Experiences of women with ovarian cancer in 22 low-income and middle-income countries (Every Woman Study LMICs): a cross-sectional study
Bibliographic record
Abstract
Background Around 70% of ovarian cancers occur in low-income and middle-income countries (LMICs), but little is known about the experiences of women with ovarian cancer in this setting. We aimed to describe the experiences and priorities of women with ovarian cancer in LMICs, and to identify potentially modifiable factors linked to these experiences. Methods We did a cross-sectional, survey-based study in LMICs, recruiting women with ovarian cancer from 82 hospitals in 22 countries. Women diagnosed with ovarian cancer (primary malignancy of the ovary, fallopian tube, or peritoneum, including borderline tumours) at a study site within the past 5 years (2017–24), who were aged 18 years or older, were eligible for inclusion. Participants completed a 59-item survey at a single timepoint up to 5 years after their diagnosis, which collected information on demographics and cancer experiences. Survey data collection ran from June 14, 2022, to May 13, 2024. Data on cancer histology and stage at diagnosis were collected from medical records. Countries were grouped according to the four Human Development Index (HDI) levels (low, medium, high, and very high). The primary study outcomes were self-reported knowledge of ovarian cancer before diagnosis and the extent of any financial impact of having ovarian cancer. Based on survey responses, knowledge of ovarian cancer was ordered from low (had never heard of it) to high (had heard of it and knew something about it), and extent of financial impact from low (not at all) to high (a great extent). Random-effects ordered logistic regression was used to investigate the association of participant-reported variables and country HDI group with each primary outcome. Findings We analysed data from 2446 women with ovarian cancer (mean age at diagnosis 49·9 years [SD 13·6]). 631 (26·1%) of 2421 participants who reported on their knowledge of ovarian cancer before diagnosis reported that they had heard of ovarian cancer and knew something about it (range: three [3·3%] of 90 participants in Nepal to 92 [63·4%] of 145 in Uzbekistan). In multivariable regression analyses of 2133 participants with relevant data on model variables, lower education level (no formal education vs tertiary or higher education, odds ratio [OR] 3·41, 95% CI 2·38–4·89, p<0·0001; and primary or secondary education vs tertiary or higher education, OR 1·96, 1·56–2·47, p<0·0001), lower household income (self-perceived as below vs above average for the country, OR 1·79, 1·28–2·50, p=0·0006), and lower HDI group (low vs very high HDI group, OR 2·32, 1·06–5·04, p=0·034; and medium vs very high HDI group, OR 1·88, 1·04–3·42, p=0·038) were associated with a decrease in ovarian cancer knowledge by one category level. 1105 (45·9%) of 2406 participants who reported on the extent of financial impact of ovarian cancer indicated that their financial situation had been affected to a great extent (range: 16 [15·2%] of 105 in Argentina to 46 [83·6%] of 55 in Uganda). In multivariable regression analyses of 2099 participants, lower household income (self-perceived as below vs above average for the country, OR 3·64, 2·58–5·14, p<0·0001; and average vs above average for the country, OR 1·78, 1·31–2·41, p=0·0002) and lower HDI group (low vs very high HDI group, OR 3·70, 1·10–12·45, p=0·035; and medium vs very high HDI group, OR 3·47, 1·40–8·59, p=0·0072) were associated with an increase in financial impact by one category level. Interpretation We have identified factors associated with ovarian cancer knowledge, experiences, and outcomes across LMICs, which could inform policy and the development of interventions to improve patient care. Given variation in patient experiences and outcomes between LMICs, interventions should be tailored to local needs and priorities. Funding International Gynecologic Cancer Society and the World Ovarian Cancer Coalition.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".