ABSTRACT
BackgroundLocal authorities (LAs) provide Cultural, Environmental, and Planning (CEP) services, such as parks, libraries, and waste collection, that are vital in supporting the health of the communities they serve. There have been significant changes to LA funding recently, most notably due to the UK government’s austerity programme. These changes have not affected all places equally. To understand potential impacts on health inequalities, we investigated the extent to which areas have been differentially affected by declines in CEP services spending based on local characteristics.MethodsWe conducted a longitudinal ecological study using routinely available data on LA expenditure, as collated in the Place-Based Longitudinal Data Resource. We used generalised estimating equations to determine how expenditure trends varied across 378 LAs in Great Britain between 2009 and 2018 on the basis of country, deprivation, rurality, and local government structure. We investigated the gross expenditure per capita on CEP services, and the CEP expenditure as a proportion of total LA budgets. We conducted analysis using R v4.0.2.ResultsExpenditure per capita for CEP services reduced by 36% between 2009 and 2018. In England, the reduction in per capita spending was steepest in the most deprived quintile of areas, falling by 5.9% [95% CI: 4.7;7.0] per year, compared to 3.3% [95% CI: 2.5;4.1] in the least deprived quintile. Budget cuts in Scotland and Wales have been more equitable, showing little differentiation between most and least deprived areas. Welsh LAs have reduced the proportion of total LA budget spent on CEP services more than any other country (-2.9% per year [95% CI: -4.0;-1.8]), followed by Scotland (-1.5% [95% CI: -2.8;-0.3]) then England (-0.5% [95% CI: -1.0;0.1). In England, rural LAs have reduced their CEP spending share more than those in urban areas, and unitary authorities have reduced their share more than those in a two-tier structure.ConclusionThere have been distinct inequalities in the reduction of spending for CEP services. LAs with a higher baseline level of deprivation, those with a single-tier local government structure, and English rural LAs have been worst affected. These inequalities in cuts to services that impact public health risk widening geographical and social health inequalities. Understanding these inequalities will provide crucial evidence to inform the UK government’s ‘levelling up’ strategy as the country recovers from the COVID-19 pandemic. One limitation of our study is that we were unable to investigate how resources have been distributed within LAs.
ABSTRACT
BACKGROUND: During the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality-including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities. METHODS: We performed a cross-sectional ecological analysis across 6789 small areas in England. We assessed the association between COVID-19 mortality in each area and five vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index. RESULTS: Four vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each SD increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, 19% 8% and 11% increase in age-adjusted COVID-19 mortality rate, respectively. CONCLUSION: Vulnerability to COVID-19 was noticeably higher in the North West, West Midlands and North East regions, with high levels of vulnerability clustered in some communities. Our analysis indicates the communities who will be most at risk from a second wave of the pandemic.