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COVID-19 community vulnerability briefingScottish Public Health Observatory 31-03-2020In addition to clinical and public health responses to the Coronavirus (COVID-19) pandemic, action will be required to mitigate a range of associated social harms. This briefing presents public health intelligence to inform the COVID-19 response in Scotland.We used data routinely available in the Scottish Public Health Observatory (ScotPHO) Health and Wellbeing Profiles to help guide the use and allocation of the public money for social mitigation of the effects of COVID-19. First, we have created a community vulnerability measure based on demographic, social and clinical indicators relevant either directly to COVID-19 or to socio-economic factors that are likely to modify the impacts of the pandemic and efforts to delay it. These measures have been developed in response to COVID-19 and may require further refinement. However, they are presented in this paper primarily to illustrate the methodology, likely outputs and potential to inform COVID-19 response planning. Second, we have presented a comparison of key demographic indicators with the rest of the United munity vulnerability measureWe used data routinely available in the Scottish Public Health Observatory (ScotPHO) Health and Wellbeing Profiles, with the exception of the Diabetes Patient Hospitalisation indicator. The indicators were chosen on the basis of being immediately available to the ScotPHO team; and were demographic or clinical indicators deemed to be directly relevant to COVID-19, clinical indicators relevant to other demands on clinical services or population health, or social factors that would be likely to modify the impact of COVID-19 on communities. We used the most recent data in all cases (2016, 2017, 2018 or 2019). A full list of the indicators used in the analysis is presented in Appendix1.We combined these indicators into a single measure for overall vulnerability across each of the three domains using an approach called principal components analysis (PCA). Further information on the methodology used is reported in Appendix 2.Key messagesA combined vulnerability score for each geographical area (Council, Intermediate Zone and Datazone) has been calculated. Each area has then been assigned a quintile based on that score, with Quintile 1 indicating the 20% of areas across Scotland with the highest vulnerability score.The councils with the highest combined vulnerability score are: East Ayrshire, Na h-Eileanan Siar, Inverclyde, North Ayrshire, South Ayrshire, West Dunbartonshire and Dundee City.Table 1 presents the number of intermediate zones and datazones in the highest overall vulnerability quintile for each council area, and also the number and percentage of the council population within these areas. All council areas have at least one datazone classified as being in the most vulnerable quintile. These findings indicate that in addition to vulnerability varying by council area, there are also small area pockets of vulnerability across Scotland. These can be viewed in more detail as an interactive map and data tables (provided as separate files).Using these tools could help at which geographic level resources are likely to be most effectively targeted. Table 1: Number of areas and population within most vulnerable quintile (quintile 1) based on combined vulnerability scoreCouncil AreasIntermediate ZonesDatazonescount of IZsum 2018 pop’n% of total pop’ncount of DZsum 2018 pop’n% of total pop’nAberdeen City418,6788%31 23,626 10%Aberdeenshire0-0%25 18,455 7%Angus313,81612%22 17,189 15%Argyll & Bute520,54924%28 17,284 20%City of Edinburgh832,1036%47 37,803 7%Clackmannanshire14,5399%15 10,708 21%Dumfries & Galloway624,11716%48 37,248 25%Dundee City1151,40335%62 49,569 33%East Ayrshire1140,72533%51 37,262 31%East Dunbartonshire311,56911%19 15,784 15%East Lothian13,2623%14 10,699 10%East Renfrewshire29,98810%17 13,419 14%Falkirk723,24114%36 25,288 16%Fife1964,09117%91 67,253 18%Glasgow City67285,75646%259 203,657 33%Highland413,9976%45 31,087 13%Inverclyde945,62358%43 27,766 36%Midlothian12,8653%13 9,172 10%Moray14,2674%11 7,852 8%Na h-Eileanan Siar12,79310%4 3,045 11%North Ayrshire1964,64348%69 49,443 37%North Lanarkshire1251,50215%97 70,525 21%Orkney Islands0-0%1 886 4%Perth & Kinross310,4537%26 22,631 15%Renfrewshire1148,16527%55 43,401 24%Scottish Borders618,20716%34 25,103 22%Shetland Islands0-0%1 668 3%South Ayrshire1040,09036%58 42,299 38%South Lanarkshire2278,99125%102 74,625 23%Stirling13,4494%9 7,240 8%West Dunbartonshire733,68338%31 23,336 26%West Lothian14,8303%32 23,296 13%Total2561,027,39518.9%13961,047,619 19.3%Comparison of key indicators with the rest of the United KingdomPopulation structure:Scotland has a higher proportion of its population in the 50-70 year age group than the rest of the UK.The proportion of the population in younger age groups (under 18 years) is lower in Scotland compared to the UK. Nineteen percent of people living in Scotland (around 1 million people) are aged 65 years and over, compared with 18% for the UK as a whole.Life Expectancy: UK Constituent Countries:Life expectancy is lower in Scotland than in the UK as a whole, for both males and females.Appendix 1: Indicators included in the community vulnerability measure by vulnerability domain IndicatorVulnerabilityGeographyPopulation income deprivedSocialDZ, IZ, CAChildren in low income familiesSocialDZ, IZ, CASingle adult dwellingsSocialDZ, IZ, CAWorking age employment derivedSocialDZ, IZ, CAPeople aged 65+ with high levels of care needs who are cared for at homeSocialCAChildren registered for free school mealsSocialCAChildren on the child protection registerSocialCAHousehold with children living in fuel povertySocialCAMid-year population estimate - aged 65+ yearsDemographicsDZ, IZ, CAMid-year population estimate - aged 75+ yearsDemographicsDZ, IZ, CAMid-year population estimate - aged 85+ yearsDemographicsDZ, IZ, CAAlcohol-related hospital admissionsClinicalDZ, IZ, CAAsthma patient hospitalisationsClinicalDZ, IZ, CACoronary heart disease (CHD) hospitalisationsClinicalDZ, IZ, CADeaths all agesClinicalDZ, IZ, CADeaths, aged 15-44 yearsClinicalDZ, IZ, CAEarly deaths from cancer, aged <75 yearsClinicalDZ, IZ, CAEarly deaths from CHD, aged <75 yearsClinicalDZ, IZ, CAEmergency patient hospitalisationsClinicalDZ, IZ, CALife expectancy, maleClinicalCALife expectancy, femaleClinicalCAMultiple emergency hospital admissions, aged >65 yearsClinicalDZ, IZ, CAChronic obstructive pulmonary disease (COPD) patient hospitalisationsClinicalDZ, IZ, CADrug-related hospital staysClinicalDZ, IZ, CACancer registrationsClinicalDZ, IZ, CAPopulation prescribed drugs for anxiety/depression/psychosisClinicalDZ, IZ, CADiabetes hospitalisationsClinicalDZ, IZ, CAPsychiatric patient hospitalisationsClinicalDZ, IZ, CA Appendix 2: MethodologyPrincipal Components Analysis (PCA) is a method for summarising the information contained in datasets with multiple variables. The technique aims to generate a small number of new variables, called principal components that capture the maximum amount of the variation in the original data, with minimal loss of information. We ran three separate PCAs (datazone, intermediate zone and council area) on the indicators in our dataset. All the indicators were scaled to have a standard deviation of one and a mean of zero. We used a package called FactoMineR within the R statistical software. For each geography we selected the first two principal components for the vulnerability measure. These two components captured 53% of the data variance at datazone level, 70% at intermediate zone level, and 73% at council area level. When we examined how the original variables had contributed to these components it was evident that the first component captured variability in the clinical and social vulnerability indicators, while the second component captured variability in the demographic variables. We labelled them ‘social/clinical vulnerability’ and ‘demographic vulnerability’ scores respectively. The combined vulnerability score was constructed by normalising and then summing these scores. ................
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