Evaluating the effects of
private-public partnerships in
public health1
Importance of partnerships:
public health perspective
Institute of Medicine Report The Future of the
Public’s Health in 21st Century (2001):
“Government public health agencies ….must build and
maintain partnerships with other organizations and
sectors of society, working closely with communities
and community based organizations, the health care
delivery system, academia, business”.
Sergey Sotnikov, PhD
Toby Merlin, MD
Division of Partnerships and Strategic Alliances
National Center for Health Marketing
Centers for Disease Control and Prevention
ann0@cdc.gov
1The findings and conclusions in this presentation have not been formally
disseminated by CDC and should not be construed to represent any agency
determination or policy.
Why partner: economic
perspective
Graph 1. Public Health Partnerships
(Source: NPHPSP)
Police
EMS
Community
Centers
Home Health
Corrections
Churches
MCOs
Health Department
Parks
Schools
Elected Officials
Doctors
Hospitals
Nursing Homes
Mass Transit
Philanthropist
Environmental Health
Civic Groups
CHCs
Fire
Tribal Health
Laboratory
Facilities
Drug Treatment
Mental Health
Employers
Benefits of partnerships:
„ reduce transaction costs
„ eliminate redundancies
„ gain resources and clientele (referrals)
„ reduce uncertainty through information exchange
Economic
Development
Costs of partnerships :
start-up costs
„ costs of maintaining relationship
„ loss of operating and decision autonomy
„ time to develop and maintain ties
„
How to evaluate partnerships?
„
„
Individual indicators of benefits and costs of
partnership outlined on previous slide are often not
observable
What is usually observed is the fact that local health
departments (LHD) has established a partnership
The goal of the presentation
„
Provide quantitative evaluation of public-private
partnerships by modeling effects of partnership choices
on overall performance of local health departments
1
Data sources
Research Questions
„
„
„
„
Outcome variable
„ Performance data for 2000 obtained from National Public
Health Performance Standards Program (NPHPSP) and
covered 147 health departments in three states.
Input, infrastructure and partnership variables
„ The 1996 NACCHO survey of public health
infrastructure
„ CDC Health Alert Network program
„ USDA rural continuum codes
What is the role of public-private partnerships in
explaining variations in performance of health
departments?
How large are the effects of partnerships on
performance?
Are these partnership effects endogenous?
Are there any differences in partnership effects at
different level of LHD performance?
NPHPSP instrument
„
„
„
„
Table 1. Descriptive Std.
statistics
Based on a concept of 10 essential pubic health
services.
Instrument contains description of 30 performance
standards across these 10 essential services.
After thorough description of a standard managers of
LHD are asked the question : “To what extent does the
local public health agency achieve the model
standard?”.
Performance score is calculated as a mean of these
responses across 30 standards
Mean
Dev.
Min
LHD Performance score
Variable name
63
15
34
93
LHD expenditures per capita served
51
45
5
329
LHD FTE per 100,000 people served
82
65
12
485
High speed internet connectivity
(Yes=1, No=0)
0.56
0.40
0
1
LHD Director has MD or MPH degree
(Yes=1, No=0)
0.30
0.46
0
1
5
3
1
9
18
6
3
32
Urbanization - 1 (heavily urbanized) to
9 (isolated rural)
Number of partners
LHD has partnership with:
Max
Mean
Local, State, Federal Government
Hospitals
Independent Care Providers
0.77
0.76
0.72
Non-Profits
Universities/Academic Centers
Community and Civic Groups
0.71
0.63
0.63
Professional Associations
Community Health Centers
Local Board of Health
0.61
0.59
0.56
Faith Communities
Businesses
0.44
0.43
Insurance Companies
0.29
Methods
„
Model 1 OLS regression of partner effects
LHD performance score = f (Expenditures, FTE, High speed
internet, LHD Director education, Number of partners,
Partnership, by type) + error
„
Model 2 Probit model of business partner choice
LHD has business partner = g (Expenditures, FTE, High
speed internet, LHD Director education, Number of partners,
Partnership, by type, except with business, Urbanization
level) + error
2
Table 2 Model 1 results
Methods – continued
„
„
Model 3 Accounting for endogeneity
Re-estimate Model 1 by OLS using predicted
probability of having business partner from Model 2
instead of dichotomous business partner variable
Model 4 Accounting for heterogeneity of
partnership effects on LHD performance
Re-estimate Model 1 by quantile regression (QR)
technique using predicted probability of having
business partner from Model 2
Dependent variable: LHD Mean Performance Score
Coeff.
Constant
Local Board of Health
Local, State, Federal Government
Universities/Academic Centers
0.000
LHD expenditures per capita
0.095
3.38
0.001
LHD FTE per 100,000 people
-0.051
-2.68
0.008
High speed internet connectivity
(Yes=1, No=0)
7.263
2.11
0.037
LHD Director has MD or MPH degree
(Yes=1, No=0)
-0.607
-0.25
0.800
Number of partners
-0.002
-0.01
0.994
Model1 - Results
t
P>t
3.108
0.98
0.329
-10.174
-2.61
0.010
11.032
3.58
0.000
Community Health Centers
0.134
0.05
0.960
Hospitals
2.843
0.69
0.492
Independent Care Providers
3.914
1.03
0.304
Insurance Companies
-3.848
-1.43
0.157
-15.880
-4.19
0.000
2.176
0.55
0.586
Community and Civic Groups
-1.981
-0.51
0.608
Businesses
10.458
3.36
0.001
Faith Communities
-0.120
-0.04
0.970
Non-Profits
Professional Associations
Model 1 Issues
„
„
„
Statistical association between partnerships and
performance does not necessarily imply causation
Some LHD may be more likely to be engaged in
partnerships due to unobserved characteristics –
endogeneity issue
Outcome variable may have skewed distribution
„
„
„
Higher mean performance scores are statistically associated
with LHD partnerships with:
Academic institutions (+11.0%)
Businesses (+10.4%)
Worse than average performance if LHD has partnerships
with:
Non-profit organizations (-15.9%)
Government agencies (-10.2%)
No statistically significant effects for other types of
partnerships
Model 2 Probit Model– Determinant of LHD
partnerships with businesses
Coef.
z
P>z
2.87
4.39
0.00
LHD expenditures per capita
-0.01
-2.12
0.03
LHD FTE per 100,000 people
0.00
1.16
0.25
High speed internet connectivity
(Yes=1, No=0)
0.31
0.93
0.35
Constant
LHD Director has MD or MPH degree
(Yes=1, No=0)
0.50
1.89
0.06
-0.14
-5.01
0.00
Total population served
0.00
-2.14
0.03
Total population served squared
0.00
2.03
0.04
-0.12
-1.94
0.05
Number of partners
„
P>t
8.29
Table 2 - continued
Coeff.
t
60.154
Will use partnerships with businesses as an example to
address these issues
Urbaization (urbanized=1...rural=9)
3
Model 2 - Results
Models 3 and 4 - Results
Public–private partnerships are more likely to develop
if:
„ LHD per capita spending are low compared to peers
„ LHD is located in more urbanized county
„ LHD director has training in public health
„ LHD is engaged in fewer partnerships
„
Public-private partnerships effects on performance are
11 percentage points higher after correction for
endogeneity
„
Partnerships with businesses have no effect on
performance at low and high end of the distribution
(Graph 2).
P e r c e n ta g e
p o in t g a in s
G ra p h 2 E f fe c t s o f p riv a t e -p u b lic p a rt n e rs h ip s o n L H D
p e rf o rm a n c e : th re e m o d e ls c o m p a re d
Conclusions
25
20
15
10
5
0
Public-private partnerships are important determinants of
LHD performance
‹ Relationship between the effects of partnerships on
performance is neither exogenous, nor linear
‹ “Returns” to LHD partnerships with businesses are
concave function of LHD performance
‹ Quantile regression is a useful public health policy tool
addressing heterogeneity of returns to partnerships across
the wide range of distribution of performance outcomes
‹
0 .1
0 .2
0 .3 0 .4 0 .5 0 .6 0 .7 0 .8
Q u a n t ile s o f p e rfo rm a n c e
Q R - c o rre c te d fo r e n d o g e n e ity
OLS
O L S - c o rre c te d fo r e n d o g e n e ity
0 .9
4