HIV testing among pregnant women with prenatal care in the United States: An analysis of the 2011–2017 National Survey of Family Growth
Bibliographic record
Abstract
Although there has been significant progress in reducing perinatal human immunodeficiency virus (HIV) transmission, the United States is yet to meet the proposed elimination goal of less than one infection per 100,000 live births. Failure to screen all pregnant women for HIV as recommended by the Centers for Disease Control and Prevention can result in missed opportunities for preventing vertical transmission of HIV with antiretroviral drugs. Using the 2011-2017 National Survey of Family Growth, this study examined HIV testing among pregnant women during prenatal care. We estimated the weighted proportion of self-reported HIV testing among women whose last pregnancy ended within 12 months prior to the interview. Logistic regression models were used to determine the factors associated with HIV testing. Of the 1566 women included in the study, 76.4% (95% confidence intervals [CI] = 72.8-80.0) reported receiving an HIV test during prenatal care. In the multivariable regression model, high school diploma (adjusted odds ratio [aOR] = 1.9, 95% CI = 1.1-3.1), two completed pregnancies (aOR = 1.7, 95% CI = 1.1-2.7), health insurance coverage in the last 12 months (aOR = 1.6, 95% CI = 1.0-2.6), Hispanic race/ethnicity (aOR = 2.8, 95% CI = 1.8-4.4), and non-Hispanic black race/ethnicity (aOR = 2.2, 95% CI = 1.3-3.8) were associated with higher odds of reporting being tested for HIV. However, household income of 300% or more of the federal poverty level (aOR = 0.6, 95% CI = 0.3-0.9) and urban residence (aOR = 0.5, 95% CI = 0.3-0.9) were associated with lower odds of reporting HIV testing. These findings suggest that HIV testing among pregnant women during prenatal care is not universal and may affect achieving the goal of elimination of mother-to-child transmission of HIV in the United States.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".