Dataset Information

Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study.

ABSTRACT:

Background

Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might be different across SES in Portugal. This work identifies the coronavirus disease 2019 (COVID-19) worst-affected municipalities at four different time points in Portugal measured by prevalence of cases, and seeks to determine if these worst-affected areas are associated with SES.

Methods

The worst-affected areas were defined using the spatial scan statistic for the cumulative number of cases per municipality. The likelihood of being in a worst-affected area was then modelled using logistic regressions, as a function of area-based SES and health services supply. The analyses were repeated at four different time points of the COVID-19 pandemic: 1 April, 1 May, 1 June, and 1 July, corresponding to two moments before and during the confinement period and two moments thereafter.

Results

Twenty municipalities were identified as worst-affected areas in all four time points, most in the coastal area in the Northern part of the country. The areas of lower unemployment were less likely to be a worst-affected area on the 1 April [adjusted odds ratio (AOR) = 0.36 (0.14-0.91)], 1 May [AOR = 0.03 (0.00-0.41)] and 1 July [AOR = 0.40 (0.16-1.05)].

Conclusion

This study shows a relationship between being in a worst-affected area and unemployment. Governments and public health authorities should formulate measures and be prepared to protect the most vulnerable groups.

SUBMITTER: Alves J

PROVIDER: S-EPMC7989252 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study.

Alves Joana J Soares Patrícia P Rocha João Victor JV Santana Rui R Nunes Carla C

European journal of public health 20211001 5

<h4>Background</h4>Previous literature shows systematic differences in health according to socioeconomic status (SES). However, there is no clear evidence that the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection might be different across SES in Portugal. This work identifies the coronavirus disease 2019 (COVID-19) worst-affected municipalities at four different time points in Portugal measured by prevalence of cases, and seeks to determine if these worst-affected areas are ...[more]

PMID: 33723606

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Project description:Hospitalizations for Ambulatory Care Sensitive Conditions (ACSC) are specific conditions for which hospitalization is thought to be avoidable through patient education, health promotion initiatives, early diagnosis and by appropriate chronic disease management, and have been shown to be greatly influenced by socioeconomic (SE) characteristics. We examined the SE inequalities in hospitalization rates for ACSC in Portugal, their evolution over time (2000-2014), and their associated financial burden.We modeled municipality-level ACSC hospitalization rates per 1000 inhabitants and ACSC hospitalization-related costs per inhabitant, for the 2000-2014 period (n = 4170), as a function of SE indicators (illiteracy and purchasing power, in quintiles), controlling for the proportion of elderly, sex, disease specific mortality rate, population density, PC supply, and time trend. The evolution of inequalities was measured interacting SE indicators with a time trend. Costs attributable to ACSC related hospitalization inequalities were measured by the predicted values for each quintile of the SE indicators.Hospitalization rate for ACSC was significantly higher in the 4th quintile of illiteracy compared with the 1st quintile (beta = 1.97; p < 0.01), and significantly lower in the 5th quintile of purchasing power, compared with the 1st quintile (beta = - 1.19; p < 0.05). ACSC hospitalization-related costs were also significantly higher in the 4th quintile of illiteracy compared with the 1st quintile (beta = 4.04€; p < 0.05), and significantly lower in the 5th quintile of purchasing power, compared with the 1st quintile (beta = - 4,69€; p < 0.01). The SE gradient significantly increased over the 2000-2014 period, and the annual cost of inequalities were estimated at more than 15 million euros for the Portuguese NHS.There was an increasing SE patterning in ACSC related hospitalizations, possibly reflecting increasing SE inequalities in early and preventive high-quality care, imposing a substantial financial burden to the Portuguese NHS.

Project description:The Joint External Evaluation (JEE) assesses national capacities to implement the International Health Regulations (IHR). Previous studies have found that higher JEE scores are associated with fewer communicable disease deaths. But given the impact of COVID-19 in many countries, including those believed to have developed IHR capacities, the validity of the JEE for pandemic preparedness has been questioned. We constructed univariable and multivariable linear regression models to investigate the relationship between JEE scores and i) deaths from communicable diseases before the pandemic and ii) deaths from COVID-19. We adjusted for country differences in age, health system access, national wealth, health expenditure, democratic governance, government restrictions, pre-pandemic tourist arrivals and testing capacity (estimated by test positivity rates). For COVID-19 deaths, we calculated cumulative deaths per 100,000 at 3, 6 and 12 months into the pandemic. A total of 91 countries were included, with a median JEE score of 50%. On multivariable linear regression the association between JEE scores and log COVID-19 deaths was significant and positive at 3 months (β 0.05, p = 0.02), becoming statistically non-significant, at 6 (β 0.02, p = 0.27) and 12 months (β -0.03, p = 0.19), while the association with log communicable disease deaths was significant and negative (β -0.03, p = 0.003). A higher Stringency Index was significantly associated with higher log COVID-19 deaths at 3 (β 0.04, p = 0.003) and 6 (β 0.04, p = 0.001) months, but not at 12 months (β 0.02, p = 0.08). Higher test positivity rates were associated with higher log COVID-19 deaths at all time points, at least partially attenuating the positive association between Stringency Index and log COVID-19 deaths. While universal health coverage indices (β -0.04 p<0.001) and international tourist arrivals were associated with log communicable disease deaths (β 0.02, p = 0.002), they were not associated with log COVID-19 deaths. Although the same tool is used to assess capacities for both epidemics and pandemics, the JEE may be better suited to small outbreaks of known diseases, compared to pandemics of unknown pathogens.

Dataset Information

Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study.

Background

Methods

Results

Conclusion

Publications

Evolution of inequalities in the coronavirus pandemics in Portugal: an ecological study.

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