Inequalities after STEMI in National Health Service: is there really a postcode lottery?. (25th November 2020) - Ferreira, C; Baptista, R; Ribeiro, A.I; Freitas, A; Ferreira, J.A; Milner, J; Martinho, A; Almeida, J.P; Goncalves, V; Campos, G; Rosa, J; Goncalves, F; et al.
Abstract: Background and purpose: Failure to address the impact of social determinants of health attenuates efficacy of proven prevention recommendations, namely because important considerations related to socioeconomic disadvantage are not captured by existing cardiovascular disease (CDV) risk stratification methods. We aimed to assess how socioeconomic determinants influence recurrent MI and all-cause death after myocardial infarction (MI) in Portugal. Methods: We conducted a retrospective, observational cohort study, including all patients with a ST-elevation MI (STEMI) admitted to and discharged alive from an intensive cardiac care unit between 2004 and 2017 (n=1809). The median (interquartile range) follow-up was 6 (4–9) years. We used survival models to assess the relationship between their municipal (i) income by purchasing power per capita (PPC), (ii) geographical accessibility to health care, (iii) illiteracy, iv) residential socioeconomic deprivation and recurrent MI and all-cause mortality. To assess residential socioeconomic deprivation, each individual's residential postcode was matched to the recently validated Portuguese version of European Deprivation Index (EDI). The index was categorized into quintiles (Q1-least deprived to Q5-most deprived). Results: The mean age was 64±14 years; 74% were male. Regarding individual socioeconomic variables, PPC (HR 1.19; 95% CI 0.97–1.47 for Tertile 1 vs Tertile 2; HR 1.28; 95% CI 1.04–1.56 for Tertile 1 vs Tertile 3 and HR 1.07; 95% CI 0.85–1.34 for Tertile 2 vs Tertile 3) and medical appointments in primary health centers per inhabitant (HR 0.90; 95% CI 0.75–1.09 for Tertile 1 vs Tertile 2; HR 1.23; 95% CI 0.95–1.61 for Tertile 1 vs Tertile 3 and HR 1.37; 95% CI 1.06–1.76 for Tertile 2 vs Tertile 3) were predictors of all-cause mortality, but not recurrent MI; however, in multivariate analysis adjusted for sex, age and ejection fraction, this association was no longer significant (HR 1.00; 95% CI 0.99–1.00 and, HR 1.00; 95% CI 0.89–1.17, respectively). Additionally, no evident association between illiteracy and all-cause mortality or MI was present. Concerning EDI, demographic data was similar among the quintiles (Table 1). Although EDI quintiles were not associated with all-cause mortality (HR 1.17; 95% CI 0.82–1.66 for Q5 vs Q1), the EDI was an independent predictor of recurrent MI (Figure 1). On multivariate analysis, adjusted for age, sex, hypertension, diabetes and LDL cholesterol, the HR for the most deprived (Q5) to the least deprived (Q1) quintile was 1.91 (95% CI 1.05–3.49) for MI. Conclusions: Our study shows clear socioeconomic differentials in cardiovascular outcomes in patients with STEMI which suggests that accounting for socioeconomic deprivation might improve risk prediction and therefore disease prognosis. Funding Acknowledgement: Type of funding source: None