Although the nation's local health departments (LHDs) share a common mission, variability in administrative structures is a barrier to identifying common, optimal management strategies. There is a gap in understanding what unifying features LHDs share as organizations that could be leveraged systematically for achieving high performance.
To explore sources of commonality and variability in a range of LHDs by comparing intraorganizational networks.
We used organizational network analysis to document relationships between employees, tasks, knowledge, and resources within LHDs, which may exist regardless of formal administrative structure.
A national sample of 11 LHDs from seven states that differed in size, geographic location, and governance.
Relational network data were collected via an on-line survey of all employees in 11 LHDs. A total of 1062 out of 1239 employees responded (84% response rate).
Network measurements were compared using coefficient of variation. Measurements were correlated with scores from the National Public Health Performance Assessment and with LHD demographics. Rankings of tasks, knowledge, and resources were correlated across pairs of LHDs.
We found that 11 LHDs exhibited compound organizational structures in which centralized hierarchies were coupled with distributed networks at the point of service. Local health departments were distinguished from random networks by a pattern of high centralization and clustering. Network measurements were positively associated with performance for 3 of 10 essential services (r > 0.65). Patterns in the measurements suggest how LHDs adapt to the population served.
Shared network patterns across LHDs suggest where common organizational management strategies are feasible. This evidence supports national efforts to promote uniform standards for service delivery to diverse populations.
This article discusses a comparative study of 11 local health department (LHD) organizational networks to document relationships between employees, tasks, knowledge, and resources within LHDs.
Jacqueline Merrill, RN, MPH, DNSc, is a Public Health Nurse and Associate Research Scientist, Department of Biomedical Informatics, College of Physicians and Surgeons, Columbia University, New York.
Jonathan W. Keeling, MS, is a Doctoral Candidate, Department of Biomedical Informatics, Columbia University, New York.
Kathleen M. Carley, PhD, is a Professor, School of Computer Science, Institute for Software Research, and Director, Center for Computational Analysis of Social and Organizational Systems, Carnegie Mellon University, Pittsburgh, Pennsylvania.
Corresponding Author: Jacqueline Merrill, RN, MPH, DNSc, Department of Biomedical Informatics, College of Physicians and Surgeons, Columbia University, New York, NY 10032 (firstname.lastname@example.org).
The Robert Wood Johnson Foundation supported this research through an HCFO PHSSR grant. J. K. is supported by National Library of Medicine grant T15-LM007079.
The authors thank Kristine Gebbie, DrPH, RN, for her insightful comments.