Comprehensive analyses of geographical inequalities in cardiovascular disease incidence and survival: A possible aid to rationalize prevention strategies

Kjærulff et al. present a comprehensive analysis of geographical inequalities in survival after acute myocardial infarction (AMI) in Denmark in the time period 2004–2014. They have previously published an analogous study on the incidence of AMI in the same setting. We acknowledge that studies of geographical inequalities in cardiovascular disease (CVD) incidence and survival are challenging. Nevertheless, such studies can help to target areas with a heavy CVD burden and/ or an unfavourable mortality rate among incident cases, which, in turn, may enable the rational allocation of prevention resources. With this perspective in mind, we comment on the solid contributions by Kjærulff et al., who have dealt with some of the challenges in a commendable way. It is a complex task to gain knowledge about the underlying causes of geographical inequalities in CVD incidence and survival after a life-threatening CVD event. Several factors may vary geographically: environmental exposures that affect risk; the neighbourhood context, which influences people’s ability to adopt a healthy lifestyle; access to preventive healthcare and screening services; and expected time to an emergency medical service (EMS). Researchers have explored ways to find evidence for the causal links between neighbourhood context and health and various theories, concepts and methods have evolved. It is a complex task to gain an understanding of such causal mechanisms. Notwithstanding such challenges, geographically linked information on CVD incidence and mortality can have a direct impact on prevention strategies. Area-level data on the factors that should be used to identify disadvantaged areas are crucial (Figure 1). Kjærulff et al. focused on sociodemographic data. They considered four dimensions of the neighbourhood sociodemographic environment in which people live: neighbourhood socioeconomic position; ethnic composition; population density; and population turnover. A key feature of their analyses was the assessment of geographical variation beyond the neighbourhood-level and individual-level sociodemographic characteristics. However, geographically linked data on factors reflecting accessibility to healthcare and EMS were lacking. Basic information about the geographical locations of relevant services may provide further insights. For example, in a study on geographical and sociodemographic disparities in the uptake of population-based screening for atrial fibrillation in people aged 75 years, we noted that the screening uptake was negatively affected if these elderly people had a long distance to travel from home to the clinic they needed to visit for the screening. In their study on survival after AMI, Kjærulff et al. considered immediate fatality, defined as death on the same day as the AMI event, as well as mortality 1–28 and 29–365 days after admission to hospital. They found more marked geographical inequalities in immediate fatality than in mortality conditional on surviving the critical initial phase or longer. This finding merits further analyses. One concern is the possible misclassification of AMI in the cases of immediate fatality that were not examined clinically, suggesting a sensitivity analysis that evaluates the potential misclassification of the registered causes of death for the out-of-hospital deaths. Furthermore, an analysis of geographical disparities in the expected time to an EMS would be helpful. Even more desirable would be an analysis of different phases of the ischaemic time period: patient’s delay; EMS delay; and time to percutaneous coronary intervention. However, register data on patient’s delay