The Impact of COVID-19 Lockdown on Cases of and Deaths From AIDS, Gonorrhea, Syphilis, Hepatitis B, and Hepatitis C: Interrupted Time Series Analysis
Bibliographic record
Abstract
BACKGROUND: China implemented a nationwide lockdown to contain COVID-19 from an early stage. Previous studies of the impact of COVID-19 on sexually transmitted diseases (STDs) and diseases caused by blood-borne viruses (BBVs) in China have yielded widely disparate results, and studies on deaths attributable to STDs and BBVs are scarce. OBJECTIVE: We aimed to elucidate the impact of COVID-19 lockdown on cases, deaths, and case-fatality ratios of STDs and BBVs. METHODS: We extracted monthly data on cases and deaths for AIDS, gonorrhea, syphilis, hepatitis B, and hepatitis C between January 2015 and December 2021 from the notifiable disease reporting database on the official website of the National Health Commission of China. We used descriptive statistics to summarize the number of cases and deaths and calculated incidence and case-fatality ratios before and after the implementation of a nationwide lockdown (in January 2020). We used negative binominal segmented regression models to estimate the immediate and long-term impacts of lockdown on cases, deaths, and case-fatality ratios in January 2020 and December 2021, respectively. RESULTS: A total of 14,800,330 cases of and 127,030 deaths from AIDS, gonorrhea, syphilis, hepatitis B, and hepatitis C were reported from January 2015 to December 2021, with an incidence of 149.11/100,000 before lockdown and 151.41/100,000 after lockdown and a case-fatality ratio of 8.21/1000 before lockdown and 9.50/1000 after lockdown. The negative binominal model showed significant decreases in January 2020 in AIDS cases (-23.4%; incidence rate ratio [IRR] 0.766, 95% CI 0.626-0.939) and deaths (-23.9%; IRR 0.761, 95% CI 0.647-0.896), gonorrhea cases (-34.3%; IRR 0.657, 95% CI 0.524-0.823), syphilis cases (-15.4%; IRR 0.846, 95% CI 0.763-0.937), hepatitis B cases (-17.5%; IRR 0.825, 95% CI 0.726-0.937), and hepatitis C cases (-19.6%; IRR 0.804, 95% CI 0.693-0.933). Gonorrhea, syphilis, and hepatitis C showed small increases in the number of deaths and case-fatality ratios in January 2020. By December 2021, the cases, deaths, and case-fatality ratios for each disease had either reached or remained below expected levels. CONCLUSIONS: COVID-19 lockdown may have contributed to fewer reported cases of AIDS, gonorrhea, syphilis, hepatitis B, and hepatitis C and more reported deaths and case-fatality ratios of gonorrhea, syphilis, and hepatitis C in China.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".