Wealth, Unemployment, Social Investment, and Risk of ST-Segment Elevation Myocardial Infarction—An Ecological Analysis of a Low-Cardiovascular-Risk European Region

TL;DR

This study explores how wealth, unemployment, and social investment relate to heart attack risk in a low-cardiovascular-risk region of Spain.

Contribution

The study identifies socioeconomic factors correlated with STEMI incidence in a high-income European region.

Findings

STEMI incidence correlates positively with unemployment rates.

STEMI incidence inversely correlates with wealth indicators like income tax and energy consumption.

No significant link was found between STEMI and public investment variables.

Abstract

Objectives: A retrospective ecological study was conducted to analyze the relationship between the incidence of myocardial infarction with ST-segment elevation (STEMI) and various sociodemographic factors in municipalities within the Community of Madrid, a high-income and low-cardiovascular-risk European region. Methods: This study analyzed a database of patients registered in the regional network for STEMI care from January 2014 to December 2018. Thirty-four municipalities with populations greater than 10,000 inhabitants were included. The mean annual incidence of STEMI (iSTEMI) was estimated for each locality, and several variables of wealth, employment and social investment were obtained from public databases. Results: During the period of the study, 2561 confirmed STEMI cases were recorded in the selected localities, with an average incidence of 23 events per 100,000 inhabitants and year. The mean age was 62, with 83% of patients being male. Among municipalities included in the study, a significant direct correlation was found between iSTEMI and unemployment rate (r = 0.354, p = 0.04). A significant inverse correlation was found with all wealth-related variables, mainly with a composed deprivation (poverty) index (r = −0.624, p < 0.001) and the percentage of employees in the financial sector (r = −0.497, p = 0.003). No correlation was found between iSTEMI and the sociodemographic or public investment variables retrieved. Multiple regression analysis showed that the model best fitted when energy billed per inhabitant and mean income tax per taxpayer were introduced. Conclusions: Residents of areas with lower incomes and higher unemployment rates may be at a greater risk of STEMI. This should be taken into account when planning cardiovascular prevention and community health management.

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