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ObjectiveHealthcare utilisation (HU) is key to improving the health of residents in urban informal settlements. This study aimed to explore household-level factors influencing HU among informal settlement households in Freetown, Sierra Leone.DesignCross-sectional survey.SettingThree informal settlements (Cockle Bay, Dwarzark and Moyiba) in Freetown, Sierra Leone.ParticipantsPrimary data from 4871 households were collected during the Health and Wellbeing survey conducted between April and May 2023, targeting households with adults aged 18 years and older.Primary outcome measuresThe primary outcomes were households HU both within and outside informal settlements. Household-level predisposing and enabling explanatory variables were derived from Andersen’s Behavioural Model of HU.ResultsDisability in households increases HU within settlements (especially in Dwarzark, 13% and Moyiba, 10%) but is less likely outside. Households engaged in income-generating activities are more likely to seek healthcare within settlements, but 12% less likely outside in Cockle Bay and Dwarzark. Food insecurity decreases HU within Dwarzark (9%) and increases HU outside by 174% in Moyiba. Longer water fetching times and water shortages were associated with higher HU (between 6% and 16%) within settlements, especially in Cockle Bay and Dwarzark. Clean water sources (eg, piped dwelling, bowser, surface, bottled) were consistently associated with higher HU both within and outside settlements. Shared sanitation facilities (such as shared toilets) were positively associated with HU both within and outside settlements, particularly in Dwarzark and Moyiba. Households with income from fishing, informal salaried work and bike riding showed higher HU both within and outside settlements, especially in Dwarzark and Moyiba.ConclusionsWe identified strong settlement-specific patterns of household-level factors that influence HU both within and outside Freetown’s informal settlements. These findings provide a foundation for developing targeted policies such as strengthening local services, addressing affordability and accessibility barriers and supporting vulnerable occupation groups.

Background The growing urban population imposes additional challenges for health systems in low- and middle-income countries (LMICs). We explored the economic burden and inequities in healthcare utilisation across slum, non-slum and levels of wealth among urban residents in LMICs. Methods This scoping review presents a narrative synthesis and descriptive analysis of studies conducted in urban areas of LMICs. We categorised studies as conducted only in slums, city-wide studies with measures of wealth and conducted in both slums and non-slums settlements. We estimated the mean costs of accessing healthcare, the incidence of catastrophic health expenditures (CHE) and the progressiveness and equity of health expenditures. The definitions of slums used in the studies were mapped against the 2018 UN-Habitat definition. We developed an evidence map to identify research gaps on the economics of healthcare access in LMICs. Results We identified 64 studies for inclusion, the majority of which were from South-East Asia (59%) and classified as city-wide (58%). We found severe economic burden across health conditions, wealth quintiles and study types. Compared with city-wide studies, slum studies reported higher direct costs of accessing health care for acute conditions and lower costs for chronic and unspecified health conditions. Healthcare expenditures for chronic conditions were highest amongst the richest wealth quintiles for slum studies and more equally distributed across all wealth quintiles for city-wide studies. The incidence of CHE was similar across all wealth quintiles in slum studies and concentrated among the poorest residents in city-wide studies. None of the definitions of slums used covered all characteristics proposed by UN-Habitat. The evidence map showed that city-wide studies, studies conducted in India and studies on unspecified health conditions dominated the current evidence on the economics of healthcare access. Most of the evidence was classified as poor quality. Conclusions Our findings indicated that city-wide and slums residents have different expenditure patterns when accessing healthcare. Financial protection schemes must consider the complexity of healthcare provision in the urban context. Further research is needed to understand the causes of inequities in healthcare expenditure in rapidly expanding and evolving cities in LMICs.

Progress toward reducing the malaria burden in Africa has been measured, or modeled, using datasets with relatively short time-windows. These restricted temporal analyses may miss the wider context of longer-term cycles of malaria risk and hence may lead to incorrect inferences regarding the impact of intervention. 1147 age-corrected Plasmodium falciparum parasite prevalence (PfPR2-10) surveys among rural communities along the Kenyan coast were assembled from 1974 to 2014. A Bayesian conditional autoregressive generalized linear mixed model was used to interpolate to 279 small areas for each of the 41 years since 1974. Best-fit polynomial splined curves of changing PfPR2-10 were compared to a sequence of plausible explanatory variables related to rainfall, drug resistance and insecticide-treated bed net (ITN) use. P. falciparum parasite prevalence initially rose from 1974 to 1987, dipped in 1991-92 but remained high until 1998. From 1998 onwards prevalence began to decline until 2011, then began to rise through to 2014. This major decline occurred before ITNs were widely distributed and variation in rainfall coincided with some, but not all, short-term transmission cycles. Emerging resistance to chloroquine and introduction of sulfadoxine/pyrimethamine provided plausible explanations for the rise and fall of malaria transmission along the Kenyan coast. Progress towards elimination might not be as predictable as we would like, where natural and extrinsic cycles of transmission confound evaluations of the effect of interventions. Deciding where a country lies on an elimination pathway requires careful empiric observation of the long-term epidemiology of malaria transmission.

ObjectiveMinority ethnic groups disproportionately experienced adverse COVID-19 outcomes, partly a consequence of disproportionate exposure to socioeconomic disadvantage and high-risk occupations. We examined whether minority ethnic groups were also disproportionately vulnerable to the consequences of socioeconomic disadvantage and high-risk occupations in Scotland.DesignWe investigated effect modification and interaction between area deprivation, education and occupational risk and ethnicity (assessed as both a binary white vs non-white variable and a multi-category variable) in relation to severe COVID-19 (hospitalisation or death). We used electronic health records linked to the 2011 census and Cox proportional hazards models, adjusting for age, sex and health board. We were principally concerned with additive interactions as a measure of vulnerability, estimated as the relative excess risk due to interaction (RERI).ResultsAnalyses considered 3 730 837 individuals aged ≥16 years (with narrower age ranges for analyses focused on education and occupation). Severe COVID-19 risk was typically higher for minority ethnic groups and disadvantaged socioeconomic groups, but additive interactions were not consistent. For example, non-white ethnicity and highest deprivation level experienced elevated risk ((HR=2.7, 95% CI: 2.4, 3.2) compared with the white least deprived group. Additive interaction was not present (RERI=−0.1, 95% CI: −0.4, 0.2), this risk being less than the sum of risks of white ethnicity/highest deprivation level (HR=2.4, 95% CI: 2.3, 2.5) and non-white ethnicity/lowest deprivation level (1.4, 95% CI: 1.2, 1.7). Similarly, non-white ethnicity/no degree education (HR=2.5, 95% CI: 2.2, 2.7; RERI=−0.1, 95% CI: −0.4, 0.2) and non-white ethnicity/high-risk occupation (RERI=0.3, 95% CI: −0.2, 0.8) did not experience greater than additive risk. No clear evidence of effect modification was identified when using the multicategory ethnicity variable or on the multiplicative scale either.ConclusionWe found no definitive evidence that minority ethnic groups were more vulnerable to the effect of social disadvantage on the risk of severe COVID-19.

Seasonal malaria chemoprevention (SMC) has been shown to be highly efficacious against clinical malaria in areas where transmission is acutely seasonal. SMC targeting depends on a complex interplay of climate, malaria transmission and population distribution. In this study a spatial decision support framework was developed to identify health districts suitable for the targeting of SMC across seven Sahelian countries and northern states of Nigeria that are members of the Nouakchott Initiative. A spatially explicit decision support framework that links information on seasonality, age-structured population, urbanization, malaria endemicity and the length of transmission season was developed to inform SMC targeting in health districts. Thresholds of seasonality, population and receptive risks were defined to delineate SMC suitable health districts and define the age range of children for targeting. Numbers of children were then computed for the period 2015-2020 in SMC districts. For 2015, this was combined with maps of length of malaria transmission seasons and WHO recommended treatment regimen to quantify the number of tablets required across the SMC health districts. A total of 597 Sahelian health districts were mapped, out of which 478 (80.1%) were considered suitable for SMC based on seasonality and endemicity thresholds. These districts had an estimated 119.8 million (85%) of the total population in 2015. In the six years from 2015-2020, it is estimated that a total of 158 million children 3m to <5 years, 121 million of whom were in rural areas, will need SMC to achieve universal coverage in the Sahel. If the upper age limit of SMC targeted children was increased to <10 years in low transmission districts, a total 177 million overall, of whom 135 million were rural children, will require chemoprevention in 2015-2020. In 2015 alone, an estimated 49-72 million SP tablets and 148-217 million AQ tablets will be needed to cover all or rural children respectively under the different scenarios of upper age limits. Our proposed framework provides a standardised approach to support targeting and scale up of SMC by the countries of the Nouakchott Initiative. Our analysis suggests that the vast majority of the population in this region are likely to benefit from SMC and substantial resources will be required to reach universal coverage each year.

Introduction Residents of informal settlements face significant intersectional inequalities, due to the overlapping and compounding effects of multiple social factors. This study aims to explore how these intersecting social factors, identified by community members, combine to shape household-level inequalities healthcare utilisation (HU) among residents of informal settlements in Freetown, Sierra Leone. Methods This study employed participatory action research to collaboratively identify key social determinants affecting healthcare utilisation in Freetown’s informal settlements. A cross-sectional health and wellbeing survey was implemented in April-May 2023 and collected data from 4,871 households in Cockle Bay, Dwazark, and Moyiba informal settlements. The survey questions were codesigned by researchers and community fieldworkers, informed by prior qualitative research. Two outcomes were analysed: HU within the settlement ( n  = 4,821), and outside the settlement ( n  = 4,616). A multilevel analysis of individual heterogeneity and discriminatory accuracy (MAIHDA) was conducted, nesting households within 122 intersectional strata. These strata were defined by six social factors: head of household gender, marital composition, engagement in income-generating activity, food security, disability and the household’s settlement. Intersectional measures included variance partition coefficient (VPC), the proportional change in variance (PCV), and residual intersectional effects. Results VPCs of 0.9% (PCV, of 92.8%) for HU within the settlements and of 3.9% (PCV, 81.7%) for HU outside the informal settlements suggest moderate but meaningful intersectional effects in shaping HU inequalities. The lowest levels of HU within informal settlements were observed among single, male, disabled individuals in Moyiba who lacked income-generating activities and experienced food insecurity. For HU outside the settlement, the lowest levels were found among female-headed households in Moyiba who were married, cohabiting, or engaged with a disabled household member, experienced food insecurity, and were engaged in income-generating activities. Conclusion This study identifies and quantifies inequalities in HU at the household level across three informal settlements in Freetown, driven by intersecting social factors. Addressing these inequalities requires policies that are universally accessible but implemented with an intensity proportionate to the level of vulnerability, ensuring that support is targeted to those most in need. Highlights The findings identify and quantify intersectional inequalities in healthcare utilisation (HU), particularly among the most vulnerable groups. Single women without income and single men with disabilities and no income in Moyiba were less likely to utilise healthcare. Households with protective factors against illness showed higher HU than those exposed to illness-enabling conditions. Stakeholders are encouraged to address HU inequalities through social security and health insurance, proportionately targeted by level of need.

IntroductionSeveral studies have shown that residents of urban informal settlements/slums are usually excluded and marginalised from formal social systems and structures of power leading to disproportionally worse health outcomes compared to other urban dwellers. To promote health equity for slum dwellers, requires an understanding of how their lived realities shape inequities especially for young children 0–4 years old (ie, under-fives) who tend to have a higher mortality compared with non-slum children. In these proposed studies, we aim to examine how key Social Determinants of Health (SDoH) factors at child and household levels combine to affect under-five health conditions, who live in slums in Bangladesh and Kenya through an intersectionality lens.Methods and analysisThe protocol describes how we will analyse data from the Nairobi Cross-sectional Slum Survey (NCSS 2012) for Kenya and the Urban Health Survey (UHS 2013) for Bangladesh to explore how SDoH influence under-five health outcomes in slums within an intersectionality framework. The NCSS 2012 and UHS 2013 samples will consist of 2199 and 3173 under-fives, respectively. We will apply Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy approach. Some of SDoH characteristics to be considered will include those of children, head of household, mothers and social structure characteristics of household. The primary outcomes will be whether a child had diarrhoea, cough, fever and acute respiratory infection (ARI) 2 weeks preceding surveys.Ethics and disseminationThe results will be disseminated in international peer-reviewed journals and presented in events organised by the Accountability and Responsiveness in Informal Settlements for Equity consortium and international conferences. Ethical approval was not required for these studies. Access to the NCSS 2012 has been given by Africa Population and Health Center and UHS 2013 is freely available.

IntroductionPeople living in slums face several challenges to access healthcare. Scarce and low-quality public health facilities are common problems in these communities. Costs and prevalence of catastrophic health expenditures (CHE) have also been reported as high in studies conducted in slums in developing countries and those suffering from chronic conditions and the poorest households seem to be more vulnerable to financial hardship. The COVID-19 pandemic may be aggravating the economic impact on the extremely vulnerable population living in slums due to the long-term consequences of the disease. The objective of this review is to report the economic impact of seeking healthcare on slum-dwellers in terms of costs and CHE. We will compare the economic impact on slum-dwellers with other city residents.Methods and analysisThis scoping review adopts the framework suggested by Arksey and O’Malley. The review is part of the accountability and responsiveness of slum-dwellers (ARISE) research consortium, which aims to enhance accountability to improve the health and well-being of marginalised populations living in slums in India, Bangladesh, Sierra Leone and Kenya. Costs of accessing healthcare will be updated to 2020 prices using the inflation rates reported by the International Monetary Fund. Costs will be presented in International Dollars by using purchase power parity. The prevalence of CHE will also be reported.Ethics and disseminationEthical approval is not required for scoping reviews. We will disseminate our results alongside the events organised by the ARISE consortium and international conferences. The final manuscript will be submitted to an open-access international journal. Registration number at the Research Registry: reviewregistry947.

A spatial decision support framework for identifying areas suitable for seasonal chemoprevention and quantifying the size of the population of target children and the amount of the required antimalarial tablets. A) Monthly Africa Rainfall Estimates version 2 (RFE 2.0) data from 2002–2009 at 10 × 10 km spatial resolution [NOAA 2013] were used to generate average long term monthly rainfall which are then used to define average seasonality (Section D in S1 File); B) Maps of total population are disaggregated by age structure (3 months to below 5 years; 5 years to below 10 years) using data from census and household surveys and by urban and rural using population density, night time lights and other land cover classifications (Section C in S1 File). L) The median number of transmission months was extracted for each health district from the climate based map of length of transmission (Section E in S1 File) and was multiplied by the estimated number of SMC targeted children and the 1 SP and 3 AQ tablets per child per month (Section F in S1 File).

BackgroundHaving high-quality ethnicity data alongside health records is crucial to monitor and redress ethnic inequalities in health. We assessed the quality of ethnicity coding in Scottish health datasets and its implications for assessing ethnic inequalities in severe COVID-19.MethodsWe compared ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other NHS datasets, with the 2011 Scottish Census as the ‘gold standard’. Measures of quality included the level of missingness (ethnicity missing compared to the Census) and misclassification (ethnicity miscoded compared to the Census). We examined the implications of misclassification, using age- and sex-adjusted Cox proportional hazards models to estimate the risk of severe COVID-19 (hospitalisation or death) by ethnicity using PHS-EL compared with Census coding.ResultsMisclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared to the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was high overall [30%] but was not higher among ethnic minority groups. PHS-EL data often underestimated severe COVID-19 risk compared to Census data. For example, in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared to the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL.ConclusionThe quality of ethnicity coding in Scottish health datasets is poorer among minority ethnic groups and this can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.