Public Health Reports®

Because of state and federal health care reform, local health departments play an increasingly prominent role leading and coordinating disease prevention programs in the United States. This case study shows how a local health department working in chronic disease prevention and management can use systems science and evidence-based decision making to inform program selection, implementation, and assessment; enhance engagement with local health systems and organizations; and possibly optimize health care delivery and population health.

The authors built a systems-science agent-based simulation model of diabetes progression for the San Antonio Metropolitan Health District, a local health department, to simulate health and cost outcomes for the population of San Antonio for a 20-year period (2015-2034) using 2 scenarios: 1 in which hemoglobin A1c (HbA1c) values for a population were similar to the current distribution of values in San Antonio, and the other with a hypothetical 1-percentage-point reduction in HbA1c values.

They projected that a 1-percentage-point reduction in HbA1c would lead to a decrease in the 20-year prevalence of end-stage renal disease from 1.7% to 0.9%, lower extremity amputation from 4.6% to 2.9%, blindness from 15.1% to 10.7%, myocardial infarction from 23.8% to 17.9%, and stroke from 9.8% to 7.2%. They estimated annual direct medical cost savings (in 2015 US dollars) from reducing HbA1c by 1 percentage point ranging from $6842 (myocardial infarction) to $39 800 (end-stage renal disease) for each averted case of diabetes complications.

Local health departments could benefit from the use of systems science and evidence-based decision making to estimate public health program effectiveness and costs, calculate return on investment, and develop a business case for adopting programs.

Given challenges to recruiting nurses to public health and the growth in national policies focused on population health, it is crucial that public health agencies develop strategies to sustain this important group of employees. The objective of this study was to examine factors that influence nurses’ decisions to work in public health agencies.

This cross-sectional study examined perspectives of nurses who worked in state and local public health departments and responded to the 2010 Council on Linkages Between Academia and Public Health Practice’s survey of public health workers. We calculated the mean rating of each recruitment and retention factor for nurses and non-nurses separately and compared differences by using t tests. We then used multivariate regression analysis to examine differences in ratings by role (ie, nurse or non-nurse).

After controlling for personal and organizational characteristics, the influence of 5 recruitment factors was significantly stronger among nurses than among non-nurses: flexibility of work schedule (P < .001), autonomy/employee empowerment (P < .001), ability to innovate (P = .002), specific duties and responsibilities (P = .005), and identifying with the mission of the organization (P = .02). The influence of 5 retention factors was stronger among nurses than among non-nurses: autonomy/employee empowerment (P < .001), flexibility of work schedule (P < .001), specific duties and responsibilities (P < .001), opportunities for training/continuing education (P = .03), and ability to innovate (P = .008).

Some factors that influence nurses to begin and remain working in local governmental public health agencies, such as flexible schedules and employee autonomy, are factors that governmental public health agencies can design into positions and highlight when recruiting from health care organizations, private industry, and academia.

Although take-home naloxone (THN) programs are integral in strategies to prevent overdose deaths among opioid users, the uptake of THN among people who use drugs (PWUD) (including non-opioid users) is unknown. The objectives of this study were to determine awareness, possession, and use of THN among PWUD in Vancouver, Canada, and identify barriers to adopting this strategy.

From December 1, 2014, to May 29, 2015, participants in 2 prospective cohort studies of PWUD in Vancouver completed a standardized questionnaire, which asked about awareness, possession, and use of THN; sociodemographic characteristics; and drug use patterns. We conducted multivariable logistic regression analyses to determine factors independently associated with awareness and possession of THN.

Of 1137 PWUD, 727 (64%) reported at least 1 previous overdose ever, and 220 (19%) had witnessed an overdose in the previous 6 months. Although 769 (68%) participants overall reported awareness of THN, only 88 of 392 (22%) opioid users had a THN kit, 18 (20%) of whom had previously administered naloxone. Factors that were positively associated with awareness of THN included witnessing an overdose in the previous 6 months (adjusted odds ratio [aOR] = 2.23; 95% confidence interval [CI], 1.49-3.34; P < .001), possession of THN (aOR = 1.85; 95% CI, 1.11-3.06; P = .02), younger age (aOR = 1.02; 95% CI, 1.01-1.04; P = .003), white race (aOR = 1.67; 95% CI, 1.27-2.19; P < .001), hepatitis C infection (aOR = 1.63; 95% CI, 1.13-2.36; P = .01), residing in Vancouver’s Downtown Eastside neighborhood (aOR = 1.93; 95% CI, 1.47-2.53; P < .001), and at least daily heroin injection (aOR = 1.69; 95% CI, 1.09-2.62; P < .02).

Efforts to improve knowledge of and participation in the THN program may contribute to reduced opioid overdose mortality in Vancouver.

We examined the survival rates after diagnosis of HIV infection stage 3 (AIDS) in the United States by population density area of residence at diagnosis.

We used data from the National HIV Surveillance System to calculate survival rates among people aged ≥13 with HIV infection stage 3 (AIDS) diagnosed from 2005 through 2010. We determined survival rates for more than 12, 24, and 36 months after diagnosis; overall and by demographic characteristics; and across 3 population density area categories (large metropolitan statistical areas [MSAs, ≥500 000 people], small-to-medium MSAs [50 000 to 499 999 people], and nonmetropolitan areas [<50 000 people]).

The survival rates for more than 12, 24, and 36 months after diagnosis were highest among people residing in large MSAs (90.2%, 87.2%, and 84.9%, respectively) and lowest among people residing in nonmetropolitan areas (87.3%, 84.1%, and 81.4%, respectively). With a few exceptions, survival rates were lower in those residing in nonmetropolitan areas than those residing in large MSAs and small-to-medium MSAs across most subgroups by age at diagnosis, race/ethnicity, sex, transmission category, region of residence, and year of diagnosis. Between 2005 and 2010, significant year-to-year increases occurred in the proportion of people surviving more than 36 months after diagnosis across all 3 population density area categories (estimated annual percentage change: large MSAs [0.88; 95% confidence interval (CI), 0.56-1.20]; small-to-medium MSAs [0.94; 95% CI, 0.06-1.83]; and nonmetropolitan areas [1.26; 95% CI, 0.07-2.46]).

Although survival rates for those with HIV infection stage 3 (AIDS) improved in all 3 population density area categories, efforts to remove barriers to care and promote treatment adherence in nonmetropolitan areas will be necessary to eliminate survival disparities.

Chronic hepatitis C virus (HCV) is the most common blood-borne infection in the United States, with an estimated 2.7 to 3.9 million cases as of 2014. In August 2012, the Centers for Disease Control and Prevention (CDC) recommended 1-time HCV testing of all baby boomers. The objectives of this study were to (1) determine the proportion of people screened for HCV in the US Department of Defense Military Health System before and after the CDC screening recommendation for baby boomers and (2) assess whether certain patient or system factors were associated with screening for HCV before and after August 2012.

We used a dataset containing 5% of beneficiaries randomly selected from the Military Health System Data Repository medical claims database for the period July 2011 through September 2013.

Of 108 223 people eligible for HCV screening during the first period (July 2011 through July 2012), 1812 (1.7%) were screened. Of 109 768 people eligible during the second period (September 2012 through September 2013), 2599 (2.4%) were screened. HCV screening receipt was related to benefit type (Prime before August 2012: adjusted odds ratio [aOR] = 2.16; 95% confidence interval [CI], 1.89-2.46; Prime after August 2012: aOR = 1.93; 95% CI, 1.73-2.16) and care source (direct care before August 2012: aOR = 1.80; 95% CI, 1.57-2.07; direct care after August 2012: aOR = 2.45; 95% CI, 2.18-2.75); male sex (aOR = 1.17; 95% CI, 1.06-1.29) and black race (aOR = 1.20; 95% CI, 1.05-1.37) were associated with HCV testing only before August 2012.

Interventions should be implemented to increase awareness and knowledge of the current national HCV testing recommendation among baby boomers to seek out testing and health care providers to perform screening.

In all health system sectors, electronic health information (EHI) is created, used, released, and reused. We examined states’ efforts to address EHI uses in law to provide an understanding of the EHI legal environment.

Attorney researchers used WestlawNext to search for EHI-related statutes and regulations of the US states, US territories, and the District of Columbia in effect as of January 2014. The researchers independently catalogued provisions by the EHI use described in the law. Researchers resolved discrepancies through peer review meetings and recorded the consensus codes for each law.

This study identified 2364 EHI-related laws representing 49 EHI uses in 54 jurisdictions. A total of 18 EHI uses were regulated by ≥10 jurisdictions. More than 750 laws addressed 2 or more EHI uses. Jurisdictions varied by the number of EHI laws in effect, with a mean of 44 laws. Texas had the most EHI laws (n = 145). Hawaii and South Carolina had the fewest (n = 14 each).

The EHI legal landscape is complex. The large quantity and diversity of laws complicate legal analysis, likely delay implementation of public health solutions, and might be detrimental to the development of emerging health information technology. Research is needed to understand the effect of EHI-related laws.