Socioeconomic Burden of Ovarian Cancer in 11 Countries
Bibliographic record
Abstract
PURPOSE: Ovarian cancer remains among the most aggressive tumors with the lowest survival probability. Projections are that ovarian cancer will claim more than 8 million lives between 2022 and 2050 without better prevention or control measures. METHODS: We built an Excel-based instrument that uses a prevalence-based cost-of-illness approach and a societal perspective to estimate the burden of ovarian cancer. The instrument leverages data from editions of the World Ovarian Cancer Coalition's Every Woman Study, contains a micro-costing framework to assess the resources and costs of providing care, and uses data from novel systematic reviews and meta-analyses conducted to estimate the effect of ovarian cancer on patient labor productivity outcomes and the time caregivers devote to caring for people living with the disease. RESULTS: Across 11 countries, we estimated US dollars 70 billion in socioeconomic losses attributable to ovarian cancer. Health expenditures to cover treatment in the first 2 years after diagnosis were 7, 41, and 118 times total health spending per capita in high-, upper-middle-, and low- and lower-middle countries, respectively. Patients spent 3,663 years traveling to or receiving treatment. Women lost labor productivity equivalent to 2.5 million workdays due to ill-health from ovarian cancer, and 9,403 women living with ovarian cancer or survivors were estimated to be missing from the workforce. Caregivers spent 17,112 person-years providing practical support to patients-an average of 33 days per woman living with ovarian cancer. CONCLUSION: This study is the first to quantify the social and economic burden of ovarian cancer in 11 countries and highlights its significant cost and defines actions needed to improve ovarian cancer outcomes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".