Web-Based Search Volume for HIV Tests and HIV-Testing Preferences During the COVID-19 Pandemic in Japan: Infodemiology Study
Bibliographic record
Abstract
BACKGROUND: Research has found a COVID-19 pandemic-related impact on HIV medical services, including clinic visits, testing, and antiviral therapy initiation in countries including Japan. However, the change in trend for HIV/AIDS testing during the COVID-19 pandemic has not been explored extensively in the Japanese population. OBJECTIVE: This infodemiology study examines the web-based search interest for two types of HIV tests, self-test kits and facility-based tests, before and during the COVID-19 pandemic in Japan. METHODS: The monthly search volume of queried search terms was obtained from Yahoo! JAPAN. Search volumes for the following terms were collected from November 2017 to October 2018: "HIV test," "HIV test kit," and "HIV test health center." The search term "Corona PCR" and the number of new COVID-19 cases by month were used as a control for the search trends. The number of new HIV cases in the corresponding study period was obtained from the AIDS Trend Committee Quarterly Report from the AIDS Prevention Foundation. RESULTS: Compared to the search volume of "corona-PCR," which roughly fluctuated corresponding to the number of new COVID-19 cases in Japan, the search volume of "HIV test" was relatively stable from 2019 to 2022. When we further stratified by the type of HIV test, the respective web-based search interest in HIV self-testing and facility-based testing showed distinct patterns from 2018 to 2022. While the search volume of "HIV test kit" remained stable, that of "HIV test health center" displayed a decreasing trend starting in 2018 and has remained low since the beginning of the COVID-19 pandemic. Around 66%-71% of the search volume of "HIV test kits" was attributable to searches made by male internet users from 2018 to 2022, and the top three contributing age groups were those aged 30-39 (27%-32%), 20-29 (19%-32%), and 40-49 (19%-25%) years. On the other hand, the search volume of "HIV test health centers" by male users decreased from more than 500 from 2018 to 2019 to fewer than 300 from 2020 to 2022. CONCLUSIONS: Our study found a notable decrease in the search volume of "HIV test health center" during the pandemic, while the search volume for HIV self-testing kits remained stable before and during the COVID-19 crisis in Japan. This suggests that the previously reported COVID-19-related decrease in the number of HIV tests mostly likely referred to facility-based testing. This sheds light on the change in HIV-testing preferences in Japan, calling for a more comprehensive application and regulatory acceptance of HIV self-instructed tests.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".