Impact of maternal HIV–HBV coinfection on pregnancy outcomes in an underdeveloped rural area of southwest China
Bibliographic record
Abstract
OBJECTIVES: Our objective was to determine the impact of maternal HIV-hepatitis B virus (HBV) coinfection on pregnancy outcomes. METHODS: The current study was conducted in a county of Yi Autonomous Prefecture in southwest China. Data were abstracted from hospitalisation records, including maternal and infant information. The seroprevalences of HIV and HBV infections and HIV-HBV coinfection were determined and the impact of maternal HIV-HBV coinfection on adverse pregnancy outcomes was assessed using logistic regression analysis. A treatment effects linear regression model was also applied to examine the effect of HBV, HIV or coinfection to quantify the absolute difference in birth weight from a reference of HBV-HIV negative participants. RESULTS: A total of 13 198 pregnant women were included in our study, and among them, 99.1% were Yi people and 90.8% lived in rural area. The seroprevalences of HIV and HBV infections and HIV-HBV coinfection were 3.6% (95% CI: 3.2% to 3.9%), 3.2% (95% CI: 2.9% to 3.5%) and 0.2% (95% CI: 0.1% to 0.2%) among the pregnant women, respectively. Maternal HIV-HBV coinfection was a risk factor for low birth weight (adjusted OR (aOR)=5.52, 95% CI: 1.97 to 15.40). Compared with the HIV mono-infection group, the risk of low birth weight was significantly higher in the HIV-HBV coinfection group (aOR=3.62, 95% CI: 1.24 to 10.56). Maternal HIV infection was associated with an increased risk of low birth weight (aOR=1.90, 95% CI: 1.38 to 2.60) and preterm delivery (aOR=2.84, 95% CI: 1.81 to 4.47). Perinatal death was more common when mothers were infected with HBV (aOR=2.85, 95% CI: 1.54 to 5.26). CONCLUSIONS: The prevalence of HIV infection was high among pregnant women of the Yi region. Both HIV and HBV infections might have adverse effects on pregnancy outcomes. Maternal HIV-HBV coinfection might be a risk factor for low birth weight in the Yi region, which needs to be confirmed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".