Evaluation of the benefits and harms of alternative strategies for cervical screening and treatment in women living with HIV: modelling to support WHO 2021 cervical screening and treatment guidelines to prevent cervical cancer
Bibliographic record
Abstract
Abstract The WHO strategy for the elimination of cervical cancer sets 2030 targets for the scale-up of HPV vaccination, cervical screening, and pre-cancer and invasive cancer treatment. Screening and treatment of precancerous disease will be critical to reduce mortality in the short term, particularly in high HIV burden settings, due to the increased risk of cervical cancer among women living with HIV. To inform the development of new screening and treatment guidelines for women living with HIV, we modelled alternate scenarios for the screening test, frequency, and interval, and assessed the benefits and potential harms of cervical screening and precancer treatment in Tanzania, a country with a high burden of both HIV and of cervical cancer. We used a dynamic model of HPV and HIV co-infection, Policy1-Cervix-HIV, parametrised to Tanzanian demographic and epidemiological data, which captured HIV and HPV interactions. With the support of the WHO Guidelines Development Group for Screening and Treatment to Prevent Cervical Cancer (GDG) we assessed the impact of seven screening algorithms including primary visual inspection with acetic acid (‘VIA’), primary cytology, and primary HPV DNA (‘primary HPV’) with no triage, or triage using HPV16/18 genotyping, colposcopy, cytology, or VIA for women aged 25-50. Screening intervals of 3 years were considered for primary VIA and cytology, and intervals of 3, 5 or 10 years were considered for primary HPV. Screening and triage test performance was informed by updated systematic review evidence. We assumed 70% of women attended each routine screen, and 90% complied with follow-up or treatment. Outcomes include reduction in cervical cancer incidence and mortality as a measure of benefits, and number of precancer treatments (NNT) needed to prevent a death and preterm delivery events directly due to precancer treatment (‘additional preterm delivery events’) as a measure of the potential harms. A range of assumptions were considered in sensitivity and supplementary analyses. We found that, without screening, 5,263 cervical cancer cases (age-standardised incidence rate [ASIR] 104.0/100,000/year) and 4,467 cervical cancer deaths (age-standardised mortality rate [ASMR] 99.8/100,000) are predicted over the lifetime of 100,000 Tanzanian WLHIV. Primary HPV testing without triage every 3-years for women aged 25-50 years was effective in terms of cancer reduction, resulting in a 63.6% reduction in ASIR and 71.7% reduction in ASMR, and an NNT of 38.7 to prevent one cervical cancer death. However, triaging HPV positive women before treatment resulted in minimal loss of effectiveness with overall reduction in ASIR of 57.3-62.2% (range depends on triaging test used) and ASMR of 66.4-70.5% and was associated with more favourable NNTs (19.7-33.0). Primary screening with VIA or cytology at 3-yearly intervals resulted in lesser reductions in cervical cancer incidence and mortality and less favourable NNTs: for VIA, the overall reduction in ASIR was 50.6% and the reduction in ASMR was 54.2%, with an NNT of 107.5 to prevent a death. Extending the HPV screening interval from 3 to 5 years slightly decreased screening program effectiveness but increased efficiency. In conclusion, primary HPV testing approaches were the most effective, and optimised benefits-to-harms compared to primary VIA or cytology. Triaging HPV positive women before treatment reduces precancer treatments and improves efficiencies with minimal loss in effectiveness. However, uncertainties in the primary data exist and further empirical data on the effectiveness of screening in WLHIV will strengthen the evidence base for screening in this population. These findings informed updated WHO cervical screening and treatment guidelines; WHO now recommends that women living with HIV have primary HPV screening with triage before treatment for ages 25-50 every 3 or 5 years.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".