New Horizons in Modeling and Simulation for Social Epidemiology and Public Health. Daniel Kim
help address the social determinants of health at a government level, in 2010, the WHO and the Government of South Australia (2010) developed the HiAP approach through the Adelaide Statement on HiAP. In this comprehensive population health strategy, health considerations in policymaking permeate and encompass multiple public sectors that may influence health, such as transportation, agriculture, housing and urban development, and education (Figure 1.4). The HiAP approach was founded on the notion that many social determinants of health are outside the purview of public health agencies. The roots of this radical approach can be traced back to the seminal ideas put forth in the Alma Ata Declaration on Primary Health Care (1978) and the Ottawa Charter for Health Promotion (1986). The HiAP approach became reinforced in the 2011 Rio Political Declaration on Social Determinants of Health (World Health Organization 2016a).
Figure 1.4 Examples of multiple public sectors collectively adopting a Health in All Policies (HiAP) approach.
The HiAP approach has been increasingly adopted in jurisdictions around the world. For example, the Department of Housing and Urban Development (HUD) in the United States has embraced a HiAP approach and is collaborating with the U.S. Department of Health and Human Services (HHS) to ensure the integration of the elderly and disabled into the community via housing and human service agencies to enable them to live as long and as healthily as possible (Bostic et al. 2012). HUD further encourages applicants to regional planning and neighborhood initiative grants to incorporate health metrics into their baseline assessments of neighborhoods and asks them to indicate how they will support regional planning efforts that consider public health impacts (Bostic et al. 2012). Moreover, to attain objectives on the social determinants of health, the HiAP approach has been encouraged by Healthy People 2020 (2010), the U.S. Centers for Disease Control and Prevention initiative that establishes national goals and objectives for policy, programs, and activities to address the major health challenges facing our country today. The Secretary's Advisory Committee on Healthy People Objectives for 2020 (Office of Disease Prevention and Health Promotion 2010) has further advised that all federal agencies (e.g. the Departments of Education, Transportation, and HUD) should be required to include Healthy People in their strategic plans.
In 2010, the US state of California created a HiAP Task Force, with representation of 19 state agencies, offices, and departments. Employing a HiAP framework, this statewide effort brought policymakers together to identify and recommend programs, policies, and strategies to improve health, including multiagency initiatives addressing transportation, housing, affordable healthy foods, safe neighborhoods, and green spaces. Additional recommendations included the development of health criteria in the discretionary funding review process and incorporating health issues into statewide data collection and survey efforts (Health in All Policies Task Force 2010).
The region of South Australia has also implemented the HiAP approach. Its HiAP model is based on the twin pillars of central governance and accountability and a “health lens” analysis process, which aims to identify key interactions and synergies between South Australia's Strategic Plan (SASP) targets, policies, and population health (Kickbusch and Buckett 2010). Notably, it was in Adelaide, the capital of South Australia, that the 2010 Adelaide Statement of HiAP was first developed. The South Australian Public Health Act was developed during the early implementation stages of HiAP and provided a legislative mandate to allow HiAP approaches to be systematically adopted across state and local governments within the region (Delany et al. 2015).
To strengthen the overall accountability for the HiAP pledges made by countries in the 2011 Rio Political Declaration on Social Determinants of Health, the WHO is currently developing a global monitoring system for intersectoral interventions on the social determinants of health to improve health equity (World Health Organization 2016b).
1.5 Conventional Approaches to Studying the Social Determinants of Health
Randomized experiments are the gold standard of study designs to establish cause‐and‐effect relationships. Yet, it is often neither feasible nor ethical to conduct experiments that randomly assign people or places to different levels of social determinants of health. As a result, evidence on the impacts of the social determinants of health has been largely based on observational studies, i.e. ecological, cohort, case–control, and cross‐sectional studies. Within such observational studies, traditional epidemiological approaches for studying the impacts of social determinants of health include multivariate analysis, which controls for factors that predict both the social determinants and health outcomes, i.e. so‐called potential “confounders.”
In addition, studies have explored these relationships by testing for single or multiple factors as potential mediators of the population health impacts of social determinants that could lend plausibility to the presence of causal associations. Because such social determinants are often contextual or area‐based factors (e.g. factors at the neighborhood or regional level), multilevel models that incorporate the hierarchical structure of data—such as individuals living within neighborhoods or states—are used to account for similarities and statistical nonindependence of individuals living within the same geographical areas (Goldstein et al. 2002).
1.6 Novel Approaches to Strengthen Causal Inference in Studying the Social Determinants of Health
A growing body of literature is attempting to reduce alternative explanations and other sources of bias in nonexperimental studies on the social determinants of health and more generally within public health. These novel approaches to strengthen causal inference include but are not limited to instrumental variable (IV) analysis, fixed effects analysis, propensity score analysis, inverse probability weighting, and natural experiments. By isolating random variation in the exposure, IV analysis can yield unbiased estimates of the causal association between an exposure and outcome, including through reducing attenuation bias due to measurement error and confounding bias due to both observed and unobserved factors (Kim 2016). Such approaches are increasingly being used to evaluate the causal roles of risk factors in public health, including obesity, neighborhood conditions, the social environment, and state policies (Davey Smith et al. 2009; Fish et al. 2010; Kim et al. 2011; Mojtabai and Crum 2013; Hawkins and Baum 2014; Kim 2016).
Similar to multivariable regression, propensity score analysis can control for imbalances between comparison groups and can thereby control for confounding. It has the advantage of being more efficient than traditional regression when there are relatively fewer events (Cepeda et al. 2003). However, like multivariable regression, propensity score analysis cannot control for unobserved or unmeasured confounders. Inverse probability weighting has also been used as an approach to estimate the counterfactual or potential outcome if all subjects were assigned to either exposure/treatment (Mansournia and Altman 2016). Finally, natural experiments or other quasi‐experimental designs such as regression discontinuity designs (Moscoe et al. 2015) can exploit random variation in exposures as in an experimental study and can thereby minimize confounding due to both observed and unobserved factors as a source of bias.
Results from individual studies can also be qualitatively reviewed in aggregate to identify existing gaps in methodological approaches, potential sources of bias, and similarities/differences in their results. Results across studies can be quantitatively summarized in meta‐analyses that yield overall point estimates of exposure–outcome associations, although, importantly, such estimates are only as good as the quality of the studies that are included in the meta‐analyses (Egger et al. 2001).
1.7 What Do We Know About the Social Determinants of Health?
As Bambra et al. (2010) have noted, there are clear limitations to the existing evidence based on the social determinants of health. First, observational studies that dominate the literature can only hint at possible