Non-HIV chronic disease burden among transgender populations globally: A systematic review and narrative synthesis
Bibliographic record
Abstract
Chronic disease is a growing concern for research, policy and clinical care. While the global burden of HIV for transgender populations has been comprehensively covered in recent systematic reviews, the same is not true for the burden of other chronic disease. The objective of this study was to review the literature on non-HIV chronic disease burden for transgender populations worldwide. A systematic review was conducted of Medline, Embase, CINAHL, PsycINFO and LGBT Life bibliographic databases for peer-reviewed scientific studies with non-HIV chronic disease prevalence data for transgender populations published any date up to February 15, 2019 without restriction on country or study design. A total of 93 studies and 665 datapoints were included in this review, comprising 48 distinct chronic disease outcomes in seven groups (cancer, cerebro/cardiovascular conditions, chronic liver and kidney disease, mental health and substance use conditions, metabolic and endocrine disorders, musculoskeletal and brain disorders, respiratory conditions, and unspecified and other conditions). The empirical literature on chronic disease among global transgender populations focuses on mental health morbidity, demonstrating an evidence gap on chronic physical health morbidity, particularly beyond that of sexual health. This review identified important gaps including in age-related conditions, inflammation-related disease and studies designed explicitly to investigate chronic disease burden among transgender populations. There is a need for high quality evidence in this area, including longitudinal population-based studies with appropriate comparison groups, and consistent measurement of both transgender status and chronic conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 source (direct Gemma or distilled Codex), 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".