Predictors of capacity in public health, environmental, and agricultural laboratories.

OBJECTIVES

Ensuring adequate capacity to address population health concerns has challenged public health for decades. Organizational and workforce characteristics are theorized to contribute to organizational capacity. This article considers 2 possible quantitative measures of organizational capacity using public health, environmental, and agricultural laboratories (PHEALs) as the unit of interest and tests their associations with workforce and human resources variables.

DESIGN

The National Laboratory Capacity Assessment was developed by the University of Michigan Center of Excellence in Public Health Workforce Studies and the Association of Public Health Laboratories. Online data collection took place from July to September 2011. All statistical analyses were performed in 2013.

SETTING

US PHEALs were invited to participate in the study. All study participants were Association of Public Health Laboratories members.

PARTICIPANTS

The Association of Public Health Laboratories distributed the National Laboratory Capacity Assessment survey to 105 PHEAL directors in all 50 states, the District of Columbia, and Puerto Rico, including 50 state public health laboratories, 41 local public health laboratories, 8 environmental laboratories, and 6 agricultural laboratories.

MAIN OUTCOME MEASURES

Logistic regression analyses were performed to assess relationships between outcome measures of overall capacity and averaged program capacity and variables representing characteristics of PHEALs and their workforce, including number of workers, proportion of scientists, education, experience, training, and equipment quality.

RESULTS

The survey achieved a 76% response rate. Both capacity models showed that PHEALs offering an array of training opportunities are 4 times more likely to report higher capacity scores. One model showed a positive association between workforce size and capacity. Worker education and equipment quality were negatively associated with capacity in both models.

CONCLUSIONS

The findings of this study provide empirical evidence that some workforce factors may influence organizational capacity of PHEALs. Techniques used to measure capacity and workforce factors must be improved to produce consistent findings across public health organizational data sets.