Effects of Preoperative Intravenous Versus Subcutaneous Tranexamic Acid on Postoperative Periorbital Ecchymosis and Edema Following Upper Eyelid Blepharoplasty: A Prospective, Randomized, Double-Blinded, Placebo-Controlled, Comparative Study.

PURPOSE: To compare the effects of preoperative tranexamic acid (TXA) administered intravenously (IV) versus subcutaneously on postoperative ecchymosis and edema in patients undergoing bilateral upper eyelid blepharoplasty. METHODS: A prospective, double-blinded, placebo-controlled study of patients undergoing bilateral upper eyelid blepharoplasty at a single-center. Eligible participants were randomized to preoperatively receive either (1) 1 g of TXA in 100 ml normal saline IV, (2) 50 µl/ml of TXA in local anesthesia, or (3) no TXA. Primary outcomes included ecchymosis and edema at postoperative day 1 (POD1) and 7 (POD7). Secondary outcomes included operative time, pain, time until resuming activities of daily living, patient satisfaction, and adverse events. RESULTS: By comparison (IV TXA vs. local subcutaneous TXA vs. no TXA), ecchymosis scores were significantly lower on POD1 (1.31 vs. 1.56 vs. 2.09, p = 0.02) and on POD7 (0.51 vs. 0.66 vs. 0.98, p = 0.04) among those that received TXA. By comparison (IV TXA vs. local subcutaneous TXA vs. no TXA), significant reductions in edema scores occurred in those that received TXA on POD1 (1.59 vs. 1.43 vs. 1.91, p = 0.005) and on POD7 (0.85 vs. 0.60 vs. 0.99, p = 0.04). By comparison (IV TXA vs. local subcutaneous TXA vs. no TXA) patients treated with intravenous and local subcutaneous TXA preoperatively were more likely to experience shorter operative times (10.8 vs. 11.8 vs. 12.9 minutes, p = 0.01), reduced time to resuming activities of daily livings (1.6 vs. 1.6 vs. 2.3 days, p < 0.0001), and higher satisfaction scores at POD1 (8.8 vs. 8.7 vs. 7.9, p = 0.0002). No adverse events occurred were reported. CONCLUSION: In an analysis of 106 patients, preoperative TXA administered either IV or subcutaneously safely reduced postoperative ecchymosis and edema in patients undergoing upper eyelid blepharoplasty. While statistical superiority between intravenous versus local subcutaneous TXA treatment was not definitively identified, our results suggest clinical superiority with IV dosing.

Changes in firearm mortality following the implementation of state laws regulating firearm access and use.

Although 39,000 individuals die annually from gunshots in the US, research examining the effects of laws designed to reduce these deaths has sometimes produced inconclusive or contradictory findings. We evaluated the effects on total firearm-related deaths of three classes of gun laws: child access prevention (CAP), right-to-carry (RTC), and stand your ground (SYG) laws. The analyses exploit changes in these state-level policies from 1970 to 2016, using Bayesian methods and a modeling approach that addresses several methodological limitations of prior gun policy evaluations. CAP laws showed the strongest evidence of an association with firearm-related death rate, with a probability of 0.97 that the death rate declined at 6 y after implementation. In contrast, the probability of being associated with an increase in firearm-related deaths was 0.87 for RTC laws and 0.77 for SYG laws. The joint effects of these laws indicate that the restrictive gun policy regime (having a CAP law without an RTC or SYG law) has a 0.98 probability of being associated with a reduction in firearm-related deaths relative to the permissive policy regime. This estimated effect corresponds to an 11% reduction in firearm-related deaths relative to the permissive legal regime. Our findings suggest that a small but meaningful decrease in firearm-related deaths may be associated with the implementation of more restrictive gun policies.

Adjustment of Patient Experience Surveys for How People Respond.

BACKGROUND: Patient surveys are the primary tool to measure patient experiences of care. Caution must be taken when analyzing these data, as responses can be influenced by factors that do not reflect the quality of care received. OBJECTIVES: To provide a practical overview of adjusting patient experience survey results to address bias related to patient case-mix, extreme response tendency, and mode of survey administration. RESEARCH DESIGN: We discuss options for adjustment for biases in how people respond to patient experience surveys. RESULTS: Case-mix adjustment (CMA) aims to compare provider performance that would have been observed if all providers had treated the same set of patients by removing the effects of patient characteristics that vary across providers. Extreme response tendency can bias the measurement of the disparities in patient experiences even after typical CMAs, since differences in patients' use of extreme response options may affect patient experience scores when they have a skewed distribution. Survey mode may affect scores for the provider entity being evaluated (eg, hospital) more than CMA if survey mode differs at the provider level. CONCLUSIONS: It is best practice to evaluate known source of bias when analyzing patient experience surveys. Failure to adjust for patient case-mix, extreme response tendency, and survey mode in patient experience surveys may lead to erroneous comparisons of providers.

Impact on Nonfirearm Deaths of Firearm Laws Affecting Firearm Deaths: A Systematic Review and Meta-Analysis.

Background. There is debate whether policies that reduce firearm suicides or homicides are offset by increases in non-firearm-related deaths.Objectives. To assess the extent to which changes in firearm homicides and suicides following implementation of various gun laws affect nonfirearm homicides and suicides.Search Methods. We performed a literature search on 13 databases for studies published between 1995 and October 31, 2018 (PROSPERO CRD42019120105).Selection Criteria. We included studies if they (1) estimated an effect of 1 of 18 included classes of gun policy on firearm homicides or suicides, (2) included a control group or comparison group and evaluated time series data to establish that policies preceded their purported effects, and (3) provided estimated effects of the policy and inferential statistics for either total or nonfirearm homicides or suicides.Data Collection and Analysis. We extracted data from each study, including study timeframe, population, and statistical methods, as well as point estimates and inferential statistics for the effects of firearm policies on firearm deaths as well as either nonfirearm or overall deaths. We assessed quality at the estimate (study-policy-outcome) level by using prespecified criteria to evaluate the validity of inference and causal identification. For each estimate, we derived the mortality multiplier (i.e., the ratio of the policy's effect on total homicides or suicides; expressed as a change in the number of deaths) as a proportion of its effect on firearm homicides or suicides. Finally, we performed a meta-analysis to estimate overall mortality multipliers for suicide and homicide that account for both within- and between-study heterogeneity.Main Results. We identified 16 eligible studies (study timeframes spanning 1977-2015). All examined state-level policies in the United States, with most estimating effects of multiple policies, yielding 60 separate estimates of the mortality multiplier. From these, we estimated that a firearm law's effect on homicide, expressed as a change in the number of total homicide deaths, is 0.99 (95% confidence interval = 0.76, 1.22) times its effect on the number of firearm homicides. Thus, on average, changes in the number of firearm homicides caused by gun policies are neither offset nor compounded by second-order effects on nonfirearm homicides. There is insufficient evidence in the existing literature on suicide to indicate the extent to which the effects of gun policy changes on firearm suicides are offset or compounded by their effects on nonfirearm suicides.Authors' Conclusions. State gun policies that reduce firearm homicides are likely to reduce overall homicides in the state by approximately the same number. It is currently unknown whether the same holds for state gun policies that significantly reduce firearm suicides. The small number of studies meeting our inclusion criteria, issues of methodological quality within those studies, and the possibility of reporting bias are potential limitations of this review.Public Health Implications. Policies that reduce firearm homicides likely have large benefits for public health as there is little evidence to support a strong substitution effect between firearm and nonfirearm homicides at the population level. Further research is needed to determine whether policies that produce population-level reductions in firearm suicides will translate to overall declines in suicide rates.

Social Marketing of Mental Health Treatment: California's Mental Illness Stigma Reduction Campaign.

Objectives. To understand the processes involved in effective social marketing of mental health treatment. Methods. California adults experiencing symptoms of probable mental illness were surveyed in 2014 and 2016 during a major stigma reduction campaign (n = 1954). Cross-sectional associations of campaign exposure with stigma, treatment overall, and 2 stages of treatment seeking (perceiving a need for treatment and use conditional on perceiving a need) were examined in covariate-adjusted multivariable regression models. Results. Campaign exposure predicted treatment use overall (odds ratio [OR] = 1.82; 95% confidence interval [CI] = 1.17, 2.83). Exposure was associated with perceived need for services (OR = 1.64; 95% CI = 1.09, 2.47) but was not significantly associated with treatment use in models conditioned on perceiving a need (OR = 1.52; 95% CI = 0.78, 2.96). Exposure was associated with less stigma, but adjustment for stigma did not affect associations between exposure and either perceived need or treatment use. Conclusions. The California campaign appears to have increased service use by leading more individuals to interpret symptoms of distress as indicating a need for treatment. Social marketing has potential for addressing underuse of mental health services and may benefit from an increased focus on perceived need.

Associations between provider communication and personal recovery outcomes.

BACKGROUND: This study examined whether two types of provider communication considered important to quality of care (i.e., shows respect and explains understandably) are associated with mental health outcomes related to personal recovery (i.e., connectedness, hope, internalized stigma, life satisfaction, and empowerment). This study also tested whether these associations varied by the type of provider seen (i.e., mental health professional versus general medical doctor). METHODS: This sample included participants from the 2014 California Well-Being Survey, a representative survey of California residents with probable mental illness, who had recently obtained mental health services (N = 429). Multiple regression was used to test associations between provider communication and personal recovery outcomes and whether these associations were modified by provider type. RESULTS: Providers showing respect was associated with better outcomes across all five of the personal recovery domains, connectedness (ß = 1.12; p < .001), hope (ß = 0.72; p < .0001), empowerment (ß = 0.38; p < .05), life satisfaction (ß = 1.10; p < .001) and internalized stigma (ß = - 0.49; p < .05). Associations between provider showing respect and recovery outcomes were stronger among those who had seen a mental health professional only versus a general medical doctor only. CONCLUSIONS: Respectful communication may result in greater personal recovery from mental health problems. Respecting consumer perspectives is a hallmark feature of both recovery-oriented services and quality care, yet these fields have operated independently of one another. Greater integration between these two areas could significantly improve recovery-oriented mental health outcomes and quality of care.

Comparing Quality of Care in Veterans Affairs and Non-Veterans Affairs Settings.

BACKGROUND: Congress, veterans' groups, and the press have expressed concerns that access to care and quality of care in Department of Veterans Affairs (VA) settings are inferior to access and quality in non-VA settings. OBJECTIVE: To assess quality of outpatient and inpatient care in VA at the national level and facility level and to compare performance between VA and non-VA settings using recent performance measure data. MAIN MEASURES: We assessed Patient Safety Indicators (PSIs), 30-day risk-standardized mortality and readmission measures, and ORYX measures for inpatient safety and effectiveness; Healthcare Effectiveness Data and Information Set (HEDIS®) measures for outpatient effectiveness; and Consumer Assessment of Healthcare Providers and Systems Hospital Survey (HCAHPS) and Survey of Healthcare Experiences of Patients (SHEP) survey measures for inpatient patient-centeredness. For inpatient care, we used propensity score matching to identify a subset of non-VA hospitals that were comparable to VA hospitals. KEY RESULTS: VA hospitals performed on average the same as or significantly better than non-VA hospitals on all six measures of inpatient safety, all three inpatient mortality measures, and 12 inpatient effectiveness measures, but significantly worse than non-VA hospitals on three readmission measures and two effectiveness measures. The performance of VA facilities was significantly better than commercial HMOs and Medicaid HMOs for all 16 outpatient effectiveness measures and for Medicare HMOs, it was significantly better for 14 measures and did not differ for two measures. High variation across VA facilities in the performance of some quality measures was observed, although variation was even greater among non-VA facilities. CONCLUSIONS: The VA system performed similarly or better than the non-VA system on most of the nationally recognized measures of inpatient and outpatient care quality, but high variation across VA facilities indicates a need for targeted quality improvement.

Doubly robust matching estimators for high dimensional confounding adjustment.

Valid estimation of treatment effects from observational data requires proper control of confounding. If the number of covariates is large relative to the number of observations, then controlling for all available covariates is infeasible. In cases where a sparsity condition holds, variable selection or penalization can reduce the dimension of the covariate space in a manner that allows for valid estimation of treatment effects. In this article, we propose matching on both the estimated propensity score and the estimated prognostic scores when the number of covariates is large relative to the number of observations. We derive asymptotic results for the matching estimator and show that it is doubly robust in the sense that only one of the two score models need be correct to obtain a consistent estimator. We show via simulation its effectiveness in controlling for confounding and highlight its potential to address nonlinear confounding. Finally, we apply the proposed procedure to analyze the effect of gender on prescription opioid use using insurance claims data.

Collaborative targeted learning using regression shrinkage.

Causal inference practitioners are routinely presented with the challenge of model selection and, in particular, reducing the size of the covariate set with the goal of improving estimation efficiency. Collaborative targeted minimum loss-based estimation (CTMLE) is a general framework for constructing doubly robust semiparametric causal estimators that data-adaptively limit model complexity in the propensity score to optimize a preferred loss function. This stepwise complexity reduction is based on a loss function placed on a strategically updated model for the outcome variable through which the error is assessed using cross-validation. We demonstrate how the existing stepwise variable selection CTMLE can be generalized using regression shrinkage of the propensity score. We present 2 new algorithms that involve stepwise selection of the penalization parameter(s) in the regression shrinkage. Simulation studies demonstrate that, under a misspecified outcome model, mean squared error and bias can be reduced by a CTMLE procedure that separately penalizes individual covariates in the propensity score. We demonstrate these approaches in an example using electronic medical data with sparse indicator covariates to evaluate the relative safety of 2 similarly indicated asthma therapies for pregnant women with moderate asthma.

Differential Association of Stigma with Perceived Need and Mental Health Service Use.

This study examined the role of stigma at two stages of the treatment-seeking process by assessing associations between various types of stigma and perceived need for mental health treatment as well as actual treatment use. We analyzed cross-sectional data from the 2014 and 2016 California Well-Being Survey, a telephone survey with a representative sample of 1954 California residents with probable mental illness. Multivariable logistic regression indicated that perceived need was associated with less negative beliefs about mental illness (odds ratio [OR] = 0.72; 95% confidence interval [CI] = 0.54, 0.95) and greater intentions to conceal a mental illness (OR = 1.47; 95% CI = 1.12-1.92). Among respondents with perceived need, treatment use was associated with greater mental health knowledge/advocacy (OR = 1.63; 95% CI = 1.03-2.56) and less negative treatment attitudes (OR = 0.66; 95% CI = 0.43-1.00). Understanding which aspects of stigma are related to different stages of the help-seeking process is essential to guiding policy and program initiatives aimed at ensuring individuals with mental illness obtain needed mental health services.

Long-term impact of evidence-based quality improvement for facilitating medical home implementation on primary care health professional morale.

BACKGROUND: Poor morale among primary care providers (PCPs) and staff can undermine the success of patient-centered care models such as the patient-centered medical home that rely on highly coordinated inter-professional care teams. Medical home literature hypothesizes that participation in quality improvement can ease medical home transformation. No studies, however, have assessed the impact of quality improvement participation on morale (e.g., burnout or dissatisfaction) during transformation. The objective of this study is to examine whether primary care practices participating in evidence-based quality improvement (EBQI) during medical home transformation reduced burnout and increased satisfaction over time compared to non-participating practices. METHODS: We used a longitudinal quasi-experimental design to examine the impact of EBQI (vs. no EBQI), a multi-level, interdisciplinary approach for engaging frontline primary care practices in developing evidence-based improvement innovations and tools for spread on PCP and staff morale following the 2010 national implementation of the medical home model in the Veterans Health Administration. The sample included 356 primary care employees (107 primary care providers and 249 staff) from 23 primary care practices (6 intervention and 17 comparison) within one Veterans Health Administration region. Three intervention practices began EBQI in 2011 (early) and three more began EBQI in 2012 (late). Three waves of surveys were administered across 42 months beginning in November 2011 and ending in January 2016 approximately 2 years 18 months apart. We used repeated measures analysis of the survey data on medical home teams. Main outcome measures were the emotional exhaustion subscale from the Maslach Burnout Inventory, and job satisfaction. RESULTS: Six of 26 approved EBQI innovations directly addressed provider and staff morale; all 26 addressed medical home implementation challenges. Survey rates were 63% for baseline and 48% for both follow-up waves. Age was associated with lower burnout among PCPs (p = .039) and male PCPs had higher satisfaction (p = .037). Controlling for practice and PCP/staff characteristics, burnout increased by 5 points for PCPs in comparison practices (p = .024) and decreased by 1.4 points for early and 6.8 points (p = .039) for the late EBQI practices. CONCLUSIONS: Engaging PCPs and staff in EBQI reduced burnout over time during medical home transformation.

Pazopanib for renal cell carcinoma leads to elevated mean arterial pressures in a murine model.

BACKGROUND: In the setting of metastatic RCC (mRCC), pazopanib is approved as first line therapy. Unfortunately treatment may lead to cardiotoxicity such as hypertension, heart failure, and myocardial ischemia. OBJECTIVE: Define the in vivo role of pazopanib in the development of cardiotoxicity. METHODS: Wild type mice were dosed for 42 days via oral gavage, and separated into control and treatment (pazopanib) groups. Baseline ECG's, echocardiograms, and blood pressures were recorded. At the conclusion of the study functional parameters were again recorded, and animals were used for pathological, histological, and protein analysis. RESULTS: After 2 weeks of dosing with pazopanib, the treatment group exhibited a statistically significant increase in mean arterial pressure compared to control mice (119 ± 11.7 mmHg versus 108 ± 8.2 mmHg, p = 0.049). Treatment with pazopanib led to a significant reduction in the cardiac output of mice. CONCLUSION: Our findings in mice clearly demonstrate that treatment with pazopanib leads to a significant elevation in blood pressure after 2 weeks of dosing and this persists for the duration of dosing. The continued development of the cardio-oncology field will be paramount in providing optimal oncologic care while simultaneously improving cardiac outcomes through further investigation into the mechanisms of CV toxicity.

The positive effects of population-based preferential sampling in environmental epidemiology.

In environmental epidemiology, exposures are not always available at subject locations and must be predicted using monitoring data. The monitor locations are often outside the control of researchers, and previous studies have shown that "preferential sampling" of monitoring locations can adversely affect exposure prediction and subsequent health effect estimation. We adopt a slightly different definition of preferential sampling than is typically seen in the literature, which we call population-based preferential sampling. Population-based preferential sampling occurs when the location of the monitors is dependent on the subject locations. We show the impact that population-based preferential sampling has on exposure prediction and health effect estimation using analytic results and a simulation study. A simple, one-parameter model is proposed to measure the degree to which monitors are preferentially sampled with respect to population density. We then discuss these concepts in the context of PM2.5 and the EPA Air Quality System monitoring sites, which are generally placed in areas of higher population density to capture the population's exposure.

Comparative effectiveness of radiotherapy with vs. without temozolomide in older patients with glioblastoma.

It is unknown whether the addition of temozolomide (TMZ) to radiotherapy (RT) is associated with improved overall survival (OS) among older glioblastoma patients. We performed a retrospective cohort SEER-Medicare analysis of 1652 patients aged ≥65 years with glioblastoma who received ≥10 fractions of RT from 2005 to 2009, or from 1995 to 1999 before TMZ was available. Three cohorts were assembled based on diagnosis year and treatment initiated within 60 days of diagnosis: (1) 2005-2009 and TMZ/RT, (2) 2005-2009 and RT only, or (3) 1995-1999 and RT only. Associations with OS were estimated using Cox proportional hazards models and propensity score analyses; OS was calculated starting 60 days after diagnosis. Pre-specified sensitivity analyses were performed among patients who received long-course RT (≥27 fractions). Median survival estimates were 7.4 (IQR, 3.3-14.7) months for TMZ/RT, 5.9 (IQR, 2.6-12.1) months for RT alone in 2005-2009, and 5.6 (IQR, 2.7-9.6) months for RT alone in 1995-1999. OS at 2 years was 10.1 % for TMZ/RT, 7.1 % for RT in 2005-2009, and 4.7 % for RT in 1995-1999. Adjusted models suggested decreased mortality risk for TMZ/RT compared to RT in 2005-2009 (AHR, 0.86; 95 % CI, 0.76-0.98) and RT in 1995-1999 (AHR, 0.71; 95 % CI, 0.57-0.90). Among patients from 2005 to 2009 who received long-course RT, however, the addition of TMZ did not significantly improve survival (AHR, 0.91; 95 % CI, 0.80-1.04). In summary, among a large cohort of older glioblastoma patients treated in a real-world setting, the addition of TMZ to RT was associated with a small survival gain.

Model averaged double robust estimation.

Researchers estimating causal effects are increasingly challenged with decisions on how to best control for a potentially high-dimensional set of confounders. Typically, a single propensity score model is chosen and used to adjust for confounding, while the uncertainty surrounding which covariates to include into the propensity score model is often ignored, and failure to include even one important confounder will results in bias. We propose a practical and generalizable approach that overcomes the limitations described above through the use of model averaging. We develop and evaluate this approach in the context of double robust estimation. More specifically, we introduce the model averaged double robust (MA-DR) estimators, which account for model uncertainty in both the propensity score and outcome model through the use of model averaging. The MA-DR estimators are defined as weighted averages of double robust estimators, where each double robust estimator corresponds to a specific choice of the outcome model and the propensity score model. The MA-DR estimators extend the desirable double robustness property by achieving consistency under the much weaker assumption that either the true propensity score model or the true outcome model be within a specified, possibly large, class of models. Using simulation studies, we also assessed small sample properties, and found that MA-DR estimators can reduce mean squared error substantially, particularly when the set of potential confounders is large relative to the sample size. We apply the methodology to estimate the average causal effect of temozolomide plus radiotherapy versus radiotherapy alone on one-year survival in a cohort of 1887 Medicare enrollees who were diagnosed with glioblastoma between June 2005 and December 2009.

A synthetic estimator for the efficacy of clinical trials with all-or-nothing compliance.

A critical issue in the analysis of clinical trials is patients' noncompliance to assigned treatments. In the context of a binary treatment with all or nothing compliance, the intent-to-treat analysis is a straightforward approach to estimating the effectiveness of the trial. In contrast, there exist 3 commonly used estimators with varying statistical properties for the efficacy of the trial, formally known as the complier-average causal effect. The instrumental variable estimator may be unbiased but can be extremely variable in many settings. The as treated and per protocol estimators are usually more efficient than the instrumental variable estimator, but they may suffer from selection bias. We propose a synthetic approach that incorporates all 3 estimators in a data-driven manner. The synthetic estimator is a linear convex combination of the instrumental variable, per protocol, and as treated estimators, resembling the popular model-averaging approach in the statistical literature. However, our synthetic approach is nonparametric; thus, it is applicable to a variety of outcome types without specific distributional assumptions. We also discuss the construction of the synthetic estimator using an analytic form derived from a simple normal mixture distribution. We apply the synthetic approach to a clinical trial for post-traumatic stress disorder.

Racial/ethnic differences in perception of need for mental health treatment in a US national sample.

PURPOSE: To resolve contradictory evidence regarding racial/ethnic differences in perceived need for mental health treatment in the USA using a large and diverse epidemiologic sample. METHODS: Samples from 6 years of a repeated cross-sectional survey of the US civilian non-institutionalized population were combined (N = 232,723). Perceived need was compared across three non-Hispanic groups (whites, blacks and Asian-Americans) and two Hispanic groups (English interviewees and Spanish interviewees). Logistic regression models were used to test for variation across groups in the relationship between severity of mental illness and perceived need for treatment. RESULTS: Adjusting statistically for demographic and socioeconomic characteristics and for severity of mental illness, perceived need was less common in all racial/ethnic minority groups compared to whites. The prevalence difference (relative to whites) was smallest among Hispanics interviewed in English, -5.8% (95% CI -6.5, -5.2%), and largest among Hispanics interviewed in Spanish, -11.2% (95% CI -12.4, -10.0%). Perceived need was significantly less common among all minority racial/ethnic groups at each level of severity. In particular, among those with serious mental illness, the largest prevalence differences (relative to whites) were among Asian-Americans, -23.3% (95% CI -34.9, -11.7%) and Hispanics interviewed in Spanish, 32.6% (95% CI -48.0, -17.2%). CONCLUSIONS: This study resolves the contradiction in empirical evidence regarding the existence of racial/ethnic differences in perception of need for mental health treatment; differences exist across the range of severity of mental illness and among those with no mental illness. These differences should be taken into account in an effort to reduce mental health-care disparities.

Does exposure prediction bias health-effect estimation?: The relationship between confounding adjustment and exposure prediction.

In environmental epidemiology, we are often faced with 2 challenges. First, an exposure prediction model is needed to estimate the exposure to an agent of interest, ideally at the individual level. Second, when estimating the health effect associated with the exposure, confounding adjustment is needed in the health-effects regression model. The current literature addresses these 2 challenges separately. That is, methods that account for measurement error in the predicted exposure often fail to acknowledge the possibility of confounding, whereas methods designed to control confounding often fail to acknowledge that the exposure has been predicted. In this article, we consider exposure prediction and confounding adjustment in a health-effects regression model simultaneously. Using theoretical arguments and simulation studies, we show that the bias of a health-effect estimate is influenced by the exposure prediction model, the type of confounding adjustment used in the health-effects regression model, and the relationship between these 2. Moreover, we argue that even with a health-effects regression model that properly adjusts for confounding, the use of a predicted exposure can bias the health-effect estimate unless all confounders included in the health-effects regression model are also included in the exposure prediction model. While these results of this article were motivated by studies of environmental contaminants, they apply more broadly to any context where an exposure needs to be predicted.

State Policies Regulating Firearms and Changes in Firearm Mortality.

Importance: Despite high social and public health costs of firearm violence in the United States, the effects of many policies designed to reduce firearm mortality remain uncertain. Objective: To estimate the individual and joint effect sizes of state firearm policies on firearm-related mortality. Design, Setting, and Participants: In this comparative effectiveness study, bayesian methods were used to model panel data of annual, state-level mortality rates (1979-2019) for all US firearm decedents, with analyses conducted in October 2023. Exposures: Six classes of firearms policies: background checks, minimum age, waiting periods, child access, concealed carry, and stand-your-ground laws. Main Outcome and Measures: Primary outcomes (total firearm deaths, firearm homicide deaths, and firearm suicide deaths) were assessed using the National Vital Statistics System. Bayesian estimation was used to estimate the partial association of changes in firearms policies with subsequent changes in firearm mortality. Results: The estimated effect sizes of individual policies 5 or more years after implementation were generally small in magnitude and had considerable uncertainty. The policy class with the highest probability of reducing firearm deaths was child-access prevention laws, estimated to reduce overall firearm mortality by 6% (80% credible interval [CrI], -2% to -9%). The policy class with the highest probability of increasing firearm deaths was stand-your-ground laws, estimated to increase firearm homicides by 6% (80% CrI, 0% to 13% increase). Estimates of association of implementing multiple firearm restrictions with subsequent changes in firearm mortality yielded larger effect sizes. Moving from the most permissive to most restrictive set of firearm policies was associated with an estimated 20% reduction in firearm deaths (80% CrI, 10% to 28% reduction), with a 0.99 probability of any reductions in firearm death rates. Conclusions and Relevance: In this comparative effectiveness study of state firearm policies, the joint effect estimates of combinations of firearm laws were calculated, showing that restrictive firearm policies were associated with substantial reductions in firearm mortality. Although policymakers would benefit from knowing the effects of individual policies, the estimated changes in firearm mortality following implementation of individual policies were often small and uncertain.

Just in Reach Pay for Success: Impact Evaluation and Cost Analysis of a Permanent Supportive Housing Program.

Discharging individuals from jails and prisons who may be poorly equipped for independent living-such as those with a history of chronic health conditions, including serious mental illness-is likely to reinforce a pattern of homelessness and recidivism. Permanent supportive housing (PSH)-which combines a long-term housing subsidy with supportive services-has been proposed as a mechanism to intervene directly on this relationship between housing and health. In Los Angeles County, jail has become a default housing and services provider to unhoused individuals with serious mental health issues. In 2017, the county initiated the Just in Reach Pay for Success (JIR PFS) project, which provided PSH as an alternative to jail for individuals with a history of homelessness and chronic behavioral or physical health conditions. The authors of this study assessed whether the project led to changes in use of several county services, including justice, health, and homeless services. The authors examined changes in county service use, before and after incarceration, by JIR PFS participants and a comparison control group and found that use of jail services was significantly reduced after JIR PFS PSH placement, while the use of mental health and other services increased. The researchers assess that the net cost of the program is highly uncertain but that it may pay for itself in terms of reducing the use of other county services and therefore provide a cost-neutral means of addressing homelessness among individuals with chronic health conditions involved with the justice system in Los Angeles County.