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Public health workforce size and composition have been difficult to accurately determine because of the wide variety of methods used to define job title terms, occupational categories, and worker characteristics. In 2014, a preliminary consensus-based public health workforce taxonomy was published to standardize the manner in which workforce data are collected and analyzed by outlining uniform categories and terms. We summarize development of the taxonomy's 2017 iteration and provide guidelines for its implementation in public health workforce development efforts. To validate its utility, the 2014 taxonomy was pilot tested through quantitative and qualitative methods to determine whether further refinements were necessary. Pilot test findings were synthesized, themed by axis, and presented for review to an 11-member working group drawn from the community of experts in public health workforce development who refined the taxonomy content and structure through a consensus process. The 2017 public health workforce taxonomy consists of 287 specific classifications organized along 12 axes, intended for producing standardized descriptions of the public health workforce. The revised taxonomy provides enhanced clarity and inclusiveness for workforce characterization and will aid public health workforce researchers and workforce planning decision makers in gathering comparable, standardized data to accurately describe the public health workforce.

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.

Context The public health, environmental, and agricultural laboratory (PHEAL) workforce is a key component of the public health infrastructure. The national laboratory workforce faces an ongoing challenge of recruitment and retention of workers often related to pay and other compensation issues. Objective To collect information on laboratory salaries and laboratory compensation practices using a national compensation survey targeting the PHEAL workforce. Participants Seventy-three of 109 (67%) PHEAL directors in the 50 states and District of Columbia collectively employ 3723/4830 (77%) PHEAL employees in the United States. Methods A standardized survey was developed and administered in 2010. Compensation data were compiled by job classification, geographic region, laboratory gross operating budget size, laboratory staff size, and laboratory type. Results Laboratory staff size ranged from 3 to 327 individuals (mean = 74 and median = 51). Median base salaries were lowest in the Southwest and South and highest in the Mountain and Pacific regions. Mean and median laboratory gross operating budgets for all participating PHEALs were $8 609 238 and $5 671 500, respectively. Extra cash compensation, used by 8 of 60 (13.3%) PHEALs, was more likely to go to a scientist-manager or scientist-supervisor. Conclusions In 2010, a standardized national compensation survey of technical and scientific public health employees working in 73 PHEALs was effective in collecting previously unavailable data about laboratory salaries, laboratory budgets, and payroll practices. Laboratory salaries varied by geographic region and there was an uneven distribution of extra cash compensation among job classifications. The compensation data collected may be useful in characterizing and improving laboratory salary structures and practices to better support workforce recruitment and retention.

This article reviews the activities of an office of public health practice in a school of public health (SPH) and assesses their impact on master's of public health student training and education. The University of Michigan SPH established a Practice Office in 2005 to develop programs in community-based public health, workforce development, student training, and practice-based research. Student training objectives included increasing practice activity offerings and participation rates, ensuring participant satisfaction with activities, and raising the number of students seeking public health practice employment. According to student survey results from the 2007-2008 and 2008-2009 academic years, the Practice Office achieved mixed success in meeting objectives. Approximately 50% of students participated in at least 1 activity, nearly 50% of students reported that the activities impacted their decision to pursue a practice career, and approximately 75% of students reported moderate to extensive public health practice experience on graduation, compared with 30% at the beginning of their degree program, although this change was not significantly different for those who had participated in Practice Office activities. Initiation of a systematic process to evaluate the impact of practice-based activities early in program development is recommended for all Practice Offices.

OBJECTIVE To identify the primary sources of information utilized by a vulnerable population during the 2009 influenza pandemic and examine disease prevention behaviors related to reports of local H1N1 influenza transmission. DESIGN Cross-sectional study. SETTING Between May 2009 and December 2009, face-to-face interviews were conducted in towns located in 3 Mississippi counties along the Gulf Coast. PARTICIPANTS Two hundred sixteen residents of the Mississippi Gulf Coast were interviewed. MAIN OUTCOME MEASURES Analysis of the interview results described awareness of the influenza outbreak/pandemic and sources of information about the situation. Chi-square tests were used to examine differences in reported disease-preventive behaviors taken by Mississippi Gulf Coast residents before and after H1N1 influenza transmission was confirmed locally. RESULTS Most subjects were aware of H1N1 influenza at the time of interview (n = 212; 98%). Television (n = 145; 69%), newspaper (n = 40; 19%), and the Internet (n = 19; 9%) were the most common sources of information regarding H1N1 influenza. Hand hygiene (n = 85; 41%) was the most reported preventive measure adopted by study subjects and increased following the confirmation of the first H1N1 influenza cases in Harrison County (χ= 4.46, p= 0.04). CONCLUSIONS The Centers for Disease Control and Prevention's emphasis on providing health information about H1N1 primarily through the Internet may not have been effective in reaching the public. Provision of health messages through various mediums, especially television, may better inform the public of disease-related prevention messages during a developing influenza pandemic.

This special supplement of the Journal of Public Health Management and Practice reflects the increasing attention focused on public health workforce research. Interest in this area has grown since the publication of a 2003 Institute of Medicine report, which documented future challenges to public health infrastructure and workforce. A credible federal effort to enumerate and characterize the national public health workforce was relatively recent and long overdue, but amidst the now constant assertions of critical worker shortages, the time has come when more refined models for assessing public health capacity are needed. In the accompanying article, “Health Human Resources Planning and the Production of Health: Development of an Extended Analytical Framework for Needs-Based Health Human Resources Planning,” Birch et al propose a needs-based approach for estimating necessary human health resources. The authors argue, convincingly, that human health resources planning has tended to be an exercise in demographic computation using age and sex of the population as proxy measures for service need while ignoring important variables such as changes in population health over time, productivity levels of providers, and the quantity of service required, all key determinants of provider requirements. Instead, their analytic framework derives number and type of providers required directly from (needs-based) service requirements. As a generic tool with potential application in a variety of healthcare settings, this model has clear relevance for public health workforce research. Particularly important for national public health leadership, this framework calls for explicitly identifying determinants of providers needed, thereby avoiding the “illusion of necessity” which generates continuously increasing estimates for the number of providers (workers) needed. Ask any local or state