Health-Related Quality of Life of HIV-Positive and HIV-Negative Pregnant Women in an Impoverished Area: Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: Liangshan prefecture of Sichuan province was an impoverished mountainous area in China, where the annual number of HIV-positive pregnant women accounted for approximately 10% of China's total population in the decades before 2020. In general, pregnant women living here are likely to be physically and mentally different from those in other places. OBJECTIVE: This study aims to explore the health-related quality of life (HRQoL) of pregnant women living with HIV in an impoverished area. METHODS: From December 2018 to January 2019, HIV-positive and HIV-negative parturients within 18 months after delivery were recruited in Liangshan Prefecture, Sichuan Province. Questionnaires were designed to collect their demographic data, while the EuroQol 5-Dimension, 3-Level questionnaire was used to measure their HRQoL when they were in the second trimester from 4 to 6 months of pregnancy, and their quantitative health scores were converted to corresponding healthy utility values by using the Chinese Utility Value Integral System (time trade-off coefficient). RESULTS: A total of 250 pregnant women (133 HIV-positive and 117 HIV-negative) were enrolled in the study. Among them, 55 (41.35%) and 75 (64.10%) of HIV-positive and HIV-negative pregnant women self-reported full health (healthy state 11111), respectively. The median health utility value of the 250 pregnant women was 0.961 (IQR -0.046 to 0.961), and those of the HIV-positive and HIV-negative pregnant women were 0.875 (0.424-0.961) and 0.961 (IQR -0.046 to 0.961), respectively. We observed a significant difference only in the dimension of anxiety or depression between the two groups (P=.002) and no significant difference in the distribution of health utility indices between the two groups in terms of maternal age, education level, occupation, annual household income, prenatal care visits, family size, and medical insurance category. Multivariate ordinal logistic regression analysis showed that age (odds ratio [OR] 0.62, P<.05) and prenatal care visit (OR 0.29, P<.01) were independent risk factors for health status. CONCLUSIONS: Most pregnant women self-reported satisfactory HRQoL in this impoverished mountainous area. HIV-negative pregnant women had an edge over HIV-positive pregnant women, and there were significant differences in anxiety or depression dimensions between the two groups.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".