Читать книгу New Horizons in Modeling and Simulation for Social Epidemiology and Public Health - Daniel Kim - Страница 20

In principle, intervening on the social determinants of health should have profound effects on population health outcomes and health equity. These outcomes include the numbers of lives saved and the occurrence of disease and other morbidity outcomes such as Disability‐Adjusted Life Years (DALYs) (Murray et al. 2015). If we consider the distal nature of these social determinants in Figure 1.2, the impacts of these determinants on population health may in fact be stronger than those of proximal biological and behavioral factors at the individual level (such as smoking and high cholesterol), because upstream social determinants likely shape many of these biological and behavioral factors.

Yet what does the empirical evidence show about the impacts of social determinants of health at a population level? Drawing on studies from the public health literature, the numbers of adult deaths attributable to six social determinants of health have been estimated (Galea et al., 2011): low education, poverty, low social support, area‐level poverty, income inequality, and racial segregation. The investigators calculated summary relative risk estimates of mortality, and used prevalence estimates for each of these social determinants to estimate the associated population attributable risks (PARs, the percentage of deaths attributed to each factor), and then project the total number of deaths attributable to each social determinant in the United States. Through this approach, the authors estimated that 245 000 deaths would have taken place among Americans in the year 2000 due to low education, 176 000 deaths to racial segregation, 162 000 deaths to low social support, 133 000 deaths to individual poverty, 119 000 deaths to income inequality, and 39 000 deaths to area‐level poverty. These estimates due to social determinants of health were comparable to the total numbers of deaths due to the leading pathophysiological causes such as heart attacks (192 898 deaths), strokes (167 661 deaths), and lung cancer (155 521 deaths) (Galea et al. 2011). To further put the size of these numbers into perspective, in the year 2000, it was estimated that smoking resulted in 269 655 deaths among men and 173 940 deaths among women in the United States (Centers for Disease Control and Prevention (CDC) 2008).

In another study, Krueger et al. (2015) estimated the mortality attributable to education under three hypothetical scenarios: (i) individuals having less than a high school degree, (ii) individuals having some college education but not completing a bachelor's degree, and (iii) individuals having any level of education but not completing a bachelor's degree. The authors used National Health Interview Survey data (1986–2004) linked to prospective mortality through 2006 and discrete‐time survival models to derive annual attributable mortality estimates. The estimated numbers of attributable deaths were striking: 45 243 deaths in the 2010 US population were attributed to individuals having less than a high school degree rather than a high school degree; 110 068 deaths were due to individuals having some college education; and 554 525 deaths were attributed to individuals having anything less than a bachelor's degree but not a bachelor's degree (Krueger et al. 2015). The total numbers of deaths due to having less than a high school degree was similar among women and men and among non‐Hispanic Blacks and Whites and was greater for cardiovascular disease than for cancer. Overall, these estimates point to the substantial impacts that policies that increase educational opportunities could have on reducing the burden of adult mortality (Krueger et al. 2015).

Using nationally‐representative data, Kim (2016) estimated the impacts of state and local spending on welfare and education on the risks of dying from major causes. Each additional $250 per capita spent on welfare predicted a 3‐percentage point lower probability of dying from any cause, and each additional $250 per capita spent on welfare and education predicted a 1.6‐percentage point lower probability and a nearly 1‐percentage point lower probability of dying from coronary heart disease (CHD). To put such numbers into context, these changes are on the order of reductions achieved through treating a patient with high blood pressure or cholesterol—representing clinically meaningful changes (Kim 2016).

In a cross‐national study that implemented IV analysis to enhance causal inference, Kim et al. (2011) further estimated the population health impacts of raising social capital across 40 countries. Among those aged 15–74 years in 40 nations with at least 40% of the country trusting of others, raising country percentages of social trust by 20 percentage points in countries with at least 30% of a country’s citizens trusting of others and by 10 percentage points in countries with 30–40% average country trust was predicted to avert nearly 287 000 deaths per year.

Finally, Kondo et al. (2009) conducted a meta‐analysis of cohort studies including roughly 60 million participants in which people living in regions with high‐income inequality had an excess risk for premature mortality independent of their SES, age, and sex. The estimated excess mortality risk was 8% for each 0.05 unit increase in the Gini coefficient (a common measure of income inequality theoretically ranging from 0, representing perfect equality, to 1, corresponding to perfect inequality). While this excess risk appears modest, all of society is exposed to income inequality, such that the aggregate effects can be significant (Kondo et al. 2009). The authors estimated that if the inequality–mortality relation is truly causal, more than 1.5 million deaths (9.6% of total adult mortality in the 15–60 age group) could be averted in 30 OECD countries by reducing the Gini coefficient to below the threshold value of 0.3 (Kondo et al. 2009).

Notably, according to Figure 1.2, there should also be substantial impacts of intervening on the social determinants of health on health inequities across population groups, as defined along social axes such as gender, race/ethnicity, and SES. For example, government spending on public assistance programs (e.g. Aid to Families with Dependent Children) and tax credit programs (e.g. the Earned Income Tax Credit) should reduce income disparities between the rich and the poor and thereby reduce associated gaps in health, since income is a strong determinant of health and disease.

As the U.S. National Academy of Sciences panel concluded in its report, if the United States fails to address its growing health disadvantage in the near future, it will lag even further behind comparable countries in life expectancy and across a wide range of other population health outcomes. By adversely affecting the productivity of the workforce through worse population health, the economy of the United States would also continue to suffer, whereas other countries would continue to reap the economic benefits of having healthier populations. Because of how much is at stake, the panel concluded that it would hence be at the United States' peril that it continue to ignore its growing health disadvantage (National Research Council and Committee on Population 2013). Meanwhile, other countries will still need to maintain their efforts on addressing the social determinants of health if they wish to sustain and/or improve their relative standings in the Health Olympics.

Overall, the findings summarized in this chapter make a strong case for intervening at the policy level on social determinants to improve population health and reduce population health inequities. It is also clear that much more empirical evidence is needed if we wish to establish the population health impacts of the social determinants of health. These evidence gaps include estimates of the effects of social determinants of health on the incidence of diseases and on morbidity outcomes such as DALYs; the estimated population‐wide health impacts of intervening on the social determinants of health through scaled‐up interventions and policies; and economic evaluations (e.g. cost‐effectiveness) of such interventions.

In the next chapter, we move beyond traditional analytic approaches to provide a rationale for the use of systems science methods. In particular, we introduce two major sets of analytical tools for modeling and simulating impacts of the social determinants of health: agent‐based modeling and microsimulation models. These two novel system science tools and their growing applications in social epidemiology and public health form the primary substance of this book.