exploring the impact of the public health system network on local health department efficiency...
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Exploring the Impact of the Public Health System Network on Local Health Department Efficiency
Rachel Hogg, DrPH, MAGlen Mays, PhD, MPH
JS Butler, PhD
University of Colorado Denver,University of Kentucky
Public Health Services and Systems Research Keeneland Conference • Lexington, KY • April 2015
Presenter Disclosures
There are no relationships to disclose.
Acknowledgements
Research support provided by:
• Robert Wood Johnson Foundation National Coordinating Center for Public Health Services & Systems Research and Public Health Practice-Based Research Networks
What We KnowLocal public health systems can be configured in a number of different ways
NLSPHS, 2012.
What We Know
• Joint production and delivery of public health services requires network coordination
• Problems with congestion and information flow may occur
• Dense networks may allow for quick information flow
• Highly centralized may maximize coordination But what is most efficient?
What We Know
• At the same time, little is understood about how efficiently LHDs operate and deliver services
• Uncertainty around the optimal use of staff and resources to deliver the largest scope of public health services
What We Know
• Resource and population inequities may require additional effort on the part of the LHD to meet population needs
• Local public health system network connections may help alleviate the negative impact external pressure and constraints have on LHD performance
Questions Driving the Study
• How does the public health system network impact the efficiency of public health service delivery?
• Question of Optimization How to best deploy and capitalize on a diverse
collection of actors, resources, responsibilities, and expectations
Data Used
• National Longitudinal Survey of Public Health Systems (NLSPHS)
• Cohort of 360 communities with at least 100,000 residents
• Surveyed in 1998, 2006, 2012 (70% response rate)• Measures the availability of 20 core public health services,
the range of organizations that deliver each service, and the perceived effectiveness of services
Scope Network Quality
Data Used
Linked with:
• Information on local public health agency and system (NACCHO)
• Community characteristics (Census, ARF, and CMS)
Analytic Approach
• Two-stage approach using results from Stochastic Frontier Analysis (SFA) and social network analysis (SNA) to examine effects of local delivery system structures on the efficiency of public health service delivery
• n=663
SFA Analytic Approach
• Time–Invariant Stochastic Frontier Analysis (SFA) of longitudinal observations Sample restricted to those respondents with completely
matched data and those with 1,000 FTE or less
• Econometric approach to measuring technical efficiency
• Uses the input/output ratio to determine an optimal bundle that can then be used as an estimation of the ideal production function Maximum output possible with the minimum amount of
inputs
Technical Efficiency Scores
05
10
Pe
rcen
t
0 .2 .4 .6 .8Technical Efficiency
Technical Efficiency Scores
• Average technical efficiency is 50%, with a low of 1.5% and a high of 88%
• Local health departments are operating at inefficiency levels as high as 98.5%
Two-Stage Analytic Approach
• Dependent Variable: Technical Efficiency
• Key Independent Variables: Average Network Degree Centrality-number of
relationships that an organization maintains with other organizations in the network
Average Network Density-proportion of total possible relationships that exist between organizations in the network
Two-Stage Analytic Approach
• Model includes quadratic terms for both centrality and density
• Model controls for governance structure
Two-Stage Multivariable Regression Results
Variable Parameter Estimate Robust Standard ErrorOrganization Density 0.813 0.211**Organization Density Squared -0.943 0.348**Organization Centrality 3.12 0.631**Organization Centrality Squared -6.67 1.99**Centralized -0.0159 0.0173Mixed 0.0205 0.0187
p<0.01**
Effect of Network Connections on Technical Efficiency
Two-Stage Results
• Average organization centrality has a standard deviation of 0.05, one standard deviation increase in centrality is associated with a 15 unit increase in technical efficiency
• Average network density has a standard deviation of 0.1, one standard deviation increase in density is associated with a 0.08 unit increase in technical efficiency
Conclusions
• Local health department efficiency is positively impacted by increasing levels of overall network centrality and density
• Partnerships and collaboration help foster a production environment that leads to greater efficiency in local health department operation Policy initiatives and incentives for collaboration
Conclusions
• Higher degrees of technical efficiency could be a powerful mechanism in showing success on the part of public health Advocacy for increased public health and system
building funding
• Diminishing returns to density and centrality Tipping points where the relationship levels out and
becomes negative Problems with congestion and coordination
Targeted leadership, engagement and communication strategies
Conclusions
• Novel analysis tools, like frontier analysis and SNA, to examine local health systems and operation may highlight important external factors and community characteristics that impact the productivity of local health department operation and service delivery
Limitations and Next Steps
• Measures are not perfect• Novel analyses that could use expansion and more
exploration• Only examining those systems serving 100,000 or
more• From the local health department perspective• Problems with NACCHO reliability and validity • Autocorrelation-problem of time and geography • Should the variables go in the 1st or 2nd stage?
