Community Behavioral Health Service Use and Criminal Recidivism of People With Mental, Substance Use, and Co-occurring Disorders.

OBJECTIVE: This study assessed the relationship between community behavioral health service (CBHS) use and criminal recidivism in a broad sample of potential beneficiaries and by diagnostic group. METHODS: Among a cohort of people on probation with any mental and/or substance use disorder (N=772), the study estimated the effect of CBHS use on rearrest with Cox proportional hazards models. RESULTS: Service use significantly predicted reduced recidivism among people with any mental disorder (hazard rate=0.36, p=0.008), but not among those with any substance use disorder or co-occurring disorders. CONCLUSIONS: CBHS use in a given week predicted a 64% reduced recidivism risk during the following week among people with any mental disorder. However, CBHS use had no clear relationship with recidivism among people with co-occurring disorders or any substance use disorder. CBHS use may reduce recidivism, depending on recipient and service characteristics.

Evaluating a Key Instrumental Variable Assumption Using Randomization Tests.

Instrumental variable (IV) analyses are becoming common in health services research and epidemiology. Most IV analyses use naturally occurring instruments, such as distance to a hospital. In these analyses, investigators must assume that the instrument is as-if randomly assigned. This assumption cannot be tested directly, but it can be falsified. Most IV falsification tests compare relative prevalence or bias in observed covariates between the instrument and exposure. These tests require investigators to make covariate-by-covariate judgments about the validity of the IV design. Often, only some covariates are well-balanced, making it unclear whether as-if randomization can be assumed for the instrument. We propose an alternative falsification test that compares IV balance or bias with the balance or bias that would have been produced under randomization. A key advantage of our test is that it allows for global balance measures as well as easily interpretable graphical comparisons. Furthermore, our test does not rely on parametric assumptions and can be used to validly assess whether the instrument is significantly closer to being as-if randomized than the exposure. We demonstrate our approach using data from (SPOT)light, a prospective cohort study carried out in 48 National Health Service hospitals in the United Kingdom between November 1, 2010, and December 31, 2011. This study used bed availability in the intensive care unit as an instrument for admission to the intensive care unit.

Randomization-based inference for Bernoulli trial experiments and implications for observational studies.

We present a randomization-based inferential framework for experiments characterized by a strongly ignorable assignment mechanism where units have independent probabilities of receiving treatment. Previous works on randomization tests often assume these probabilities are equal within blocks of units. We consider the general case where they differ across units and show how to perform randomization tests and obtain point estimates and confidence intervals. Furthermore, we develop rejection-sampling and importance-sampling approaches for conducting randomization-based inference conditional on any statistic of interest, such as the number of treated units or forms of covariate balance. We establish that our randomization tests are valid tests, and through simulation we demonstrate how the rejection-sampling and importance-sampling approaches can yield powerful randomization tests and thus precise inference. Our work also has implications for observational studies, which commonly assume a strongly ignorable assignment mechanism. Most methodologies for observational studies make additional modeling or asymptotic assumptions, while our framework only assumes the strongly ignorable assignment mechanism, and thus can be considered a minimal-assumption approach.

Tuning electric field aligned CNT architectures via chemistry, morphology, and sonication from micro to macroscopic scale.

Electric-field alignment of carbon nanotubes (CNT) is widely used to produce composite materials with anisotropic mechanical, electrical, and optical properties. Nevertheless, consistent results are difficult to achieve, and even under identical electric field conditions the resulting aligned morphologies can vary over µm to cm length scales. In order to improve reproducibility, this study addresses (1) how solution processing steps (oxidation, sonication) affect CNT properties, and (2) how CNT chemistry, morphology, and dispersion influence alignment. Aligned CNT were deposited onto PVDF membranes using a combination of electric-field alignment and vacuum-filtration. At each step in solution processing, the CNT chemistry (oxygen content) and morphology (length/diameter) were characterized and compared to the final aligned morphology. Well-dispersed CNT with high oxygen content (>8.5%O) yielded uniform membrane coatings and microscopically aligned CNT, whereas CNT with low oxygen CNT (<2.2%O) produced aligned bundles visible at a macroscopic level, but microscopically the individual CNT remained disordered. Based on regression analysis, CNT with larger mean length and diameter, smaller length and diameter variation, and higher oxygen content yielded increased electrical anisotropy, and bath sonication was slightly preferable to probe sonication for initial dispersion.