Estimating the size of hard-to-reach populations: a novel method using HIV testing data compared to other methods
Bibliographic record
Abstract
OBJECTIVE: To estimate population size of hard-to-reach groups such as injecting drug users and men who have sex with men. DESIGN: Several different methods were used to estimate the size of these populations in Canada's three largest cities (Toronto, Montreal and Vancouver). METHODS: A novel method (referred to as the indirect method) was developed for use in Toronto and Vancouver that combines HIV serodiagnostic information with data on HIV testing behavior. Population size estimates were obtained by dividing the number of injecting drug users or men who have sex with men recorded in HIV serodiagnostic databases in a given year by the proportion of the corresponding group that reported being tested in a 1-year period. Results of this method were compared with four other methods: (1) population surveys; (2) capture-recapture (for injecting drug users only); (3) a modified Delphi technique; and (4) a method based on the proportion of never-married men aged 45 and over (for men who have sex with men only). Only these other methods were used in Montreal. RESULTS: The survey method gave the lowest estimates which are best viewed as minimum estimates given the relative inability of surveys to access these populations and the reluctance of participants to admit to sensitive behaviors. The indirect method produced results more closely comparable with those obtained by other methods, but they are probably slight overestimates, at least for injecting drug users, due to possible underestimation of the proportion tested for HIV. Point estimates using the indirect method were 17,700 and 17,500 for injecting drug users in Toronto and Vancouver, respectively, and 39,100 and 15,900 for men who have sex with men. In Toronto, results for the other methods ranged from 12,300-13,360 for injecting drug users and 18,800-35,000 for men who have sex with men. For Vancouver, these ranges were 6400-11,670 and 7000-26,500, respectively. In Montreal, ranges were 4300-12,500 for injecting drug users and 18,500-40,000 for men who have sex with men. CONCLUSIONS: This novel method provides estimates of population size of hard-to-reach groups such as injecting drug users and men who have sex with men that are comparable with results derived by other methods. These estimates may be useful for the purposes of planning, implementing and evaluating prevention and care services, especially when they are combined with the results of other estimation methods to improve the degree of confidence in the resulting estimates.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".