IntroductionAfrican American women have higher rates of obesity and related chronic disease than other demographic groups. The poorer health of African American women compared with other groups may be explained by allostatic load, or cumulative physiologic stress, due to chronic socioeconomic disadvantage. The objective of this study was to evaluate neighborhood and individual factors contributing to allostatic load in African American women at risk for obesity-related diseases.MethodsThis study evaluated the relationship of allostatic load with neighborhood disadvantage, individual socioeconomic determinants, and synergism between neighborhood and socioeconomic disadvantage, along with health behaviors and other factors as mediators in African American women. Our sample consisted of 220 African American women at risk of obesity-related diseases enrolled in the Better Me Within program (mean [standard deviation] age, 50.1 [11.2] y; mean [standard deviation] body mass index,36.7 [8.4] kg/m2). Allostatic load score for each participant was calculated by summing the number of biomarkers (of 9 biomarkers) that were determined to be in the high-risk quartile.ResultsPoisson regression of neighborhood disadvantage and individual socioeconomic determinants found that neighborhood disadvantage, but not education level or household income, was significantly associated with allostatic load (β = 0.22, SE, 0.10, P = .04). Tests for mediators showed that household income and alcohol consumption partially mediated the relationship between allostatic load score and neighborhood disadvantage but were not significant.ConclusionMore research is necessary to determine the mechanisms by which neighborhoods can exacerbate and attenuate cumulative disadvantage among African American women. Policies and interventions that focus on neighborhood health may improve the outcomes of individual-level health interventions among women who reside in disadvantaged communities.

Rotavirus is the leading cause of severe dehydrating gastroenteritis and death due to diarrhea among children under 5, causing over 180,000 under-5 deaths annually. Safe, effective rotavirus vaccines have been available for over a decade and are used in over 98 countries. In addition to the globally available, WHO-prequalified ROTARIX (GSK) and RotaTeq (Merck), several new rotavirus vaccines have attained national licensure -ROTAVAC (Bharat Biotech) and ROTASIIL (Serum Institute of India), licensed and manufactured in India and now WHO-prequalified, and Rotavin-M1 (PolyVac), licensed and manufactured in Vietnam. In this review, we summarize the available clinical trial and post-introduction evidence for these three new orally administered rotavirus vaccines. All three vaccines have demonstrated safety and efficacy against rotavirus diarrhea, although publicly available preclinical data are limited in some cases. This expanding product landscape presents a range of options to optimize immunization programs, and new presentations of each vaccine are currently under development.

In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables. In this viewpoint, we review the rationale behind the push to integrate SBDH data into health care predictive models and explore the technical, strategic, and ethical challenges faced as this process unfolds across the United States. We also offer several recommendations to overcome these challenges to reach the promise of SBDH predictive analytics to improve health and reduce health care disparities.

Introduction: African American (AA) women have disproportionately higher risk of cardiovascular disease than White women, which may be explained by the uniquely higher allostatic load (AL) found in AA women. No studies have tested the effect of lifestyle interventions on AL in AA women. Our objectives were to assess the change in allostatic load following a lifestyle intervention and explore the roles of life­style behaviors and socioeconomic factors on allostatic load change.Methods: Participants were non-diabetic (mean age and SD: 48.8±11.2 y) AA women (n=221) enrolled in a church-based, cluster randomized trial testing a standard diabetes prevention program (DPP) and a faith-enhanced DPP with 4-months of follow-up. We assessed the relation­ships of changes in diet, physical activity, neighborhood disadvantage, individual socioeconomic factors, and other lifestyle variables to changes in AL at 4-months using a multilevel multinomial logistic regression model.Results: Average AL decreased (-.13±.99, P=.02) from baseline to 4-months. After adjusting for other variables, a high school education or less (OR:.1, CI:.02–.49) and alcohol use (OR: .31, CI: .09-.99) contrib­uted to increased AL. Living in a disadvan­taged neighborhood was responsible for increased AL, though it was not statistically significant. There were no statistically sig­nificant associations between AL and other health behavior changes.Conclusions: Lower education levels may dampen the benefits of lifestyle interventions in reducing AL. Although a significant reduction in AL was found after participation in a lifestyle intervention, more research is needed to determine how lifestyle behaviors and socioeconomic factors influence AL in AA women.Ethn Dis. 2019;29(2):297-308; doi:10.18865/ ed.29.2.297

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Background Prescription Drug Monitoring Programs (PDMPs) are electronic databases that track controlled substance prescriptions in a state. They are underused tools in preventing opioid abuse. Most PDMP education research measures changes in knowledge or confidence rather than behavior. Objective To evaluate the impact of online case-based training on healthcare provider use of the Maryland (USA) PDMP. Methods We used e-mail distribution lists to recruit providers to complete a brief educational module. Using a pre-training and post-training survey in the module, we measured self-reported PDMP use patterns and perceived PDMP value in specific clinical situations and compared pre- and post-training responses. Within the module, we presented three fictional pain cases and asked participants how they would manage each, both before, and then after presenting prescription drug history simulating a PDMP report. We measured changes in the fictional case treatment plans before and after seeing prescription history. Finally, we measured and compared how often each participant accessed the Maryland PDMP database before and after completing the educational module. We used multivariate logistic regression to measure the effect of the intervention on actual PDMP use frequency. Results One hundred and fifty participants enrolled and completed the training module, and we successfully retrieved real-world PDMP use data of 137 of them. Participants’ decisions to prescribe opioids changed significantly after reviewing PDMP data in each of the fictional cases provided in the module. In the months following the training, the rate of PDMP use increased by a median of four use-cases per month among providers in practice for less than 20 years (p = 0.039) and two use-cases per month among infrequent opioid prescribers (p = 0.014). Conclusion A brief online case-based educational intervention was associated with a significant increase in the rate of PDMP use among infrequent opioid prescribers and those in practice less than 20 years.

UNSTRUCTURED In an era of accelerating health information technology capability, healthcare organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and healthcare costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Not surprisingly, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of healthcare prediction models. In this article, we review the rationale behind the push to integrate SBDH data into healthcare predictive models. We also explore the technical, strategic, and ethical challenges faced as this process unfolds across the nation. We then offer several recommendations to overcome these challenges in order to reach the promise of SBDH predictive analytics to improve health and decrease healthcare disparities.