Prevalence and predictors of HIV-syphilis co-infection among HIV-infected pregnant women in China, 2011-2018
Bibliographic record
Abstract
Abstract Background The co-infection of Human Immunodeficiency Virus (HIV) and syphilis is risky for pregnant women and their expected children. In 2015, the Integrated Prevention of Mother-to-Child Transmission (iPMTCT) programwas established to offer all pregnant women with free screening, counseling, and testing of HIV and syphilis during regular obstetric inspections. To summarize the phase progress of this program, we reported the trends of maternal HIV-syphilis co-infection in China. We tried to socioeconomic factors associated with HIV-syphilis co-infection to inform the stratified control strategy for future work. Methods We obtained the prevalence data of HIV and syphilis over 2011–2018 by reviewing the Sexually Transmitted Infection (STI) monthly update reporting to the central surveillance system. With health status, background characteristics, and health outcomes reported, we collected the case reports from 2,578 HIV-positive pregnant women who accepted the screening at the local clinic. The trends of HIV and syphilis prevalence were examined using the Cochran-Armitage trend test. Logistic regression was applied to detect the features associated with syphilis infection among HIV-positive women and the potential risk factor to neonatal death. Results The prevalence of HIV decreased from 0.076–0.039% among registered pregnant women but increased slightly to 0.054% in 2018. The trend of syphilis prevalence in HIV-infected pregnant women fluctuated slightly around an average of 1.80% (p = .378). Multivariate logistic regression indicated finishing education of junior high school or below (aOR: 1.79, 95%CI: 1.31–2.43; p < .001), on regular Antiretroviral Therapy (ART) (aOR: 1.89, 95%CI: 1.47–2.45; p < .001) and exposed HIV from injective drug use (aOR: 5.49, 95%CI: 3.51–8.61; p < .001) are associated with high syphilis infection risk. Syphilis co-infection with HIV (aOR: 2.81, 95%CI: 1.32–5.96; p < .007) significantly increases the risk of newborns death. Conclusion Syphilis infection is still very prevalent in HIV-positive pregnant women five years after the implementation of iPMTCT program. Promoting the health education for maternal infection of STIs and increasing the availability of early intervention to link more marginalized women with care service should be the focuses of work in the next stage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".