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BackgroundOpioid tapering is increasingly utilized by providers to decrease risks of chronic opioid therapy, but it is unknown whether tapering is associated with termination of care.ObjectiveTo determine whether patients taking chronic opioid therapy who experienced opioid tapers were at greater risk of subsequently terminating their care compared with those who were continued on their doses.DesignRetrospective cohort study of patients in a large, urban health system between 2008 and 2012 with 2 years of follow-up.ParticipantsAdult patients prescribed a stable baseline dose of chronic opioid therapy of at least 25 morphine milligram equivalents per day during a baseline year.Main MeasuresAn opioid taper during an exposure year, defined as a reduction in the average daily dose of at least 30% from the baseline dose in both of the two 6-month periods in the year following the baseline year. Opioid dose continuation was defined as any increase in dose, no change in dose, or any decrease up to 30% compared with baseline dose in the exposure year. The primary outcome was termination of care, defined as no outpatient encounters in the health system, in the year following the exposure year.Key ResultsOf 1624 patients on chronic opioid therapy, 207 (15.5%) experienced an opioid taper and 78 (4.8%) experienced termination of care. Compared with opioid dose continuation, opioid taper was significantly associated with termination of care (AOR 4.3 [95% CI 2.2–8.5]).ConclusionsOpioid taper is associated with subsequent termination of care. These findings invite caution and demonstrate the need to fully understand the risks and benefits of opioid tapers.

Background In countless number of clinical trials, measurements of outcomes rely on instrument questionnaire items which however often suffer measurement error problems which in turn affect statistical power of study designs. The Cronbach alpha or coefficient alpha, here denoted by C α , can be used as a measure of internal consistency of parallel instrument items that are developed to measure a target unidimensional outcome construct. Scale score for the target construct is often represented by the sum of the item scores. However, power functions based on C α have been lacking for various study designs. Methods We formulate a statistical model for parallel items to derive power functions as a function of C α under several study designs. To this end, we assume fixed true score variance assumption as opposed to usual fixed total variance assumption. That assumption is critical and practically relevant to show that smaller measurement errors are inversely associated with higher inter-item correlations, and thus that greater C α is associated with greater statistical power. We compare the derived theoretical statistical power with empirical power obtained through Monte Carlo simulations for the following comparisons: one-sample comparison of pre- and post-treatment mean differences, two-sample comparison of pre-post mean differences between groups, and two-sample comparison of mean differences between groups. Results It is shown that C α is the same as a test-retest correlation of the scale scores of parallel items, which enables testing significance of C α . Closed-form power functions and samples size determination formulas are derived in terms of C α , for all of the aforementioned comparisons. Power functions are shown to be an increasing function of C α , regardless of comparison of interest. The derived power functions are well validated by simulation studies that show that the magnitudes of theoretical power are virtually identical to those of the empirical power. Conclusion Regardless of research designs or settings, in order to increase statistical power, development and use of instruments with greater C α , or equivalently with greater inter-item correlations, is crucial for trials that intend to use questionnaire items for measuring research outcomes. Discussion Further development of the power functions for binary or ordinal item scores and under more general item correlation strutures reflecting more real world situations would be a valuable future study.

Contact investigations among individuals living with drug-susceptible tuberculosis patients (source cases) have shown a high yield of tuberculosis disease and latent tuberculosis, but the yield of such investigations in households of drug-resistant tuberculosis source cases is unknown. In this systematic review and meta-analysis, we found 25 studies that evaluated a median of 111 (interquartile range, 21–302) household contacts of drug-resistant tuberculosis source cases. The pooled yield was 7.8% (95% CI, 5.6%–10.0%) for active tuberculosis and 47.2% (95% CI, 30.0%–61.4%) for latent tuberculosis, although there was significant statistical heterogeneity (P < .0001). More than 50% of secondary cases with drug susceptibility test results were concordant with those of the source case. Among studies that followed household members, the majority of secondary cases were detected within 1 year of the source case's diagnosis. Household contact investigation around drug-resistant tuberculosis patients is a high-yield intervention for detection of drug-resistant tuberculosis and prevention of ongoing transmission.

Introduction: Behavioral and mental health conditions present significant challenges in the United States where access to care is limited. Family medicine physicians play a crucial role in addressing these challenges, often serving as frontline clinicians for behavioral and mental health conditions. Methods: This study examined the current behavioral and mental health system in a predominantly rural 10-county region in the Southeastern United States through gap analysis in addition to a survey of preparedness and barriers among family medicine physicians in the region. Results: Gap analysis results indicated that (1) stigma and lack of accessible education about behavioral and mental health, (2) fragmented resources, (3) inaccessible care, and (4) workforce shortage and burnout were primary drivers of poor outcomes in the region. Survey results indicated that physicians feel prepared to treat anxiety and depression but feel less prepared to manage bipolar disorder, schizophrenia, and substance use disorders. Respondents disagreed that there are adequate local resources and referral options for patients with behavioral and mental health conditions. Lack of timely access, distance, cost/insurance status, were all cited by respondents as barriers to appropriate care. Conclusion and Recommendations: Findings underscore the importance of supporting family medicine physicians to enhance behavioral and mental healthcare outcomes. Behavioral health integration in primary care settings is a promising strategy to improve care accessibility and clinician preparedness. Bridging gaps in health care outcomes requires collaborative efforts, enhanced training, and policy advocacy within the family medicine community to ensure comprehensive and equitable behavioral and mental healthcare delivery.

Background/ObjectivesThree-dimensional optical (3DO) imaging systems that rapidly and accurately provide body shape and composition information are increasingly available in research and clinical settings. Recently, relatively low-cost and space efficient 3DO systems with the ability to report and track individual assessments were introduced to the consumer market for home use. This study critically evaluated the first 3DO imaging device intended for personal operation, the Naked Body Scanner (NBS), against reference methods.Participants/MethodsCircumferences at six standardized anatomic sites were measured with a flexible tape in 90 participants ranging in age (5–74 years), ethnicity, and adiposity. Regression analysis and Bland-Altman plots compared these direct measurements and dual-energy X-ray absorptiometry (DXA) %fat estimates to corresponding NBS values. Method precision was analyzed from duplicate anthropometric and NBS measurements in a subgroup of 51 participants.ResultsThe NBS exhibited greater variation in test–retest reliability (CV, 0.4–2.7%) between the six measured anatomic locations when compared with manually measured counterparts (0.2–0.4%). All six device-derived circumferences correlated with flexible tape references (R2s, 0.84–0.97; p < 0.0001). Measurement bias was apparent for three anatomic sites while mean differences were present for five. The NBS’s %fat estimates also correlated with DXA results (R2 = 0.73, p < 0.0001) with no significant bias.ConclusionsThis system opens a new era of digital home-based assessments that can be incorporated into weight loss or exercise interventions accessible to clinical investigators as well as individual users.

Randomization is an important tool used to establish causal inferences in studies designed to further our understanding of questions related to obesity and nutrition. To take advantage of the inferences afforded by randomization, scientific standards must be upheld during the planning, execution, analysis, and reporting of such studies. We discuss ten errors in randomized experiments from real-world examples from the literature and outline best practices for their avoidance. These ten errors include: representing nonrandom allocation as random, failing to adequately conceal allocation, not accounting for changing allocation ratios, replacing subjects in nonrandom ways, failing to account for non-independence, drawing inferences by comparing statistical significance from within-group comparisons instead of between-groups, pooling data and breaking the randomized design, failing to account for missing data, failing to report sufficient information to understand study methods, and failing to frame the causal question as testing the randomized assignment per se. We hope that these examples will aid researchers, reviewers, journal editors, and other readers to endeavor to a high standard of scientific rigor in randomized experiments within obesity and nutrition research.

Background Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification— in which external factors differentially impact intervention and control clusters. Results In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.

The Mini-Mental State Examination (MMSE) is a widely employed screening tool for the severity of cognitive impairment. Among the MMSE items, the pentagon copying test (PCT) requires participants to accurately replicate a sample of two interlocking pentagons. While the PCT is traditionally scored on a binary scale, there have been limited developments of granular scoring scale to assess task performance. In this paper, we present a novel three-stage algorithm, called Quantification of Interlocking Pentagons (QIP) which quantifies PCT performance by computing the areas of individual pentagons and their intersection areas, and a balance ratio between the areas of the two individual pentagons. The three stages of the QIP algorithm include: (1) detection of line segments, (2) unraveling of the interlocking pentagons, and (3) quantification of areas. A set of 497 PCTs from 84 participants including their baseline and follow-up PCTs from the Rush Memory and Aging Project was selected blinded about their cognitive and clinical status. Analysis of the quantified data revealed a significant inverse relationship between age and balance ratio (beta = − 0.49, p  = 0.0033), indicating that older age was associated with a smaller balance ratio. In addition, balance ratio was associated with perceptual speed (r = 0.71, p  = 0.0135), vascular risk factors (beta = − 3.96, p  = 0.0269), and medical conditions (beta = − 2.78, p  = 0.0389). The QIP algorithm can serve as a useful tool for enhancing the scoring of performance in the PCT.

Background Changes have recently been proposed to both the visit frequency and setting for delivery of prenatal care, including decreasing frequency of scheduled visits or using group visits or virtual visits. The impacts of participant engagement patterns with prenatal care on preterm birth (PTB) are not clearly understood. We aimed to characterize prenatal care visit patterns, examine their associations with PTB, and provide optimal cutoffs for care patterns. Methods This study is a secondary analysis of prenatal care visit data obtained from the randomized CRADLE study that tested the effects of group (GPNC) versus individual (IPNC) prenatal care on PTB. We analyzed prenatal care visit data from N  = 1,989 medically low-risk pregnant women who had at least one prenatal care visit between study enrollment and gestational age (GA) week 37. Prenatal care visit patterns before GA week 37 were predictors, characterized in terms of the number of IPNC and GPNC visits, duration of care, total hours of care, GA week at last visit, minimum gap (> 21 vs. ≤ 21 days) between any or GPNC visits, and discontinuation before the third trimester. PTB was the study outcome, defined as delivery < 37 GA weeks. Simple and multivariable logistic regression models and ROC analysis were applied to test associations and determine optimal cutoff points. Results Overall, > 7 visits during pregnancy (OR = 0.58, 95%CI: 0.43–0.79, p  < .001), > 3 visits in the third trimester (OR = 0.42, 95%CI: 0.30–0.57, p  < .001), > 2.25 care hours during pregnancy (OR = 0.67, 95%CI: 0.49–0.92, p  = .014), > 0.75 care hours during the third trimester (OR = 0.50, 95%CI: 0.37, 0.67, p  < .001), and > 147 days in care (OR = 0.41, 95%CI: 0.30–0.56, p  < .001) were all significantly associated with lower PTB rates. A minimum care gap of > 21 days was associated with higher PTB (aOR = 2.87, 95%CI: 1.76–4.69, p  < .001) and discontinuation of care before the third trimester was the strongest correlate of PTB (aOR = 12.6, 95%CI: 6.5–24.5), p  < .001) in terms of aOR compared to that of all the other pattern variables. Conclusion Patient engagement with prenatal care providers, including both duration and frequency, was associated with reduced risk of PTB. Any proposed revision to the schedule or frequency of prenatal care which decreases patient contact with healthcare providers may risk worsening birth outcomes. Clinical trial registration This study was registered on December 20, 2015, at ClinicalTrials.gov ( www.clinicaltrials.gov , NCT02640638) with a title, A RCT of Centering Pregnancy on Birth Outcomes (CRADLE).

Background: Personal protective measures help prevent infection and disease transmission during health crises such as Coronavirus disease 2019 (COVID-19). Populations facing barriers to adhering to these measures are more vulnerable to the health crisis. This study investigated the association of social drivers of health (SDoH), self-efficacy, and adverse substance use behavior changes with ability to practice COVID-19 personal protective behaviors among persons who inject drugs (PWID) with hepatitis C virus (HCV) infection history. Methods: This study used the Hepatitis C Real Options (HERO) study’s COVID-19 survey data (n = 157). The association of inability to practice COVID-19 personal protective behaviors (hand washing, social distancing, etc.) with (a) SDoH difficulties (employment, housing, etc.); (b) adverse substance use behavior change (overdose, injecting behavior, etc.); and (c) self-efficacy was tested using logistic regression. Results: Inability to practice any personal protective behaviors was more likely among those experiencing any vs. no SDoH difficulties [adjusted odds ratio (aOR) (95% confidence interval (CI))] = 4.57 (1.57, 16.40); p = 0.003] but less likely for those with higher overall self-efficacy [aOR (95% CI) = 0.55 (0.32, 0.93); p = 0.025] and self-efficacy for setting goals [aOR (95% CI) = 0.63 (0.40, 0.96); p = 0.031]. The association between adverse substance use behavior changes and the outcome was not significant. Conclusions: Greater SDoH difficulties and lower self-efficacy were associated with greater inability to practice COVID-19 personal protective behaviors. Interventions to meet SDoH-related challenges and increase self-efficacy could help encourage practice of personal protective behaviors and economically reduce disease burden during health crises.