New Horizons in Modeling and Simulation for Social Epidemiology and Public Health. Daniel Kim
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.
References
1 Adema, W., Fron, P., and Ladaique, M. (2011). Is the European welfare state really more expensive?: indicators on social spending, 1980–2012; and a manual to the OECD social expenditure database (SOCX). OECD Social, Employment and Migration working papers, No. 124, OECD Publishing.
2 Bambra, C., Gibson, M., Amanda, S. et al. (2010). Tackling the wider social determinants of health and health inequalities: evidence from systematic reviews. Journal of Epidemiology and Community Health 64: 284–291.
3 Bezo, B., Maggi, S., and Roberts, W.L. (2012). The rights and freedoms gradient of health: evidence from a cross‐national study. Frontiers in Psychology 3: 441.
4 Bostic, R.W., Thornton, R.L.J., Rudd, E.C., and Sternthal, M.J. (2012). Health in all policies: the role of the US Department of Housing and Urban Development and present and future challenges. Health Affairs 31: 2130–2137.
5 Braveman, P., Egerter, S., and Williams, D.R. (2011). The social determinants of health: coming of age. Annual Review of Public Health 32: 381–398.
6 Centers for Disease Control and Prevention (CDC) (2008). Smoking‐attributable mortality, years of potential life lost, and productivity losses‐United States, 2000–2004. Morbidity and Mortality Weekly Report 57: 1226–1228.
7 Cepeda, M.S., Boston, R., Farrar, J.T., and Strom, B.L. (2003). Comparison of logistic regression versus propensity score when the number of events is low and there are multiple confounders. American Journal of Epidemiology 158 (3): 280–287.
8 Charter, O. (1986, November). Ottawa Charter for health promotion. In First International Conference on Health Promotion, pp. 17–21.
9 Chung, H. and Muntaner, C. (2006). Political and welfare state determinants of infant and child health indicators: an analysis of wealthy countries. Social Science and Medicine 63: 829–842.
10 Davey Smith, G., Sterne, J.A.C., Fraser, A. et al. (2009). The associatiodczcn between BMI and mortality using offspring BMI as an indicator of own BMI: large intergenerational mortality study. British Medical Journal 339: b5043.
11 Declaration of Alma‐Ata. (1978). Pan American Health Organization. https://www.paho.org/English/DD/PIN/alma‐ata_declaration.htm (accessed 1 July 2019).
12 Delany, T., Lawless, A., Baum, F. et al. (2015). Health in all policies in South Australia: what has supported early implementation? Health Promotion International 31 (4): 888–898.
13 Egger, M., Smith, G.D., and Sterne, J.A. (2001). Uses and abuses of meta‐analysis. Clinical Medicine 1 (6): 478–484.
14 Fish, J.S., Ettner, S., Ang, A. et al. (2010). Association of perceived neighborhood safety on body mass index. American Journal of Public Health 100: 2296–2303.
15 Fowler, K.A., Dahlberg, L.L., Haileyesus, T., and Annest, J.L. (2015). Firearm injuries in the United States. Preventive Medicine 79: 5–14.
16 Galea, S., Tracy, M., Hoggatt, K.J. et al. (2011). Estimated deaths attributable to social factors in the United States. American Journal of Public Health 101: 1456–1465.
17 Goldstein, H., Browne, W., and Rasbash, J. (2002). Multilevel modeling of medical data. Statistics in Medicine 21 (21): 3291–3315.
18 Hawkins, S.S. and Baum, C. (2014). Impact of state cigarette taxes on disparities in maternal smoking during pregnancy. American Journal of Public Health 104 (8): 1464–1470.
19 Health in All Policies Task Force (2010). Health in All Policies Task Force Report to the Strategic Growth Council Executive Summary. Sacramento: Health in All Policies Task Force.
20 Kickbusch, I. and Buckett, K. (eds.) (2010). Implementing Health in All Policies: Adelaide 2010. Adelaide: Health in All Policies Unit, Department of Health; Government of South Australia.
21 Kim, D. (2016). The associations between US state and local social spending, income inequality, and individual all‐cause and cause‐specific mortality: the National Longitudinal Mortality Study. Preventive Medicine 84: 62–68.
22 Kim, D. and Saada, A. (2013). The social determinants of infant mortality and birth outcomes in western developed nations: a cross‐country systematic review. International Journal of Environmental Research and Public Health 10: 2296–2335.
23 Kim, D., Baum, C.F., Ganz, M.L. et al. (2011). The contextual effects of social capital on health: a cross‐national instrumental variable analysis. Social Science and Medicine 73: 1689–1697.
24 Kochanek, K.D., Murphy, S.L., Xu, J.Q., and Arias, E. (2017). Mortality in the United States, 2016. NCHS Data Brief, no 293. Hyattsville, MD: National Center for Health Statistics.
25 Kondo, N., Sembajwe, G., Kawachi, I. et al. (2009). Income inequality, mortality, and self rated health: meta‐analysis of multilevel studies. British Medical Journal 339: b4471.
26 Krueger, P.M., Tran, M.K., Hummer, R.A., and Chang, V.W. (2015). Mortality attributable to low levels of education in the United States. PlOS ONE 10 (7): e0131809.
27 Mansournia, M.A. and Altman, D.G. (2016). Inverse probability weighting. BMJ 352: 189.
28 Marmot, M.G. and Bell, R. (2009). Action on health disparities in the United States: commission on Social Determinants of Health. Journal of the American Medical Association 301 (11): 1169–1171.
29 Mojtabai, R. and Crum, R.M. (2013). Cigarette smoking and onset of mood and anxiety disorders. American Journal of Public Health 103: 1656–1665.
30 Moscoe, E., Bor, J., and Bärnighausen, T. (2015). Regression discontinuity designs are underutilized in medicine, epidemiology, and public health: a review of current and best practice. Journal of Clinical Epidemiology 68 (2): 122–133.
31 Muntaner, C., Chung, H., Benach, J., and Ng, E. (2012). Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low‐ and middle‐income countries. BMC Public Health 12: 286.
32 Murray, C.J., Barber, R.M., Foreman, K.J. et al. (2015). Global, regional, and national disability‐adjusted life years (DALYs) for 306 acute diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition. The Lancet 386 (10009): 2145–2191.
33 National Center for Health Statistics (2017). Provisional counts of drug overdose deaths. https://www.cdc.gov/nchs/data/health_policy/monthly‐drug‐overdose‐death‐estimates.pdf