Prevention of Parent to Child Transmission (PPTCT) Program Data in India: An Emerging Data Set for Appraising the HIV Epidemic
Bibliographic record
Abstract
BACKGROUND: Evidence based resource allocation and decentralized planning of an effective HIV/AIDS response requires reliable information on levels and trends of HIV at national and sub-national geographic levels. HIV sentinel surveillance data from antenatal clinics (HSS-ANC) has been an important data source to assess the HIV/AIDS epidemic in India, but has a number of limitations. We assess the value of Prevention of Parent to Child Transmission (PPTCT) programme data to appraise the HIV epidemic in India. METHODS/FINDINGS: HIV data from PPTCT sites were compared to HSS-ANC and general population level surveys at various geographic levels in the states of Karnataka, Maharashtra and Andhra Pradesh. Chi-square tests were used to ascertain statistical significance. PPTCT HIV prevalence was significantly lower than HSS-ANC HIV prevalence (0.92% vs. 1.22% in Andhra Pradesh, 0.65% vs. 0.89% in Karnataka, 0.52% vs. 0.60% in Maharashtra, p<0.001 for all three states). In all three states, HIV prevalence from PPTCT centres that were part of the sentinel surveillance was comparable to HSS-ANC prevalence but significantly higher than PPTCT centres that were not part of the sentinel surveillance. HIV prevalence from PPTCT data was comparable to that from general population surveys. In all three states, significant declines in HIV prevalence between 2007 and 2010 were observed with the PPTCT data set. District level analyses of HIV trends and sub-district level analysis of HIV prevalence were possible using the PPTCT and not the HSS-ANC data sets. CONCLUSION: HIV prevalence from PPTCT may be a better proxy for general population prevalence than HSS-ANC. PPTCT data allow for analysis of HIV prevalence and trends at smaller geographic units, which is important for decentralized planning of HIV/AIDS programming. With further improvements to the system, India could replace its HSS-ANC with PPTCT programme data for surveillance.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".