Experiences of women with ovarian cancer in 22 low-income and middle-income countries (Every Woman Study LMICs): a cross-sectional study
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».