Faculty Bibliography

INTRODUCTION/UNASSIGNED:There is growing interest in using electronic health records (EHRs) for chronic disease surveillance. However, these data are convenience samples of in-care individuals, which are not representative of target populations for public health surveillance, generally defined, for the relevant period, as resident populations within city, state, or other jurisdictions. We focus on using EHR data for estimation of diabetes prevalence among young adults in New York City, as rising diabetes burden in younger ages call for better surveillance capacity. METHODS/UNASSIGNED:This article applies common nonprobability sampling methods, including raking, post-stratification, and multilevel regression with post-stratification, to real and simulated data for the cross-sectional estimation of diabetes prevalence among those aged 18-44 years. Within real data analyses, we externally validate city- and neighborhood-level EHR-based estimates to gold-standard estimates from a local health survey. Within data simulations, we probe the extent to which residual biases remain when selection into the EHR sample is non-ignorable. RESULTS/UNASSIGNED:Within the real data analyses, these methods reduced the impact of selection biases in the citywide prevalence estimate compared to gold standard. Residual biases remained at the neighborhood-level, where prevalence tended to be overestimated, especially in neighborhoods where a higher proportion of residents were captured in the sample. Simulation results demonstrated these methods may be sufficient, except when selection into the EHR is non-ignorable, depending on unmeasured factors or on diabetes status. CONCLUSIONS/UNASSIGNED:While EHRs offer potential to innovate on chronic disease surveillance, care is needed when estimating prevalence for small geographies or when selection is non-ignorable.

The surtvep package is an open-source software designed for estimating time-varying effects in survival analysis using the Cox non-proportional hazards model in R. With the rapid increase in large-scale time-to-event data from national disease registries, detecting and accounting for time-varying effects in medical studies have become crucial. Current software solutions often face computational issues such as memory limitations when handling large datasets. Furthermore, modeling time-varying effects for time-to-event data can be challenging due to small at-risk sets and numerical instability near the end of the follow-up period. surtvep addresses these challenges by implementing a computationally efficient Kronecker product-based proximal algorithm, supporting both unstratified and stratified models. The package also incorporates P-spline and smoothing spline penalties to improve estimation (Eilers & Marx, 1996). Cross-validation and information criteria are available to determine the optimal tuning parameters. Parallel computation is enabled to further enhance computational efficiency. A variety of operating characteristics are provided, including estimated time-varying effects, confidence intervals, hypothesis testing, and estimated hazard functions and survival probabilities. The surtvep package thus offers a comprehensive and flexible solution to analyzing large-scale time-to-event data with dynamic effect trajectories.

PURPOSE/OBJECTIVE:We undertook a trial to test the efficacy of a technology-assisted health coaching intervention for weight management, called Goals for Eating and Moving (GEM), within primary care. METHODS:). The primary outcome (weight change at 12 months) and exploratory outcomes (eg, program attendance, diet, physical activity) were analyzed according to intention to treat. RESULTS:= .48). There were no statistically significant differences in secondary outcomes. Exploratory analyses showed that the GEM arm had a greater change than the EUC arm in mean number of weekly minutes of moderate to vigorous physical activity other than walking, a finding that may warrant further exploration. CONCLUSIONS:The GEM intervention did not achieve clinically important weight loss in primary care. Although this was a negative study possibly affected by health system resource limitations and disruptions, its findings can guide the development of similar interventions. Future studies could explore the efficacy of higher-intensity interventions and interventions that include medication and bariatric surgery options, in addition to lifestyle modification.

Despite facing many social and structural challenges inside and outside of health systems, transgender and gender diverse (TGD) Brazilian immigrants in the U.S. are understudied, and their barriers to care are largely unnamed. In this commentary, we build on existing literature and our experiences at a safety-net community health system that sees a high volume of Brazilian patients to discuss challenges facing TGD Brazilian immigrant populations. We highlight that while Brazilian TGD populations face discrimination in Brazil, major challenges persist upon immigrating to the U.S., and include: difficulty updating identity documents and changing immigration status, barriers seeking general and specialized health care (including finding bilingual and bicultural providers), challenges navigating complex health and insurance systems, and a lack of community supports. We end by recommending more coordinated efforts between health care and community organizations to help ensure the health and wellness of TGD Brazilian immigrants in the United States.

BACKGROUND:Quitting smoking may lead to improvement in substance use, psychiatric symptoms, and pain, especially among high-risk populations who are more likely to experience comorbid conditions. However, causal inferences regarding smoking cessation and its subsequent benefits have been limited. METHODS:We emulated a hypothetical open-label randomized control trial of smoking cessation using longitudinal observational data of HIV-positive and HIV-negative US veterans from 2003-2015 in the Veterans Aging Cohort Study. We followed individuals from the first time they self-reported current cigarette smoking (baseline). We categorized participants as quitters or non-quitters at the first follow-up visit (approximately 1 year after baseline). Using inverse probability weighting to adjust for confounding and selection bias, we estimated odds ratios for improvement of co-occurring conditions (unhealthy alcohol use, cannabis use, illicit opioid use, cocaine use, depressive symptoms, anxiety symptoms, and pain symptoms) at second follow-up (approximately 2 years after baseline) for those who quit smoking compared to those who did not, among individuals who had the condition at baseline. RESULTS:Of 4,165 eligible individuals (i.e., current smokers at baseline), 419 reported no current smoking and 2,330 reported current smoking at the first follow-up. Adjusted odds ratios (95% confidence intervals) for associations between quitting smoking and improvement of each condition at second follow-up were: 2.10 (1.01, 4.35) for unhealthy alcohol use, 1.75 (1.00, 3.06) for cannabis use, 1.10 (0.58, 2.08) for illicit opioid use, and 2.25 (1.20, 4.24) for cocaine use, 0.78 (0.44, 1.38) for depressive symptoms, 0.93 (0.58, 1.49) for anxiety symptoms, and 1.31 (0.84, 2.06) for pain symptoms. CONCLUSIONS:While a causal interpretation of our findings may not be warranted, we found evidence for decreased substance use among veterans who quit cigarette smoking but none for the resolution of psychiatric conditions or pain symptoms. Findings suggest the need for additional resources combined with smoking cessation to reduce psychiatric and pain symptoms for high-risk populations.

BACKGROUND/UNASSIGNED:Loneliness has been declared an "epidemic" associated with negative physical, mental, and cognitive health outcomes such as increased dementia risk. Less is known about the relationship between loneliness and advanced neuroimaging correlates of Alzheimer's disease (AD). OBJECTIVE/UNASSIGNED:To assess whether loneliness was associated with advanced neuroimaging markers of AD using neuroimaging data from Framingham Heart Study (FHS) participants without dementia. METHODS/UNASSIGNED:In this cross-sectional observational analysis, we used functional connectivity MRI (fcMRI), amyloid-β (Aβ) PET, and tau PET imaging data collected between 2016 and 2019 on eligible FHS cohort participants. Loneliness was defined as feeling lonely at least one day in the past week. The primary fcMRI marker was Default Mode Network intra-network connectivity. The primary PET imaging markers were Aβ deposition in precuneal and FLR (frontal, lateral parietal and lateral temporal, retrosplenial) regions, and tau deposition in the amygdala, entorhinal, and rhinal regions. RESULTS/UNASSIGNED:Of 381 participants (mean age 58 [SD 10]) who met inclusion criteria for fcMRI analysis, 5% were classified as lonely (17/381). No association was observed between loneliness status and network changes. Of 424 participants (mean age 58 [SD = 10]) meeting inclusion criteria for PET analyses, 5% (21/424) were lonely; no associations were observed between loneliness and either Aβ or tau deposition in primary regions of interest. CONCLUSIONS/UNASSIGNED:In this cross-sectional study, there were no observable associations between loneliness and select fcMRI, Aβ PET, and tau PET neuroimaging markers of AD risk. These findings merit further investigation in prospective studies of community-based cohorts.

Planetary health influences mental health and a better management of climate, biodiversity and pollution has co-benefits of improving mental health outcomes. The recognition and treatment of mental health, however, has been marginalized within environmental and climate change sciences and a greater understanding of the complex underlying processes and societal costs is required to appropriately manage and motivate policy responses. The paper provides seven recommendations underscoring that public policy developers and implementors need to be aware of the combined costs of inaction - that might accrue from neglecting mental health and environmental sciences- two areas that have been historically marginalized. Improved methodologies in conducting studies on the nature and mental health nexus are needed. The trajectories and models of adaptation and mitigation of climate change and environmental damage can be strengthened through adoption of mental and behavioral sciences approach.

BACKGROUND:We assessed longitudinal effects of e-cigarette use on respiratory symptoms in a nationally representative sample of US adults by combustible tobacco smoking status. METHODS:We analyzed Waves 4-5 public-use data from the Population Assessment of Tobacco and Health Study. Study sample included adult respondents who reported no diagnosis of respiratory diseases at Wave 4, and completed Waves 4-5 surveys with no missing data on analytic variables (N = 15,291). Outcome was a validated index of functionally important respiratory symptoms based on 7 wheezing/cough questions (range 0-9). An index score of ≥2 was defined as having important respiratory symptoms. Weighted lagged logistic regression models were performed to examine the association between e-cigarette use status at Wave 4 (former/current vs. never use) and important respiratory symptoms at Wave 5 by combustible tobacco smoking status (i.e., never/former/current smokers), adjusting for Wave 4 respiratory symptom index, sociodemographic characteristics, secondhand smoke exposure, body mass index, and chronic disease. RESULTS:Among current combustible tobacco smokers, e-cigarette use was associated with increased odds of reporting important respiratory symptoms (former e-cigarette use: adjusted odds ratio [AOR] = 1.39, 95% confidence interval [CI]: 1.07-1.81; current e-cigarette use: AOR = 1.55, 95% CI: 1.17-2.06). Among former combustible tobacco smokers, former e-cigarette use (AOR = 1.51, 95% CI: 1.06-2.15)-but not current e-cigarette use (AOR = 1.59, 95% CI: 0.91-2.78)-was associated with increased odds of important respiratory symptoms. Among never combustible tobacco smokers, no significant association was detected between e-cigarette use and important respiratory symptoms (former e-cigarette use: AOR = 1.62, 95% CI: 0.76-3.46; current e-cigarette use: AOR = 0.82, 95% CI: 0.27-2.56). CONCLUSIONS:The association between e-cigarette use and respiratory symptoms varied by combustible tobacco smoking status. Current combustible tobacco smokers who use e-cigarettes have an elevated risk of respiratory impairments.