Faculty Bibliography

BACKGROUND:Team-based care (TBC), a team of ≥2 healthcare professionals working collaboratively toward a shared clinical goal, is a recommended strategy to manage blood pressure (BP). However, the most effective and cost-effective TBC strategy is unknown. METHODS:A meta-analysis of clinical trials in US adults (aged ≥20 years) with uncontrolled hypertension (≥140/90 mm Hg) was performed to estimate the systolic BP reduction for TBC strategies versus usual care at 12 months. TBC strategies were stratified by the inclusion of a nonphysician team member who could titrate antihypertensive medications. The validated BP Control Model-Cardiovascular Disease Policy Model was used to project the expected BP reductions out to 10 years and simulate cardiovascular disease events, direct healthcare costs, quality-adjusted life years, and cost-effectiveness of TBC with physician and nonphysician titration. RESULTS:Among 19 studies comprising 5993 participants, the 12-month systolic BP change versus usual care was -5.0 (95% CI, -7.9 to -2.2) mm Hg for TBC with physician titration and -10.5 (-16.2 to -4.8) mm Hg for TBC with nonphysician titration. Relative to usual care at 10 years, TBC with nonphysician titration was estimated to cost $95 (95% uncertainty interval, -$563 to $664) more per patient and gain 0.022 (0.003-0.042) quality-adjusted life years, costing $4400/quality-adjusted life year gained. TBC with physician titration was estimated to cost more and gain fewer quality-adjusted life years than TBC with nonphysician titration. CONCLUSIONS:TBC with nonphysician titration yields superior hypertension outcomes compared with other strategies and is a cost-effective way to reduce hypertension-related morbidity and mortality in the United States.

In data collection for predictive modeling, under-representation of certain groups, based on gender, race/ethnicity, or age, may yield less-accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: i) fairness is often achieved by compromising accuracy for some groups; ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a Joint Fairness Model (JFM) approach for logistic regression models for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an Accelerated Smoothing Proximal Gradient Algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across-group parity, in comparison with the single fairness model, group-separate model, and group-ignorant model, especially when the minority group's sample size is small. Finally, we demonstrate the utility of the JFM method in a real-world example to obtain fair risk predictions for under-represented older patients diagnosed with coronavirus disease 2019 (COVID-19). This article is protected by copyright. All rights reserved.

BACKGROUND/UNASSIGNED:Research investigating adverse effects from drug use has focused extensively on poisonings and mortality. This study focuses on drug-related adverse effects not necessarily resulting in hospitalization or death among a population known for high prevalence of party drug use-electronic dance music (EDM) nightclub and festival attendees. METHODS/UNASSIGNED: = 1952). Those reporting past-month use of a drug were asked whether they had experienced a harmful or very unpleasant effect after use. We examined 20 drugs and drug classes with a particular focus on alcohol, cannabis, cocaine, and ecstasy. Prevalence and correlates of adverse effects were estimated. RESULTS/UNASSIGNED:Almost half (47.6%) of adverse effects involved alcohol and 19.0% involved cannabis. 27.6% of those using alcohol reported an adverse effect, while 19.5%, 15.0%, and 14.9% of participants reported an effect from use of cocaine, ecstasy, and cannabis, respectively. Use of less prevalent drugs, such as NBOMe, methamphetamine, fentanyls, and synthetic cathinones, tended to be associated with higher prevalence of adverse effects. The most consistent risk factor was younger age, while past-month use of a greater number of drugs was often a protective factor against adverse effects. For most drugs, taking too much was the most common perceived reason for the adverse effect, and visiting a hospital after use was most prevalent among those experiencing an adverse effect from cocaine (11.0%). CONCLUSIONS/UNASSIGNED:Adverse drug effects are common in this population and results can inform prevention and harm reduction in this population and the general population.

Reproducible translation of transcriptomics data has been hampered by the ubiquitous presence of batch effects. Statistical methods for managing batch effects were initially developed in the setting of sample group comparison and later borrowed for other settings such as survival outcome prediction. The most notable such method is ComBat, which adjusts for batches by including it as a covariate alongside sample groups in a linear regression. In survival prediction, however, ComBat is used without definable groups for survival outcome and is done sequentially with survival regression for a potentially batch-confounded outcome. To address these issues, we propose a new method called BATch MitigAtion via stratificatioN (BatMan). It adjusts batches as strata in survival regression and uses variable selection methods such as the regularized regression to handle high dimensionality. We assess the performance of BatMan in comparison with ComBat, each used either alone or in conjunction with data normalization, in a resampling-based simulation study under various levels of predictive signal strength and patterns of batch-outcome association. Our simulations show that (1) BatMan outperforms ComBat in nearly all scenarios when there are batch effects in the data and (2) their performance can be worsened by the addition of data normalization. We further evaluate them using microRNA data for ovarian cancer from the Cancer Genome Atlas and find that BatMan outforms ComBat while the addition of data normalization worsens the prediction. Our study thus shows the advantage of BatMan and raises caution about the use of data normalization in the context of developing survival prediction models. The BatMan method and the simulation tool for performance assessment are implemented in R and publicly available at LXQin/PRECISION.survival-GitHub.

PURPOSE/OBJECTIVE:To evaluate whether underlying infertility and mode of conception are associated with childhood behavioral disorders. METHODS:Oversampling on fertility treatment exposure using vital records, the Upstate KIDS Study followed 2057 children (of 1754 mothers) from birth to 11 years. Type of fertility treatment and time to pregnancy (TTP) were self-reported. Mothers completed annual questionnaires reporting symptomology, diagnoses, and medications at 7-11 years of age. The information identified children with probable attention-deficit/hyperactivity disorder, anxiety or depression, and conduct or oppositional defiant disorders. We estimated adjusted relative risks (aRR) for disorders by underlying infertility (TTP > 12 months) or treatment exposure groups compared to children born to parents with TTP ≤ 12 months. RESULTS:Children conceived with fertility treatment (34%) did not have an increased risk of attention-deficit/hyperactivity disorder (aRR): 1.21; 95% CI: 0.88, 1.65), or conduct or oppositional defiant disorders (aRR: 1.31; 0.91, 1.86), but did have an increased risk of anxiety or depression (aRR: 1.63; 1.18, 2.24), which remained elevated even after adjusting for parental mood disorders (aRR: 1.40; 0.99, 1.96). Underlying infertility without the use of treatment was also associated with a risk of anxiety or depression (aRR: 1.82; 95% CI: 0.96, 3.43). CONCLUSIONS:Underlying infertility or its treatment was not associated with risk of attention-deficit/hyperactivity disorder. Observations of increased anxiety or depression require replication.

OBJECTIVES:This secondary analysis evaluated opioid-specific validation results of the Tobacco, Alcohol, Prescription Medication, and Other Substances (TAPS) tool for screening in primary care. METHODS:This study is a secondary data analysis of the TAPS validation study. Performance of the TAPS tool for screening for unhealthy opioid use (with a score of 1+ for heroin and/or prescription opioids representing a positive screen) was evaluated. Discriminative ability was examined in comparison with reference standard measures across the spectrum of unhealthy opioid use: timeline follow-back with and without oral fluid testing identifying past-month use and the modified Composite International Diagnostic Interview for past-year problem use, opioid use disorder (OUD), and moderate-severe OUD. RESULTS:In a sample of 2000 primary care patients, 114 screened positive for opioids on the TAPS tool. With a TAPS cutoff equal to 1+, the TAPS accurately identified past-month use, problem use, any OUD, and moderate-severe OUD (sensitivities = 68%-85%, specificities = 97%-98%, area under the curve = 0.80-0.91). When past-month use was expanded to include timeline follow-back with oral fluid testing, accuracy declined (52% sensitivity [95% confidence interval, 43%-60%], 98% specific [95% confidence interval, 97%-98%]). CONCLUSIONS:While further testing in a larger population sample may be warranted, given their brevity, simplicity, and accuracy when self-administered, the TAPS opioid items can be used in primary care settings for a spectrum of unhealthy opioid use; however, self-disclosure remains an issue in primary care settings.

BACKGROUND:There are well-established guidelines for treating hypertension (HTN), yet only half of patients with HTN meet the defined target of < 140/90. Team-based care (TBC) is an evidence-based strategy for improving blood pressure (BP) management and control. TBC is defined as the provision of health services by at least two health professionals "who work collaboratively with patients and their caregivers to accomplish shared goals to achieve coordinated, high-quality care". However, primary care practices experience challenges to implementing TBC principles and care processes; these are more pronounced in small independent practice settings (SIPs). Practice facilitation (PF) is an implementation strategy that may overcome barriers to adopting evidence-based TBC to improve HTN management in SIPs. METHODS:Using a stepped wedge randomized controlled trial design, we will test the effect of PF on the adoption of TBC to improve HTN management in small practices (< 5 FTE clinicians) in New York City, and the impact on BP control compared with usual care. We will enroll 90 SIPs and randomize them into one of three 12-month intervention waves. Practice facilitators will support SIPs to adopt TBC principles to improve implementation of five HTN management strategies (i.e., panel management, population health, measuring BP, supporting medication adherence, self-management). The primary outcome is the adoption of TBC for HTN management measured at baseline and 12 months. Secondary outcomes include the rate of BP control and sustainability of TBC and BP outcomes at 18 months. Aggregated data on BP measures are collected every 6 months in all clusters so that each cluster provides data points in both the control and intervention conditions. Using a mixed methods approach, we will also explore factors that influence the effectiveness of PF at the organization and team level. DISCUSSION/CONCLUSIONS:This study will provide much-needed guidance on how to optimize adoption and sustainability of TBC in independent primary care settings to reduce the burden of disease related to suboptimal BP control and advance understanding of how facilitation works to improve implementation of evidence-based interventions. TRIAL REGISTRATION/BACKGROUND:ClinicalTrials.gov; NCT05413252 .