1. Evidence from California County Departments of Public Health How Effective are Public Health Departments at Improving Health Status and Preventing Mortality? Evidence from California County Departments of Public Health Timothy T. Brown, PhD School of Public Health University of California, Berkeley 10 th Annual Public Health Finance Roundtable November 16, 2014

2. Funding Robert Wood Johnson Foundation Public Health Services and System Research ID Number 67617

3. Roadmap  What do Departments of Public Health do?  Prevention takes time  How do we figure this out?  Results  Limitations

10. Prevention Takes Time  Prevention will usually affect health status before mortality with very short or no lag  It generally takes at least a decade for changes in health status to fully impact mortality rates  Models need to incorporate lag times to account for the overall impact of public health spending on mortality

12. Prevention Takes Time

13. How do we figure this out?  Short-term and long-term relationships  Lags are likely, possibly extended lags  Requires panel data

14. Koyck Distributed Lag Model  Flexible lag structure – based on fit to data  Instrumental variables to obtain causal estimates (correct reverse causation, measurement error, omitted variable bias).

15. Koyck Distributed Lag Model

16. Results – Overall Pattern  Model analyzing self-rated health finds approximately 200,000 improve their health immediately – CAUSAL EFFECT  Over a decade 26,937 lives per year are saved (about 14%) – CAUSAL EFFECT  Thus, with every round of funding, approximately 200,000 improve their health status. Of these 200,000, approximately 27,000 do not die who otherwise would have.

17. Results – Overall Pattern  Average long-run impact  9.1 lives saved per 100,000 for every $10 per capita invested.  Cost per life saved: $109,514 (limited societal perspective of public health agencies – does not include costs to individuals using programs – e.g., cost of any lifestyle changes)  26,937 lives per year

18. Discussion - Comparisons  Cost per life saved: $109,514  Flu-vaccine for adults over age 50 $35,000 per life saved  Mammography $100,000 per life saved  Higher nurse-to-patient ratio $136,000 to $449,000 per life saved  Mandated mental health insurance $1.3 million per life saved

19. Discussion – Overall Pattern  Results can be expressed differently - Can value statistical lives ($7.9 million – EPA) - Can include the value of change in health status from other research - Using the above information and adding additional information, results can be expressed in cost- benefit terms or “societal ROI” terms

20. Limitations  Only valued mortality, value of improved health status is not included (societal benefits are underestimated)  Longer panel of data may yield different results  California population is racially diverse and culturally distinct, which may limit external validity

21. Publications Brown, TT. (2014). How Effective are Health Departments at Preventing Mortality? Economics and Human Biology 13, 34-45. (Released online in 2013). PHSR Article of the Year Brown TT, Publications Brown, TT. (2014). How Effective are Health Departments at Preventing Mortality? Economics and Human Biology 13, 34-45. (Released online in 2013). PHSR Article of the Year Brown TT, Martinez-Gutierrez MS, Navab B. (2014). The Impact of Changes in County Public Health Expenditures on General Health in the Population. Health Economics, Policy and Law 9, 251-269.

22. Thank You!

23. Econometric Estimation  Generalized method of moments  Unit root test  Clustered standard errors (by county)  Lewbel instrumental variables  Weak instrument test  Underidentification test  Overidentification test

27. Data (2001-2008)  California Department of Health Services  California State Controller’s Office: Counties Annual Report  U.S. Census (estimates)  U.S. Bureau of Economic Analysis  RAND  HealthLeaders-InterStudy  San Francisco County, Alpine County omitted  56 counties x 8 years = 448 observations

28. Plenty of within-county variation  All-cause mortality per 100,000  Within-county standard deviation: 12.11 to 190.97 (median: 34.04)  Public health expenditures per capita  Within-county standard deviation: $2.10 to $92.10 (median: $8.17)

29. Koyck Distributed Lag Model y = all-cause mortality per 100,000 x = public health expenditures per capita k = vector of private insurance, Medicare, Medicaid, proportion of population by age, proportion of population by race/ethnicity, crime index, relative per capita income, unemployment, education proxies, population density f = year fixed effects

30. Koyck Distributed Lag Model