Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions

Abstract

Reducing health inequalities has become a major public health priority internationally. However, how best to achieve this goal is not well understood. Population health intervention research has the potential to address some of this knowledge gap. This review argues that simulation studies can produce unique evidence to build the population health intervention research evidence base on reducing social inequalities in health. To this effect, the advantages of using simulation models over other population health intervention research methods are discussed. Key questions regarding the potential challenges of developing simulation models to investigate population health intervention research on reducing social inequalities in health and the types of population health intervention research questions that can be answered using this methodology are reviewed. We use the example of social inequalities in coronary heart disease to illustrate how simulation models can elucidate the effectiveness of a number of 'what-if' counterfactual population health interventions on reducing social inequalities in coronary heart disease. Simulation models are a flexible, cost-effective, evidence-based research method with the capacity to inform public health policy-makers regarding the implementation of population health interventions to reduce social inequalities in health.

Keywords: Epidemiological methods; Modelling; Public Health Policy; Social Inequalities.

Figure 1

Building a simulation model to investigate the impact of population health interventions on social inequalities in coronary heart disease (CHD). (A) The selection of measures of inequalities, mediators and outcomes and the need to determine the causal relationships between variables, steps (1) and (2). Step (3) in (B) illustrates three different potential targets for population health interventions to reduce CHD, the effects of which can be evaluated and compared by running different ‘what-if’ counterfactual scenarios in the model. Step (4) in (C) represents the possibility of evaluating three different interventions for the same risk factor to evaluate which intervention will result in the greatest reduction in CHD. Interventions represented in step (4) should be parameterised to allow for different effectiveness by socioeconomic group.