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印度 15-54 岁男性人群中糖尿病+高血压共病(定义为糖尿病+高血压)的预测因素:城乡分析。

Predictors of Multimorbidity (Defined as Diabetes + Hypertension) Amongst Males Aged 15-54 in India: An Urban/Rural Split Analysis.

机构信息

Department of Epidemiology and Biostatistics, Western University, London, ON, Canada.

Interfaculty Program in Public Health, Western University, London, ON, Canada.

出版信息

Int J Public Health. 2024 Feb 1;69:1606660. doi: 10.3389/ijph.2024.1606660. eCollection 2024.

Abstract

This study aimed to determine which sociodemographic and lifestyle factors may act as predictors of multimorbidity (defined as diabetes + hypertension) amongst men aged 15-54 within urban and rural areas of India. Data from the latest 2019-2021 India NFHS-5 survey were utilized. Presumed cases of multimorbidity were defined as men who had DM + HTN. A total of 22,411 men in urban areas and 66,768 rural men were analyzed using mixed-effect multi-level binary logistic regression models. Various predictors were found to have a statistically significant association to multimorbidity. Urban areas: Age, region of residence, wealth, religion, occupation, and BMI. Rural areas: Age, education, region of residence, wealth, occupation, caste, BMI, alcohol consumption, media exposure, and tobacco consumption. Departing from the broad operational definitions often studied within literature, this study provided insight into one of the most prevalent specific multimorbidities across India. The urban/rural split analyses revealed substantial differences in high-risk characteristics across both areas, which have commonly been overlooked. These findings may better inform policymakers and assist in effectively reducing multimorbidity-related burden through area-specific preventative programs.

摘要

本研究旨在确定哪些社会人口和生活方式因素可能是印度城乡地区 15-54 岁男性合并症(定义为糖尿病+高血压)的预测因素。本研究使用了最新的 2019-2021 年印度 NFHS-5 调查的数据。合并症的假定病例定义为同时患有 DM+HTN 的男性。采用混合效应多级二项逻辑回归模型对城市地区的 22411 名男性和农村地区的 66768 名男性进行了分析。结果发现,各种预测因素与合并症具有统计学显著关联。城市地区:年龄、居住地、财富、宗教、职业和 BMI。农村地区:年龄、教育、居住地、财富、职业、种姓、BMI、饮酒、媒体接触和吸烟。本研究与文献中经常研究的广泛操作定义不同,它深入了解了印度最常见的特定合并症之一。城乡分割分析揭示了两个地区高风险特征的显著差异,这些差异通常被忽视。这些发现可以为政策制定者提供更好的信息,并通过针对特定地区的预防计划来有效减轻与合并症相关的负担。

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