Impact of sociodemographic and economic determinants of health on COVID-19 infection: incidence variation between reference periods

OBJECTIVES

The COVID-19 pandemic hit Portugal in March 2020, causing widespread disruption to various aspects of society. While extensive research has been conducted on the significance of socio-economic disparities in infection risk, this study aims to enhance our understanding of their evolving relationship over time by analysing four distinct periods in 2020.

STUDY DESIGN AND METHODS

This retrospective observational ecological study included individuals residing in the Primary Healthcare Cluster areas of Almada-Seixal and Western Lisbon and Oeiras, who tested positive for SARS-CoV-2 through a polymerase chain reaction (PCR) test between the 2nd of March and the 8th of November of 2020. Using incidence rates for each specific neighbourhood (n = 29) and period, we explored the relationship between neighbourhood-level socio-economic variables and the risk of infection using negative-binomial regression models.

RESULTS

In the analysed period, a total of 8562 confirmed COVID-19 cases were identified. Overall incidence rates for each period were sequentially 2.74, 5.03, 3.99 and 14.29 COVID-19 cases per 100,000 person-days. Housing overcrowding, illiteracy rate and place of birth were associated with increased risk of infection, while age, congregate living, and employment in the secondary sector exhibited the opposite association. No association was consistent across all time periods.

CONCLUSIONS

Our findings support the idea that the influence of socio-economic determinants of health is not immutable throughout time. In a pandemic context where information, knowledge, beliefs, and behaviours are ever-changing and evolving, a dynamic, inclusive, and adaptable approach to disease control can lead to a more equitable distribution of improved outcomes, benefiting all strata of society.

Migrant integration policies and health inequalities in Europe

BACKGROUND

Research on socio-economic determinants of migrant health inequalities has produced a large body of evidence. There is lack of evidence on the influence of structural factors on lives of fragile groups, frequently exposed to health inequalities. The role of poor socio-economic status and country level structural factors, such as migrant integration policies, in explaining migrant health inequalities is unclear. The objective of this paper is to examine the role of migrant socio-economic status and the impact of migrant integration policies on health inequalities during the recent economic crisis in Europe.

METHODS

Using the 2012 wave of Eurostat EU-SILC data for a set of 23 European countries, we estimate multilevel mixed-effects ordered logit models for self-assessed poor health (SAH) and self-reported limiting long-standing illnesses (LLS), and multilevel mixed-effects logit models for self-reported chronic illness (SC). We estimate two-level models with individuals nested within countries, allowing for both individual socio-economic determinants of health and country-level characteristics (healthy life years expectancy, proportion of health care expenditure over the GDP, and problems in migrant integration policies, derived from the Migrant Integration Policy Index (MIPEX).

RESULTS

Being a non-European citizen or born outside Europe does not increase the odds of reporting poor health conditions, in accordance with the "healthy migrant effect". However, the country context in terms of problems in migrant integration policies influences negatively all of the three measures of health (self-reported health status, limiting long-standing illnesses, and self-reported chronic illness) in foreign people living in European countries, and partially offsets the "healthy migrant effect".

CONCLUSIONS

Policies for migrant integration can reduce migrant health disparities.

Frailty prevalence and associated factors in the Mexican health and aging study: A comparison of the frailty index and the phenotype

BACKGROUND

Frailty is a relatively new phenomenon described mainly in the older population. There are a number of different tools that aim at categorizing an older adult as frail. Two of the main tools for this purpose are the Fried's frailty phenotype (FFP) and the frailty index (FI). The aim of this report is to determine the prevalence of frailty and associated factors using both FFP and the FI.

METHODS

Secondary analysis of 1108 individuals aged 60 or older is participating in the third (2012) wave from the Mexican Health and Aging Study (MHAS). The FFP and the FI were constructed and a set of variables from different domains were used to explore associations. Domains included were: socio-demographic, health-related, and psychological factors. Regarding prevalence, concordance was tested with a kappa statistic. To test significant associations when classifying with each of the tools, multiple logistic regression models were fitted.

RESULTS

Mean (SD) age was 69.8 (7.6) years, and 54.6% (n=606) were women. The prevalence of frailty with FFP was 24.9% (n=276) while with FI 27.5% (n=305). Kappa statistics for concordance between tools was 0.34 (p<0.001). Age, years in school, number of past days in bed due to health problems, number of times that consulted a physician last year for health problems, having smoked in the past, and life satisfaction were associated with frailty when using any of the tools.

CONCLUSIONS

There is a persistent heterogeneity on how frailty is measured that should be addressed in future research.

Developing empirically supported theories of change for housing investment and health

The assumption that improving housing conditions can lead to improved health may seem a self-evident hypothesis. Yet evidence from intervention studies suggests small or unclear health improvements, indicating that further thought is required to refine this hypothesis. Articulation of a theory can help avoid a black box approach to research and practice and has been advocated as especially valuable for those evaluating complex social interventions like housing. This paper presents a preliminary theory of housing improvement and health based on a systematic review conducted by the authors. Following extraction of health outcomes, data on all socio-economic impacts were extracted by two independent reviewers from both qualitative and quantitative studies. Health and socio-economic outcome data from the better quality studies (n = 23/34) were mapped onto a one page logic models by two independent reviewers and a final model reflecting reviewer agreement was prepared. Where there was supporting evidence of links between outcomes these were indicated in the model. Two models of specific improvements (warmth & energy efficiency; and housing led renewal), and a final overall model were prepared. The models provide a visual map of the best available evidence on the health and socio-economic impacts of housing improvement. The use of a logic model design helps to elucidate the possible pathways between housing improvement and health and as such might be described as an empirically based theory. Changes in housing factors were linked to changes in socio-economic determinants of health. This points to the potential for longer term health impacts which could not be detected within the lifespan of the evaluations. The developed theories are limited by the available data and need to be tested and refined. However, in addition to providing one page summaries for evidence users, the theory may usefully inform future research on housing and health.

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Theories of health impacts of complex interventions are valuable but poorly developed.

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Including socio-economic data may explain why health impacts are observed or not.

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A theory of healthy housing improvement was prepared using systematic review data.

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Quantitative and qualitative data on health and socio-economic impacts were used.

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Warmth, affordability & suitable space provision may mediate health impacts of housing.