1. Exploring the Impact of the Public Health System Network on Local Health Department Efficiency Rachel Hogg, DrPH, MA Glen 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

2. Presenter Disclosures There are no relationships to disclose.

3. 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

4. What We Know Local public health systems can be configured in a number of different ways NLSPHS, 2012.

5. 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?

6. 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

7. 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

8. 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

9. 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

10. Data Used Linked with: Information on local public health agency and system (NACCHO) Community characteristics (Census, ARF, and CMS)

11. 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

12. 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

13. Technical Efficiency Scores

14. 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%

16. 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

17. Two-Stage Analytic Approach Model includes quadratic terms for both centrality and density Model controls for governance structure

18. Two-Stage Multivariable Regression Results VariableParameter EstimateRobust Standard Error Organization Density 0.8130.211** Organization Density Squared -0.9430.348** Organization Centrality 3.120.631** Organization Centrality Squared -6.671.99** Centralized -0.01590.0173 Mixed 0.02050.0187 p<0.01** Effect of Network Connections on Technical Efficiency

19. 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

20. 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

21. 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

22. 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

23. 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 1 st or 2 nd stage?

24. Questions? For more information contact: Rachel Hogg Rachel.hogg@ucdenver.edu