Kundu Jhumki, Goli Srinivas, James K S
Centre for Ageing Studies, International Institute for Population Sciences, Mumbai, Maharashtra 400088, India.
Department of Fertility and Social Demography, International Institute for Population Sciences, Mumbai, Maharashtra 400088, India.
Int Health. 2025 Mar 4;17(2):168-178. doi: 10.1093/inthealth/ihae037.
While the association between education and non-communicable diseases (NCDs) is well established, it remains unclear whether this association varies by gender. The aim of this study was to examine two critical research questions: whether the association of education and NCDs is conditioned by gender and, if so, what are the factors contributing to this?
Data from the Longitudinal Aging Study in India Wave 1 (2017-2018) was used for the empirical analysis. The study employs bivariate, binary logistic regression and Oaxaca decomposition analyses.
The results reveal that the net likelihood of having at least one chronic NCD increases with an increase in education level for men (<5 y of schooling: odds ratio [OR] 1.18 [95% confidence interval {CI} 1.09 to 1.28]; ≥10 y of schooling: OR 1.43 [95% CI 1.33 to 1.53]). However, for women, the result showed a contrasting pattern. The decomposition analysis revealed that the distinctive roles of marital status and working status in the diagnosis of morbidity for men and women are the key factors behind the gendered heterogeneous relationship of education and NCDs in India.
The study found that it is important to acknowledge the potential impact of self-reporting bias in morbidity data while examining the relationship between education and NCDs.
虽然教育与非传染性疾病(NCDs)之间的关联已得到充分证实,但这种关联是否因性别而异仍不清楚。本研究的目的是探讨两个关键研究问题:教育与非传染性疾病之间的关联是否受性别影响,如果是,促成这种情况的因素有哪些?
印度纵向老龄化研究第一轮(2017 - 2018年)的数据用于实证分析。该研究采用双变量、二元逻辑回归和奥瓦卡分解分析。
结果显示,男性中至少患有一种慢性非传染性疾病的净可能性随着教育水平的提高而增加(受教育年限<5年:比值比[OR]为1.18[95%置信区间{CI}为1.09至1.28];受教育年限≥10年:OR为1.43[95%CI为1.33至1.53])。然而,对于女性而言,结果呈现出相反的模式。分解分析表明,婚姻状况和工作状况在男性和女性发病诊断中的独特作用是印度教育与非传染性疾病性别异质关系背后的关键因素。
该研究发现,在研究教育与非传染性疾病之间的关系时,认识到发病数据中自我报告偏差的潜在影响很重要。