How Effective are Public Health Departments at Improving Health Status and Preventing Mortality?

Evidence from California County Departments of Public Health

Timothy T. Brown, PhDSchool of Public Health

University of California, Berkeley

10th Annual Public Health Finance RoundtableNovember 16, 2014

Funding

Robert Wood Johnson Foundation

Public Health Services and System Research

ID Number 67617

Roadmap

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

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

Prevention Takes Time

How do we figure this out?

Short-term and long-term relationships

Lags are likely, possibly extended lags

Requires panel data

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).

Koyck Distributed Lag Model

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.

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

Discussion - ComparisonsCost 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

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

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

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. 

Thank You!

Econometric Estimation Generalized method of moments Unit root test Clustered standard errors (by county) Lewbel instrumental variables

Weak instrument testUnderidentification testOveridentification test

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

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 capitaWithin-county standard deviation: $2.10 to $92.10

(median: $8.17)

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

Koyck Distributed Lag Model

Koyck Distributed Lag Model