Recruitment of practices for large-scale practice quality improvement transformation initiatives is difficult and costly. The cost of recruiting practices without existing partnerships is expensive, costing 7 times more than reaching out to familiar practices. Investigators initiating and studying practice quality improvement initiatives should budget adequate funds to support high-touch recruitment strategies, including building trusted relationships over a long time frame, for a year or more.

COVID-19 was recognized as a pandemic in the United States in March 2020. Since the emergence, research has explored conditions associated with the illness; however, racial disparities remain underexplored. The purpose of this paper is to explore disparities in conditions associated with an increased severity risk of COVID-19 including race, personal factors, healthcare accessibility, and affordability. Using data from the 2018 National Health Interview Survey (NHIS), univariate and multivariate analysis were performed. More Non-Hispanic (NH) Blacks (61.1%) and NH Whites (61.2%) had conditions associated with increased severity risk of COVID-19 compared to Hispanics (47.1%) (p < .001). Racial differences revealed a higher proportion of NH Blacks with increased severity risk of COVID-19 were female (p < .001), not married (p < .001), not employed for wages (p < .001), had accessibility issues with transportation (p < .001), and had affordability issues with paying for medicine (p < .001). A higher proportion of Hispanic persons had a health place change (p = .020), had accessibility issues (e.g. telephone (p < .001), longer wait times (p < .001), closed facility (p = .038)) and had affordability issue with worrying about pay (p < .001). Significant predictors that were positively associated with increased severity risk of COVID-19 for all racial/ethnic groups were being NH Black, older age, having appointment issues, and affordability issues with medicine. Differences in magnitude across racial group dynamics were observed. Racial disparities exist in conditions associated with increased severity risk of COVID-19. As future policies and interventions are developed, it is important to consider differentials across racial group dynamics.

Background: Effective quality improvement (QI) strategies are needed for small practices. Objective: The objective of this study was to compare practice facilitation implementing point-of-care (POC) QI strategies alone versus facilitation implementing point-of-care plus population management (POC+PM) strategies on preventive cardiovascular care. Design: Two arm, practice-randomized, comparative effectiveness study. Participants: Small and mid-sized primary care practices. Interventions: Practices worked with facilitators on QI for 12 months to implement POC or POC+PM strategies. Measures: Proportion of eligible patients in a practice meeting “ABCS” measures: (Aspirin) Aspirin/antiplatelet therapy for ischemic vascular disease, (Blood pressure) Controlling High Blood Pressure, (Cholesterol) Statin Therapy for the Prevention and Treatment of Cardiovascular Disease, and (Smoking) Tobacco Use: Screening and Cessation Intervention, and the Change Process Capability Questionnaire. Measurements were performed at baseline, 12, and 18 months. Results: A total of 226 practices were randomized, 179 contributed follow-up data. The mean proportion of patients meeting each performance measure was greater at 12 months compared with baseline: Aspirin 0.04 (95% confidence interval: 0.02–0.06), Blood pressure 0.04 (0.02–0.06), Cholesterol 0.05 (0.03–0.07), Smoking 0.05 (0.02–0.07); P<0.001 for each. Improvements were sustained at 18 months. At 12 months, baseline-adjusted difference-in-differences in proportions for the POC+PM arm versus POC was: Aspirin 0.02 (−0.02 to 0.05), Blood pressure −0.01 (−0.04 to 0.03), Cholesterol 0.03 (0.00–0.07), and Smoking 0.02 (−0.02 to 0.06); P>0.05 for all. Change Process Capability Questionnaire improved slightly, mean change 0.30 (0.09–0.51) but did not significantly differ across arms. Conclusion: Facilitator-led QI promoting population management approaches plus POC improvement strategies was not clearly superior to POC strategies alone.

The morbidity and mortality rates of African American men consistently rank among the lowest across all groups in the United States. African American men have one of the highest mortality rates for heart disease, cancer, stroke unintentional injuries and homicide (Gilbert et al., 2016). The leading cause of death in African American men age 24-34 years old is homicide (CDC, 2011). A majority of the health disparities experienced by African American men are the result of socioeconomic disadvantage, racism and residing in resource-poor communities (Bharmal et al., 2011). With disproportionate access to care and community stressors, there is a critical need to explore the health of African American men in high violent neighbourhoods. Many African American men live in disadvantaged communities marked by strenuous poverty, residential instability, joblessness, violent crime and educational shortages (Simning, Wijngaarden, & Conwell, 2012). African American men are also more likely not to have regular care, live in food deserts, work in unsafe environments and engage in unhealthy behaviours like tobacco use and alcohol consumption (Metzl, 2013). Dwelling in these communities, leave African American men at risk for adverse experiences that impact their health behaviours and outcomes. Community violence is an important consideration of the physical environment. Community violence is characterised by physical assault, sexual assault, homicide, mugging, gang violence, unnecessary force by authorities, theft and family violence (Walling, Eriksson, Putman, & Foy, 2011). Additionally, community violence exposure has been linked to higher rates of weapon involvement especially in African Americans, potentially due to self-defence, fear of violence or association with delinquency and aggression (Shetgiri, Boots, Lin, & Cheng, 2016). In particular, gun violence disproportionately impacts communities with social and economic inequities (Santilli et al., 2017). Inequities in communities of colour are due to structural racism such as segregation. Communities plagued with

BackgroundThe literature indicates that peer relations are an important aspect of the treatment and recovery of adolescents with substance use disorder (SUD). Unfortunately, no standard measure of peer relations exists. The objective of this research is to use exploratory factor analysis to examine the underlying factor structure of a 14-item peer relations scale for use in this treatment population.MethodsParticipants are 509 adolescents discharged from primary substance abuse treatment from 2003–2010. The data are from research conducted between six and twelve months post discharge via a 230-item questionnaire that included the 14-item peer relations scale. The scale has questions that assess the degree to which the adolescent's social contacts conform to norms of positive behavior and therefore foster non-use and recovery. The response rate was 62%.ResultsThe scale was decomposed by principal component factor analysis. When the matrix was rotated by varimax a three factor solution explaining 99.99% of the common variance emerged. The first factor yielded ten items that measure association with peers who engage in positive versus delinquent social behavior (positive versus negative social behavior). The three items in the second factor specify association with peers who use versus those who don’t use drugs, and thereby encourage recovery and discourage drug use (drug use). The third and factor contained two items measuring the degree to which the recovering adolescent associates with new or previous friends (post treatment peer association).ConclusionsThis scale is useful as a standard measure in that it begins to identify the measurable dimensions of peer relations that influence sustaining post treatment recovery.

Background: The United States experienced severe mental health budget cuts in many states across the nation during the years of the largest recession since the Great Depression. Illinois had one of the hardest hit mental health budgets in the country. The massive mental health funding cuts in Illinois, combined with the state's budget impasse, left fewer facilities available to provide treatment and support to those in need. Many of Illinois's most vulnerable populations either had reduced access, or no access to care. Serious spillover effects were felt by emergency rooms, community hospitals, and the criminal justice system. Therefore, the purpose of this research is to examine disparities in Health Related Quality of Life for those with depression after the funding cuts in Illinois. Methods: Data from the 2017 Behavior Risk Factor Surveillance System was analyzed by using multivariate logistic regression models of the Health Related Quality of Life measures for Illinoisans diagnosed with depressive disorders. Results: According to the regression models in this study, disparities exist in HRQOL for Illinoisans with depressive disorders. In all of the HRQOL models, income was associated with a reduction in HRQOL. Additionally, disparities exist in HRQOL for certain age groups and those who are unemployed. Interestingly, the models did not show any racial disparities as anticipated. Conclusion: Without the basic policy-level deficiencies addressed, disparities in Health Related Quality of Life for Illinois's most vulnerable populations will continue to exist as will costly economic spillover effects.

treated with docetaxel-based chemotherapy. EQ-5D and FACT-P data were collected for a subset of patients at baseline and throughout the study until treatment discontinuation. We compared three statistical mapping techniques to estimate patients' EQ-5D index scores determined using the UK-tariff: (a) linear regression estimated by generalized estimating equation (GEE) algorithms; (b) two-part model (TPM) combining logistic and linear regression estimated by GEE algorithms; (c) separate mapping algorithms for patients with poor health defined as FACT-P Յ78.To select the best model specification, four different sets of explanatory variables were compared. The models were fitted to the full dataset and cross-validated using a 10-fold in-sample cross-validation. The variance explained by the model was assessed by the marginal R2. Model performance was assessed by comparing predicted and observed mean EQ-5D scores, the mean absolute error (MAE) and the root mean squared Error (RMSE). RESULTS: Values for both FACT-P and EQ-5D were available for 234 patients. The TPM model including the FACT-P sub-domain scores and demographic variables was the best-performing model (marginal R2 ϭ 0.689) providing the most accurate predictions (MAE ϭ 0.125; RMSE ϭ 0.170).

Purpose The passage of the Affordable Care Act in the US resulted in more Americans with health insurance coverage as well as expanded health benefits. However, barriers in accessing health care still exist in the US especially as it relates to some of the most vulnerable Americans including those with depressive disorders. The purpose of this cross-sectional secondary data analysis was to examine the differences in health-related quality of life for individuals with depressive disorders in early years of the implementation of the Affordable Care Act as compared to later years of implementation. Methods This study used a repeated cross-sectional design that pooled data from the 2011–2017 Behavioral Risk Factor Surveillance System which is a nationally representative survey of the non-institutionalized U.S. population. Logistic regression models were used to evaluate the before and after impact of the Affordable Care Act on health related quality of life for those with depressive disorders. Results Those with depressive disorders in early years of implementation of the Affordable Care Act were less likely to report 14 or more days of poor physical health (AOR  =  0.96; 95% CI: 0.95, 0.98), were less likely to report 14 or more days of poor mental health (AOR  =  0.93; 95% CI: 0.92, 0.94), and less likely to report 14 or more days of overall poor physical and mental health (AOR  =  0.93; 95% CI: 0.90, 0.96) as opposed to later years of implementation. Conclusions Our results indicate poorer health related quality of life for those with depressive disorders in later years of implementation of the Affordable Care Act. Despite expanded mental health benefits under the Affordable Care Act, those benefits do not always translate into improved access or improved patient-reported outcomes. The federal government needs to comprehensively address mental health services in order to improve patient-reported outcomes and mental health treatment for those with depression.