Abstract P031: Does The Place Of Residence Influence Your Risk Of Being Hypertensive? A Case Study From Nepal
Notice bibliographique
Résumé
Introduction: Area level factors could be an independent risk factor for disease and negative health outcomes. Despite the multiple studies on risk factors for hypertension, area-level influence is poorly investigated especially in low- and middle-income countries. Hypothesis: This study aims to test the hypothesis that area-level deprivation (AD) is significantly associated with the risk of hypertension independent of individual risk factors in the context of Nepal. Methods: The study is based on the latest Nepal Demographic Health Survey conducted in 2016. The area-level deprivation index was constructed and validated based on the material (household assets, household structure), social and geospatial features. The index ranged from 52 to 146 with higher scores representing higher levels of deprivation. Individuals aged 15 and above were identified as hypertensive if they met any of the following criteria: i) systolic blood pressure ≥140 mmHg; ii) diastolic blood pressure ≥90 mmHg; iii) taking antihypertensive medication irrespective of their blood pressure readings during the survey; iv) history of earlier diagnosis of hypertension by any health care provider. Associations between the quartiles of AD and hypertension were investigated using two-level logistic regression. Further, potential cross-level interaction and mediated pathways between AD and hypertension were explored. Results: The overall prevalence of hypertension was approximately 23% of which nearly 50% were unaware of their high blood pressure status at the time of the survey. Individuals from the least deprived areas seemed to have higher odds of hypertension compared to highly deprived areas (Odds Ratio=1.54 (95% CI 1.26, 1.89); p-trend=<0.001). Literate individuals from deprived areas were likely to have a higher risk of hypertension compared to those without formal education. However, the association was not significant in the least deprived areas (p-interaction <0.001). Overweight/obesity seems to explain a significant magnitude of the indirect pathway (67%) between AD and hypertension in the context of Nepal. Conclusion: Residing in the least deprived or more affluent areas might increase an individual’s risk of being hypertensive. Association between individual risk factors and hypertension is likely to vary across the areas with varying levels of deprivation. These results seem paradoxical and are inconsistent with epidemiological data from high-income countries. Over-nutrition and changing lifestyle, driven by affluence and higher disposable income, could explain this counterintuitive association in the context of a country like Nepal which is undergoing a rapid socio-demographic and epidemiological transition.
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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 | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».