Predictors of health insurance enrolment and wealth-related inequality in Nepal: evidence from Multiple Indicator Cluster Survey (MICS) 2019.
Predictors of health insurance enrolment and wealth-related inequality in Nepal: evidence from Multiple Indicator Cluster Survey (MICS) 2019.
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DOI：
10.1136/bmjopen-2021-050922
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发表时间：
2021-11-26
期刊：
影响因子：
2.9
通讯作者：
Sapkota VP
中科院分区：
文献类型：
--
作者：
Bhusal UP;Sapkota VP
我们分析了尼泊尔医疗保险参保率的预测因素，衡量了与财富相关的不平等，并将不平等分解为其促成因素。横断面研究。我们使用了基于《2019年尼泊尔多指标群调查》的具有全国代表性的数据。在这项研究的10个 958个家庭中，6.95%的家庭至少参加了一项医疗保险计划。衡量健康保险(任何类型)的投保率。户主受过高中或以上教育的家庭比没有受过正规教育的家庭更有可能成为医疗保险会员(调整后的OR为1.87；95%CI：1.32至2.64)。有大众媒体曝光率的家庭参加计划的可能性几乎是他们的同龄人的三倍(调整后的OR2.96；95%的 CI为2.03至4.31)。与非印度教徒相比，印度教徒被录取的几率更大(调整后的OR1.82；95%的 CI为1.20%至2.77%)。与婆罗门人、切特里人和马德西人相比，达利特人入伍的可能性较小(调整后的OR0.66；95% CI为0.47%至0.94%)。与来自第一省的家庭相比，来自第二省、巴格马蒂和苏图尔巴斯奇姆的家庭不太可能拥有医疗保险会员资格。与财富最低五分之一的家庭相比，来自较富裕和最富有的五分之一的家庭拥有医疗保险成员的可能性高出两倍多。积极的集中度指数0.25%(95%可信区间0.21%至0.30%；P<；0.001)表明富裕家庭的医疗保险参保率高得不成比例。户主受教育程度、接触大众媒体、宗教和种族背景、地理位置(省)和财富状况是尼泊尔医疗保险参保率的关键预测因素。在医疗保险从属关系中，与财富相关的不平等现象十分严重。该研究建议定期监测人口和社会经济群体在医疗保险参保方面的不平等，以确保在实现全民健康覆盖方面取得进展。
We analysed predictors of health insurance enrolment in Nepal, measured wealth-related inequality and decomposed inequality into its contributing factors. Cross-sectional study. We used nationally representative data based on Nepal Multiple Indicator Cluster Survey 2019. Out of 10 958 households included in this study, 6.95% households were enroled in at least one health insurance scheme. measures health insurance (of any type) enrolment. Households were more likely to have health insurance membership when household head have higher secondary education or above compared with households without formal education (adjusted OR 1.87; 95% CI: 1.32 to 2.64)). Households with mass media exposure were nearly three times more likely to get enroled into the schemes compared with their counterparts (adjusted OR 2.96; 95% CI 2.03 to 4.31). Hindus had greater odds of being enroled (adjusted OR 1.82; 95% CI 1.20 to 2.77) compared with non-Hindus. Dalits were less likely to get enroled compared with Brahmin, Chhetri and Madhesi (adjusted OR 0.66; 95% CI 0.47 to 0.94). Households from province 2, Bagmati and Sudurpaschim were less likely to have membership compared with households from province 1. Households from Richer and Richest wealth quintiles were more than two times more likely to have health insurance membership compared with households from the poorest wealth quintile. A positive concentration index 0.25 (95% CI 0.21 to 0.30; p<0.001) indicated disproportionately higher health insurance enrolment among wealthy households. Education of household head, exposure to mass media, religious and ethnic background, geographical location (province) and wealth status were key predictors of health insurance enrolment in Nepal. There was a significant wealth-related inequality in health insurance affiliation. The study recommends regular monitoring of inequality in health insurance enrolment across demographic and socioeconomic groups to ensure progress towards Universal Health Coverage.
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