Further Exploring the Public Health Implications of the Network Scale-Up Method: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: The decline in the number of new HIV infections among adults has slowed down, gradually becoming the biggest obstacle to achieving the 2030 target of ending the HIV/AIDS epidemic. Thus, a political declaration to ensure that 90% of people at high risk of HIV infection can access comprehensive prevention services was proposed by the United Nations General Assembly. Therefore, obtaining an accurate estimated size of high-risk populations is required as a prior condition to plan and implement HIV prevention services. The network scale-up method (NSUM) was recommended by the United Nations Programme on HIV/AIDS and the World Health Organization to estimate the sizes of populations at high risk of HIV infection; however, we found that the NSUM also revealed underlying population characteristics of female sex workers in addition to being used to estimate the population size. Such information on underlying population characteristics is very useful in improving the planning and implementation of HIV prevention services. This is especially relevant for people who inject drugs, where in addition to stigma and discrimination, criminalization further hinders access to HIV prevention services. Objective: We aimed to conduct a further exploration of the public health implications of the NSUM by using it to estimate the population size, popularity ratio, and information transmission rate among people who inject drugs. Methods: A stratified 2-stage cluster survey of the general population and a respondent-driven sampling survey of people who inject drugs were conducted in the urban district of Taiyuan, China, in 2021. Results: The estimated size of the population of people who inject drugs in Taiyuan was 1241.9 (95% CI 1009.2-1474.9), corresponding to 4.4×10-2% (95% CI 3.6×10-2% to 5.2×10-2%) of the adult population aged 15-64 years. The estimated popularity ratio of people who inject drugs was 53.6% (95% CI 47.2%-60.1%), and the estimated information transmission rate was 87.9% (95% CI 86.5%-89.3%). Conclusions: In addition to being used to estimate the size of the population of people who inject drugs, the NSUM revealed that they have smaller-sized personal social networks while concealing their drug use, and these underlying population characteristics are extremely useful for planning appropriate service delivery approaches with the fewest barriers for people who inject drugs to access HIV prevention services. Therefore, more cost-effectiveness brings new public health implications for the NSUM, which makes it even more promising for its application.
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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 | 0.072 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".