Research School of Population Health, Australian National University, Canberra, Australian Capital Territory, Australia
International Institute for Population Sciences, Mumbai, India.
BMJ Glob Health. 2021 Nov;6(11). doi: 10.1136/bmjgh-2021-007399.
Estimates of excess mortality are required to assess and compare the impact of the COVID-19 pandemic across populations. For India, reliable baseline prepandemic mortality patterns at national and subnational level are necessary for such assessments. However, available data from the Civil Registration System (CRS) is affected by incompleteness of death recording that varies by sex, age and location.
Under-reporting of CRS 2019 deaths was assessed for three age groups (< 5 years, 15-59 years and ≥60 years) at subnational level, through comparison with age-specific death rates from alternate sources. Age-specific corrections for under-reporting were applied to derive adjusted death counts by sex for each location. These were used to compute life expectancy (LE) at birth by sex in 2019, which were compared with subnational LEs from the Global Burden of Disease (GBD) 2019 Study.
A total of 9.92 million deaths (95% UI 9.70 to 10.02) were estimated across India in 2019, about 2.28 million more than CRS reports. Adjustments to under-five and elderly mortality accounted for 30% and 56% of additional deaths, respectively. Adjustments in Bihar, Jharkhand, Madhya Pradesh, Maharashtra, Rajasthan and Uttar Pradesh accounted for 75% of all additional deaths. Adjusted LEs were below corresponding GBD estimates by ≥2 years for males at national level and in 20 states, and by ≥1 year for females in 12 states.
These results represent the first-ever subnational mortality estimates for India derived from CRS reported deaths, and serve as a baseline for assessing excess mortality from the COVID-19 pandemic. Adjusted life expectancies indicate higher mortality patterns in India than previously perceived. Under-reporting of infant deaths and those among women and the elderly is evident in many locations. Further CRS strengthening is required to improve the empirical basis for local mortality measurement across the country.
为了评估和比较 COVID-19 大流行对不同人群的影响,需要估计超额死亡率。对于印度,在国家和次国家层面上,需要有可靠的大流行前死亡率模式作为此类评估的基础。然而,来自民事登记系统(CRS)的数据受到死亡记录完整性的影响,这种完整性因性别、年龄和地点而异。
通过与来自其他来源的特定年龄死亡率进行比较,评估了 CRS 2019 年三个年龄组(<5 岁、15-59 岁和≥60 岁)的死亡率报告不足情况。对每个地点的性别特定死亡率报告不足情况进行了年龄修正,得出了修正后的死亡人数。利用这些数据计算了 2019 年出生时的预期寿命(LE),并与全球疾病负担(GBD)2019 研究中的次国家 LE 进行了比较。
2019 年,印度全国估计有 992 万人死亡(95%UI 970 万至 1002 万),比 CRS 报告的数字多出 228 万。对五岁以下和老年人群的死亡率进行调整,分别占额外死亡人数的 30%和 56%。比哈尔邦、恰蒂斯加尔邦、中央邦、马哈拉施特拉邦、拉贾斯坦邦和北方邦的调整占所有额外死亡人数的 75%。在国家一级和 20 个邦,男性的调整后预期寿命低于 GBD 估计值至少 2 年,在 12 个邦,女性的调整后预期寿命低于 GBD 估计值至少 1 年。
这些结果代表了印度首次从 CRS 报告的死亡人数中得出的次国家死亡率估计数,是评估 COVID-19 大流行期间超额死亡率的基准。调整后的预期寿命表明,印度的死亡率模式比以前认为的要高。许多地方都存在婴儿死亡人数以及妇女和老年人死亡人数报告不足的情况。需要进一步加强 CRS,以改善全国各地区死亡率衡量的实证基础。