Mohanty Sanjay K, Khan Junaid, Maiti Suraj, Kämpfen Fabrice, Maurer Jürgen, O'Donnell Owen
Department of Population and Development, International Institute for Population Sciences, Mumbai, 400088, India.
Department of Statistics, Vivekananda College, University of Calcutta, Kolkata, 700 063, India.
Soc Sci Med. 2025 Jan;364:117535. doi: 10.1016/j.socscimed.2024.117535. Epub 2024 Nov 20.
Given the large and growing number of older (45+ years) people in India, inequitable access to healthcare in this population would slow global progress toward universal health coverage. We used a 2017-18 nationally representative sample of this population (n = 53,687) to estimate healthcare inequality and inequity by economic status. We used an extensive battery of indicators in nine health domains, plus age and sex, to adjust for need. We measured economic status by monthly per capita consumption expenditure and used a concentration index to measure inequalities in probabilities of any doctor visit and any hospitalisation within 12 months. We decomposed inequality with a regression method that allowed for economic and urban/rural heterogeneity in partial associations between healthcare and both need and non-need covariates. We used the associations achieved by the richest fifth of urban dwellers to predict a need-justified distribution of healthcare and compared the actual distribution with that benchmark to identify inequity. We found pro-rich inequalities in doctor visits and hospitalisations, which were driven by use of private healthcare. Adjustment for the greater need of poorer individuals revealed pro-rich inequity that exceeded inequality by about 65% and 39% for doctor visits and hospitalisations, respectively. These adjustments would have been substantially smaller, and inequity underestimated, without allowing for use-need heterogeneity, which accounted for 11% of the inequity in doctor visits and was 373% of inequity in hospitalisations. Targeting service coverage on poorer and rural groups, and increasing their access to private providers, would both reduce inequity and raise average coverage in the older population of India.
鉴于印度45岁及以上老年人数量众多且不断增加，这一人群在医疗保健方面的不公平获取将减缓全球实现全民健康覆盖的进程。我们使用了2017 - 18年该人群具有全国代表性的样本（n = 53687），按经济状况估计医疗保健的不平等和不公平。我们在九个健康领域以及年龄和性别方面使用了一系列广泛的指标来调整需求。我们通过人均月消费支出衡量经济状况，并使用集中指数来衡量12个月内任何医生就诊和任何住院概率的不平等。我们使用回归方法分解不平等，该方法考虑了医疗保健与需求和非需求协变量之间部分关联中的经济和城乡异质性。我们利用城市最富裕五分之一居民所达到的关联来预测医疗保健需求合理的分布，并将实际分布与该基准进行比较以确定不公平。我们发现，在医生就诊和住院方面存在有利于富人的不平等现象，这是由使用私立医疗保健推动的。对较贫困个体更大需求的调整显示，有利于富人的不公平现象在医生就诊和住院方面分别比不平等现象高出约65%和39%。如果不考虑使用 - 需求异质性，这些调整会小得多，不公平现象也会被低估，使用 - 需求异质性分别占医生就诊不公平现象的11%和住院不公平现象的373%。将服务覆盖目标对准较贫困和农村群体，并增加他们获得私立医疗服务提供者的机会，将既能减少不公平现象，又能提高印度老年人口的平均覆盖率。
