Comprehensive approach to costing cervical cancer prevention and control: a case study in the United Republic of Tanzania using the Cervical Cancer Prevention and Control Costing (C4P) tool
Bibliographic record
Abstract
BACKGROUND: The World Health Organization (WHO) has developed a costing tool, the Cervical Cancer Prevention and Control Costing (C4P) tool, to estimate the comprehensive cost of cervical cancer primary, secondary and tertiary prevention in low- and middle-income countries. The tool was piloted in the United Republic of Tanzania, a country with a high incidence of cervical cancer with 62.5 cases per 100,000 women in 2020. This paper presents the costing tool methods as well as the results from the pilot in Tanzania. METHODS: The C4P tool estimates the incremental costs of cervical cancer prevention and control programmes. It estimates the financial (monetary costs to the government) and economic costs (opportunity costs). For the pilot, the study team collected data on costs and programme assumptions for human papillomavirus (HPV) vaccination of 14-year-old girls and scaling up of cervical cancer screening (visual inspection with acetic acid and HPV-DNA testing) and treatment for women for 2020-2024. Assumptions were made on how vaccination coverage would increase over the 5 years as well as developing additional screening and treatment capacity through health personnel training and infrastructure strengthening. RESULTS: The total financial and economic costs of the comprehensive programme during 2020-2024 are projected to be US$68 million and US$124 million, respectively. The financial and economic costs of a fully immunized girl with HPV vaccine are estimated to be US$6.68 and US$17.31, respectively, while the costs per woman screened for cervical cancer are, on average, US$4.02 and US$5.83, respectively; US$6.44 and US$9.37 for pre-cancer treatment, respectively; and US$101 and US$107 for diagnosis of invasive cancer, respectively. The cost of treating and managing invasive cancer range from US$7.05 and US$7.83 for outpatient palliative care to US$800.21 and US$893.80 for radiotherapy, respectively. CONCLUSIONS: The C4P costing tool can assist national cervical cancer programmes to estimate monetary resources needed as well as opportunity costs of reducing national cervical cancer incidence through primary, secondary and tertiary prevention.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".