Joseph L. Goulet

Compared to society overall, LGBTQ+ people with disabilities are a minority within a minority, often facing increased health disparities that are underrecognized in clinical care (1–6). Situated wi...

Suicide is a serious public health crisis, and lesbian, gay, bisexual, and transgender (LGBT) adults are at particularly high risk. The present study examined risk factors for mortality by suicide and other causes in LGBT versus non-LGBT middle-aged and older adult veterans who used Veterans Affairs health care services between 2009 and 2019 (n = 834,774). We hypothesized that LGBT veterans (n = 167,676) would be at increased risk of premature mortality from suicide, overdose, and other causes of death compared with non-LGBT veterans (n = 667,774), due to minority stress as measured by proxy risk factors of health and social determinants of health. In bivariate models, LGBT veterans had significantly higher rates of death by suicide and overdose compared with non-LGBT veterans. After controlling for proxy risk factors of minority stress in multivariate analyses, the risk associated with LGBT status was nonsignificant. Sociodemographic variables (e.g., race/ethnicity, age), mental health diagnoses, substance use, and other risk factors contributed significantly to differences in risk. Findings highlight the critical importance of interventions (e.g., U.S. Department of Veterans Affairs Whole Health) for LGBT veterans to manage multiple health risk factors that contribute to mortality risk. Suicide prevention is crucial, especially for middle-aged, older adult, and LGBT veterans. Initiating targeted suicide prevention earlier in life may prevent suicide deaths. Findings also implicate subgroups of veterans who may particularly benefit from systematic improvements to health care delivery. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Background: The use of patient-reported outcome measures (PROMs) is an expected component of high-quality, measurement-based chiropractic care. The largest health care system offering integrated chiropractic care is the Veterans Health Administration (VHA). Challenges limit monitoring PROM use as a care quality metric at a national scale in the VHA. Structured data are unavailable, with PROMs often embedded within clinic text notes as unstructured data requiring time-intensive, peer-conducted chart review for evaluation. Natural language processing (NLP) of clinic text notes is one promising solution to extracting care quality data from unstructured text. Objective: This study aims to test NLP approaches to identify PROMs documented in VHA chiropractic text notes. Methods: VHA chiropractic notes from October 1, 2017, to September 30, 2020, were obtained from the VHA Musculoskeletal Diagnosis/Complementary and Integrative Health Cohort. A rule-based NLP model built using medspaCy and spaCy was evaluated on text matching and note categorization tasks. SpaCy was used to build bag-of-words, convoluted neural networks, and ensemble models for note categorization. Performance metrics for each model and task included precision, recall, and F-measure. Cross-validation was used to validate performance metric estimates for the statistical and machine-learning models. Results: Our sample included 377,213 visit notes from 56,628 patients. The rule-based model performance was good for soft-boundary text-matching (precision=81.1%, recall=96.7%, and F-measure=88.2%) and excellent for note categorization (precision=90.3%, recall=99.5%, and F-measure=94.7%). Cross-validation performance of the statistical and machine learning models for the note categorization task was very good overall, but lower than rule-based model performance. The overall prevalence of PROM documentation was low (17.0%). Conclusions: We evaluated multiple NLP methods across a series of tasks, with optimal performance achieved using a rule-based method. By leveraging NLP approaches, we can overcome the challenges posed by unstructured clinical text notes to track documented PROM use. Overall documented use of PROMs in chiropractic notes was low and highlights a potential for quality improvement. This work represents a methodological advancement in the identification and monitoring of documented use of PROMs to ensure consistent, high-quality chiropractic care for veterans.

Background: Seven million lesbian, gay, and bisexual (LGB) adults will be aged >50 years by 2030; assessing and addressing their risk for cardiovascular disease is critical. Methods and Results: We analyzed a nationwide cohort using the Veterans Health Administration data. Sexual orientation (SO) was classified via a validated natural language processing algorithm. Prevalent atherosclerotic cardiovascular disease (ASCVD) (history of acute myocardial infarction, ischemic stroke, or revascularization) was identified via International Classification of Diseases, Ninth and Tenth Revision ( ICD‐9 and ICD‐10 ) codes. The index date was the date of the first primary care appointment on or after October 1, 2009. We ascertained covariates and prevalent ASCVD in the year following the index date; the baseline date was 1 year after the index date. We calculated sample statistics by sex and SO and used logistic regression analyses to assess associations between SO and prevalent ASCVD. Of 1 102 193 veterans with natural language processing‐defined SO data, 170 861 were classified as LGB. Prevalent ASCVD was present among 25 031 (4105 LGB). Adjusting for age, sex, race, and Hispanic ethnicity, LGB veterans had 1.24 [1.19–1.28] greater odds of prevalent ASCVD versus non‐LGB identified veterans. This association remained significant upon additional adjustment for the ASCVD risk factors substance use, anxiety, and depression (odds ratio [OR],1.17 [95% CI, 1.13–1.21]). Among a subset with self‐reported SO, findings were consistent (OR, 1.53 [95% CI, 1.20–1.95]). Conclusions: This is one of the first studies to examine cardiovascular risk factors and disease of the veteran population stratified by natural language processing‐defined SO. Future research must explore psychological, behavioral, and physiological mechanisms that result in poorer cardiovascular health among LGB veterans.

Background: Pain assessment is performed in many healthcare systems, such as the Veterans Health Administration, but prior studies have not assessed whether pain screening varies in sexual and gender minority populations that include individuals who identify as lesbian, gay, bisexual, and/or transgender (LGBT). Objective: The purpose of this study was to evaluate pain screening and reported pain of LGBT Veterans compared to non-LGBT Veterans. Methods: Using a retrospective cross-sectional cohort, data from the Corporate Data Warehouse, a national repository with clinical/administrative data, were analyzed. Veterans were classified as LGBT using natural language processing. We used a robust Poisson model to examine the association between LGBT status and binary outcomes of pain screening, any pain, and persistent pain within one year of entry in the cohort. All models were adjusted for demographics, mental health, substance use, musculoskeletal disorder(s), and number of clinic visits. Results: There were 1,149,486 Veterans (218,154 (19%) classified as LGBT) in our study. Among LGBT Veterans, 94% were screened for pain compared to 89% among those not classified as LGBT (non-LGBT) Veterans. In adjusted models, LGBT Veterans’ probability of being screened for pain compared to non-LGBT Veterans was 2.5% higher (95% CI 2.3%, 2.6%); risk of any pain was 2.1% lower (95% CI 1.6%, 2.6%); and there was no significant difference between LGBT and non-LGBT Veterans in persistent pain (RR = 1.00, 95% CI (0.99, 1.01), p = 0.88). Conclusions: In a nationwide sample, LGBT Veterans were more likely to be screened for pain but had lower self-reported pain scores, though adjusted differences were small. It was notable that transgender and Black Veterans reported the greatest pain. Reasons for these findings require further investigation.

Deep neural network (DNN) is a powerful technology that is being utilized by a growing number and range of research projects, including disease risk prediction models. One of the key strengths of DNN is its ability to model non-linear relationships, which include covariate interactions. We developed a novel method called interaction scores for measuring the covariate interactions captured by DNN models. As the method is model-agnostic, it can also be applied to other types of machine learning models. It is designed to be a generalization of the coefficient of the interaction term in a logistic regression; hence, its values are easily interpretable. The interaction score can be calculated at both an individual level and population level. The individual-level score provides an individualized explanation for covariate interactions. We applied this method to two simulated datasets and a real-world clinical dataset on Alzheimer’s disease and related dementia (ADRD). We also applied two existing interaction measurement methods to those datasets for comparison. The results on the simulated datasets showed that the interaction score method can explain the underlying interaction effects, there are strong correlations between the population-level interaction scores and the ground truth values, and the individual-level interaction scores vary when the interaction was designed to be non-uniform. Another validation of our new method is that the interactions discovered from the ADRD data included both known and novel relationships.