Predictors of cervical cancer screening among Kenyan women: results of a nested case-control study in a nationally representative survey
Notice bibliographique
Résumé
BACKGROUND: Cervical cancer is a major public health concern in Kenya. It is the leading cause of cancer morbidity and mortality among women. Although screening is an effective prevention method, uptake is low among eligible women. Little is known about predictors of cervical cancer screening uptake. This study explored relationship between uptake of cervical cancer screening, socio-demographic, behavioral and biological risk factors. METHODS: Nested case-control study within STEPS survey, a population-based cross-sectional household survey conducted between April and June 2015.Cases were women who had undergone cervical cancer screening and controls were unscreened women. Study participants were women eligible for cervical cancer screening (30-49 years). Variables included socio-demographic; behavioral risk factors such as physical activity, tobacco and alcohol use diet and biological factors like diabetes and hypertension. Outcome of interest was cervical cancer screening. Data analysis was done using STATA version 14. Logistic regression model was used to assess relationship between cervical cancer screening and socio-demographic, behavioral and biological risk factors. RESULTS: Of 1180 women interviewed, 16.4% (n = 194) had been screened for cervical cancer. Of unscreened women (n = 986), 67.9% were aware of cervical cancer screening. Higher screening rates were observed in more educated women (25.2%), highest income quintile (29.6%) and living in urban areas (23%) than in women with no formal education (3.2%), poorest (3.6%) and living in rural areas (13.8%). Younger women (35-39) and those with low High-density lipoprotein (HDL) were less likely to be screened [OR = 0.56; 95% CI = (0.34, 0.93); p-value = 0.025] and [OR = 0.51; 95% CI = (0.29, 0.91); p = value 0.023] respectively. Self-employed women, those in the fourth wealth quintile, binge drinkers, high sugar consumption and insufficient physical activity were more likely to be screened [OR 2.55 (1.12, 5.81) p value 0.026], [OR 3.56 (1.37, 9.28) p value 0.009], [OR 5.94 (1.52, 23.15) p value 0.010], [OR 2.99 (1.51, 5.89) p value 0.002] and [OR 2.79 (1.37, 5.68) p value 0.005] respectively. CONCLUSION: Uptake of cervical cancer screening is low despite high awareness. Strategies to improve cervical cancer screening in Kenya should be implemented with messages targeting persons with both risky and non-risky lifestyles especially younger women with no formal education living in rural areas.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 tête enseignante, 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 ».