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Despite extensive evidence of work as a key social determinant of hypertension, risk prediction equations incorporating this information are lacking. Such limitations hinder clinicians’ ability to tailor patient care and comprehensively address hypertension risk factors. This study examined whether including work characteristics in hypertension risk equations improves their predictive accuracy. Using occupation ratings from the Occupational Information Network database, we measured job demand, job control, and supportiveness of supervisors and coworkers for occupations in the United States economy. We linked these occupation-based measures with the employment status and health data of participants in the Coronary Artery Risk Development in Young Adults (CARDIA) study. We fit logistic regression equations to estimate the probability of hypertension onset in five years among CARDIA participants with and without variables reflecting work characteristics. Based on the Harrell’s c- and Hosmer–Lemeshow’s goodness-of-fit statistics, we found that our logistic regression models that include work characteristics predict hypertension onset more accurately than those that do not incorporate these variables. We also found that the models that rely on occupation-based measures predict hypertension onset more accurately for White than Black participants, even after accounting for a sample size difference. Including other aspects of work, such as workers’ experience in the workplace, and other social determinants of health in risk equations may eliminate this discrepancy. Overall, our study showed that clinicians should examine workers’ work-related characteristics to tailor hypertension care plans appropriately.

In the United States (US), Black—particularly Black female—healthcare workers are more likely to hold occupations with high job demand, low job control with limited support from supervisors or coworkers and are more vulnerable to job loss than their white counterparts. These work-related factors increase the risk of hypertension. This study examines the extent to which occupational segregation explains the persistent racial inequity in hypertension in the healthcare workforce and the potential health impact of workforce desegregation policies. We simulated a US healthcare workforce with four occupational classes: health diagnosing professionals (i.e., highest status), health treating professionals, healthcare technicians, and healthcare aides (i.e., lowest status). We simulated occupational segregation by allocating 25-year-old workers to occupational classes with the race- and gender-specific probabilities estimated from the American Community Survey data. Our model used occupational class attributes and workers’ health behaviors to predict hypertension over a 40-year career. We tracked the hypertension prevalence and the Black–white prevalence gap among the simulated workers under the staus quo condition (occupational segregation) and the experimental conditions in which occupational segregation was eliminated. We found that the Black–white hypertension prevalence gap became approximately one percentage point smaller in the experimental than in the status quo conditions. These findings suggest that policies designed to desegregate the healthcare workforce may reduce racial health inequities in this population. Our microsimulation may be used in future research to compare various desegregation policies as they may affect workers’ health differently.

Background: Hospitalizations involving opioid use disorder (OUD) are increasing. Medications for opioid use disorder (MOUD) reduce mortality and acute care utilization. Hospitalization is a reachable moment for initiating MOUD and arranging for ongoing MOUD engagement following hospital discharge. Despite existing quality metrics for MOUD initiation and engagement, few hospitals provide hospital based opioid treatment (HBOT). This protocol describes a cluster-randomized hybrid type-2 implementation study comparing low-intensity and high-intensity implementation support strategies to help community hospitals implement HBOT. Methods: Four state implementation hubs with expertise in initiating HBOT programs will provide implementation support to 24 community hospitals (6 hospitals/hub) interested in starting HBOT. Community hospitals will be randomized to 24-months of either a low-intensity intervention (distribution of an HBOT best-practice manual, a lecture series based on the manual, referral to publicly available resources, and on-demand technical assistance) or a high-intensity intervention (the low-intensity intervention plus funding for a hospital HBOT champion and regular practice facilitation sessions with an expert hub). The primary efficacy outcome, adapted from the National Committee on Quality Assurance, is the proportion of patients engaged in MOUD 34-days following hospital discharge. Secondary and exploratory outcomes include acute care utilization, non-fatal overdose, death, MOUD engagement at various time points, hospital length of stay, and discharges against medical advice. Primary, secondary, and exploratory outcomes will be derived from state Medicaid data. Implementation outcomes, barriers, and facilitators are assessed via longitudinal surveys, qualitative interviews, practice facilitation contact logs, and HBOT sustainability metrics. We hypothesize that the proportion of patients receiving care at hospitals randomized to the high-intensity arm will have greater MOUD engagement following hospital discharge. Discussion: Initiation of MOUD during hospitalization improves MOUD engagement post hospitalization. Few studies, however, have tested different implementation strategies on HBOT uptake, outcome, and sustainability and only one to date has tested implementation of a specific type of HBOT (addiction consultation services). This cluster-randomized study comparing different intensities of HBOT implementation support will inform hospitals and policymakers in identifying effective strategies for promoting HBOT dissemination and adoption in community hospitals. Trial registration: NCT04921787.

In the USA, Black and Hispanic or Latinx individuals continue to be disproportionately affected by HIV. Applying a distributional cost-effectiveness framework, we estimated the cost-effectiveness and epidemiological impact of two combination implementation approaches to identify the approach that best meets the dual objectives of improving population health and reducing racial or ethnic health disparities.

The purpose of our study was to estimate the cost-effectiveness of complementary and integrative approaches for chronic back or neck pain. We developed a state transition or Markov model to estimate the benefits, harms, and costs of common treatments for back and neck pain over a five-year time period. Treatments assessed included acupuncture, cognitive behavioral therapy, home exercise and advice, mindfulness-based stress reduction, non-steroidal anti-inflammatory medications, spinal manipulation, supervised exercise, and yoga. We assessed the impact of treatments in multiple populations including those with high-impact pain and older adults. We found that yoga resulted in the lowest costs and largest health benefits relative to all other treatments across multiple populations. Other complementary and integrative approaches such as massage, mindfulness-based stress reduction, cognitive behavioral therapy, and spinal manipulation were also shown to be cost-effective options relative to home exercise and advice for chronic spine pain across different populations. Findings for these treatments were not sensitive to changes in key model parameters impacting costs or effectiveness. In conclusion, multiple treatments including yoga, cognitive behavioral therapy, massage, mindfulness-based stress reduction, and spinal manipulation are likely cost-effective for chronic spine pain relative to guideline recommended self-management approach of home exercise and advice. NCCIH K01AT008965.

(Abstracted from JAMA2016;315(17):1864–1873)Antibiotic-resistant infections, driven by inappropriate outpatient antibiotic use, are widespread in the United States. A number of initiatives, including the Centers for Disease Control and Prevention's (CDC's) Get Smart: Know When Antibiotics Work, the National Action Plan for Combating Antibiotic-Resistant Bacteria, and Healthcare Effectiveness Data and Information Set (HEDIS) have been undertaken to promote appropriate outpatient antibiotic use; however, the fraction that is inappropriate and has to be reduced is not known.

Networks are often used to incorporate heterogeneity in contact patterns in mathematical models of pathogen spread. However, few tools exist to evaluate whether potential transmission pathways in a population are adequately represented by an observed contact network. Here, we describe a novel permutation-based approach, the network k-test, to determine whether the pattern of cases within the observed contact network are likely to have resulted from transmission processes in the network, indicating that the network represents potential transmission pathways between nodes. Using simulated data of pathogen spread, we compare the power of this approach to other commonly used analytical methods. We test the robustness of this technique across common sampling constraints, including undetected cases, unobserved individuals and missing interaction data. We also demonstrate the application of this technique in two case studies of livestock and wildlife networks. We show that the power of the k-test to correctly identify the epidemiologic relevance of contact networks is substantially greater than other methods, even when 50% of contact or case data are missing. We further demonstrate that the impact of missing data on network analysis depends on the structure of the network and the type of missing data.