INTRODUCTION:A rate-based understanding of home care aides' adverse occupational outcomes related to their work location and care tasks is lacking.METHODS:Within a 30-month, dynamic cohort of 43 394 home care aides in Washington State, injury rates were calculated by aides' demographic and work characteristics. Injury narratives and focus groups provided contextual detail.RESULTS:Injury rates were higher for home care aides categorized as female, white, 50 to <65 years old, less experienced, with a primary language of English, and working through an agency (versus individual providers). In addition to direct occupational hazards, variability in workload, income, and supervisory/social support is of concern.CONCLUSIONS:Policies should address the roles and training of home care aides, consumers, and managers/supervisors. Home care aides' improved access to often-existing resources to identify, manage, and eliminate occupational hazards is called for to prevent injuries and address concerns related to the vulnerability of this needed workforce.

Home Care Aides (HCAs) have nearly four times the rate of injury as the general U.S. work force. In 2015, the Service Employees International Union 775 Benefits Group conducted a health and safety survey with 672 HCAs in Washington State. The goal was to identify the risk factors for injury and to better assess injury rates through self-report. Quantitative analyses assessed injury prevalence and barriers in reporting injury. Overall, 13 percent responded that they had ever had an injury that required medical attention while working as an HCA. These rates are significantly higher for HCAs employed by home care agencies compared with HCAs who work independently. Over a third reported moderate to high levels of hesitancy in reporting an on-the-job injury; these rates were even higher for independent providers. Study findings suggest that HCAs are well informed about appropriate next steps following workplace injury, but strong barriers may prevent them from attempting the reporting process.

OBJECTIVES To characterize the leading causes of death for the urban American Indian/Alaska Native (AI/AN) population and compare with urban White and rural AI/AN populations. METHODS We linked Indian Health Service patient registration records with the National Death Index to reduce racial misclassification in death certificate data. We calculated age-adjusted urban AI/AN death rates for the period 1999-2009 and compared those with corresponding urban White and rural AI/AN death rates. RESULTS The top-5 leading causes of death among urban AI/AN persons were heart disease, cancer, unintentional injury, diabetes, and chronic liver disease and cirrhosis. Compared with urban White persons, urban AI/AN persons experienced significantly higher death rates for all top-5 leading causes. The largest disparities were for diabetes and chronic liver disease and cirrhosis. In general, urban and rural AI/AN persons had the same leading causes of death, although urban AI/AN persons had lower death rates for most conditions. CONCLUSIONS Urban AI/AN persons experience significant disparities in death rates compared with their White counterparts. Public health and clinical interventions should target urban AI/AN persons to address behaviors and conditions contributing to health disparities.

OBJECTIVE:To determine the relationship between depression and diabetes management among urban American Indians/Alaska Natives (AI/ANs).DESIGN:Retrospective, cross-sectional analysis of medical records.SETTING:33 Urban Indian Health Organizations that participated in the Indian Health Service Diabetes Care and Outcomes Audit.PATIENTS:3,741 AI/AN patient records.MAIN OUTCOME MEASURES:Diabetes management outcomes, including HbA1c, smoking, BMI, systolic blood pressure, creatinine, total cholesterol, and receipt of preventive services.RESULTS:Individuals with depression and diabetes were 1.5 times more likely to smoke than individuals with diabetes but without depression (OR=1.51; 95% Cl: 1.23, 1.86), controlling for age, sex, and facility. After adjustment, the geometric mean BMI in diabetes patients with depression was 3% higher than in patients without depression (β=.034; 95% CI: .011, .057).CONCLUSIONS:Urban AI/ANs with diabetes and depression are more likely to smoke and have higher BMI than those with diabetes but without depression. These findings inform programmatic efforts to address the care of patients with both depression and diabetes.