Temporal and Geographic Patterns of Documentation of Sexual Orientation and Gender Identity Keywords in Clinical Notes

Sexual and Gender Minority Status and Suicide Mortality: An Explainable Artificial Intelligence Analysis

Objectives: Suicide risk is elevated in lesbian, gay, bisexual, and transgender (LGBT) individuals. Limited data on LGBT status in healthcare systems hinder our understanding of this risk. This study used natural language processing to extract LGBT status and a deep neural network (DNN) to examine suicidal death risk factors among US Veterans. Methods: Data on 8.8 million veterans with visits between 2010 and 2017 was used. A case-control study was performed, and suicide death risk was analyzed by a DNN. Feature impacts and interactions on the outcome were evaluated. Results: The crude suicide mortality rate was higher in LGBT patients. However, after adjusting for over 200 risk and protective factors, known LGBT status was associated with reduced risk compared to LGBT-Unknown status. Among LGBT patients, black, female, married, and older Veterans have a higher risk, while Veterans of various religions have a lower risk. Conclusion: Our results suggest that disclosed LGBT status is not directly associated with an increase suicide death risk, however, other factors (e.g., depression and anxiety caused by stigma) are associated with suicide death risks.

Emphasizing Sexual Orientation and Gender Identity Data Capture for Improved Cardiovascular Care of the LGBTQ+ Population

Importance

The rising self-identifying lesbian, gay, bisexual, transgender, and queer (LGBTQ+) population makes understanding the unique health care needs of sexual and gender minoritized patients an urgent one. The interaction between minority stress and cardiovascular disease has been well described among underrepresented minoritized populations. The underrepresentation of minoritized populations in clinical research is partly responsible for worse cardiovascular outcomes in these populations. The absence of sexual orientation and gender identity and expression (SOGIE) data makes it difficult to understand the cardiovascular health of LGBTQ+ adults, thereby widening health care disparities in this population. Advancing cardiovascular health equity for LGBTQ+ patients must begin with careful and accurate SOGIE data collection.

Observations

Current SOGIE data capture remains inadequate despite federal mandates. Challenges in data collection include political and regulatory discrimination, patient/practitioner hesitancy, lack of supportive guidance on SOGIE data collection, improper terminology, regulatory inertia, and inadequate and often incorrect integration of SOGIE data into electronic health records (EHRs). Additional challenges include grouping participants as "others" for statistical significance. The inclusion of SOGIE data has demonstrated an impact in other fields like cancer survivorship and surgery. The same needs to be done for cardiology.

Conclusions and Relevance

Potential solutions for improving much-needed SOGIE data collection include (1) implementing LGBTQ+ inclusive policies, (2) integrating SOGIE data into the EHR, (3) educating health care professionals on the relevance of SOGIE to patient-centered care, and (4) creating a diverse cardiovascular workforce. These steps can substantially enhance the ability to collect SOGIE data to address LGBTQ+ cardiovascular health care disparities.

Relationship Between Pain and LGBT Status Among Veterans in Care in a Retrospective Cross-Sectional Cohort

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.