Prevalence of Social Determinants of Health Among Health System Employees.

INTRODUCTION/OBJECTIVES: Many health systems screen patients for social determinants of health and refer patients with social needs to community service organizations for assistance. However, little is known about social determinants of health among health system employees. We sought to examine the prevalence of social determinants among employees of The MetroHealth System, a large safety-net health system in Cleveland, Ohio. METHODS: We invited participants in an employee wellness program to answer the same screening questions that patients answer about 9 social determinants of health, including food insecurity, financial strain, transportation difficulty, inability to pay for housing or utilities, intimate partner violence, social isolation, infrequent physical activity, daily stress, and lack of internet access. We then determined the percentage of employees who met pre-defined criteria for being at risk for each social determinant. We also examined how these percentages varied across employee job categories. RESULTS: Of 4191 full-time employees, 1932 (46%) completed the survey. The percentage of employees at risk for each social determinant were: food insecurity (11%), financial strain (12%), transportation difficulty (4%), inability to pay for housing or utilities (10%), intimate partner violence (4%), social isolation (48%), infrequent physical activity (10%), daily stress (58%), and lack of internet access (3%). Being at risk for specific social determinants was more common among support staff compared to staff physicians and nurses. For example, the survey participants included 436 administrative support staff, a job category that includes secretaries and patient service representatives. Among this group, 20% reported food insecurity, 20% financial strain, and 17% inability to pay for housing or utilities. CONCLUSIONS: Social determinants of health are common among health system employees, especially among workers in lower paid job categories. Health systems should routinely screen employees for social determinants and adjust salaries, benefits, and assistance programs to address their social needs.

Ecological correlates of blue whale movement behavior and its predictability in the California Current Ecosystem during the summer-fall feeding season.

BACKGROUND: Species distribution models have shown that blue whales (Balaenoptera musculus) occur seasonally in high densities in the most biologically productive regions of the California Current Ecosystem (CCE). Satellite telemetry studies have additionally shown that blue whales in the CCE regularly switch between behavioral states consistent with area-restricted searching (ARS) and transiting, indicative of foraging in and moving among prey patches, respectively. However, the relationship between the environmental correlates that serve as a proxy of prey relative to blue whale movement behavior has not been quantitatively assessed. METHODS: We investigated the association between blue whale behavioral state and environmental predictors in the coastal environments of the CCE using a long-term satellite tracking data set (72 tagged whales; summer-fall months 1998-2008), and predicted the likelihood of ARS behavior at tracked locations using nonparametric multiplicative regression models. The models were built using data from years of cool, productive conditions and validated against years of warm, low-productivity conditions. RESULTS: The best model contained four predictors: chlorophyll-a, sea surface temperature, and seafloor aspect and depth. This model estimated highest ARS likelihood (> 0.8) in areas with high chlorophyll-a levels (> 0.65 mg/m3), intermediate sea surface temperatures (11.6-17.5 °C), and shallow depths (< 850 m). Overall, the model correctly predicted behavioral state throughout the coastal environments of the CCE, while the validation indicated an ecosystem-wide reduction in ARS likelihood during warm years, especially in the southern portion. For comparison, a spatial coordinates model (longitude × latitude) performed slightly better than the environmental model during warm years, providing further evidence that blue whales exhibit strong foraging site fidelity, even when conditions are not conducive to successful foraging. CONCLUSIONS: We showed that blue whale behavioral state in the CCE was predictable from environmental correlates and that ARS behavior was most prevalent in regions of known high whale density, likely reflecting where large prey aggregations consistently develop in summer-fall. Our models of whale movement behavior enhanced our understanding of species distribution by further indicating where foraging was more likely, which could be of value in the identification of key regions of importance for endangered species in management considerations. The models also provided evidence that decadal-scale environmental fluctuations can drive shifts in the distribution and foraging success of this blue whale population.