Background Methamphetamine (MA) use has been linked anecdotally to rampant dental disease. The authors sought to determine the relative prevalence of dental comorbidities in MA users, verify whether MA users have more quantifiable dental disease and report having more dental problems than nonusers and establish the influence of mode of MA administration on oral health outcomes. Methods Participating physicians provided comprehensive medical and oral assessments for adults dependent on MA (n = 301). Trained interviewers collected patients' self-reports regarding oral health and substance-use behaviors. The authors used propensity score matching to create a matched comparison group of nonusers from participants in the the Third National Health and Nutrition Examination Survey (NHANES III). Results Dental or oral disease was one of the most prevalent (41.3 percent) medical cormorbidities in MA users who otherwise were generally healthy. On average, MA users had significantly more missing teeth than did matched NHANES III control participants (4.58 versus 1.96, P < .001) and were more likely to report having oral health problems (P < .001). Significant subsets of MA users expressed concerns with their dental appearance (28.6 percent), problems with broken or loose teeth (23.3 percent) and tooth grinding (bruxism) or erosion (22.3 percent). The intravenous use of MA was significantly more likely to be associated with missing teeth than was smoking MA (odds ratio = 2.47; 95 percent confidence interval = 1.3-4.8). Conclusions Overt dental disease is one of the key distinguishing comorbidities in MA users. MA users have demonstrably higher rates of dental disease and report long-term unmet oral health needs. Contrary to common perception, users who smoke or inhale MA have lower rates of dental disease than do those who inject the drug. Many MA users are concerned with the cosmetic aspects of their dental disease, and these concerns could be used as behavioral triggers for targeted interventions. Clinical Implications Dental disease may provide a temporally stable MA-specific medical marker with discriminant utility in identifying MA users. Dentists can play a crucial role in the early detection of MA use and participate in the collaborative care of MA users.

Background Whether hospitals with the highest risk-standardized readmission rates (RSRRs) subsequently experienced the greatest improvement after passage of the Medicare Hospital Readmissions Reduction Program (HRRP) is unknown. Objective To evaluate whether passage of the HRRP was followed by acceleration in improvement in 30-day RSRRs after hospitalizations for acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia and whether the lowest-performing hospitals had faster acceleration in improvement after passage of the law than hospitals that were already performing well. Design Pre–post analysis stratified by hospital performance groups. Setting U.S. acute care hospitals. Patients 15 170 008 Medicare patients discharged alive from 2000 to 2013. Intervention Passage of the HRRP. Measurements 30-day readmission rates after hospitalization for AMI, CHF, or pneumonia for hospitals in the highest-performance (0% penalty), average-performance (>0% and <0.50% penalty), low-performance (≥0.50% and <0.99% penalty), and lowest-performance (≥0.99% penalty) groups. Results Of 2868 hospitals serving 1 109 530 Medicare discharges annually, 30.1% were highest performers, 44.0% were average performers, 16.8% were low performers, and 9.0% were lowest performers. After controlling for prelaw trends, an additional 67.6 (95% CI, 66.6 to 68.4), 74.8 (CI, 74.0 to 75.4), 85.4 (CI, 84.0 to 86.8), and 95.1 (CI, 92.6 to 97.5) readmissions per 10 000 discharges were found to have been averted per year in the highest-, average-, low-, and lowest-performance groups, respectively, after passage of the law. Limitation Inability to distinguish between improvement caused by the magnitude of the penalty or by different levels of health improvement in different patient populations. Conclusion After passage of the HRRP, 30-day RSRRs for myocardial infarction, heart failure, and pneumonia decreased more rapidly than before the law’s passage. Improvement was most marked for hospitals with the lowest prelaw performance.

Summary Methods based on the propensity score comprise one set of valuable tools for comparative effectiveness research and for estimating causal effects more generally. These methods typically consist of two distinct stages: 1) a propensity score stage where a model is fit to predict the propensity to receive treatment (the propensity score), and 2) an outcome stage where responses are compared in treated and untreated units having similar values of the estimated propensity score. Traditional techniques conduct estimation in these two stages separately; estimates from the first stage are treated as fixed and known for use in the second stage. Bayesian methods have natural appeal in these settings because separate likelihoods for the two stages can be combined into a single joint likelihood, with estimation of the two stages carried out simultaneously. One key feature of joint estimation in this context is “feedback” between the outcome stage and the propensity score stage, meaning that quantities in a model for the outcome contribute information to posterior distributions of quantities in the model for the propensity score. We provide a rigorous assessment of Bayesian propensity score estimation to show that model feedback can produce poor estimates of causal effects absent strategies that augment propensity score adjustment with adjustment for individual covariates. We illustrate this phenomenon with a simulation study and with a comparative effectiveness investigation of carotid artery stenting vs. carotid endarterectomy among 123,286 Medicare beneficiaries hospitlized for stroke in

Causal inference with observational data frequently relies on the notion of the propensity score (PS) to adjust treatment comparisons for observed confounding factors. As decisions in the era of “big data” are increasingly reliant on large and complex collections of digital data, researchers are frequently confronted with decisions regarding which of a high-dimensional covariate set to include in the PS model in order to satisfy the assumptions necessary for estimating average causal effects. Typically, simple or ad-hoc methods are employed to arrive at a single PS model, without acknowledging the uncertainty associated with the model selection. We propose three Bayesian methods for PS variable selection and model averaging that 1) select relevant variables from a set of candidate variables to include in the PS model and 2) estimate causal treatment effects as weighted averages of estimates under different PS models. The associated weight for each PS model reflects the data-driven support for that model’s ability to adjust for the necessary variables. We illustrate features of our proposed approaches with a simulation study, and ultimately use our methods to compare the effectiveness of surgical vs. nonsurgical treatment for brain tumors among 2,606 Medicare beneficiaries. Supplementary materials are available online.

Both space and membership in geographically-embedded administrative units can produce variations in health, resulting in geographic clusters of good and poor health. Despite important differences between these two types of dependence, one is easily mistaken for the other, and the possibility that both are at work is commonly ignored. We fit a series of hierarchical and spatially-explicit multilevel models to a U.S. county-level life dataset of life expectancy in 1999 to demonstrate approaches for data analysis and interpretation when multiple sources of area-clustering are present. We demonstrate the methods to detect, interpret, and differentiate evidence of spatial and geographic membership effects and discuss key considerations for analyzing data with spatial or/and membership dimensions. We find evidence that life expectancy is driven by both within-state geographic process, and by spatial processes. We argue that considering spatial and membership processes simultaneously yields valuable insights into the patterning of area variations in health.

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed distance adjusted propensity score matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade-offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.

The translation of salivary alpha-amylase (sAA) to the ambulatory assessment of stress hinges on the development of technologies capable of speedy and accurate reporting of sAA levels. Here, we describe the developmental validation and usability testing of a point-of-care, colorimetric, sAA biosensor. A disposable test strip allows for streamlined sample collection and a corresponding hand-held reader with integrated analytic capabilities permits rapid analysis and reporting of sAA levels. Bioanalytical validation utilizing saliva samples from 20 normal subjects indicates that, within the biosensor’s linear range (10–230 U/ml), its accuracy (R2 = 0.989), precision (CV < 9%), and measurement repeatability (range −3.1% to + 3.1%) approach more elaborate laboratory-based, clinical analyzers. The truncated sampling-reporting cycle (< 1 minute) and the excellent performance characteristics of the biosensor has the potential to take sAA analysis out of the realm of dedicated, centralized laboratories and facilitate future sAA biomarker qualification studies.

Methods for causal inference regarding health effects of air quality regulations are met with unique challenges because (1) changes in air quality are intermediates on the causal pathway between regulation and health, (2) regulations typically affect multiple pollutants on the causal pathway towards health, and (3) regulating a given location can affect pollution at other locations, that is, there is interference between observations. We propose a principal stratification method designed to examine causal effects of a regulation on health that are and are not associated with causal effects of the regulation on air quality. A novel feature of our approach is the accommodation of a continuously scaled multivariate intermediate response vector representing multiple pollutants. Furthermore, we use a spatial hierarchical model for potential pollution concentrations and ultimately use estimates from this model to assess validity of assumptions regarding interference. We apply our method to estimate causal effects of the 1990 Clean Air Act Amendments among approximately 7 million Medicare enrollees living within 6 miles of a pollution monitor.