Examining Health Inequalities in Latvia: A Decade of Association between Socioeconomic Position and Perceived Health Status

Understanding health inequalities in Wales using the Blinder-Oaxaca decomposition method

Background

Throughout Wales and the world, health inequality remains a problem that is interconnected with a wider and complex social, economic and environmental dynamic. Subsequently, action to tackle inequality in health needs to take place at a structural level, acknowledging the constraints affecting an individual's (or community's) capability and opportunity to enable change. While the 'social determinants of health' is an established concept, fully understanding the composition of the health gap is dependent on capturing the relative contributions of a myriad of social, economic and environmental factors within a quantitative analysis.

Method

The decomposition analysis sought to explain the differences in the prevalence of these outcomes in groups stratified by their ability to save at least £10 a month, whether they were in material deprivation, and the presence of a limiting long-standing illness, disability of infirmity. Responses to over 4,200 questions within the National Survey for Wales (n = 46,189; 2016-17 to 2019-20) were considered for analysis. Variables were included based on (1) their alignment to a World Health Organization (WHO) health equity framework ("Health Equity Status Report initiative") and (2) their ability to allow for stratification of the survey sample into distinct groups where considerable gaps in health outcomes existed. A pooled Blinder-Oaxaca model was used to analyse inequalities in self-reported health (fair/poor health, low mental well-being and low life satisfaction) and were stratified by the variables relating to financial security, material deprivation and disability status.

Results

The prevalence of fair/poor health was 75% higher in those who were financially insecure and 95% higher in those who are materially deprived. Decomposition of the outcome revealed that just under half of the health gap was "explained" i.e., 45.5% when stratifying by the respondent's ability to save and 46% when stratifying by material deprivation status. Further analysis of the explained component showed that "Social/Human Capital" and "Income Security/Social Protection" determinants accounted the most for disparities observed; it also showed that "Health Services" determinants accounted the least. These findings were consistent across the majority of scenarios modeled.

Conclusion

The analysis not only quantified the significant health gaps that existed in the years leading up to the COVID-19 pandemic but it has also shown what determinants of health were most influential. Understanding the factors most closely associated with disparities in health is key in identifying policy levers to reduce health inequalities and improve the health and well-being across populations.

Mortality Related to Cold Temperatures in Two Capitals of the Baltics: Tallinn and Riga

Background and objectives: Despite global warming, the climate in Northern Europe is generally cold, and the large number of deaths due to non-optimal temperatures is likely due to cold temperatures. The aim of the current study is to investigate the association between cold temperatures and all-cause mortality, as well as cause-specific mortality, in Tallinn and Riga in North-Eastern Europe. Materials and Methods: We used daily information on deaths from state death registries and minimum temperatures from November to March over the period 1997-2015 in Tallinn and 2009-2015 in Riga. The relationship between the daily minimum temperature and mortality was investigated using the Poisson regression, combined with a distributed lag non-linear model considering lag times of up to 21 days. Results: We found significantly higher all-cause mortality owing to cold temperatures both in Tallinn (Relative Risk (RR) = 1.28, 95% Confidence Interval (CI) 1.01-1.62) and in Riga (RR = 1.41, 95% CI 1.11-1.79). In addition, significantly increased mortality due to cold temperatures was observed in the 75+ age group (RR = 1.64, 95% CI 1.17-2.31) and in cardiovascular mortality (RR = 1.83, 95% CI 1.31-2.55) in Tallinn and in the under 75 age group in Riga (RR = 1.58, 95% CI 1.12-2.22). In this study, we found no statistically significant relationship between mortality due to respiratory or external causes and cold days. The cold-related attributable fraction (AF) was 7.4% (95% CI -3.7-17.5) in Tallinn and 8.3% (95% CI -0.5-16.3) in Riga. This indicates that a relatively large proportion of deaths in cold periods can be related to cold in North-Eastern Europe, where winters are relatively harsh.