Validating a Case Definition for Transgender Adults Using Administrative Data
Bibliographic record
Abstract
Importance: Administrative health data serve as promising data sources to study transgender health at a population level in the absence of self-reported gender identity. Objective: To develop and validate case definitions identifying transgender adults in administrative data compared with the reference standard of self-reported gender identity in a universal health care setting. Design, Setting, and Participants: In this cohort study conducted in Alberta, Canada, data from provincial administrative health data sources including inpatient hospitalizations, emergency department encounters, primary care visits, prescription drug dispensations, and the provincial health insurance registry were linked and used to develop 15 case definitions (9 for transgender women and 6 for transgender men). Participants aged 18 years or older with a provincial health care number between April 1, 1994, and March 31, 2021, were included and stratified by sex marker (eg, female or male) at study entry. Data analysis was from December 2023 to March 2024. Main Outcomes and Measures: For each case definition, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated against the reference standard of self-reported gender identity. Results: In this cohort study of 5 375 735 individuals, the reference standard consisted of 141 self-identified transgender women, 174 self-identified transgender men, 111 self-identified cisgender women, and 65 self-identified cisgender men. The final cohort representing transgender women participants who met at least 1 case definition and/or were part of the standard reference totaled 63 977. Combining a case definition employing male sex registry identification and 2 or more dispensations of estrogen or a case definition employing male sex registry identification and at least 1 gender-related diagnostic code demonstrated a sensitivity of 86.6% (95% CI, 79.9%-91.7%), specificity of 62.5% (95% CI, 51.5%-72.6%), PPV of 78.8% (95% CI, 71.6%-85.0%), and NPV of 74.3% (95% CI, 62.8%-83.8%). The final cohort representing transgender men participants who met at least 1 case definition and/or were part of the standard reference totaled 26 852. Combining a case definition employing female sex registry identification and 2 or more dispensations of testosterone or a case definition employing female sex registry identification and at least 1 gender-related diagnostic code demonstrated a sensitivity of 78.2% (95% CI, 71.3%-84.1%), specificity of 89.2% (95% CI, 82.2%-94.1%), PPV of 91.3% (95% CI, 85.5%-95.3%), and NPV of 73.8% (95% CI, 65.8%-80.7%). Conclusion and Relevance: These findings suggest that case definitions using transgender-related diagnostic codes and gender-affirming hormone prescriptions can be used to study the epidemiology, disease burden, and health care utilization of transgender populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".