Geographic access to COVID-19 healthcare in Brazil using a balanced float catchment area approach

Pereira, Rafael H. M.; Braga, Carlos Kauê Vieira; Servo, Luciana Mendes Santos; Serra, Bernardo; Amaral, Pedro; Gouveia, Nélson; Páez, Antonio

doi:10.1016/j.socscimed.2021.113773

Cited by 68 publications

(48 citation statements)

References 38 publications

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“…This should be considered when planning hospitals and health centres in rapidly growing developing countries, since an unequal distribution of hospitals with a concentration in the central metropolitan areas is currently common [ 26 ]. Consistent with the findings of Pereira et al [ 32 ] in Brazil, our study found that there is an intense spatial inequality between downtown areas and marginal poor neighbourhoods with respect to access to PHs and PVCs. Our findings also suggest that a significant number of CTs around the city had virtually no access to COVID-19 vaccinations due to the dearth of hospitals there.…”

Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

“…A study in Brazil measured the geographic access to COVID-19 healthcare services using a balanced float CA approach and identified substantial social and spatial inequalities in access to health services during the pandemic [ 32 ]. Their findings indicated that ICU equipment availability varied considerably between cities and was substantially lower among Black communities and those of the poor [ 32 ]. Bauer et al [ 33 ] measured access to ICU beds in 14 European countries using a regional ratio of ICU beds to 100 000 population as the accessibility index and the distance to the closest ICU and arrived at high indices in Germany, Estonia and Austria, with the lowest in Sweden and Denmark.…”

Section: Introductionmentioning

confidence: 99%

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Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

et al. 2021

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Background There are only limited studies on access to COVID-19 vaccines and identifying the most appropriate health centres for performing vaccination in metropolitan areas. This study aimed to measure potential spatial access to COVID-19 vaccination centres in Mashhad, the second-most populous city in Iran. Methods The 2021 age structure of the urban census tracts was integrated into the enhanced two-step floating catchment area model to improve accuracy. The model was developed based on three different access scenarios: only public hospitals, only public healthcare centres and both (either hospitals or healthcare centres) as potential vaccination facilities. The weighted decision-matrix and analytic hierarchy process, based on four criteria (i.e. service area, accessibility index, capacity of vaccination centres and distance to main roads), were used to choose potential vaccination centres looking for the highest suitability for residents. Global Moran’s index (GMI) was used to measure the spatial autocorrelation of the accessibility index in different scenarios and the proposed model. Results There were 26 public hospitals and 271 public healthcare centres in the study area. Although the exclusive use of public healthcare centres for vaccination can provide the highest accessibility in the eastern and north-eastern parts of the study area, our findings indicate that including both public hospitals and public healthcare centres provide high accessibility to vaccination in central urban part. Therefore, a combination of public hospitals and public healthcare centres is recommended for efficient vaccination coverage. The value of GMI for the proposed model (accessibility to selected vaccination centres) was calculated as 0.53 (Z = 162.42, P < 0.01). Both GMI and Z-score values decreased in the proposed model, suggesting an enhancement in accessibility to COVID-19 vaccination services. Conclusions The periphery and poor areas of the city had the least access to COVID-19 vaccination centres. Measuring spatial access to COVID-19 vaccination centres can provide valuable insights for urban public health decision-makers. Our model, coupled with geographical information systems, provides more efficient vaccination coverage by identifying the most suitable healthcare centres, which is of special importance when only few centres are available. Graphic abstract

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Section: Discussionsupporting

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

et al. 2021

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“…Yet, previous studies have found that access to COVID-19 health services tends to be lower in less developed regions in the country, 41 42 particularly among low-income and black communities. 43 Only 25% of Brazilians have access to private healthcare via health insurance, reflecting how inequality in access to quality healthcare is largely driven by income. 44 This leaves 75% of the population solely reliant on a chronically underfunded public healthcare system, which highlights a double disadvantage for low-income and non-White populations, who are more likely to be infected and deprived of care.…”

Section: Discussionmentioning

confidence: 99%

Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil

Pereira

Prete

et al. 2021

BMJ Glob Health

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IntroductionLittle evidence exists on the differential health effects of COVID-19 on disadvantaged population groups. Here we characterise the differential risk of hospitalisation and death in São Paulo state, Brazil, and show how vulnerability to COVID-19 is shaped by socioeconomic inequalities.MethodsWe conducted a cross-sectional study using hospitalised severe acute respiratory infections notified from March to August 2020 in the Sistema de Monitoramento Inteligente de São Paulo database. We examined the risk of hospitalisation and death by race and socioeconomic status using multiple data sets for individual-level and spatiotemporal analyses. We explained these inequalities according to differences in daily mobility from mobile phone data, teleworking behaviour and comorbidities.ResultsThroughout the study period, patients living in the 40% poorest areas were more likely to die when compared with patients living in the 5% wealthiest areas (OR: 1.60, 95% CI 1.48 to 1.74) and were more likely to be hospitalised between April and July 2020 (OR: 1.08, 95% CI 1.04 to 1.12). Black and Pardo individuals were more likely to be hospitalised when compared with White individuals (OR: 1.41, 95% CI 1.37 to 1.46; OR: 1.26, 95% CI 1.23 to 1.28, respectively), and were more likely to die (OR: 1.13, 95% CI 1.07 to 1.19; 1.07, 95% CI 1.04 to 1.10, respectively) between April and July 2020. Once hospitalised, patients treated in public hospitals were more likely to die than patients in private hospitals (OR: 1.40%, 95% CI 1.34% to 1.46%). Black individuals and those with low education attainment were more likely to have one or more comorbidities, respectively (OR: 1.29, 95% CI 1.19 to 1.39; 1.36, 95% CI 1.27 to 1.45).ConclusionsLow-income and Black and Pardo communities are more likely to die with COVID-19. This is associated with differential access to quality healthcare, ability to self-isolate and the higher prevalence of comorbidities.

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“…In this context, one practical limitation can be the lack of access to spatially-explicit health data like, for instance, the geographic origin of hospitalised patients. To circumvent this issue, we here propose an analytical framework based on the definition of hospital catchment areas (HCAs), a concept that has previously been used to study the geographic access to healthcares in the context of the COVID-19 epidemic in Brazil [ 5 ]. The overall objective of our study is to present and describe an analytical framework based on the delimitation of such HCA units and that allows to analyse the dynamic of potential drivers of COVID-19 hospitalisation incidence in a spatially-explicit context.…”

Section: Introductionmentioning

confidence: 99%

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case

et al. 2021

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Background The COVID-19 pandemic is affecting nations globally, but with an impact exhibiting significant spatial and temporal variation at the sub-national level. Identifying and disentangling the drivers of resulting hospitalisation incidence at the local scale is key to predict, mitigate and manage epidemic surges, but also to develop targeted measures. However, this type of analysis is often not possible because of the lack of spatially-explicit health data and spatial uncertainties associated with infection. Methods To overcome these limitations, we propose an analytical framework to investigate potential drivers of the spatio–temporal heterogeneity in COVID-19 hospitalisation incidence when data are only available at the hospital level. Specifically, the approach is based on the delimitation of hospital catchment areas, which allows analysing associations between hospitalisation incidence and spatial or temporal covariates. We illustrate and apply our analytical framework to Belgium, a country heavily impacted by two COVID-19 epidemic waves in 2020, both in terms of mortality and hospitalisation incidence. Results Our spatial analyses reveal an association between the hospitalisation incidence and the local density of nursing home residents, which confirms the important impact of COVID-19 in elderly communities of Belgium. Our temporal analyses further indicate a pronounced seasonality in hospitalisation incidence associated with the seasonality of weather variables. Taking advantage of these associations, we discuss the feasibility of predictive models based on machine learning to predict future hospitalisation incidence. Conclusion Our reproducible analytical workflow allows performing spatially-explicit analyses of data aggregated at the hospital level and can be used to explore potential drivers and dynamic of COVID-19 hospitalisation incidence at regional or national scales.

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Geographic access to COVID-19 healthcare in Brazil using a balanced float catchment area approach

Cited by 68 publications

References 38 publications

Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

Measuring COVID-19 vaccination coverage: an enhanced age-adjusted two-step floating catchment area model

Higher risk of death from COVID-19 in low-income and non-White populations of São Paulo, Brazil

Investigating the drivers of the spatio-temporal heterogeneity in COVID-19 hospital incidence—Belgium as a study case

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