Social inequalities in mild and severe myocardial infarction: how large is the gap in health expectancies?

Abstract

Background Acute myocardial infarction (MI) remains a frequent health event and a major contributor to long-term impairments globally. So far, research on social inequalities in MI incidence and mortality with respect to MI severity is limited. Furthermore, evidence is lacking on disparities in the length of life affected by MI. This study investigates social inequalities in MI incidence and mortality as well as in life years free of MI and affected by the consequences of mild or severe MI. Methods The study is based on data of a large German statutory health insurance provider covering the years 2008 to 2017 (N = 1,253,083). Income inequalities in MI incidence and mortality risks and in life years with mild or severe MI and without MI were analysed using multistate analyses. The assessment of MI severity is based on diagnosed heart failure causing physical limitations. Results During the study period a total of 39,832 mild MI, 22,844 severe MI, 276,582 deaths without MI, 15,120 deaths after mild MI and 16,495 deaths after severe MI occurred. Clear inequalities were found in MI incidence and mortality, which were strongest among men and in severe MI incidence. Moreover, substantial inequalities were found in life years free of MI in both genders to the disadvantage of those with low incomes and increased life years after mild MI in men with higher incomes. Life years after severe MI were similar across income groups. Conclusions Social inequalities in MI incidence and mortality risks led to clear disparities in the length of life free of MI with men with low incomes being most disadvantaged. Our findings stress the importance of primary and secondary prevention focusing especially on socially disadvantaged groups.

Socioeconomic inequalities in lung cancer – a time trend analysis with German health insurance data

“…While it had been shown that the age and sex distribution of the insurance population is comparable to those of the total German population, individuals with low income and lower occupational position are overrepresented [36]. More detailed information on data characteristics can be found in previous studies [37][38][39][40]. The analyses were performed for all individuals aged 20 and older.…”

“…The income information contained in the data is based on the individual annual income reported to the insurer by the employer (working population) or by the Federal Pension Fund (retired population) [37][38][39][40]. Self-employed persons were also included in the analyses, as their insurance contributions also depend on their gross earnings [37][38][39][40].…”

“…The income information contained in the data is based on the individual annual income reported to the insurer by the employer (working population) or by the Federal Pension Fund (retired population) [37][38][39][40]. Self-employed persons were also included in the analyses, as their insurance contributions also depend on their gross earnings [37][38][39][40]. As in previous studies, we defined income in relation to the German average income of a given year and adjusted it for inflation, which allows direct comparability over time as the purchasing power is kept constant [37][38][39][40].…”

“…Self-employed persons were also included in the analyses, as their insurance contributions also depend on their gross earnings [37][38][39][40]. As in previous studies, we defined income in relation to the German average income of a given year and adjusted it for inflation, which allows direct comparability over time as the purchasing power is kept constant [37][38][39][40]. Individuals were classified into three income groups according to their relative income level: Individuals with less than 60% of the German average income were assigned to the low, with 60 to 80% to the middle, and with more than 80% to the higher-income group [38].…”

“…Social inequalities in incidence risks were estimated using a two-stage approach, which has been well applied in previous studies (e.g. [37,39,40]). First, general inequalities in LC incidence risks among men and women were estimated by combining the data of the three periods and fitting parametric exponential proportional hazard regression models with constant baseline hazard over time.…”