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Abstract Background Two propensity score (PS) based balancing covariate methods, the overlap weighting method (OW) and the fine stratification method (FS), produce superb covariate balance. OW has been compared with various weighting methods while FS has been compared with the traditional stratification method and various matching methods. However, no study has yet compared OW and FS. In addition, OW has not yet been evaluated in large claims data with low prevalence exposure and with low frequency outcomes, a context in which optimal use of balancing methods is critical. In the study, we aimed to compare OW and FS using real-world data and simulations with low prevalence exposure and with low frequency outcomes. Methods We used the Texas State Medicaid claims data on adult beneficiaries with diabetes in 2012 as an empirical example (N = 42,628). Based on its real-world research question, we estimated an average treatment effect of health center vs. non-health center attendance in the total population. We also performed simulations to evaluate their relative performance. To preserve associations between covariates, we used the plasmode approach to simulate outcomes and/or exposures with N = 4,000. We simulated both homogeneous and heterogeneous treatment effects with various outcome risks (1-30% or observed: 27.75%) and/or exposure prevalence (2.5-30% or observed:10.55%). We used a weighted generalized linear model to estimate the exposure effect and the cluster-robust standard error (SE) method to estimate its SE. Results In the empirical example, we found that OW had smaller standardized mean differences in all covariates (range: OW: 0.0–0.02 vs. FS: 0.22–3.26) and Mahalanobis balance distance (MB) ( 0.049) than FS. In simulations, OW also achieved smaller MB (homogeneity: 0.04; heterogeneity: 0.0-0.11 vs. 0.07–0.29), relative bias (homogeneity: 4.04–56.20 vs. 20–61.63; heterogeneity: 7.85–57.6 vs. 15.0-60.4), square root of mean squared error (homogeneity: 0.332–1.308 vs. 0.385–1.365; heterogeneity: 0.263-0.526 vs 0.313-0.620), and coverage probability (homogeneity: 0.0–80.4% vs. 0.0-69.8%; heterogeneity: 0.0-97.6% vs. 0.0-92.8%), than FS, in most cases. Conclusions These findings suggest that OW can yield nearly perfect covariate balance and therefore enhance the accuracy of average treatment effect estimation in the total population.

Abstract Background Limited research has explored the performance of health centers (HCs) compared to other primary care settings among children in the United States. We evaluated utilization, quality, and expenditures for pediatric Medicaid enrollees receiving care in HCs versus non-HCs. Methods This national cross-sectional study utilized 2012 Medicaid Analytic eXtract (MAX) claims to examine children 0–17 years with a primary care visit, stratified by whether majority (> 50%) of primary care visits were at HCs or non-HCs. Outcome measures include utilization (primary care visits, non-primary care outpatient visits, prescription claims, Emergency Department (ED) visits, hospitalizations) and quality (well-child visits, avoidable ED visits, avoidable hospitalizations). For children enrolled in fee-for-service Medicaid, we also measured expenditures. Propensity score-based overlap weighting was used to balance covariates. Results A total of 2,383,270 Medicaid-enrolled children received the majority of their primary care at HCs, while 18,540,743 did at non-HCs. In adjusted analyses, HC patients had 20% more primary care visits, 15% less non-primary care outpatient visits, and 21% less prescription claims than non-HC patients. ED visits were similar across the two groups, while HC patients had 7% lower chance of hospitalization than non-HC. Quality of care outcomes favored HC patients in main analyses, but results were less robust when excluding managed care beneficiaries. Total expenditures among the fee-for-service subpopulation were lower by $239 (8%) for HC patients. Conclusions In this study of nationwide claims data to evaluate healthcare utilization, quality, and spending among Medicaid-enrolled children who receive primary care at HCs versus non-HCs, findings suggest primary care delivery in HCs may be associated with a more cost-effective model of healthcare for children.

Introduction This nationwide study builds on prior research, which suggests that Federally Qualified Health Centers (FQHCs) and other primary care providers are associated with increased access to opioid use disorder (OUD) treatment. We compare health care utilization, spending, and quality for Medicaid patients diagnosed with OUD who receive primary care at FQHCs and Medicaid patients who receive most primary care in other settings, such as physician offices (non-FQHCs). We hypothesized that the integrated care model of FQHCs would be associated with greater access to medication for opioid use disorder (MOUD) and/or behavioral health therapy and lower rates of potentially inappropriate co-prescribing. Methods This cross-sectional study examined 2012 Medicaid Analytic eXtract files for patients diagnosed with OUD receiving most (>50%) primary care at FQHCs (N = 37,142) versus non-FQHCs (N = 196,712) in all 50 states and Washington DC. We used propensity score overlap weighting to adjust for measurable confounding between patients who received care at FQHCs versus non-FQHCs and increase generalizability of findings given variation in Medicaid programs and substance use policies across states. Results FQHC patients displayed higher primary care utilization and fee-for-service spending, and similar or lower utilization and fee-for-service spending for other health service categories. Contrary to our hypotheses, non-FQHC patients were more likely to receive timely (≤90 days) MOUD (buprenorphine, methadone, naltrexone, or suboxone) (Relative Risk [RR] = 1.10, 95% CI: 1.07, 1.12) and more likely be retained in medication treatment (>180 days) (RR = 1.12, 95% CI: 1.09, 1.14). However, non-FQHC patients were less likely to receive behavioral health therapy (mental health or substance use therapy) (RR = 0.90, 95% CI: 0.88, 0.92) and less likely to remain in behavioral health treatment (RR = 0.92, 95% CI: 0.89, 0.94). Non-FQHC patients were more likely to fill potentially inappropriate prescriptions of benzodiazepines and opioids after OUD diagnosis (RR = 1.35, 95% CI: 1.30, 1.40). Conclusions Observed patterns suggest that Medicaid patients diagnosed with OUD who obtained primary care at FQHCs received more integrated care compared to non-FQHC patients. Greater care integration may be associated with increased access to behavioral health therapy and quality of care (lower potentially inappropriate co-prescribing) but not necessarily greater access to MOUD.

IntroductionThis nationwide study builds on prior research, which suggests that Federally Qualified Health Centers (FQHCs) and other primary care providers are associated with increased access to opioid use disorder (OUD) treatment. We compare health care utilization, spending, and quality for Medicaid patients diagnosed with OUD who receive primary care at FQHCs and Medicaid patients who receive most primary care in other settings, such as physician offices (non-FQHCs). We hypothesized that the integrated care model of FQHCs would be associated with greater access to medication for opioid use disorder (MOUD) and/or behavioral health therapy and lower rates of potentially inappropriate co-prescribing.MethodsThis cross-sectional study examined 2012 Medicaid Analytic eXtract files for patients diagnosed with OUD receiving most (>50%) primary care at FQHCs (N = 37,142) versus non-FQHCs (N = 196,712) in all 50 states and Washington DC. We used propensity score overlap weighting to adjust for measurable confounding between patients who received care at FQHCs versus non-FQHCs and increase generalizability of findings given variation in Medicaid programs and substance use policies across states.ResultsFQHC patients displayed higher primary care utilization and fee-for-service spending, and similar or lower utilization and fee-for-service spending for other health service categories. Contrary to our hypotheses, non-FQHC patients were more likely to receive timely (≤90 days) MOUD (buprenorphine, methadone, naltrexone, or suboxone) (Relative Risk [RR] = 1.10, 95% CI: 1.07, 1.12) and more likely be retained in medication treatment (>180 days) (RR = 1.12, 95% CI: 1.09, 1.14). However, non-FQHC patients were less likely to receive behavioral health therapy (mental health or substance use therapy) (RR = 0.90, 95% CI: 0.88, 0.92) and less likely to remain in behavioral health treatment (RR = 0.92, 95% CI: 0.89, 0.94). Non-FQHC patients were more likely to fill potentially inappropriate prescriptions of benzodiazepines and opioids after OUD diagnosis (RR = 1.35, 95% CI: 1.30, 1.40).ConclusionsObserved patterns suggest that Medicaid patients diagnosed with OUD who obtained primary care at FQHCs received more integrated care compared to non-FQHC patients. Greater care integration may be associated with increased access to behavioral health therapy and quality of care (lower potentially inappropriate co-prescribing) but not necessarily greater access to MOUD.

Abstract Globally, congenital toxoplasmosis remains a significant cause of morbidity and mortality, and outbreaks of infection with T. gondii represent a significant, emerging public health burden, especially in the developing world. This parasite is a threat to public health. Disease often is not recognized and is inadequately managed. Herein, we analyze the status of congenital toxoplasmosis in Morocco, Colombia, the United States, and France. We identify the unique challenges faced by each nation in the implementation of optimal approaches to congenital toxoplasmosis as a public health problem. We suggest that developed and developing countries use a multipronged approach, modeling their public health management protocols after those in France. We conclude that education, screening, appropriate treatment, and the development of novel modalities will be required to intervene successfully in caring for individuals with this infection. Gestational screening has been demonstrated to be cost-effective, morbidity-sparing, and life-saving. Recognition of the value and promise of public health interventions to prevent human suffering from this emerging infection will facilitate better patient and societal outcomes.

BackgroundStreet imagery is a promising and growing big data source providing current and historical images in more than 100 countries. Studies have reported using this data to audit road infrastructure and other built environment features. Here we explore a novel application, using Google Street View (GSV) to predict travel patterns at the city level.MethodsWe sampled 34 cities in Great Britain. In each city, we accessed 2000 GSV images from 1000 random locations. We selected archived images from time periods overlapping with the 2011 Census and the 2011-2013 Active People Survey (APS). We manually annotated the images into seven categories of road users. We developed regression models with the counts of images of road users as predictors. The outcomes included Census-reported commute shares of four modes (combined walking plus public transport, cycling, motorcycle, and car), as well as APS-reported past-month participation in walking and cycling.ResultsWe found high correlations between GSV counts of cyclists ('GSV-cyclists') and cycle commute mode share (r = 0.92)/past-month cycling (r = 0.90). Likewise, GSV-pedestrians was moderately correlated with past-month walking for transport (r = 0.46), GSV-motorcycles was moderately correlated with commute share of motorcycles (r = 0.44), and GSV-buses was highly correlated with commute share of walking plus public transport (r = 0.81). GSV-car was not correlated with car commute mode share (r = -0.12). However, in multivariable regression models, all outcomes were predicted well, except past-month walking. The prediction performance was measured using cross-validation analyses. GSV-buses and GSV-cyclists are the strongest predictors for most outcomes.ConclusionsGSV images are a promising new big data source to predict urban mobility patterns. Predictive power was the greatest for those modes that varied the most (cycle and bus). With its ability to identify mode of travel and capture street activity often excluded in routinely carried out surveys, GSV has the potential to be complementary to new and traditional data. With half the world's population covered by street imagery, and with up to 10 years historical data available in GSV, further testing across multiple settings is warranted both for cross-sectional and longitudinal assessments.