Computational phenotyping within electronic healthcare data to identify transgender people in the United States: A narrative review

PURPOSE

With the expansion of research utilizing electronic healthcare data to identify transgender (TG) population health trends, the validity of computational phenotype (CP) algorithms to identify TG patients is not well understood. We aim to identify the current state of the literature that has utilized CPs to identify TG people within electronic healthcare data and their validity, potential gaps, and a synthesis of future recommendations based on past studies.

METHODS

Authors searched the National Library of Medicine's PubMed, Scopus, and the American Psychological Association PsycInfo's databases to identify studies published in the United States that applied CPs to identify TG people within electronic healthcare data.

RESULTS

Twelve studies were able to validate or enhance the positive predictive value (PPV) of their CP through manual chart reviews (n = 5), hierarchy of code mechanisms (n = 4), key text-strings (n = 2), or self-surveys (n = 1). CPs with the highest PPV to identify TG patients within their study population contained diagnosis codes and other components such as key text-strings. However, if key text-strings were not available, researchers have been able to find most TG patients within their electronic healthcare databases through diagnosis codes alone.

CONCLUSION

CPs with the highest accuracy to identify TG patients contained diagnosis codes along with components such as procedural codes or key text-strings. CPs with high validity are essential to identifying TG patients when self-reported gender identity is not available. Still, self-reported gender identity information should be collected within electronic healthcare data as it is the gold standard method to better understand TG population health patterns.

Patient and Surgery Characteristics of Inpatient Hysterectomies Among Transgender Individuals

Purpose: The purpose of this study is to estimate population-based rates of inpatient hysterectomy and accompanying bilateral salpingo-oophorectomy by indication and evaluate surgical patient characteristics by indication, year, patient age, and hospital location. Methods: We used 2016 and 2017 cross-sectional data from the Nationwide Inpatient Sample to estimate the hysterectomy rate for individuals aged 18-54 years with a primary indication for gender-affirming care (GAC) compared to other indications. Outcome measures were population-based rates for inpatient hysterectomy and bilateral salpingo-oophorectomy by indication. Results: The population-based rate of inpatient hysterectomy for GAC per 100,000 was 0.05 (95% confidence interval [CI] = 0.02-0.09) in 2016 and 0.09 (95% CI = 0.03-0.15) in 2017. For comparison, the rates per 100,000 for fibroids were 85.76 in 2016 and 73.25 in 2017. Rates of bilateral salpingo-oophorectomy in the setting of hysterectomy were higher in the GAC group (86.4%) than in comparison groups (22.7%-44.1% for all other benign indications, 77.4% for cancer) across all age ranges. A higher rate of hysterectomies performed for GAC was done laparoscopically or robotically (63.6%) than other indications, and none was done vaginally, as opposed to comparison groups (0.7%-9.8%). Conclusion: The population-based rate for GAC was higher in 2017 compared to 2016 and low compared to other hysterectomy indications. Rates of concomitant bilateral salpingo-oophorectomy were more prevalent for GAC than for other indications at similar ages. The patients in the GAC group tended to be younger, insured, and most procedures occurred in the Northeast (45.5%) and West (36.4%).