Economics, Dartmouth College, Hanover, New Hampshire, USA
Stanford University, Stanford, California, USA.
BMJ Open. 2020 Dec 16;10(12):e043165. doi: 10.1136/bmjopen-2020-043165.
To model how known COVID-19 comorbidities affect mortality rates and the age distribution of mortality in a large lower-middle-income country (India), and to identify which health conditions drive differences with high-income countries.
Modelling study.
England and India.
Individual data were obtained from the fourth round of the District Level Household Survey and Annual Health Survey in India, and aggregate data were obtained from the Health Survey for England and the Global Burden of Disease, Risk Factors and Injuries Studies.
The primary outcome was the modelled age-specific mortality in each country due to each COVID-19 mortality risk factor (diabetes, hypertension, obesity and respiratory illness, among others). The change in overall mortality and in the share of deaths under age 60 from the combination of risk factors was estimated in each country.
Relative to England, Indians have higher rates of diabetes (10.6% vs 8.5%) and chronic respiratory disease (4.8% vs 2.5%), and lower rates of obesity (4.4% vs 27.9%), chronic heart disease (4.4% vs 5.9%) and cancer (0.3% vs 2.8%). Population COVID-19 mortality in India, relative to England, is most increased by uncontrolled diabetes (+5.67%) and chronic respiratory disease (+1.88%), and most reduced by obesity (-5.47%), cancer (-3.65%) and chronic heart disease (-1.20%). Comorbidities were associated with a 6.26% lower risk of mortality in India compared with England. Demographics and population health explain a third of the difference in share of deaths under age 60 between the two countries.
Known COVID-19 health risk factors are not expected to have a large effect on mortality or its age distribution in India relative to England. The high share of COVID-19 deaths from people under age 60 in low- and middle-income countries (LMICs) remains unexplained. Understanding the mortality risk associated with health conditions prevalent in LMICs, such as malnutrition and HIV/AIDS, is essential for understanding differential mortality.
在一个中低收入国家(印度)建立模型,以了解已知的 COVID-19 合并症如何影响死亡率和死亡率的年龄分布,并确定哪些健康状况导致与高收入国家的差异。
模型研究。
英格兰和印度。
从印度第四轮地区家庭调查和年度健康调查中获得个体数据,并从英格兰健康调查和全球疾病、风险因素和伤害研究中获得综合数据。
主要结果是由于每个 COVID-19 死亡率风险因素(例如糖尿病、高血压、肥胖和呼吸系统疾病)导致的每个国家特定年龄的死亡率。在每个国家中,估计了由于风险因素组合导致的总体死亡率和 60 岁以下死亡人数的比例变化。
与英格兰相比,印度人糖尿病发病率更高(10.6%对 8.5%)和慢性呼吸道疾病发病率更高(4.8%对 2.5%),肥胖发病率更低(4.4%对 27.9%),慢性心脏病发病率更低(4.4%对 5.9%)和癌症发病率更低(0.3%对 2.8%)。与英格兰相比,印度的 COVID-19 人口死亡率增加最多的是未经控制的糖尿病(+5.67%)和慢性呼吸道疾病(+1.88%),减少最多的是肥胖(-5.47%),癌症(-3.65%)和慢性心脏病(-1.20%)。与英格兰相比,合并症使印度的死亡率降低了 6.26%。人口统计学和人口健康状况解释了两国 60 岁以下死亡人数比例差异的三分之一。
与英格兰相比,已知的 COVID-19 健康风险因素预计不会对印度的死亡率或死亡率的年龄分布产生重大影响。低收入和中等收入国家(LMICs)中 60 岁以下 COVID-19 死亡人数的高比例仍然无法解释。了解在 LMICs 中普遍存在的健康状况(例如营养不良和艾滋病毒/艾滋病)与死亡率相关的风险对于理解差异死亡率至关重要。