encouraging local adaptation, and careful monitoring of effects on the poor. Yet governments in LMICs rarely focus on the poor in their policies or the implementation or monitoring of health service strategies. There are also new innovations in financing, delivery, and regulation of health services that hold promise for improving access to the poor, such as the use of health equity funds, conditional cash transfers, and coproduction and regulation of health services. The challenge remains to find ways to ensure that vulnerable populations have a say in how strategies are developed, implemented, and accounted for in ways that demonstrate improvements in access by the poor. ? 2008 New York Academy of Sciences.","author":[{"dropping-particle":"","family":"Peters","given":"David H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Garg","given":"Anu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bloom","given":"Gerry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Walker","given":"Damian G.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brieger","given":"William R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hafizur Rahman","given":"M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Annals of the New York Academy of Sciences","id":"ITEM-3","issued":{"date-parts":[["2008"]]},"page":"161-171","title":"Poverty and access to health care in developing countries","type":"article-journal","volume":"1136"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1146/annurev.nutr.22.120701.141104","ISSN":"0199-9885","abstract":"Key Words health and development, inequities and health, obesity and poverty s Abstract This paper is an attempt to discuss the problem of malnutrition within the framework of the global need for development and the challenges posed by the trends of neoliberalism and globalization. We argue that there is a two-way link between poverty and health in which nutrition plays an important role both as an active and as a mediating factor. Key concepts are exposed and expanded: (a) Development per se does not ensure better health; (b) unequal distribution of income has an independent effect on health indicators after adjusting for total income; (c) improving health can make an important contribution to reducing poverty; (d) improving nutrition throughout the whole life course is an indispensable strategy for better health; (e) obesity has to be included amongst the most critical health problems, has different traits, and presents with different challenges in the developing world and in the industrialized countries.","author":[{"dropping-particle":"","family":"Pe?a","given":"Manuel","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bacallao","given":"Jorge","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Annual Review of Nutrition","id":"ITEM-4","issue":"1","issued":{"date-parts":[["2002"]]},"page":"241-253","title":"MALNUTRITION AND POVERTY","type":"article-journal","volume":"22"},"uris":[""]}],"mendeley":{"formattedCitation":"(Akinboade & Adeyefa, 2018; Ferguson, Bovaird Mph, & Mueller, 2007; Pe?a & Bacallao, 2002; Peters et al., 2008)","plainTextFormattedCitation":"(Akinboade & Adeyefa, 2018; Ferguson, Bovaird Mph, & Mueller, 2007; Pe?a & Bacallao, 2002; Peters et al., 2008)","previouslyFormattedCitation":"(Akinboade & Adeyefa, 2018; Ferguson, Bovaird Mph, & Mueller, 2007; Pe?a & Bacallao, 2002; Peters et al., 2008)"},"properties":{"noteIndex":0},"schema":""}(Akinboade & Adeyefa, 2018; Ferguson, Bovaird Mph, & Mueller, 2007; Pe?a & Bacallao, 2002; Peters et al., 2008). Additionally, living in poor housing conditions associated with poverty is an exposure to poor health ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pmed.1003055","ISBN":"1111111111","ISSN":"15491676","abstract":"BACKGROUND: Housing is essential to human well-being but neglected in global health. Today, housing in Africa is rapidly improving alongside economic development, creating an urgent need to understand how these changes can benefit health. We hypothesised that improved housing is associated with better health in children living in sub-Saharan Africa (SSA). We conducted a cross-sectional analysis of housing conditions relative to a range of child health outcomes in SSA. METHODS AND FINDINGS: Cross-sectional data were analysed for 824,694 children surveyed in 54 Demographic and Health Surveys, 21 Malaria Indicator Surveys, and two AIDS Indicator Surveys conducted in 33 countries between 2001 and 2017 that measured malaria infection by microscopy or rapid diagnostic test (RDT), diarrhoea, acute respiratory infections (ARIs), stunting, wasting, underweight, or anaemia in children aged 0-5 years. The mean age of children was 2.5 years, and 49.7% were female. Housing was categorised into a binary variable based on a United Nations definition comparing improved housing (with improved drinking water, improved sanitation, sufficient living area, and finished building materials) versus unimproved housing (all other houses). Associations between house type and child health outcomes were determined using conditional logistic regression within surveys, adjusting for prespecified covariables including age, sex, household wealth, insecticide-treated bed net use, and vaccination status. Individual survey odds ratios (ORs) were pooled using random-effects meta-analysis. Across surveys, improved housing was associated with 8%-18% lower odds of all outcomes except ARI (malaria infection by microscopy: adjusted OR [aOR] 0.88, 95% confidence intervals [CIs] 0.80-0.97, p = 0.01; malaria infection by RDT: aOR 0.82, 95% CI 0.77-0.88, p < 0.001; diarrhoea: aOR 0.92, 95% CI 0.88-0.97, p = 0.001; ARI: aOR 0.96, 95% CI 0.87-1.07, p = 0.49; stunting: aOR 0.83, 95% CI 0.77-0.88, p < 0.001; wasting: aOR 0.90, 95% CI 0.83-0.99, p = 0.03; underweight: aOR 0.85, 95% CI 0.80-0.90, p < 0.001; any anaemia: aOR 0.87, 95% CI 0.82-0.92, p < 0.001; severe anaemia: aOR 0.89, 95% CI 0.84-0.95, p < 0.001). In comparison, insecticide-treated net use was associated with 16%-17% lower odds of malaria infection (microscopy: aOR 0.83, 95% CI 0.78-0.88, p < 0.001; RDT: aOR 0.84, 95% CI 0.79-0.88, p < 0.001). Drinking water source and sanitation facility alone were not associated with diarrhoea. The main…","author":[{"dropping-particle":"","family":"Tusting","given":"Lucy S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gibson","given":"Harry S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Greenwood","given":"Brian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Knudsen","given":"Jakob","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lindsay","given":"Steve W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatt","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS medicine","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2020"]]},"page":"e1003055","title":"Housing and child health in sub-Saharan Africa: A cross-sectional analysis","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"(Tusting et al., 2020)","plainTextFormattedCitation":"(Tusting et al., 2020)","previouslyFormattedCitation":"(Tusting et al., 2020)"},"properties":{"noteIndex":0},"schema":""}(Tusting et al., 2020). Poverty therefore acts in two ways by decreasing access to positive health factors and by exposing and increasing risk of poor health such as infant and child mortality ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1136/bmjopen-2019-029424","ISSN":"20446055","abstract":"Objective To determine whether there were inequalities in the sustained rise in infant mortality in England in recent years and the contribution of rising child poverty to these trends. Design This is an analysis of trends in infant mortality in local authorities grouped into five categories (quintiles) based on their level of income deprivation. Fixed-effects regression models were used to quantify the association between regional changes in child poverty and regional changes in infant mortality. Setting 324 English local authorities in 9 English government office regions. Participants Live-born children under 1 year of age. Main outcome measure Infant mortality rate, defined as the number of deaths in children under 1 year of age per 100 000 live births in the same year. Results The sustained and unprecedented rise in infant mortality in England from 2014 to 2017 was not experienced evenly across the population. In the most deprived local authorities, the previously declining trend in infant mortality reversed and mortality rose, leading to an additional 24 infant deaths per 100 000 live births per year (95% CI 6 to 42), relative to the previous trend. There was no significant change from the pre-existing trend in the most affluent local authorities. As a result, inequalities in infant mortality increased, with the gap between the most and the least deprived local authority areas widening by 52 deaths per 100 000 births (95% CI 36 to 68). Overall from 2014 to 2017, there were a total of 572 excess infant deaths (95% CI 200 to 944) compared with what would have been expected based on historical trends. We estimated that each 1% increase in child poverty was significantly associated with an extra 5.8 infant deaths per 100 000 live births (95% CI 2.4 to 9.2). The findings suggest that about a third of the increases in infant mortality between 2014 and 2017 can be attributed to rising child poverty (172 deaths, 95% CI 74 to 266). Conclusion This study provides evidence that the unprecedented rise in infant mortality disproportionately affected the poorest areas of the country, leaving the more affluent areas unaffected. Our analysis also linked the recent increase in infant mortality in England with rising child poverty, suggesting that about a third of the increase in infant mortality from 2014 to 2017 may be attributed to rising child poverty.","author":[{"dropping-particle":"","family":"Taylor-Robinson","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lai","given":"Eric T.C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wickham","given":"Sophie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rose","given":"Tanith","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Norman","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bambra","given":"Clare","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Whitehead","given":"Margaret","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Barr","given":"Ben","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"BMJ Open","id":"ITEM-1","issue":"10","issued":{"date-parts":[["2019"]]},"title":"Assessing the impact of rising child poverty on the unprecedented rise in infant mortality in England, 2000-2017: time trend analysis","type":"article-journal","volume":"9"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1177/0022146510383498","author":[{"dropping-particle":"","family":"Phelan","given":"Jo C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Link","given":"Bruce G","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tehranifar","given":"Parisa","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of health and social behavior","id":"ITEM-2","issued":{"date-parts":[["2010"]]},"page":"S28-S40","title":"Social conditions as fundamental causes of health inequalities: Theory, evidence and policy implications","type":"article-journal","volume":"51"},"uris":[""]}],"mendeley":{"formattedCitation":"(Phelan, Link, & Tehranifar, 2010; Taylor-Robinson et al., 2019)","plainTextFormattedCitation":"(Phelan, Link, & Tehranifar, 2010; Taylor-Robinson et al., 2019)","previouslyFormattedCitation":"(Phelan, Link, & Tehranifar, 2010; Taylor-Robinson et al., 2019)"},"properties":{"noteIndex":0},"schema":""}(Phelan, Link, & Tehranifar, 2010; Taylor-Robinson et al., 2019). When examining global wealth trends, it was found that global wealth increased by 66% from 1995- 2014 ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1596/978-1-4648-1046-6","abstract":"Countries regularly track gross domestic product (GDP) as an indicator of their economic progress, but not wealth—the assets such as infrastructure, forests, minerals, and human capital that produce GDP. In contrast, corporations routinely report on both their income and assets to assess their economic health and prospects for the future. Wealth accounts allow countries to take stock of their assets to monitor the sustainability of development, an urgent concern today for all countries. The Changing Wealth of Nations 2018: Building a Sustainable Future covers national wealth for 141 countries over 20 years (1995–2014) as the sum of produced capital, 19 types of natural capital, net foreign assets, and human capital overall as well as by gender and type of employment. Great progress has been made in estimating wealth since the fi rst volume, Where Is the Wealth of Nations? Measuring Capital for the 21st Century, was published in 2006. New data substantially improve estimates of natural capital, and, for the first time, human capital is measured by using household surveys to estimate lifetime earnings. The Changing Wealth of Nations 2018 begins with a review of global and regional trends in wealth over the past two decades and provides examples of how wealth accounts can be used for the analysis of development patterns. Several chapters discuss the new work on human capital and its application in development policy. The book then tackles elements of natural capital that are not yet fully incorporated in the wealth accounts: air pollution, marine fisheries, and ecosystems. This book targets policy makers but will engage anyone committed to building a sustainable future for the planet.","author":[{"dropping-particle":"","family":"Lange","given":"Glenn-Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wodon","given":"Quentin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carey","given":"Kevin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World Bank Group","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher-place":"Washington, DC","title":"The Changing Wealth of Nations 2018: Building a Sustainable Future","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Lange, Wodon, & Carey, 2018)","plainTextFormattedCitation":"(Lange, Wodon, & Carey, 2018)","previouslyFormattedCitation":"(Lange, Wodon, & Carey, 2018)"},"properties":{"noteIndex":0},"schema":""}(Lange, Wodon, & Carey, 2018). However, global wealth held by low and middle income countries (LMIC) only grew 5% during the same time period ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1596/978-1-4648-1046-6","abstract":"Countries regularly track gross domestic product (GDP) as an indicator of their economic progress, but not wealth—the assets such as infrastructure, forests, minerals, and human capital that produce GDP. In contrast, corporations routinely report on both their income and assets to assess their economic health and prospects for the future. Wealth accounts allow countries to take stock of their assets to monitor the sustainability of development, an urgent concern today for all countries. The Changing Wealth of Nations 2018: Building a Sustainable Future covers national wealth for 141 countries over 20 years (1995–2014) as the sum of produced capital, 19 types of natural capital, net foreign assets, and human capital overall as well as by gender and type of employment. Great progress has been made in estimating wealth since the fi rst volume, Where Is the Wealth of Nations? Measuring Capital for the 21st Century, was published in 2006. New data substantially improve estimates of natural capital, and, for the first time, human capital is measured by using household surveys to estimate lifetime earnings. The Changing Wealth of Nations 2018 begins with a review of global and regional trends in wealth over the past two decades and provides examples of how wealth accounts can be used for the analysis of development patterns. Several chapters discuss the new work on human capital and its application in development policy. The book then tackles elements of natural capital that are not yet fully incorporated in the wealth accounts: air pollution, marine fisheries, and ecosystems. This book targets policy makers but will engage anyone committed to building a sustainable future for the planet.","author":[{"dropping-particle":"","family":"Lange","given":"Glenn-Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wodon","given":"Quentin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carey","given":"Kevin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World Bank Group","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher-place":"Washington, DC","title":"The Changing Wealth of Nations 2018: Building a Sustainable Future","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Lange et al., 2018)","plainTextFormattedCitation":"(Lange et al., 2018)","previouslyFormattedCitation":"(Lange et al., 2018)"},"properties":{"noteIndex":0},"schema":""}(Lange et al., 2018). Furthermore, many LMICs have experienced large population growth, thus when examining wealth trends per capita, many LMICs actually declined. Specifically, Kenya’s total wealth per capita decreased by 5% between 1995-2014 ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1596/978-1-4648-1046-6","abstract":"Countries regularly track gross domestic product (GDP) as an indicator of their economic progress, but not wealth—the assets such as infrastructure, forests, minerals, and human capital that produce GDP. In contrast, corporations routinely report on both their income and assets to assess their economic health and prospects for the future. Wealth accounts allow countries to take stock of their assets to monitor the sustainability of development, an urgent concern today for all countries. The Changing Wealth of Nations 2018: Building a Sustainable Future covers national wealth for 141 countries over 20 years (1995–2014) as the sum of produced capital, 19 types of natural capital, net foreign assets, and human capital overall as well as by gender and type of employment. Great progress has been made in estimating wealth since the fi rst volume, Where Is the Wealth of Nations? Measuring Capital for the 21st Century, was published in 2006. New data substantially improve estimates of natural capital, and, for the first time, human capital is measured by using household surveys to estimate lifetime earnings. The Changing Wealth of Nations 2018 begins with a review of global and regional trends in wealth over the past two decades and provides examples of how wealth accounts can be used for the analysis of development patterns. Several chapters discuss the new work on human capital and its application in development policy. The book then tackles elements of natural capital that are not yet fully incorporated in the wealth accounts: air pollution, marine fisheries, and ecosystems. This book targets policy makers but will engage anyone committed to building a sustainable future for the planet.","author":[{"dropping-particle":"","family":"Lange","given":"Glenn-Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wodon","given":"Quentin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Carey","given":"Kevin","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World Bank Group","id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher-place":"Washington, DC","title":"The Changing Wealth of Nations 2018: Building a Sustainable Future","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Lange et al., 2018)","plainTextFormattedCitation":"(Lange et al., 2018)","previouslyFormattedCitation":"(Lange et al., 2018)"},"properties":{"noteIndex":0},"schema":""}(Lange et al., 2018). Because poverty disproportionately affects LMICs, measuring the wealth trends of communities in LMICs is essential for understanding and improving health outcomes ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"0195211294","abstract":"At the start of a new century, poverty remains a global problem of huge proportions. Of the world's 6 billion people, 2.8 billion live on less than $2 a day and 1.2 billion on less than $1 a day. Eight of every 100 infants do not live to see their fifth birthday. Nine of every 100 boys and 14 of every 100 girls who reach school age do not attend school. Poverty is also evident in poor people's lack of political power and voice and in their extreme vulnerability to ill health, economic dislocation, personal violence, and natural disasters. And the scourge of HIV/AIDS, the frequency and brutality of civil conflicts, and rising disparities between rich countries and the developing world have increased the sense of deprivation and injustice for many. World Development Report 2000/2001: Attacking Poverty (which follows two other World Development Reports on poverty, in 1980 and 1990) argues nevertheless that major reductions in all these dimensions of poverty are indeed possible-that the interaction of markets, state institutions, and civil society can harness the forces of economic integration and technological change to serve the interests of poor people and increase their share of society's prosperity. Actions are needed in three complementary areas: promoting economic opportunities for poor people through equitable growth, better access to markets, and expanded assets; facilitating empowerment by making state institutions more responsive to poor people and removing social barriers that exclude women, ethnic and racial groups, and the socially disadvantaged; and enhancing security by preventing and managing economywide shocks and providing mechanisms to reduce the sources of vulnerability that poor people face. But actions by countries and communities will not be enough. Global actions need to complement national and local initiatives to achieve maximum benefit for poor people throughout the world.","author":[{"dropping-particle":"","family":"World Bank","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2000"]]},"title":"World Development Report: Attacking Poverty","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(World Bank, 2000)","plainTextFormattedCitation":"(World Bank, 2000)","previouslyFormattedCitation":"(World Bank, 2000)"},"properties":{"noteIndex":0},"schema":""}(World Bank, 2000). We used a longitudinal dataset that provided information on housing materials, assets, education and mortality to examine the wealth and SES trends in a rural Kenyan community during a seven year period (2011-2018). Literature ReviewIn an effort to understand the importance of measuring household wealth, and how to do so in LMIC, substantial literature was reviewed. The following articles examine the importance of measuring wealth and propose various methodologies for doing so in LMIC settings. It has been clearly demonstrated that there is a positive relationship between socioeconomic status (SES) and health outcomes ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Feinstein","given":"Jonathan S","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Milbank Quarterly","id":"ITEM-1","issue":"2","issued":{"date-parts":[["1993"]]},"number-of-pages":"279-322","title":"The relationship between socioeconomic status and health: A review of the literature","type":"report","volume":"71"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.2989/16085906.2018.1475401","ISSN":"1727-9445","author":[{"dropping-particle":"","family":"Haacker","given":"Markus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Birungi","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"African Journal of AIDS Research","id":"ITEM-2","issue":"2","issued":{"date-parts":[["2018"]]},"page":"145-151","title":"Poverty as a barrier to antiretroviral therapy access for people living with HIV/AIDS in Kenya","type":"article-journal","volume":"17"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1080/00185860209597925","ISSN":"1939-9278","author":[{"dropping-particle":"","family":"Bruder","given":"Paul","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Hospital Topics","id":"ITEM-3","issue":"1","issued":{"date-parts":[["2002"]]},"page":"34-36","title":"In context: Healthcare and public policy: Patterns of health and illness: The effects of poverty on health","type":"article-journal","volume":"80"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1037/a0028015","ISSN":"0003066X","PMID":"22583341","abstract":"This article considers the implications for prevention science of recent advances in research on family poverty and children's mental, emotional, and behavioral health. First, we describe definitions of poverty and the conceptual and empirical challenges to estimating the causal effects of poverty on children's mental, emotional, and behavioral health. Second, we offer a conceptual framework that incorporates selection processes that affect who becomes poor as well as mechanisms through which poverty appears to influence child and youth mental health. Third, we use this conceptual framework to selectively review the growing literatures on the mechanisms through which family poverty influences the mental, emotional, and behavioral health of children. We illustrate how a better understanding of the mechanisms of effect by which poverty impacts children's mental, emotional, and behavioral health is valuable in designing effective preventive interventions for those in poverty. Fourth, we describe strategies to directly reduce poverty and the implications of these strategies for prevention. This article is one of three in a special section (see also Biglan, Flay, Embry, & Sandler, 2012; Mu?oz, Beardslee, & Leykin, 2012) representing an elaboration on a theme for prevention science developed by the 2009 report of the National Research Council and Institute of Medicine.","author":[{"dropping-particle":"","family":"Yoshikawa","given":"Hirokazu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aber","given":"J. Lawrence","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beardslee","given":"William R.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American Psychologist","id":"ITEM-4","issue":"4","issued":{"date-parts":[["2012"]]},"page":"272-284","title":"The effects of poverty on the mental, emotional, and behavioral health of children and youth","type":"article-journal","volume":"67"},"uris":[""]}],"mendeley":{"formattedCitation":"(Bruder, 2002; Feinstein, 1993; Haacker & Birungi, 2018; Yoshikawa, Aber, & Beardslee, 2012)","plainTextFormattedCitation":"(Bruder, 2002; Feinstein, 1993; Haacker & Birungi, 2018; Yoshikawa, Aber, & Beardslee, 2012)","previouslyFormattedCitation":"(Bruder, 2002; Feinstein, 1993; Haacker & Birungi, 2018; Yoshikawa, Aber, & Beardslee, 2012)"},"properties":{"noteIndex":0},"schema":""}(Bruder, 2002; Feinstein, 1993; Haacker & Birungi, 2018; Yoshikawa, Aber, & Beardslee, 2012). This phenomenon is clearly evidenced in Kenya, which is considered a LMIC. A study conducted in Kenya analyzed the impact of poverty on childhood disability risk factors ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1080/1034912X.2015.1048670","author":[{"dropping-particle":"","family":"T Mugoya","given":"George C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mutua","given":"Kagendo N","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Disability, Development and Education","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2015"]]},"page":"501-517","title":"Childhood disability risk factors in Kenya: Impact of poverty and other socio-demographics","type":"article-journal","volume":"62"},"uris":[""]}],"mendeley":{"formattedCitation":"(T Mugoya & Mutua, 2015)","plainTextFormattedCitation":"(T Mugoya & Mutua, 2015)","previouslyFormattedCitation":"(T Mugoya & Mutua, 2015)"},"properties":{"noteIndex":0},"schema":""}(T Mugoya & Mutua, 2015). Secondary data analysis conducted using Kenyan Demographic Health Survey (DHS) data, specifically analyzed data on women with children under five years old and the associated children’s data. Logistic regression analyses were conducted to examine the association between SES and childhood disability factors and the results demonstrated that urban women from low SES were approximately ten times more likely to have never received antenatal care. This study demonstrates how poverty can decrease access to health care and increase risk factors for child disability. The correlation between SES and health outcomes is prevalent in many LMIC and further emphasizes the importance of managing the SES of communities in order to influence health outcomes positively. Living in poverty serves as an exposing factor for poor health outcomes. Tusting et al. (2020) suggests that adequate housing is essential to human well-being and positive health. A cross-sectional analysis of 33 African countries was done to examine the association between housing conditions and disease among children. The results demonstrated that improved housing reduced the odds of malaria infection, stunting, diarrhea and anemia by up to 18%. This study reveals the close relationship between housing conditions and health and the importance of implementing improved housing material, water and sanitation to improve health outcomes.Additionally, more research on the wealth of communities and how it influences public health is needed to inform better policy changes. ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1136/jech.2004.021147","ISSN":"0143005X","abstract":"This article argues that public health researchers have often ignored the analysis of wealth in the quest to understand the social determinants of health. Wealth concentration and the inequities in wealth between and within countries are increasing. Despite this scare accurate data are available to assist the analysis of the health impact of this trend. Improved data collection on wealth distribution should be encouraged. Epidemiologists and political economy of health researchers should pay more attention to understanding the dynamics of wealth and its consequences for population health. Policy research to underpin policies designed to reduce inequities in wealth distribution should be intensified.","author":[{"dropping-particle":"","family":"Baum","given":"Fran","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of Epidemiology and Community Health","id":"ITEM-1","issue":"7","issued":{"date-parts":[["2005"]]},"page":"542-545","title":"Wealth and health: The need for more strategic public health research","type":"article","volume":"59"},"uris":[""]}],"mendeley":{"formattedCitation":"(Baum, 2005)","manualFormatting":"Baum (2005)","plainTextFormattedCitation":"(Baum, 2005)","previouslyFormattedCitation":"(Baum, 2005)"},"properties":{"noteIndex":0},"schema":""}Baum (2005) suggests that more concentration on the dynamics and distribution of wealth is necessary to formulate policies and adequately address social inequalities. Baum (2005) argues that public health research has been lacking when examining the impacts of wealth, especially wealth distribution within countries. If more public health research was devoted to understanding the distribution of wealth within communities, impactful policies could be implemented in an effort to improve wealth and health trends. Creation of Wealth Indices:In high income countries, SES is often measured by household income, education or occupation ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2020","3","26"]]},"author":[{"dropping-particle":"","family":"Association","given":"American Psychological","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2007"]]},"title":"Socioeconomic status","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(Association, 2007)","manualFormatting":"(APA, 2007)","plainTextFormattedCitation":"(Association, 2007)","previouslyFormattedCitation":"(Association, 2007)"},"properties":{"noteIndex":0},"schema":""}(APA, 2007). In the United States for example, the Census Bureau evaluates total family income and sets thresholds for poverty based on family size and consumption ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2020","3","26"]]},"author":[{"dropping-particle":"","family":"Census Bureau United States","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"Income & Poverty","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(Census Bureau United States, 2019)","plainTextFormattedCitation":"(Census Bureau United States, 2019)","previouslyFormattedCitation":"(Census Bureau United States, 2019)"},"properties":{"noteIndex":0},"schema":""}(Census Bureau United States, 2019). This metric is standard throughout the United States and gives a general understanding of the wealth of the population. This standard metric, however, is not representative in LMIC. Accurate household income is not available because many individuals work in an informal sector without income taxation, their income varies or they are self-employed ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Rutstein","given":"Shea Oscar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Kiersten","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2004"]]},"publisher-place":"Calverton, Maryland","title":"The DHS Wealth Index","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Rutstein & Johnson, 2004)","plainTextFormattedCitation":"(Rutstein & Johnson, 2004)","previouslyFormattedCitation":"(Rutstein & Johnson, 2004)"},"properties":{"noteIndex":0},"schema":""}(Rutstein & Johnson, 2004). One common alternative to measurement of income is measurement of household consumption. The Global Consumption Database, managed by The World Bank, collects data about household consumption in sectors of food, housing, energy and is a comprehensive proxy for household SES ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2020","3","26"]]},"author":[{"dropping-particle":"","family":"World Bank","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["0"]]},"title":"Global Consumption Database","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(World Bank, n.d.)","manualFormatting":"(World Bank, 2019)","plainTextFormattedCitation":"(World Bank, n.d.)","previouslyFormattedCitation":"(World Bank, n.d.)"},"properties":{"noteIndex":0},"schema":""}(World Bank, 2019). The Theory of the Consumption Function, stated by ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Friedman","given":"Milton","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["1957"]]},"publisher":"National Bureau of Economic Research, Inc","title":"A Theory of the Consumption Function","type":"book"},"uris":[""]}],"mendeley":{"formattedCitation":"(Friedman, 1957)","plainTextFormattedCitation":"(Friedman, 1957)","previouslyFormattedCitation":"(Friedman, 1957)"},"properties":{"noteIndex":0},"schema":""}(Friedman, 1957), is that household consumption expenditure is a highly dependable and stable function of current income, therefore serving as an accurate proxy for income and SES. Consumption data, when available, is a smooth and less-variable measure of wealth ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Deaton","given":"Angus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zaidi","given":"Salman","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2002"]]},"publisher-place":"Washington, DC","title":"Guidelines for constructing consumption aggregates for welfare analysis","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Deaton & Zaidi, 2002)","plainTextFormattedCitation":"(Deaton & Zaidi, 2002)","previouslyFormattedCitation":"(Deaton & Zaidi, 2002)"},"properties":{"noteIndex":0},"schema":""}(Deaton & Zaidi, 2002). However, consumption data is also difficult to measure in LMICs because of the large number of informal transactions and lack of detailed expenditure accountsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Deaton","given":"Angus","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Zaidi","given":"Salman","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2002"]]},"publisher-place":"Washington, DC","title":"Guidelines for constructing consumption aggregates for welfare analysis","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Deaton & Zaidi, 2002)","plainTextFormattedCitation":"(Deaton & Zaidi, 2002)","previouslyFormattedCitation":"(Deaton & Zaidi, 2002)"},"properties":{"noteIndex":0},"schema":""}(Deaton & Zaidi, 2002). Therefore, a widely acceptable and common method of measuring wealth is an asset-based index. Asset-based indices are created from household materials and durable assets which are collected through household surveys ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Rutstein","given":"Shea Oscar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Kiersten","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2004"]]},"publisher-place":"Calverton, Maryland","title":"The DHS Wealth Index","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Rutstein & Johnson, 2004)","plainTextFormattedCitation":"(Rutstein & Johnson, 2004)","previouslyFormattedCitation":"(Rutstein & Johnson, 2004)"},"properties":{"noteIndex":0},"schema":""}(Rutstein & Johnson, 2004). The basis of creating a wealth index from household characteristics assumes that if a household has more and better characteristics (including assets), they are wealthier. After collection of the household characteristics, analyses are performed, and households are divided into wealth quintiles. Significant research has been conducted to determine the validity and accuracy of using such proxies as a determinate for wealth and have demonstrated positive results ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1742-7622-5-3","ISSN":"17427622","abstract":"Background. Epidemiological studies often require measures of socio-economic position (SEP). The application of principal components analysis (PCA) to data on asset-ownership is one popular approach to household SEP measurement. Proponents suggest that the approach provides a rational method for weighting asset data in a single indicator, captures the most important aspect of SEP for health studies, and is based on data that are readily available and/or simple to collect. However, the use of PCA on asset data may not be the best approach to SEP measurement. There remains concern that this approach can obscure the meaning of the final index and is statistically inappropriate for use with discrete data. In addition, the choice of assets to include and the level of agreement between wealth indices and more conventional measures of SEP such as consumption expenditure remain unclear. We discuss these issues, illustrating our examples with data from the Malawi Integrated Household Survey 2004-5. Methods. Wealth indices were constructed using the assets on which data are collected within Demographic and Health Surveys. Indices were constructed using five weighting methods: PCA, PCA using dichotomised versions of categorical variables, equal weights, weights equal to the inverse of the proportion of households owning the item, and Multiple Correspondence Analysis. Agreement between indices was assessed. Indices were compared with per capita consumption expenditure, and the difference in agreement assessed when different methods were used to adjust consumption expenditure for household size and composition. Results. All indices demonstrated similarly modest agreement with consumption expenditure. The indices constructed using dichotomised data showed strong agreement with each other, as did the indices constructed using categorical data. Agreement was lower between indices using data coded in different ways. The level of agreement between wealth indices and consumption expenditure did not differ when different consumption equivalence scales were applied. Conclusion. This study questions the appropriateness of wealth indices as proxies for consumption expenditure. The choice of data included had a greater influence on the wealth index than the method used to weight the data. Despite the limitations of PCA, alternative methods also all had disadvantages. ? 2008 Howe et al; licensee BioMed Central Ltd.","author":[{"dropping-particle":"","family":"Howe","given":"Laura D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hargreaves","given":"James R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Huttly","given":"Sharon R.A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Emerging Themes in Epidemiology","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2008"]]},"title":"Issues in the construction of wealth indices for the measurement of socio-economic position in low-income countries","type":"article-journal","volume":"5"},"uris":[""]}],"mendeley":{"formattedCitation":"(Howe, Hargreaves, & Huttly, 2008)","plainTextFormattedCitation":"(Howe, Hargreaves, & Huttly, 2008)","previouslyFormattedCitation":"(Howe, Hargreaves, & Huttly, 2008)"},"properties":{"noteIndex":0},"schema":""}(Howe, Hargreaves, & Huttly, 2008). Additionally, measuring SES can be done by using an asset-based index in conjunction with other demographic data such as education and mortality. The Kombewa Health and Demographic Surveillance System (HDSS) nearly replicates the household asset index used by the Demographic and Health Survey and therefore collects data on housing materials, durable assets and other demographics. Because this is the available data, asset based wealth indices were created to determine the wealth trends of the Kombewa community and a multidimensional measurement of wealth was used to asses SES. Through the literature, three common methods of wealth and SES indices were identified: principal component analysis (PCA), multiple correspondence (MCA) and multidimensional poverty index (MPI).Principal Component Analysis (PCA):PCA is a very common method used for creating a wealth index and has been used by the DHS program, UNICEF, The World Bank and more ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Rutstein","given":"Shea Oscar","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Johnson","given":"Kiersten","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2004"]]},"publisher-place":"Calverton, Maryland","title":"The DHS Wealth Index","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Rutstein & Johnson, 2004)","plainTextFormattedCitation":"(Rutstein & Johnson, 2004)","previouslyFormattedCitation":"(Rutstein & Johnson, 2004)"},"properties":{"noteIndex":0},"schema":""}(Rutstein & Johnson, 2004). PCA uses household material and asset data to rank households into quintiles. Many studies have used PCA to measure wealth in LMIC and the process has been described in detail by the DHS Program ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/1742-7622-4-4","ISSN":"17427622","abstract":"Background. Accurate tools for assessing household wealth are essential for many health studies in developing countries. Household survey and participatory wealth ranking (PWR) are two approaches to generate data for this purpose. Methods. A household survey and PWR were conducted among eight villages in rural South Africa. We developed three indicators of household wealth using the data. One indicator used PWR data only, one used principal components analysis to combine data from the survey, while the final indicator used survey data combined in a manner informed by the PWR. We assessed internal consistency of the indices and assessed their level of agreement in ranking household wealth. Results. Food security, asset ownership, housing quality and employment were important indicators of household wealth. PWR, consisting of three independent rankings of 9671 households, showed a high level of internal consistency (intraclass correlation coefficient 0.81, 95% CI 0.79-0.82). Data on 1429 households were available from all three techniques. There was moderate agreement in ranking households into wealth tertiles between the two indicators based on survey data (spearman rho = 0.69, kappa = 0.43), but only limited agreement between these techniques and the PWR data (spearman rho = 0.38 and 0.31, kappa = 0.20 and 0.17). Conclusion. Both PWR and household survey can provide a rapid assessment of household wealth. Each technique had strengths and weaknesses. Reasons for differences might include data inaccuracies or limitations in the methods by which information was weighted. Alternatively, the techniques may measure different things. More research is needed to increase the validity of measures of socioeconomic position used in health studies in developing countries. ? 2007 Hargreaves et al; licensee BioMed Central Ltd.","author":[{"dropping-particle":"","family":"Hargreaves","given":"James R","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Morison","given":"Linda A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gear","given":"John S S","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kim","given":"Julia C","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Makhubele","given":"Mzamani B","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Porter","given":"John D H","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Watts","given":"Charlotte","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pronyk","given":"Paul M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Emerging Themes in Epidemiology","id":"ITEM-1","issued":{"date-parts":[["2007"]]},"title":"Assessing household wealth in health studies in developing countries: A comparison of participatory wealth ranking and survey techniques from rural South Africa","type":"article-journal","volume":"4"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1111/roiw.12286","ISSN":"14754991","abstract":"Asset indices are widely used, particularly in the analysis of Demographic and Health Surveys, where they have been routinely constructed as &#8220;wealth indices.&#8221; Such indices have been externally validated in a number of contexts. Nevertheless, we show that they often fail an internal validity test, that is, ranking individuals with &#8220;rural&#8221; assets below individuals with no assets at all. We consider from first principles what sort of indexes might make sense, given the predominantly dummy variable nature of asset schedules. We show that there is, in fact, a way to construct an asset index which does not violate some basic principles and which also has the virtue that it can be used to construct &#8220;asset inequality&#8221; measures. However, there is a need to pay careful attention to the components of the index. We show this with South African data.","author":[{"dropping-particle":"","family":"Wittenberg","given":"Martin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leibbrandt","given":"Murray","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Review of Income and Wealth","id":"ITEM-2","issue":"4","issued":{"date-parts":[["2017"]]},"page":"706-730","title":"Measuring Inequality by Asset Indices: A General Approach with Application to South Africa","type":"article-journal","volume":"63"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.4269/ajtmh.2011.10-0442","ISSN":"00029637","abstract":"The association of wealth and infections with Giardia, Cryptosporidium, Cyclospora, and microsporidia were examined in a longitudinal cohort conducted in Peru from 2001 to 2006. Data from 492 participants were daily clinical manifestations, weekly copro-parasitological diagnosis, and housing characteristics and assets owned (48 variables), and these data were used to construct a global wealth index using principal component analysis. Data were analyzed using continuous and categorical (wealth tertiles) models. Participant's mean age was 3.43 years (range = 0-12 years), with average follow-up of 993 days. Univariate and multivariate analyses identified significant associations between wealth and infections with Giardia and microsporidia. Participants with greater wealth indexes were associated with protection against Giardia (P < 0.001) and persistent Giardia infections (> 14 days). For microsporidia, greater wealth was protective (P = 0.066 continuous and P = 0.042 by tertiles). Contrarily, infections with Cryptosporidium and Cyclospora were independent of wealth. Thus, subtle differences in wealth may affect the frequency of specific parasitic infections within low-income communities. Copyright ? 2011 by The American Society of Tropical Medicine and Hygiene.","author":[{"dropping-particle":"","family":"Nundy","given":"Shantanu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gilman","given":"Robert H.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Xiao","given":"Lihua","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cabrera","given":"Lilia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cama","given":"Rosa","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ortega","given":"Ynes R.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kahn","given":"Geoffrey","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cama","given":"Vitaliano A.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"American Journal of Tropical Medicine and Hygiene","id":"ITEM-3","issue":"1","issued":{"date-parts":[["2011"]]},"page":"38-42","title":"Wealth and its associations with enteric parasitic infections in a low-income community in Peru: Use of principal component analysis","type":"article-journal","volume":"84"},"uris":[""]},{"id":"ITEM-4","itemData":{"DOI":"10.1016/j.econlet.2013.08.014","ISSN":"01651765","abstract":"Commonly available survey data for developing countries often do not include income or expenditure data. This data limitation puts severe constraints on standard poverty and inequality analyses. We provide a simple approach to simulate household income based on publicly available Demographic and Health Surveys (DHS) and macroeconomic data. We illustrate our approach with DHS data for Bolivia, Indonesia and Zambia. ? 2013 Elsevier B.V.","author":[{"dropping-particle":"","family":"Harttgen","given":"Kenneth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vollmer","given":"Sebastian","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Economics Letters","id":"ITEM-4","issue":"2","issued":{"date-parts":[["2013"]]},"page":"257-262","title":"Using an asset index to simulate household income","type":"article-journal","volume":"121"},"uris":[""]}],"mendeley":{"formattedCitation":"(Hargreaves et al., 2007; Harttgen & Vollmer, 2013; Nundy et al., 2011; Wittenberg & Leibbrandt, 2017)","plainTextFormattedCitation":"(Hargreaves et al., 2007; Harttgen & Vollmer, 2013; Nundy et al., 2011; Wittenberg & Leibbrandt, 2017)","previouslyFormattedCitation":"(Hargreaves et al., 2007; Harttgen & Vollmer, 2013; Nundy et al., 2011; Wittenberg & Leibbrandt, 2017)"},"properties":{"noteIndex":0},"schema":""}(Hargreaves et al., 2007; Harttgen & Vollmer, 2013; Nundy et al., 2011; Wittenberg & Leibbrandt, 2017).The philosophy of the DHS Wealth Index is that wealth is an unobserved variable where a household’s relative wealth position is associated with several indicator variables. The indicator variables are thought to be correlated to a household’s economic status, meaning that a household that has more indicator variables (assets) is wealthier. The creation of a wealth index was initiated because income and consumption data are often unavailable or inaccurate in LMIC. An important distinction about the PCA wealth index is that it is an economic measurement and not a socioeconomic measurement because it only analyzes assets and doesn’t measure education, occupation or other measurements of SES. PCA is one of the most common methods used for creating a wealth index because of its relative simplicity and its direct relationship to DHS.Multiple Correspondence Analysis (MCA): MCA is a statistical method similar to PCA but does not assume the variables are linear and can use categorical variables ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-1-4614-5263-8_7","ISSN":"23641088","abstract":"The measurement of multidimensional poverty has been advocated by most welfare scholars and is experiencing a growth in interest, partly explained by controversial debates that have emerged across academics and practitioners. This paper follows one of the least explored approaches -- Multiple Correspondence Analysis -- to assess multidimensional poverty in Morocco between 2001 and 2007. Multiple Correspondence Analysis provides two major advantages for the measurement of multidimensional poverty: it generates a matrix of \"weights\" based on the variance-covariance matrix of all welfare dimensions selected and provides a natural approach for constructing a composite welfare indicator that satisfies essential poverty ordering axioms. The application shows that poverty in Morocco has declined according to both monetary and multidimensional indicators and that these findings are robust to stochastic dominance tests. The paper concludes that the sustained positive growth that Morocco experienced during the last decade has translated in improvements in living conditions well beyond monetary returns.","author":[{"dropping-particle":"","family":"Ezzrari","given":"Abdeljaouad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verme","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Economic policy, poverty and gender, Middle East and North Africa region","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"number-of-pages":"181-209","title":"A multiple correspondence analysis approach to the measurement of multidimensional poverty in Morocco 2001–2007","type":"report","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"(Ezzrari & Verme, 2012)","plainTextFormattedCitation":"(Ezzrari & Verme, 2012)","previouslyFormattedCitation":"(Ezzrari & Verme, 2012)"},"properties":{"noteIndex":0},"schema":""}(Ezzrari & Verme, 2012). Several studies have determined the validity of MCA as a measurement of poverty and it is starting to be utilized more ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1057/9780230582354_5","ISBN":"978-0230004894","author":[{"dropping-particle":"","family":"Asselin","given":"Louise-Marie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Anh","given":"Vu Tuanh","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Quantitative Approaches to Multidimensional Poverty Measurement","id":"ITEM-1","issued":{"date-parts":[["2008"]]},"page":"80-103","title":"Multidimensional poverty measurement with multiple correspondence analysis","type":"article-journal"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1371/journal.pone.0184616","ISBN":"1111111111","ISSN":"19326203","abstract":"Material wealth is a key factor shaping human development and well-being. Every year, hundreds of studies in social science and policy fields assess material wealth in low- and middle-income countries assuming that there is a single dimension by which households can move from poverty to prosperity. However, a one-dimensional model may miss important kinds of prosperity, particularly in countries where traditional subsistence-based livelihoods coexist with modern cash economies. Using multiple correspondence analysis to analyze representative household data from six countries-Nepal, Bangladesh, Ethiopia, Kenya, Tanzania and Guatemala-across three world regions, we identify a number of independent dimension of wealth, each with a clear link to locally relevant pathways to success in cash and agricultural economies. In all cases, the first dimension identified by this approach replicates standard one-dimensional estimates and captures success in cash economies. The novel dimensions we identify reflect success in different agricultural sectors and are independently associated with key benchmarks of food security and human growth, such as adult body mass index and child height. The multidimensional models of wealth we describe here provide new opportunities for examining the causes and consequences of wealth inequality that go beyond success in cash economies, for tracing the emergence of hybrid pathways to prosperity, and for assessing how these different pathways to economic success carry different health risks and social opportunities.","author":[{"dropping-particle":"","family":"Hruschka","given":"Daniel J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hadley","given":"Craig","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hackman","given":"Joseph","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS ONE","id":"ITEM-2","issue":"9","issued":{"date-parts":[["2017"]]},"title":"Material wealth in 3D: Mapping multiple paths to prosperity in low- and middle- income countries","type":"article-journal","volume":"12"},"uris":[""]},{"id":"ITEM-3","itemData":{"DOI":"10.1016/j.worlddev.2007.10.008","ISSN":"0305750X","abstract":"Using comparable, nationally representative surveys and extending the work of [Sahn, D. E., & Stifel, D. C. (2000). Poverty comparisons over time and across countries in Africa. World Development, 28(12), 2123-2155], an asset index is used to investigate changes in poverty in seven African countries. Poverty declined in five of the seven countries. Improvements in the asset index are driven by progress in the accumulation of private assets, while access to public services has deteriorated. However, the method has some shortcomings. Assets are slow-changing and discrete. The index therefore may not capture changes in well-being accurately. The poor discrimination ability of the index at the lower end of the scale also makes it an inappropriate tool for studying ultra-poverty. ? 2008 Elsevier Ltd. All rights reserved.","author":[{"dropping-particle":"","family":"Booysen","given":"Frikkie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Berg","given":"Servaas","non-dropping-particle":"van der","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burger","given":"Ronelle","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"von","family":"Maltitz","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"DU","family":"Rand","given":"Gideon","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World Development","id":"ITEM-3","issue":"6","issued":{"date-parts":[["2008"]]},"page":"1113-1130","title":"Using an asset index to assess trends in poverty in seven Sub-Saharan African countries","type":"article-journal","volume":"36"},"uris":[""]}],"mendeley":{"formattedCitation":"(Asselin & Anh, 2008; Booysen, van der Berg, Burger, Maltitz, & Rand, 2008; Hruschka, Hadley, & Hackman, 2017)","plainTextFormattedCitation":"(Asselin & Anh, 2008; Booysen, van der Berg, Burger, Maltitz, & Rand, 2008; Hruschka, Hadley, & Hackman, 2017)","previouslyFormattedCitation":"(Asselin & Anh, 2008; Booysen, van der Berg, Burger, Maltitz, & Rand, 2008; Hruschka, Hadley, & Hackman, 2017)"},"properties":{"noteIndex":0},"schema":""}(Asselin & Anh, 2008; Booysen, van der Berg, Burger, Maltitz, & Rand, 2008; Hruschka, Hadley, & Hackman, 2017).A study conducted in Western Kenya used DHS to create wealth indices over time ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/j.actatropica.2015.01.006","ISSN":"18736254","abstract":"Continuous monitoring in health and demographic surveillance sites (HDSS) allows for collection of longitudinal demographic data, health related, and socio-economic indicators of the site population. We sought to use household survey data collected between 2002 and 2006 in the Kenya Medical Research Institute in collaboration with Centers for Disease Control and prevention (KEMRI/CDC) HDSS site in Asembo and Gem Western Kenya to estimate socio-economic status (SES) and assess changes of SES over time and space. Data on household assets and characteristics, mainly source of drinking water, cooking fuel, and occupation of household head was annually collected from 44,313 unique households during the study period. An SES index was calculated as a weighted average of assets using weights generated via Principal Component Analysis (PCA), Polychoric PCA, and Multiple Correspondence Analysis (MCA) methods applied to the pooled data. The index from the best method was used to rank households into SES quintiles and assess their transition over time across SES categories. Kriging was employed to produce SES maps at the start and the end of the study period. First component of PCA, Polychoric PCA, and MCA accounted for 13.7%, 31.8%, and 47.3%, respectively of the total variance of all variables. The gap between the poorest and the least poor increased from 1% at the start to 6% at the end of the study period. Spatial analysis revealed that the increase in least poor households was centered in the lower part of study area (Asembo) over time. No significant changes were observed in Gem. The HDSS sites can provide a platform to assess spatial-temporal changes in the SES status of the population. Evidence on how SES varied over time and space within the same geographical area may provide a useful tool to design interventions in health and other areas that have a close bearing to the SES of the population.","author":[{"dropping-particle":"","family":"Amek","given":"Nyaguara","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Vounatsou","given":"Penelope","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Obonyo","given":"Benson","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hamel","given":"Mary","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Odhiambo","given":"Frank","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Slutsker","given":"Laurence","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Laserson","given":"Kayla","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Acta Tropica","id":"ITEM-1","issued":{"date-parts":[["2015"]]},"page":"24-30","title":"Using health and demographic surveillance system (HDSS) data to analyze geographical distribution of socio-economic status; an experience from KEMRI/CDC HDSS","type":"article-journal","volume":"144"},"uris":[""]}],"mendeley":{"formattedCitation":"(Amek et al., 2015)","plainTextFormattedCitation":"(Amek et al., 2015)","previouslyFormattedCitation":"(Amek et al., 2015)"},"properties":{"noteIndex":0},"schema":""}(Amek et al., 2015). The wealth indices were created by three methods: PCA, polychoric PCA and MCA. The results concluded that MCA was the best method because the standardized weights for variables were higher and had the highest variation. MCA was highly and statistically significant with PCA and placed 93% of the households in the same quintile. This study is substantial because it created three different wealth indices to understand the strengths and weaknesses of each measurement and informed our decision to use PCA and MCA as methods for creating wealth indices. Although it is not used as often, MCA can be an excellent method of measuring economic status.Multidimensional Poverty Index:Unlike PCA and MCA, MPI is a method used to measure SES, as opposed to wealth. The MPI identifies people’s deprivations based on three equally weighted categories: education, health and living standards ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"978-1-907194-22-1","ISSN":"2040-8188","abstract":"This paper presents a new Multidimensional Poverty Index (MPI) for 104 developing countries. It is the first time multidimensional poverty is estimated using micro datasets (household surveys) for such a large number of countries which cover about 78 percent of the world?s population. The MPI has the mathematical structure of one of the Alkire and Foster poverty multidimensional measures and it is composed of ten indicators corresponding to same three dimensions as the Human Development Index: Education, Health and Standard of Living. The MPI captures a set of direct deprivations that batter a person at the same time. This tool could be used to target the poorest, track the Millennium Development Goals, and design policies that directly address the interlocking deprivations poor people experience. This paper presents the methodology and components in the MPI, describes main results, and shares basic robustness tests.","author":[{"dropping-particle":"","family":"Alkire,S. & Santos","given":"M.E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2010"]]},"number":"38","number-of-pages":"1-139","title":"Acute Multidimensional Poverty: A New Index for Developing Countries","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Alkire,S. & Santos, 2010)","plainTextFormattedCitation":"(Alkire,S. & Santos, 2010)","previouslyFormattedCitation":"(Alkire,S. & Santos, 2010)"},"properties":{"noteIndex":0},"schema":""}(Alkire,S. & Santos, 2010). The MPI was developed by the Oxford Poverty and Human Development Initiative to be a comprehensive measurement of SES that identifies people who are deprived and non-deprived ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"978-1-907194-22-1","ISSN":"2040-8188","abstract":"This paper presents a new Multidimensional Poverty Index (MPI) for 104 developing countries. It is the first time multidimensional poverty is estimated using micro datasets (household surveys) for such a large number of countries which cover about 78 percent of the world?s population. The MPI has the mathematical structure of one of the Alkire and Foster poverty multidimensional measures and it is composed of ten indicators corresponding to same three dimensions as the Human Development Index: Education, Health and Standard of Living. The MPI captures a set of direct deprivations that batter a person at the same time. This tool could be used to target the poorest, track the Millennium Development Goals, and design policies that directly address the interlocking deprivations poor people experience. This paper presents the methodology and components in the MPI, describes main results, and shares basic robustness tests.","author":[{"dropping-particle":"","family":"Alkire,S. & Santos","given":"M.E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2010"]]},"number":"38","number-of-pages":"1-139","title":"Acute Multidimensional Poverty: A New Index for Developing Countries","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Alkire,S. & Santos, 2010)","plainTextFormattedCitation":"(Alkire,S. & Santos, 2010)","previouslyFormattedCitation":"(Alkire,S. & Santos, 2010)"},"properties":{"noteIndex":0},"schema":""}(Alkire,S. & Santos, 2010). The MPI became a popular method of measuring poverty because economic experts Anand and Sen declared that “the need for a multidimensional view of poverty and deprivation guides the search for an adequate indicator of poverty” ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"Anand","given":"Sudhir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sen","given":"Amartya","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Poverty and Human Development: Human Development Papers","id":"ITEM-1","issued":{"date-parts":[["1997"]]},"page":"1-20","title":"Concepts of human development and poverty: A multidimensional perspective","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"(Anand & Sen, 1997)","plainTextFormattedCitation":"(Anand & Sen, 1997)","previouslyFormattedCitation":"(Anand & Sen, 1997)"},"properties":{"noteIndex":0},"schema":""}(Anand & Sen, 1997). This pushed researchers to develop a more comprehensive way of measuring poverty that included indicators beyond household assets. A study conducted in seven areas in sub-Saharan Africa used MPI to measure SES and relate SES to mortality. For creation of the wealth index, ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1080/16549716.2019.1608013","ISSN":"16549880","abstract":"(2019) A comparison of all-cause and cause-specific mortality by household socioeconomic status across seven INDEPTH network health and demographic surveillance systems in sub-Saharan Africa, ABSTRACT Background: Understanding socioeconomic disparities in all-cause and cause-specific mortality can help inform prevention and treatment strategies. Objectives: To quantify cause-specific mortality rates by socioeconomic status across seven health and demographic surveillance systems (HDSS) in five countries (Ethiopia, Kenya, Malawi, Mozambique, and Nigeria) in the INDEPTH Network in sub-Saharan Africa. Methods: We linked demographic residence data with household survey data containing living standards and education information we used to create a poverty index. Person-years lived and deaths between 2003 and 2016 (periods varied by HDSS) were stratified in each HDSS by age, sex, year, and number of deprivations on the poverty index (0-8). Causes of death were assigned to each death using the InterVA-4 model based on responses to verbal autopsy questionnaires. We estimated rate ratios between socioeconomic groups (2-4 and 5-8 deprivations on our poverty index compared to 0-2 deprivations) for specific causes of death and calculated life expectancy for the deprivation groups. Results: Our pooled data contained almost 3.5 million person-years of observation and 25,038 deaths. All-cause mortality rates were higher among people in households with 5-8 deprivations on our poverty index compared to 0-2 deprivations, controlling for age, sex, and year (rate ratios ranged 1.42 to 2.06 across HDSS sites). The poorest group had consistently higher death rates in communicable, maternal, neonatal, and nutritional conditions (rate ratios ranged 1.34-4.05) and for non-communicable diseases in several sites (1.14-1.93). The disparities in mortality between 5-8 deprivation groups and 0-2 deprivation groups led to lower life expectancy in the higher-deprivation groups by six years in all sites and more than 10 years in five sites. Conclusions: We show large disparities in mortality on the basis of socioeconomic status across seven HDSS in sub-Saharan Africa due to disparities in communicable disease mortality and from non-communicable diseases in some sites. Life expectancy gaps between socioeconomic groups within sites were similar to the gaps between high-income and lower-middle-income countries. Prevention and treatment efforts can benefit from understanding subpopulations …","author":[{"dropping-particle":"","family":"Coates","given":"Matthew M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kamanda","given":"Mamusu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kintu","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arikpo","given":"Iwara","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chauque","given":"Alberto","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mengesha","given":"Melkamu Merid","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alison J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sifuna","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wamukoya","given":"Marylene","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sacoor","given":"Charfudin N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogwang","given":"Sheila","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Assefa","given":"Nega","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crampin","given":"Amelia C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V.","family":"Macete","given":"Eusebio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kyobutungi","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meremikwu","given":"Martin M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Walter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Adjaye-Gbewonyo","given":"Kafui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marx","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Byass","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sankoh","given":"Osman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bukhman","given":"Gene","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Global Health Action","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2019"]]},"page":"1-12","publisher":"Taylor & Francis","title":"A comparison of all-cause and cause-specific mortality by household socioeconomic status across seven INDEPTH network health and demographic surveillance systems in sub-Saharan Africa","type":"article-journal","volume":"12"},"uris":[""]}],"mendeley":{"formattedCitation":"(Coates et al., 2019)","plainTextFormattedCitation":"(Coates et al., 2019)","previouslyFormattedCitation":"(Coates et al., 2019)"},"properties":{"noteIndex":0},"schema":""}(Coates et al., 2019) modified the MPI by removing the health indicator and only including education and household assets. They then split households into deprivation groups and the results showed a positive association between SES and mortality ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1080/16549716.2019.1608013","ISSN":"16549880","abstract":"(2019) A comparison of all-cause and cause-specific mortality by household socioeconomic status across seven INDEPTH network health and demographic surveillance systems in sub-Saharan Africa, ABSTRACT Background: Understanding socioeconomic disparities in all-cause and cause-specific mortality can help inform prevention and treatment strategies. Objectives: To quantify cause-specific mortality rates by socioeconomic status across seven health and demographic surveillance systems (HDSS) in five countries (Ethiopia, Kenya, Malawi, Mozambique, and Nigeria) in the INDEPTH Network in sub-Saharan Africa. Methods: We linked demographic residence data with household survey data containing living standards and education information we used to create a poverty index. Person-years lived and deaths between 2003 and 2016 (periods varied by HDSS) were stratified in each HDSS by age, sex, year, and number of deprivations on the poverty index (0-8). Causes of death were assigned to each death using the InterVA-4 model based on responses to verbal autopsy questionnaires. We estimated rate ratios between socioeconomic groups (2-4 and 5-8 deprivations on our poverty index compared to 0-2 deprivations) for specific causes of death and calculated life expectancy for the deprivation groups. Results: Our pooled data contained almost 3.5 million person-years of observation and 25,038 deaths. All-cause mortality rates were higher among people in households with 5-8 deprivations on our poverty index compared to 0-2 deprivations, controlling for age, sex, and year (rate ratios ranged 1.42 to 2.06 across HDSS sites). The poorest group had consistently higher death rates in communicable, maternal, neonatal, and nutritional conditions (rate ratios ranged 1.34-4.05) and for non-communicable diseases in several sites (1.14-1.93). The disparities in mortality between 5-8 deprivation groups and 0-2 deprivation groups led to lower life expectancy in the higher-deprivation groups by six years in all sites and more than 10 years in five sites. Conclusions: We show large disparities in mortality on the basis of socioeconomic status across seven HDSS in sub-Saharan Africa due to disparities in communicable disease mortality and from non-communicable diseases in some sites. Life expectancy gaps between socioeconomic groups within sites were similar to the gaps between high-income and lower-middle-income countries. Prevention and treatment efforts can benefit from understanding subpopulations …","author":[{"dropping-particle":"","family":"Coates","given":"Matthew M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kamanda","given":"Mamusu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kintu","given":"Alexander","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Arikpo","given":"Iwara","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Chauque","given":"Alberto","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mengesha","given":"Melkamu Merid","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Price","given":"Alison J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sifuna","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wamukoya","given":"Marylene","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sacoor","given":"Charfudin N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogwang","given":"Sheila","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Assefa","given":"Nega","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Crampin","given":"Amelia C.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"V.","family":"Macete","given":"Eusebio","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kyobutungi","given":"Catherine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meremikwu","given":"Martin M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Walter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Adjaye-Gbewonyo","given":"Kafui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Marx","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Byass","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sankoh","given":"Osman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bukhman","given":"Gene","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Global Health Action","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2019"]]},"page":"1-12","publisher":"Taylor & Francis","title":"A comparison of all-cause and cause-specific mortality by household socioeconomic status across seven INDEPTH network health and demographic surveillance systems in sub-Saharan Africa","type":"article-journal","volume":"12"},"uris":[""]}],"mendeley":{"formattedCitation":"(Coates et al., 2019)","plainTextFormattedCitation":"(Coates et al., 2019)","previouslyFormattedCitation":"(Coates et al., 2019)"},"properties":{"noteIndex":0},"schema":""}(Coates et al., 2019). This study demonstrates the utility of using MPI as a measure of SES and examines poverty from multiple lenses other than wealth. Although different from PCA and MCA, MPI can provide novel insights to the overall well-being of populations. Chapter 2: MethodsMethodsStudy designThe analysis in this paper is based on household material and asset data collected by the Kombewa HDSS. We analyzed change in wealth over time.SettingThe HDSS operates out of the United States Army Medical Research Unit- Kenya, a research site that collaborates with the Walter Reed Army Institute of Research (WRAIR) and Kenya Medical Research Institute (KEMRI) ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/ije/dyu139","ISSN":"14643685","abstract":"The Kombewa Health and Demographic Surveillance System (HDSS) grew out of the Kombewa Clinical Research Centre in 2007 and has since established itself as a platform for the conduct of regulated clinical trials, nested studies and local disease surveillance. The HDSS is located in a rural part of Kisumu County, Western Kenya, and covers an area of about 369 km(2) along the north-eastern shores of Lake Victoria. A dynamic cohort of 141 956 individuals drawn from 34 718 households forms the HDSS surveillance population. Following a baseline survey in 2011, the HDSS continues to monitor key population changes through routine biannual household surveys. The intervening period between set-up and baseline census was used for preparatory work, in particular Global Positioning System (GPS) mapping. Routine surveys capture information on individual and households including residency, household relationships, births, deaths, migrations (in and out) and causes of morbidity (syndromic incidence and prevalence) as well as causes of death (verbal autopsy). The Kombewa HDSS platform is used to support health research activities, that is clinical trials and epidemiological studies evaluating diseases of public health importance including malaria, HIV and global emerging infectious diseases such as dengue fever. Formal data request and proposed collaborations can be submitted at Kombewadssdata@usamru-.","author":[{"dropping-particle":"","family":"Sifuna","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyugi","given":"Mary","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogutu","given":"Bernhards","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Andagalu","given":"Ben","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Allan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Owira","given":"Victorine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otsyula","given":"Nekoye","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyieko","given":"Janet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cowden","given":"Jessica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Lucas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Walter","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Epidemiology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2014"]]},"page":"1097-1104","title":"Health and demographic surveillance system profile: The Kombewa health and demographic surveillance system (Kombewa HDSS)","type":"article-journal","volume":"43"},"uris":[""]}],"mendeley":{"formattedCitation":"(Sifuna et al., 2014)","plainTextFormattedCitation":"(Sifuna et al., 2014)","previouslyFormattedCitation":"(Sifuna et al., 2014)"},"properties":{"noteIndex":0},"schema":""}(Sifuna et al., 2014). The Kombewa HDSS is located in rural Western Kenya, in Kisumu County. It covers about 369 km2and stretches along Lake Victoria, about 40 km west of Kisumu city ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/ije/dyu139","ISSN":"14643685","abstract":"The Kombewa Health and Demographic Surveillance System (HDSS) grew out of the Kombewa Clinical Research Centre in 2007 and has since established itself as a platform for the conduct of regulated clinical trials, nested studies and local disease surveillance. The HDSS is located in a rural part of Kisumu County, Western Kenya, and covers an area of about 369 km(2) along the north-eastern shores of Lake Victoria. A dynamic cohort of 141 956 individuals drawn from 34 718 households forms the HDSS surveillance population. Following a baseline survey in 2011, the HDSS continues to monitor key population changes through routine biannual household surveys. The intervening period between set-up and baseline census was used for preparatory work, in particular Global Positioning System (GPS) mapping. Routine surveys capture information on individual and households including residency, household relationships, births, deaths, migrations (in and out) and causes of morbidity (syndromic incidence and prevalence) as well as causes of death (verbal autopsy). The Kombewa HDSS platform is used to support health research activities, that is clinical trials and epidemiological studies evaluating diseases of public health importance including malaria, HIV and global emerging infectious diseases such as dengue fever. Formal data request and proposed collaborations can be submitted at Kombewadssdata@usamru-.","author":[{"dropping-particle":"","family":"Sifuna","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyugi","given":"Mary","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogutu","given":"Bernhards","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Andagalu","given":"Ben","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Allan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Owira","given":"Victorine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otsyula","given":"Nekoye","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyieko","given":"Janet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cowden","given":"Jessica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Lucas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Walter","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Epidemiology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2014"]]},"page":"1097-1104","title":"Health and demographic surveillance system profile: The Kombewa health and demographic surveillance system (Kombewa HDSS)","type":"article-journal","volume":"43"},"uris":[""]}],"mendeley":{"formattedCitation":"(Sifuna et al., 2014)","plainTextFormattedCitation":"(Sifuna et al., 2014)","previouslyFormattedCitation":"(Sifuna et al., 2014)"},"properties":{"noteIndex":0},"schema":""}(Sifuna et al., 2014). The area has a total of 37 sub-locations and 357 villages and the HDSS research site is located at the heart of the HDSS area. 1060786070Figure 1: Location of Kombewa HDSS area00Figure 1: Location of Kombewa HDSS areaParticipantsThe HDSS monitored over 200,00 individuals from 46,045 households between 2011-2018. After deleting the households that did not have more than one observation over time, 20,370 households remained, and all analyses were done on these households. We created three time variables: T1, T2 and T3. Every household in our analysis has data for T1 and T2. During T3, only 4413 households had available data. VariablesAs our primary outcome, we assessed how wealth changed over time. Specifically, we looked at the change in proportion of households in quintiles and deprivations over time. As a secondary outcome we examined SES and how specific assets changed. Data sources The Kombewa HDSS was established in 2007 to conduct clinical trials, nested studies and disease surveillance ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/ije/dyu139","ISSN":"14643685","abstract":"The Kombewa Health and Demographic Surveillance System (HDSS) grew out of the Kombewa Clinical Research Centre in 2007 and has since established itself as a platform for the conduct of regulated clinical trials, nested studies and local disease surveillance. The HDSS is located in a rural part of Kisumu County, Western Kenya, and covers an area of about 369 km(2) along the north-eastern shores of Lake Victoria. A dynamic cohort of 141 956 individuals drawn from 34 718 households forms the HDSS surveillance population. Following a baseline survey in 2011, the HDSS continues to monitor key population changes through routine biannual household surveys. The intervening period between set-up and baseline census was used for preparatory work, in particular Global Positioning System (GPS) mapping. Routine surveys capture information on individual and households including residency, household relationships, births, deaths, migrations (in and out) and causes of morbidity (syndromic incidence and prevalence) as well as causes of death (verbal autopsy). The Kombewa HDSS platform is used to support health research activities, that is clinical trials and epidemiological studies evaluating diseases of public health importance including malaria, HIV and global emerging infectious diseases such as dengue fever. Formal data request and proposed collaborations can be submitted at Kombewadssdata@usamru-.","author":[{"dropping-particle":"","family":"Sifuna","given":"Peter","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyugi","given":"Mary","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ogutu","given":"Bernhards","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Andagalu","given":"Ben","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Allan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Owira","given":"Victorine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otsyula","given":"Nekoye","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Oyieko","given":"Janet","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cowden","given":"Jessica","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Lucas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Otieno","given":"Walter","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"International Journal of Epidemiology","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2014"]]},"page":"1097-1104","title":"Health and demographic surveillance system profile: The Kombewa health and demographic surveillance system (Kombewa HDSS)","type":"article-journal","volume":"43"},"uris":[""]}],"mendeley":{"formattedCitation":"(Sifuna et al., 2014)","plainTextFormattedCitation":"(Sifuna et al., 2014)","previouslyFormattedCitation":"(Sifuna et al., 2014)"},"properties":{"noteIndex":0},"schema":""}(Sifuna et al., 2014). From 2008-2010, household structures were mapped with GPI coordinates and basic demographic data from households was collected in preparation for survey collection the following year. In 2011, baseline census surveys were conducted and have been conducted every six months since, including, births, in and out migration, pregnancy, morbidity and mortality. Demographic information such as education, marital status and occupation and household characteristic data are collected every two years. The PCA and MCA wealth indices are based on household characteristics and the MPI is based on household characteristics as well as educational and mortality data for the Kombewa population. The household indicators used in the creation of wealth indices were housing materials (source of water, type of sanitation, floor material, roof material, wall material, source of cooking fuel and electricity) and durable assets (car, motorcycle, bicycle, tin lamp, refrigerator, television, radio, solar panel, hi fi stereo, electric iron, fan, cellphone, sofa set, table, flash light, kerosene lamp, kerosene stove and motorboat). Data on livestock was also collected but collection only started in 2014, therefore livestock was not included in our analyses. Table 1 explains all the indicators used for PCA and MCA. Table 2 details the definitions of improved and unimproved sources for housing materials. The MPI consists of three main indicators: health, education and standard of living. We used a modified version of the MPI and only used child mortality (<18) as opposed to child mortality and nutrition. The Kombewa HDSS does not collect data on nutrition and therefore was not available. The remaining indicators: education and standard of living, followed MPI guidelines. Educational deprivations were calculated based on years of schooling and child attendance in primary education, and standard of living was calculated using housing characteristic and asset data. Table 3 explains all the MPI indicators and their respective weights.Statistical methodsAll statistical analyses were conducted using R version 3.5.2, a programming software used for statistical computing and graphicsADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"author":[{"dropping-particle":"","family":"R Core Team","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2018"]]},"publisher":"R Foundation for Statistical Computing","publisher-place":"Vienna, Austria","title":"R: a languange and environment for statistical computing","type":"article"},"uris":[""]}],"mendeley":{"formattedCitation":"(R Core Team, 2018)","plainTextFormattedCitation":"(R Core Team, 2018)","previouslyFormattedCitation":"(R Core Team, 2018)"},"properties":{"noteIndex":0},"schema":""}(R Core Team, 2018) . We used two methods to create the wealth indices: PCA, MCA and used an MPI to quantify SES. PCA is an explanatory data analysis tool that reduces the number of variables in a data set into smaller dimensions and is a common statistical method used to create wealth indices. PCA first transforms the initial set of n correlated variables into uncorrelated components ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/heapol/czl029","ISSN":"02681080","abstract":"Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.","author":[{"dropping-particle":"","family":"Vyas","given":"Seema","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kumaranayake","given":"Lilani","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health Policy and Planning","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2006"]]},"page":"459-468","title":"Constructing socio-economic status indices: How to use principal components analysis","type":"article-journal","volume":"21"},"uris":[""]}],"mendeley":{"formattedCitation":"(Vyas & Kumaranayake, 2006)","plainTextFormattedCitation":"(Vyas & Kumaranayake, 2006)","previouslyFormattedCitation":"(Vyas & Kumaranayake, 2006)"},"properties":{"noteIndex":0},"schema":""}(Vyas & Kumaranayake, 2006). Each component is a linear weighted combination of the initial variables. The weights from the first principal component are used as a dependent variable and a wealth score is calculated for each household to indicate wealth status ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/heapol/czl029","ISSN":"02681080","abstract":"Theoretically, measures of household wealth can be reflected by income, consumption or expenditure information. However, the collection of accurate income and consumption data requires extensive resources for household surveys. Given the increasingly routine application of principal components analysis (PCA) using asset data in creating socio-economic status (SES) indices, we review how PCA-based indices are constructed, how they can be used, and their validity and limitations. Specifically, issues related to choice of variables, data preparation and problems such as data clustering are addressed. Interpretation of results and methods of classifying households into SES groups are also discussed. PCA has been validated as a method to describe SES differentiation within a population. Issues related to the underlying data will affect PCA and this should be considered when generating and interpreting results.","author":[{"dropping-particle":"","family":"Vyas","given":"Seema","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kumaranayake","given":"Lilani","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Health Policy and Planning","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2006"]]},"page":"459-468","title":"Constructing socio-economic status indices: How to use principal components analysis","type":"article-journal","volume":"21"},"uris":[""]}],"mendeley":{"formattedCitation":"(Vyas & Kumaranayake, 2006)","plainTextFormattedCitation":"(Vyas & Kumaranayake, 2006)","previouslyFormattedCitation":"(Vyas & Kumaranayake, 2006)"},"properties":{"noteIndex":0},"schema":""}(Vyas & Kumaranayake, 2006). The PCA index score for household i is the linear combination:Equation 1: yi=α1x1-x1s1+α2x2-x2s2+?+αkxk-xkskwhere, xk and sk are the mean and standard deviation of asset xk. αk is the weight assigned to each asset, which remains the same for every household ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"he Latin American Public Opinion Project (LAPOP) research program relies heavily on basic measures of individual economic status. For some time we have been attempting to refine those measures, and in this Insights study, 1 we focus on measuring relative wealth. In doing so, we focus on a critical issue in the social sciences, namely how to obtain valid and reliable measures of personal economic well-being. Our ultimate goal is to develop solid measures of individual economic status to assess the consequences of poverty and economic inequality for democratic political culture in Latin American and Caribbean countries. Research has shown that expenditure-based economic status indicators have been found to be more reliable than indices that are income-based (Deaton 1997). A major reason for this is","author":[{"dropping-particle":"","family":"Córdova","given":"Abby","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Americas Barometer Insights","id":"ITEM-1","issued":{"date-parts":[["2008"]]},"number":"6","number-of-pages":"1-9","title":"Methodological Note: Measuring relative wealth using household asset indicators","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Córdova, 2008)","plainTextFormattedCitation":"(Córdova, 2008)","previouslyFormattedCitation":"(Córdova, 2008)"},"properties":{"noteIndex":0},"schema":""}(Córdova, 2008).MCA is also an explanatory data analysis tool that builds a matrix of uncorrelated linear combinations to enhance the divergence in the original variables ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-1-4614-5263-8_7","ISSN":"23641088","abstract":"The measurement of multidimensional poverty has been advocated by most welfare scholars and is experiencing a growth in interest, partly explained by controversial debates that have emerged across academics and practitioners. This paper follows one of the least explored approaches -- Multiple Correspondence Analysis -- to assess multidimensional poverty in Morocco between 2001 and 2007. Multiple Correspondence Analysis provides two major advantages for the measurement of multidimensional poverty: it generates a matrix of \"weights\" based on the variance-covariance matrix of all welfare dimensions selected and provides a natural approach for constructing a composite welfare indicator that satisfies essential poverty ordering axioms. The application shows that poverty in Morocco has declined according to both monetary and multidimensional indicators and that these findings are robust to stochastic dominance tests. The paper concludes that the sustained positive growth that Morocco experienced during the last decade has translated in improvements in living conditions well beyond monetary returns.","author":[{"dropping-particle":"","family":"Ezzrari","given":"Abdeljaouad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verme","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Economic policy, poverty and gender, Middle East and North Africa region","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"number-of-pages":"181-209","title":"A multiple correspondence analysis approach to the measurement of multidimensional poverty in Morocco 2001–2007","type":"report","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"(Ezzrari & Verme, 2012)","plainTextFormattedCitation":"(Ezzrari & Verme, 2012)","previouslyFormattedCitation":"(Ezzrari & Verme, 2012)"},"properties":{"noteIndex":0},"schema":""}(Ezzrari & Verme, 2012). It can also be used for qualitative variables, categorical variables and imposes less constraints on the data ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1371/journal.pone.0089271","ISSN":"19326203","abstract":"BACKGROUND: Stroke is a growing public health concern in low- and middle- income countries. Improved knowledge about the association between socioeconomic status and stroke in these countries would enable the development of effective stroke prevention and management strategies. This study presents the association between socioeconomic status and the prevalence of stroke in Morocco, a lower middle-income country. METHODS: Data on the prevalence of stroke and stroke-related risk factors were collected during a large population-based survey. The diagnosis of stroke in surviving patients was confirmed by neurologists while health, demographic, and socioeconomic characteristics of households were collected using structured questionnaires. We used Multiple Correspondence Analysis to develop a wealth index based on characteristics of the household dwelling as well as ownership of selected assets. We used logistic regressions controlling for multiple variables to assess the statistical association between socioeconomic status and stroke. FINDINGS: Our results showed a significant association between household socioeconomic status and the prevalence of stroke. This relationship was non-linear, with individuals from both the poorest (mainly rural) and richest (mainly urban) households having a lower prevalence of stroke as compared to individuals with medium wealth level. The latter belonged mainly to urban households with a lower socioeconomic status. When taking into account the urban population only, we observed that a third of poorest households experienced a significantly higher prevalence of stroke compared to the richest third (OR = 2.06; CI 95%: 1.09; 3.89). CONCLUSION: We conclude that individuals from the most deprived urban households bear a higher risk of stroke than the rest of the population in Morocco. This result can be explained to a certain extent by the higher presence of behavioral risk factors in this specific category of the population, which leads in turn to metabolic and physiological risk factors of stroke, such as obesity, diabetes, and hypertension.","author":[{"dropping-particle":"","family":"Engels","given":"Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Baglione","given":"Quentin","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Audibert","given":"Martine","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Viallefont","given":"Anne","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mourji","given":"Fouzi","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Faris","given":"Mustapha El Alaoui","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Yahyaoui","given":"Mohamed","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Slassi","given":"Ilham","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Aidi","given":"Saadia","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS ONE","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2014"]]},"title":"Socioeconomic status and stroke prevalence in morocco:results from the rabat-casablanca study","type":"article-journal","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"(Engels et al., 2014)","plainTextFormattedCitation":"(Engels et al., 2014)","previouslyFormattedCitation":"(Engels et al., 2014)"},"properties":{"noteIndex":0},"schema":""}(Engels et al., 2014). MCA gives more weight to indicators with smaller frequency and has been shown to be more sensitive to deprivations than PCA ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-1-4614-5263-8_7","ISSN":"23641088","abstract":"The measurement of multidimensional poverty has been advocated by most welfare scholars and is experiencing a growth in interest, partly explained by controversial debates that have emerged across academics and practitioners. This paper follows one of the least explored approaches -- Multiple Correspondence Analysis -- to assess multidimensional poverty in Morocco between 2001 and 2007. Multiple Correspondence Analysis provides two major advantages for the measurement of multidimensional poverty: it generates a matrix of \"weights\" based on the variance-covariance matrix of all welfare dimensions selected and provides a natural approach for constructing a composite welfare indicator that satisfies essential poverty ordering axioms. The application shows that poverty in Morocco has declined according to both monetary and multidimensional indicators and that these findings are robust to stochastic dominance tests. The paper concludes that the sustained positive growth that Morocco experienced during the last decade has translated in improvements in living conditions well beyond monetary returns.","author":[{"dropping-particle":"","family":"Ezzrari","given":"Abdeljaouad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verme","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Economic policy, poverty and gender, Middle East and North Africa region","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"number-of-pages":"181-209","title":"A multiple correspondence analysis approach to the measurement of multidimensional poverty in Morocco 2001–2007","type":"report","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"(Ezzrari & Verme, 2012)","plainTextFormattedCitation":"(Ezzrari & Verme, 2012)","previouslyFormattedCitation":"(Ezzrari & Verme, 2012)"},"properties":{"noteIndex":0},"schema":""}(Ezzrari & Verme, 2012). The MCA index score for household i is: Equation 2: Zi=Ri1W1+Ri2W2+…+RijWjwhere, Rij is the response of household i to an asset j and Wj is the MCA weight assigned to each asset, which remains the same for every household ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/978-1-4614-5263-8_7","ISSN":"23641088","abstract":"The measurement of multidimensional poverty has been advocated by most welfare scholars and is experiencing a growth in interest, partly explained by controversial debates that have emerged across academics and practitioners. This paper follows one of the least explored approaches -- Multiple Correspondence Analysis -- to assess multidimensional poverty in Morocco between 2001 and 2007. Multiple Correspondence Analysis provides two major advantages for the measurement of multidimensional poverty: it generates a matrix of \"weights\" based on the variance-covariance matrix of all welfare dimensions selected and provides a natural approach for constructing a composite welfare indicator that satisfies essential poverty ordering axioms. The application shows that poverty in Morocco has declined according to both monetary and multidimensional indicators and that these findings are robust to stochastic dominance tests. The paper concludes that the sustained positive growth that Morocco experienced during the last decade has translated in improvements in living conditions well beyond monetary returns.","author":[{"dropping-particle":"","family":"Ezzrari","given":"Abdeljaouad","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Verme","given":"Paolo","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Economic policy, poverty and gender, Middle East and North Africa region","id":"ITEM-1","issued":{"date-parts":[["2012"]]},"number-of-pages":"181-209","title":"A multiple correspondence analysis approach to the measurement of multidimensional poverty in Morocco 2001–2007","type":"report","volume":"9"},"uris":[""]}],"mendeley":{"formattedCitation":"(Ezzrari & Verme, 2012)","plainTextFormattedCitation":"(Ezzrari & Verme, 2012)","previouslyFormattedCitation":"(Ezzrari & Verme, 2012)"},"properties":{"noteIndex":0},"schema":""}(Ezzrari & Verme, 2012). MPI is a measurement of SES instead of wealth and uses deprivation cutoffs to indicate poverty. An n x d matrix, where n is the number of households and d is the number of deprivations, is used. There are three main dimensions of poverty that are all equally weighted: health, education and standard of living ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"978-1-907194-22-1","ISSN":"2040-8188","abstract":"This paper presents a new Multidimensional Poverty Index (MPI) for 104 developing countries. It is the first time multidimensional poverty is estimated using micro datasets (household surveys) for such a large number of countries which cover about 78 percent of the world?s population. The MPI has the mathematical structure of one of the Alkire and Foster poverty multidimensional measures and it is composed of ten indicators corresponding to same three dimensions as the Human Development Index: Education, Health and Standard of Living. The MPI captures a set of direct deprivations that batter a person at the same time. This tool could be used to target the poorest, track the Millennium Development Goals, and design policies that directly address the interlocking deprivations poor people experience. This paper presents the methodology and components in the MPI, describes main results, and shares basic robustness tests.","author":[{"dropping-particle":"","family":"Alkire,S. & Santos","given":"M.E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2010"]]},"number":"38","number-of-pages":"1-139","title":"Acute Multidimensional Poverty: A New Index for Developing Countries","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Alkire,S. & Santos, 2010)","plainTextFormattedCitation":"(Alkire,S. & Santos, 2010)","previouslyFormattedCitation":"(Alkire,S. & Santos, 2010)"},"properties":{"noteIndex":0},"schema":""}(Alkire,S. & Santos, 2010). Within the three categories, there are typically ten indicators and a person is identified as multidimensionally poor if the sum of all deprivations is 33% or greater ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"978-1-907194-22-1","ISSN":"2040-8188","abstract":"This paper presents a new Multidimensional Poverty Index (MPI) for 104 developing countries. It is the first time multidimensional poverty is estimated using micro datasets (household surveys) for such a large number of countries which cover about 78 percent of the world?s population. The MPI has the mathematical structure of one of the Alkire and Foster poverty multidimensional measures and it is composed of ten indicators corresponding to same three dimensions as the Human Development Index: Education, Health and Standard of Living. The MPI captures a set of direct deprivations that batter a person at the same time. This tool could be used to target the poorest, track the Millennium Development Goals, and design policies that directly address the interlocking deprivations poor people experience. This paper presents the methodology and components in the MPI, describes main results, and shares basic robustness tests.","author":[{"dropping-particle":"","family":"Alkire,S. & Santos","given":"M.E.","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-1","issued":{"date-parts":[["2010"]]},"number":"38","number-of-pages":"1-139","title":"Acute Multidimensional Poverty: A New Index for Developing Countries","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(Alkire,S. & Santos, 2010)","plainTextFormattedCitation":"(Alkire,S. & Santos, 2010)","previouslyFormattedCitation":"(Alkire,S. & Santos, 2010)"},"properties":{"noteIndex":0},"schema":""}(Alkire,S. & Santos, 2010). Because nutrition data was missing from the HDSS, our analysis had a total of nine indicators. Each household is classified as multidimensionally poor or non- poor based on their weighted total. This is represented by the equation:Equation 3: Ci=j=1d=9wj where Ci is the weighted score for household i, j is the first indicator and d is the 9th indicator. wj represents the weight that is applied to each indicator j. Then, each household i is given a poverty score, pk. Equation 4: pk=1 if Ci≥33%pk=0 if Ci<33%BiasIn these analyses, a large portion of data is missing. All 46,045 households collect demographic data every year and collect household characteristic data every two years. However, the inconsistency of household characteristic data collection resulted in missing data and only 20,370 households were available to use for analyses. Additionally, because no households had data for every year between 2011-2018, time frames (T1, T2 and T3) were created. Altering the number of households and the time variable are potential biases in this analysis.Additionally, one potential bias for the MPI is the lack of nutrition data. The original MPI guidelines include child mortality and nutrition as indicators for the health deprivation. Because nutrition data was unavailable for the Kombewa HDSS, our health deprivation just included child mortality. The child mortality indicator was weighted at 33.4% and a household is considered multidimensionally poor at 33 %. Therefore, any household with a deprivation in the health dimension will automatically be classified as multidimensionally poor, which can be a potential bias.Chapter 3: Results and DiscussionResultsPCA and MCATable 4 shows the proportion of households owning particular asset items between the three time periods: T1, T2, T3 as well as their PCA and MCA weights. During T1 and T2, 20,370 households were included and in T3, 4,413 households were included. The results show that wealth increased over time. PCA assigned the highest weight to owning a television followed by having improved floor material and having electricity. It assigned the lowest weight to owning a motorboat, tin lamp and table. MCA assigned the highest weight to owning a refrigerator followed by owning a car and fan and the lowest weight to having an unimproved roof, owning no cellphone and no radio. Generally, assets with higher weights represent higher wealth. The weights assigned by PCA were greater than the weights assigned by MCA for every asset. Despite these differences, the household wealth scores for PCA and MCA were highly and statistically significant (r = 0.956, p < .01). Additionally, PCA and MCA quintiles were highly and statistically significant (r=0.971, p<.01) with a kappa statistic of .863. Both PCA and MCA demonstrate a slight increase of wealth over time. Notable improvements include wall material, electricity and ownership of a cellphone. In T1, both indices classified 22% of households in the first quintile and then decreased to 16% in T3. Similarly, approximately 19% of households in T1 were classified in the fifth quintile and increased to approximately 23% in T3. Table 5 and Table 6 detail the change in quintile over time for PCA and MCA respectively. Figure 2 and Figure 3 represent the change in quintile in a stacked bar plot. The poorest and wealthiest quintiles changed most drastically, and the third quintile changed the least over time. Further examination of the housing materials showed that roof, floor and wall material and access to electricity increased substantially over time. However, cooking fuel remained stagnant and sanitation and water decreased significantly. Figure 4 represents the change in housing material over time. MPIAlthough PCA and MCA scores demonstrated a total increase of wealth, MPI results indicated that multidimensional wealth, or SES, is staying constant over time. In all time periods, MPI indicated that approximately 63% of households were non-deprived and 37% were deprived. Table 7 shows the proportion of households and their deprivations for specific indicators between the three time periods. During T1 and T2, 20,336 households were included and during T3, 4,409 were included. Electricity, housing and assets increased while education, mortality, and cooking fuel did not change over time. However, access to improved sanitation and water decreased. During T1, 36.5% of households were deprived in water access and increased to 48.5 % of households during T3. Similarly, 97.7% of households were deprived in sanitation during T1 and increased to 99.3% during T3. Table 8 details the change in deprivation over time and Figure 5 represents the change in a stacked bar plot. DiscussionKey resultsOur study, using longitudinal household survey data, suggests that wealth slightly increased over time in Kombewa, Kenya. However, SES, demonstrated by the MPI, remained constant over time. The MPI differs from PCA and MCA in its measurement of SES and its assignment of weights. Individual assets are categorized into one indicator and housing materials are also categorized into one indicator. However, sanitation and water are their own indicators meaning they hold more weight in relation to other indicators when compared with PCA and MCA. Because of the larger weighting and the significant decrease in water accessibility, the MPI is a representation of the decrease in sanitation over time. The results suggest that ownership of most assets and improved housing material increased, but necessary structural changes to water source and sanitation decreased. Hence, while households are getting wealthier, they are not necessarily getting healthier.InterpretationThe increase in assets such as electricity, refrigerator and cellphone is consistent with other rural sub-Saharan African communities and suggests that wealth is increasing ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1007/s11205-016-1397-z","ISSN":"15730921","abstract":"? 2016, The Author(s). Understanding the distribution of socioeconomic status (SES) and its temporal dynamics within a population is critical to ensure that policies and interventions adequately and equitably contribute to the well-being and life chances of all individuals. This study assesses the dynamics of SES in a typical rural South African setting over the period 2001–2013 using data on household assets from the Agincourt Health and Demographic Surveillance System. Three SES indices, an absolute index, principal component analysis index and multiple correspondence analysis index, are constructed from the household asset indicators. Relative distribution methods are then applied to the indices to assess changes over time in the distribution of SES with special focus on location and shape shifts. Results show that the proportion of households that own assets associated with greater modern wealth has substantially increased over time. In addition, relative distributions in all three indices show that the median SES index value has shifted up and the distribution has become less polarized and is converging towards the middle. However, the convergence is larger from the upper tail than from the lower tail, which suggests that the improvement in SES has been slower for poorer households. The results also show persistent ethnic differences in SES with households of former Mozambican refugees being at a disadvantage. From a methodological perspective, the study findings demonstrate the comparability of the easy-to-compute absolute index to other SES indices constructed using more advanced statistical techniques in assessing household SES.","author":[{"dropping-particle":"","family":"Kabudula","given":"Chodziwadziwa W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Houle","given":"Brian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Collinson","given":"Mark A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Kahn","given":"Kathleen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Tollman","given":"Stephen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Clark","given":"Samuel","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Social Indicators Research","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2017"]]},"page":"1047-1073","title":"Assessing changes in household socioeconomic status in Rural South Africa, 2001–2013: A distributional analysis using household asset indicators","type":"article-journal","volume":"133"},"uris":[""]}],"mendeley":{"formattedCitation":"(Kabudula et al., 2017)","plainTextFormattedCitation":"(Kabudula et al., 2017)","previouslyFormattedCitation":"(Kabudula et al., 2017)"},"properties":{"noteIndex":0},"schema":""}(Kabudula et al., 2017). The increase of assets can build capacity for obtaining future assets and increasing wealth ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.5243/jsswr.2012.20","ISSN":"2334-2315","abstract":"Asset development is a key strategy to promote economic and social development in Sub-Saharan Africa. Research has found associations between asset ownership and household well-being. However, to date there has been little rigorous research on impacts of asset-building interventions for families in Sub-Saharan Africa. Data were obtained from AssetsAfrica, a demonstration and research initiative designed to test asset-building innovations in Masindi, Uganda. The study sample consists of 393 individuals assigned to the intervention group (n = 203) or the comparison group (n =190). The intervention is a structured, matched-savings account offered to the intervention group for a 3-year period. In addition, the program participants were offered financial education and asset-management training. Participants who successfully reach their savings goals receive matched funds at a 1:1 ratio. Propensity score optimal matching and matching estimators are used to investigate the impact of the intervention on financial and productive assets. Results indicate a positive effect of the intervention on family financial assets; that is, individuals who receive the asset-building intervention have almost $39 more in financial assets than those in the comparison group. Further, the matching estimators indicate a statistically significant larger treatment effect on the treated group. However, the impact of the intervention on ownership of productive assets is less conclusive. Overall, results of this study show that asset-building interventions have potential utility as a policy solution for improving the economic well-being of poor households in Sub-Saharan Africa.","author":[{"dropping-particle":"","family":"Chowa","given":"Gina A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Masa","given":"Rainier D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sherraden","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Journal of the Society for Social Work and Research","id":"ITEM-1","issue":"4","issued":{"date-parts":[["2012"]]},"page":"329-345","title":"Wealth Effects of an Asset-Building Intervention Among Rural Households in Sub-Saharan Africa","type":"article-journal","volume":"3"},"uris":[""]}],"mendeley":{"formattedCitation":"(Chowa, Masa, & Sherraden, 2012)","plainTextFormattedCitation":"(Chowa, Masa, & Sherraden, 2012)","previouslyFormattedCitation":"(Chowa, Masa, & Sherraden, 2012)"},"properties":{"noteIndex":0},"schema":""}(Chowa, Masa, & Sherraden, 2012). Additionally, the GDP of Kenya increased by approximately 60% during the time period, indicating that the increase in wealth in Kombewa is consistent with the trends of the countryADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"URL":"","accessed":{"date-parts":[["2020","4","15"]]},"author":[{"dropping-particle":"","family":"The World Bank","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"World Development Indicators","id":"ITEM-1","issued":{"date-parts":[["2019"]]},"title":"GDP, PPP (constant international $) - Kenya","type":"webpage"},"uris":[""]}],"mendeley":{"formattedCitation":"(The World Bank, 2019)","plainTextFormattedCitation":"(The World Bank, 2019)"},"properties":{"noteIndex":0},"schema":""}(The World Bank, 2019). Furthermore, the improvement of housing material is linked with increased wealth. Finished housing material is associated with positive health outcomes and improved SES so these improvements indicate that Kombewa is heading in a positive direction ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/s12936-018-2463-6","ISSN":"14752875","abstract":"Background: Malaria remains one of the major causes of morbidity and mortality among under-five (U5) children in Nigeria. Though different environmental factors have been assessed to influence the distribution and transmission of malaria vectors, there is a dearth of information on how housing type may influence malaria transmission among U5 children in Nigeria. This study assessed the relationship between housing type and malaria prevalence among U5s in Nigeria. Methods: A cross-sectional analysis of the nationally representative 2015 Nigeria malaria indicator survey data was done. A representative sample of 8148 households in 329 clusters was selected for the survey. Children aged 6-59 months in the selected households were tested for anaemia and malaria using the rapid diagnostic test (RDT) and the microscopy. Data were analysed using descriptive statistics, Pearson Chi square (χ2) and logistic regression models at 5% level of significance. Results: The odds of malaria infection was significantly higher among older children aged 24-59 months (aOR = 4.8, CI 2.13-10.99, p < 0.001), and children who lived in houses built completely with unimproved materials (aOR = 1.4, CI 1.08-1.80, p = 0.01). Other predictors of malaria infection include living in a rural area (aOR = 1.5, CI 1.25-1.91, p = 0.01), ever slept under a long-lasting insecticide-treated net (aOR = 1.1, CI 0.26-4.79, p = 0.89) and in a room not sprayed with insecticide (aOR = 1.2, CI 0.64-2.31, p = 0.56). Children who were malaria positive showed a higher prevalence of severe anaemia on RDT (87.6%) and Microscopy (67.4%) than those who were not anaemic (RDT = 31.6%, Microscopy = 12.9%). Conclusions: Non-improved housing predicted malaria infection among U5s in Nigeria. Improved housing is a promising means to support a more integrated and sustainable approach to malaria prevention. Education of the Nigerian people on the role of improved housing on malaria protection and empowerment of the public to adopt improved housing as well as overall enlightenment on ways to prevent malaria infection can help to augment the current malaria control measures among U5 children.","author":[{"dropping-particle":"","family":"Morakinyo","given":"Oyewale M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Balogun","given":"Folusho M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fagbamigbe","given":"Adeniyi F","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Malaria Journal","id":"ITEM-1","issue":"1","issued":{"date-parts":[["2018"]]},"page":"311","title":"Housing type and risk of malaria among under-five children in Nigeria: Evidence from the malaria indicator survey","type":"article-journal","volume":"17"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1371/journal.pmed.1003055","ISBN":"1111111111","ISSN":"15491676","abstract":"BACKGROUND: Housing is essential to human well-being but neglected in global health. Today, housing in Africa is rapidly improving alongside economic development, creating an urgent need to understand how these changes can benefit health. We hypothesised that improved housing is associated with better health in children living in sub-Saharan Africa (SSA). We conducted a cross-sectional analysis of housing conditions relative to a range of child health outcomes in SSA. METHODS AND FINDINGS: Cross-sectional data were analysed for 824,694 children surveyed in 54 Demographic and Health Surveys, 21 Malaria Indicator Surveys, and two AIDS Indicator Surveys conducted in 33 countries between 2001 and 2017 that measured malaria infection by microscopy or rapid diagnostic test (RDT), diarrhoea, acute respiratory infections (ARIs), stunting, wasting, underweight, or anaemia in children aged 0-5 years. The mean age of children was 2.5 years, and 49.7% were female. Housing was categorised into a binary variable based on a United Nations definition comparing improved housing (with improved drinking water, improved sanitation, sufficient living area, and finished building materials) versus unimproved housing (all other houses). Associations between house type and child health outcomes were determined using conditional logistic regression within surveys, adjusting for prespecified covariables including age, sex, household wealth, insecticide-treated bed net use, and vaccination status. Individual survey odds ratios (ORs) were pooled using random-effects meta-analysis. Across surveys, improved housing was associated with 8%-18% lower odds of all outcomes except ARI (malaria infection by microscopy: adjusted OR [aOR] 0.88, 95% confidence intervals [CIs] 0.80-0.97, p = 0.01; malaria infection by RDT: aOR 0.82, 95% CI 0.77-0.88, p < 0.001; diarrhoea: aOR 0.92, 95% CI 0.88-0.97, p = 0.001; ARI: aOR 0.96, 95% CI 0.87-1.07, p = 0.49; stunting: aOR 0.83, 95% CI 0.77-0.88, p < 0.001; wasting: aOR 0.90, 95% CI 0.83-0.99, p = 0.03; underweight: aOR 0.85, 95% CI 0.80-0.90, p < 0.001; any anaemia: aOR 0.87, 95% CI 0.82-0.92, p < 0.001; severe anaemia: aOR 0.89, 95% CI 0.84-0.95, p < 0.001). In comparison, insecticide-treated net use was associated with 16%-17% lower odds of malaria infection (microscopy: aOR 0.83, 95% CI 0.78-0.88, p < 0.001; RDT: aOR 0.84, 95% CI 0.79-0.88, p < 0.001). Drinking water source and sanitation facility alone were not associated with diarrhoea. The main…","author":[{"dropping-particle":"","family":"Tusting","given":"Lucy S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gibson","given":"Harry S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Greenwood","given":"Brian","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Knudsen","given":"Jakob","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lindsay","given":"Steve W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatt","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"PLoS medicine","id":"ITEM-2","issue":"3","issued":{"date-parts":[["2020"]]},"page":"e1003055","title":"Housing and child health in sub-Saharan Africa: A cross-sectional analysis","type":"article-journal","volume":"17"},"uris":[""]}],"mendeley":{"formattedCitation":"(Morakinyo, Balogun, & Fagbamigbe, 2018; Tusting et al., 2020)","plainTextFormattedCitation":"(Morakinyo, Balogun, & Fagbamigbe, 2018; Tusting et al., 2020)","previouslyFormattedCitation":"(Morakinyo, Balogun, & Fagbamigbe, 2018; Tusting et al., 2020)"},"properties":{"noteIndex":0},"schema":""}(Morakinyo, Balogun, & Fagbamigbe, 2018; Tusting et al., 2020). The Kombewa housing trends are consistent with other rural African communities ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1038/s41586-019-1050-5","ISSN":"14764687","abstract":"Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10–12%) to 23% (21–25%). However, 53 (50–57) million urban Africans (47% (44–50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.","author":[{"dropping-particle":"","family":"Tusting","given":"Lucy S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bisanzio","given":"Donal","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Alabaster","given":"Graham","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cameron","given":"Ewan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Cibulskis","given":"Richard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Davies","given":"Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Flaxman","given":"Seth","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gibson","given":"Harry S.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Knudsen","given":"Jakob","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mbogo","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Okumu","given":"Fredros O.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Seidlein","given":"Lorenz","non-dropping-particle":"von","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Weiss","given":"Daniel J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lindsay","given":"Steve W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gething","given":"Peter W.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatt","given":"Samir","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Nature","id":"ITEM-1","issue":"7752","issued":{"date-parts":[["2019"]]},"page":"391-394","publisher":"Springer US","title":"Mapping changes in housing in sub-Saharan Africa from 2000 to 2015","type":"article-journal","volume":"568"},"uris":[""]}],"mendeley":{"formattedCitation":"(Tusting et al., 2019)","plainTextFormattedCitation":"(Tusting et al., 2019)","previouslyFormattedCitation":"(Tusting et al., 2019)"},"properties":{"noteIndex":0},"schema":""}(Tusting et al., 2019). Although wealth and SES in the form of housing material has improved, the decrease in improved water source and toilet type indicates that structural change in sanitation needs to be made. Although the new Kenyan constitution, created in 2010, assigned responsibility of water and sanitation to county governments, local authorities have limited resources to implement change and create sustainable water solutions. An analysis of the implementation of water in Kisumu County showed that urban parts of Kisumu were given financial resources for water pipe implementation, yet rural areas received no financial assistance ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISSN":"2455-3689","abstract":"EXECUTIVE SUMMARY: The provision of water and sanitation globally, is threatened by climate change and pollution. Many industrialized and developing countries alike are struggling to achieve universal safe water coverage of 100% piped water connection to households and adequate sanitation. Water scarcity has imposed burden on women in their daily lives. Inadequate sanitation and outbreaks of infections have affected the health of the general population in Africa, Kenya in particular. Further to the above background, this study endeavored to establish the overall influence of determinants of implementation of water and sanitation programme in Maseno Division of Kisumu West Sub-county. Anchored on the role's theory, the target population for the study was 31600 households. From this, a sample size of 380 respondents plus one water company manager and two field officers was drawn for Maseno division under study. Data was collected through questionnaire forms administered by research assistants. Pilot testing of the study was administered at Manyatta slums in Kisumu Central Sub-county. To uphold the content validity, the contents of qualitative data was discussed with the supervisors before conclusions and generalizations were made. Reliability was tested by Kunder-Richardson(K-R)20 formula based on the split half reliabilities of data from all possible halves of instruments. Quantitative approaches using statistical package namely frequencies, means and percentages were analyzed and presented in tables. On the first objective, the study established that the majority of the households (48.7%) noted that lack of adequate financial allocation led to underdeveloped pipe network. The County government did not put in place adequate measures to address the issues of rural water capital investment programmes in successive financial budgetary provisions. On the second objective, the study established that there was lack of skilled human resource on the ground to ensure sustainability in service delivery. On the third objective, the study established that, although 89.5% of the respondents were aware of the county government's role in the provision of water and sanitation services, most of the water projects in the community were initiated by the community themselves and donor NGOs. The study further established that necessary active participation by stakeholders was not fully accommodated by the county government and this did not promote the decision-making process…","author":[{"dropping-particle":"","family":"Karan Charles","given":"Babu","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nyonje","given":"Raphael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Leonard Ogweno","given":"Kwama","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Peter Anyang Nyongo","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Invention Journal of Research Technology in Engineering & Management (IJRTEM) ","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2019"]]},"page":"32-46","title":"Determinants of implementation of county water and sanitation programme in Maseno Division of Kisumu West sub-county, Kisumu County","type":"article-journal","volume":"3"},"uris":[""]}],"mendeley":{"formattedCitation":"(Karan Charles, Nyonje, Leonard Ogweno, & Peter Anyang Nyongo, 2019)","plainTextFormattedCitation":"(Karan Charles, Nyonje, Leonard Ogweno, & Peter Anyang Nyongo, 2019)","previouslyFormattedCitation":"(Karan Charles, Nyonje, Leonard Ogweno, & Peter Anyang Nyongo, 2019)"},"properties":{"noteIndex":0},"schema":""}(Karan Charles, Nyonje, Leonard Ogweno, & Peter Anyang Nyongo, 2019). This could be a possible explanation for the decrease in improved water source in Kombewa and demonstrates that Kisumu county needs to improve resource allocation to its rural communities. In 2009, it was shown that only 58% of rural water sources were functionalADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"abstract":"The architecture of the water supply and sanitation subsectors in Kenya has undergone significant change in the last decade, in response to a slow deterioration of urban services through the 1980s and ’90s. Initiated with a new Water Act in 2002, significant policy revision and restructuring of institutional roles is still ongoing and will need to be aligned with the new Constitution of Kenya 2010. Most of the reform emphasis has been in the water supply subsectors, especially urban, but sanitation is now regaining emphasis with a new policy published in 2007 and a strategy and investment plan in development. These reforms of the enabling environment are beginning to yield impacts in the coverage and quality of services. Kenya’s challenge is to finalize the reform of enabling aspects such as strategies and investment plans, further clarifying roles and responsibilities, at the same time as significantly scaling up resources and systems for implementing the development of new services on the ground.","author":[{"dropping-particle":"","family":"AMCOW Country Status Overview","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"An African Minister's Council on Water Country Status Overview","id":"ITEM-1","issued":{"date-parts":[["2011"]]},"page":"1-15","title":"Water Supply and Sanitation in Kenya: Turning Finance into Services for 2015 and Beyond","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"(AMCOW Country Status Overview, 2011)","plainTextFormattedCitation":"(AMCOW Country Status Overview, 2011)","previouslyFormattedCitation":"(AMCOW Country Status Overview, 2011)"},"properties":{"noteIndex":0},"schema":""}(AMCOW Country Status Overview, 2011). This was also demonstrated in the WHO report on sanitation where rural households increased their usage of unimproved sanitation sources ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"ISBN":"9789241512893","ISSN":"924151289X","abstract":"The JMP 2017 update report presents indicators and baseline estimates for the drinking water, sanitation and hygiene targets within the Sustainable Development Goals (SDGs). The report introduces the indicators of safely managed drinking water and sanitation services, which go beyond use of improved facilities, to include consideration of the quality of services provided. For the first time, hygiene estimates are reported for 70 countries.","author":[{"dropping-particle":"","family":"WHO","given":"","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"UNICEF","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Update and SDG Baselines","id":"ITEM-1","issued":{"date-parts":[["2017"]]},"number-of-pages":"66","publisher-place":"Geneva","title":"Progress on drinking water, sanitation and hygiene","type":"report"},"uris":[""]}],"mendeley":{"formattedCitation":"(WHO & UNICEF, 2017)","plainTextFormattedCitation":"(WHO & UNICEF, 2017)","previouslyFormattedCitation":"(WHO & UNICEF, 2017)"},"properties":{"noteIndex":0},"schema":""}(WHO & UNICEF, 2017). These findings indicate that Kombewa is consistent with other rural African communities but should indicate to governments and households that improvements to water and sanitation are needed to increase SES and improve health outcomes. ConclusionThe overall increase in wealth is a positive indication of the well-being of the households in Kombewa. After creating three wealth indices: PCA, MCA and MPI, we concluded that ownership of assets and improved housing materials increased. The PCA and MCA indices show that the proportion of households in wealthier quintiles were greater in T3 than T1, indicating that wealth increased slightly over time. The MPI, however, shows that SES did not change over time. This can be explained by the decrease of access to improved sources of sanitation and water. More structural changes need to be implemented to fully increase the SES, well-being and health of the Kombewa community.ReferencesADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY Akinboade, O. A., & Adeyefa, S. A. (2018). An Analysis of Variance of Food Security by its Main Determinants Among the Urban Poor in the City of Tshwane, South Africa. Social Indicators Research, 137(1), 61–82. . & Santos, M. E. (2010). 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American Psychologist, 67(4), 272–284. 1: PCA and MCA indicators and descriptionsIndicatorDescriptionSource of drinking waterImproved / Non-improvedType of toilet facilityImproved / Non-improvedFloor materialImproved/ Non-improvedRoof materialImproved/ Non-improvedWall materialImproved/ Non-improvedType of cooking fuelImproved/ Non-improvedElectricityYes /NoMotorcycleYes /NoBicycleYes /NoTin lampYes /NoRefrigeratorYes /NoTelevisionYes /NoRadioYes /NoSolar PanelYes /NoHi Fi StereoYes /NoElectric IronYes /NoFanYes /NoCell phoneYes /NoSofaYes /NoTableYes /NoFlashlightYes /NoKerosene lampYes /NoKerosene stoveYes /NoMotorboatYes /NoTable 2: Definitions of improved and unimproved housing characteristicsIndicatorImprovedNon- improvedSource of drinking waterBuying tapsPiped into compoundPiped to neighborsPiped at water kioskPublic tapBorehole River / streamProtected spring waterBuying tanksPublic well Unprotected wellUnprotected spring waterRainwaterTanker truckBuying water from river/ streamPond/ lake Type of toilet facilityFlush toilet (own and shared)VIP latrine (own and shared)Flush trenchPit latrine (own and shared)No facility / bush Floor materialWood planksPolished wood / vinyl / tilesCementMud / dung/ sandRoof materialMetal sheet/ tinIron sheet TilesGrass / thatchPlastic sheetCardboard sheetWood / timberWall materialWood / timberIron sheetCement mudTin/ metal sheetMudObservation bricksCardboards sheetsCarton / plasticsType of cooking fuelGasElectricityKerosene/ ParaffinCharcoalFirewoodAnimal wasteCrop residue / saw dustDimensions of PovertyIndicatorHousehold is deprived if:Relative WeightEducationYears of schoolingNo household member aged 10 years or older has completed six year of schooling16.7%School attendance Any school aged- child is not attending school up to the age at which he/she would complete class 8.16.7%HealthChild mortalityAny child (less than 18) has died in the family33.4 %Standard of Living Cooking fuelThe household uses unimproved source5.6%SanitationThe household uses unimproved source5.6%Drinking waterThe household uses unimproved source5.6%ElectricityThe household has no electricity 5.6%HousingHousing materials for at least one roof, walls or floor are unimproved5.6%Assets The household does not own more than one of these assets: radio, TV, telephone, bicycle, motorbike or refrigerator AND does not own a car5.6% Table 3: MPI indicators, descriptions and respective weightsTable 4: Proportion of Assets and Respective PCA and MCA Weights Over TimeAssetsCategoriesPercentage Of AssetsOwnedWeights???T1(n = 20370)T2 (n = 20370)T3 (n = 4413)PCA Index MCA IndexSource of drinking waterImproved drinking water63.556.451.60.120.00005058Unimproved drinking water36.543.648.4-0.0000732Toilet facilityImproved toilet3.40.60.70.250.00089Unimproved toilet 96.699.499.3-0.00001706Floor materialImproved floor 32.146.251.90.580.0003435Unimproved floor 67.953.848.1-0.0002329Roof materialImproved roof 83.493.696.70.270.0000456Unimproved roof 16.66.43.3-0.0003809Wall materialImproved wall 18.329.034.00.320.0002717Unimproved wall 81.771.066.0-0.00008893---Cooking fuelImproved cooking fuel0.80.80.70.310.001674Unimproved cooking fuel99.299.299.3-0.00001361---ElectricityOwns electricity6.413.117.60.610.0008788No electricity93.686.982.4-0.0001034---CarOwns car1.11.01.40.400.001864No car98.999.098.6-0.0000209---MotorcycleOwns motorcycle2.12.73.00.260.0007862No motorcycle97.997.397.0-0.0000199---BicycleOwns bicycle28.913.610.80.250.0002383No bicycle71.186.489.2-0.00006047---Tin lampOwns tin lamp3.578.982.8< 0.1-0.00003722No tin lamp96.521.117.20.00003075---RefrigeratorOwns refrigerator1.21.21.80.490.002116No refrigerator98.898.898.2-0.0000273---TelevisionOwns television11.012.414.60.670.0008949No television89.087.685.4-0.0001217---RadioOwns radio70.272.974.50.420.0001275No radio29.827.125.5-0.0003255---Solar panelOwns solar panel3.620.628.60.260.0003184No solar panel96.479.471.4-0.00005049---Hi fi stereoOwns hi fi stereo2.84.86.30.360.0008657No hi fi stereo97.295.293.7-0.0000365---Electric ironOwns electric iron2.53.43.80.560.001568No electric iron97.596.696.2-0.00004889---FanOwns fan0.80.71.80.350.001829No fan99.299.398.2-0.00001594---CellphoneOwns cellphone69.182.781.60.390.0001063No cellphone30.917.318.4-0.000345---SofaOwns sofa60.365.678.00.420.0001516No sofa39.734.422.0-0.0002741---TableOwns table96.695.496.50.110.00001123No table3.44.63.5-0.0002716---FlashlightOwns flashlight43.228.927.80.390.000256No flashlight56.871.172.2-0.0001394---Kerosene lampOwns kerosene lamp52.632.420.90.370.00022001No kerosene lamp47.467.679.1-0.000149---Kerosene stoveOwns kerosene stove28.315.317.40.450.0004208No kerosene stove71.784.782.6-0.0001144---MotorboatOwns motorboat0.00.10.3<0.10.00149No motorboat100.099.999.7-0.00000121Table 5: PCA Quintile ChangeQuintile?T1(n = 20370)?T2 (n = 20370)?T3 (n = 4413)122.1%18.8%16.1%219.9%20.6%17.8%319.8%20.3%19.9%419.1%20.1%23.3%5?19.1%?20.2%?22.8%Table 6: MCA Quintile ChangeQuintile?T1(n = 20370)?T2 (n = 20370)?T3 (n = 4413)121.7%19.2%16.2%221.0%19.6%17.0%319.6%20.4%20.2%418.9%20.4%22.7%5?18.8%?20.3%?23.9%Table 7: Percentage of asset deprivations for MPIIndicatorCategoriesPercentage of households in deprivation categoryT1(n = 20336)T2 (n = 20336)T3 (n = 4409)Years of schoolingDeprived20.620.619.7Non-deprived79.479.480.3School attendanceDeprived6.36.38.1Non-deprived93.793.791.9Child mortalityDeprived4.74.74.5Non-deprived95.395.395.5Cooking fuelDeprived96.897.798.0Non-deprived3.22.32.0SanitationDeprived97.799.699.3Non-deprived2.30.40.7Drinking waterDeprived36.543.648.5Non-deprived63.556.451.5ElectricityDeprived93.686.982.4Non-deprived6.413.117.6HousingDeprived84.373.368.3Non-deprived15.726.731.7AssetsDeprived37.531.230.4Non-deprived62.568.869.6Table 8: MPI Deprivation over timeDeprivationT1(n=20336)T2(n=20336)T3(n=4409)Deprived63.4%63.7%63.0%Non-deprived?36.6%?36.3%?37.0%Figure 2: Stacked bar plot of PCA quintile change over time-400050200025Figure 3: Stacked bar plot of MCA quintile change over time-398780196215Figure 4: Housing material change over timeFigure 5: Stacked bar plot of MPI deprivation change over time ................
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