a Department of Global Health and Social Medicine, Program in Global Noncommunicable Diseases and Social Change , Harvard Medical School , Boston , USA.
b INDEPTH Network , Accra , Ghana.
Glob Health Action. 2019;12(1):1608013. doi: 10.1080/16549716.2019.1608013.
Understanding socioeconomic disparities in all-cause and cause-specific mortality can help inform prevention and treatment strategies.
To quantify cause-specific mortality rates by socioeconomic status across seven health and demographic surveillance systems (HDSS) in five countries (Ethiopia, Kenya, Malawi, Mozambique, and Nigeria) in the INDEPTH Network in sub-Saharan Africa.
We linked demographic residence data with household survey data containing living standards and education information we used to create a poverty index. Person-years lived and deaths between 2003 and 2016 (periods varied by HDSS) were stratified in each HDSS by age, sex, year, and number of deprivations on the poverty index (0-8). Causes of death were assigned to each death using the InterVA-4 model based on responses to verbal autopsy questionnaires. We estimated rate ratios between socioeconomic groups (2-4 and 5-8 deprivations on our poverty index compared to 0-2 deprivations) for specific causes of death and calculated life expectancy for the deprivation groups.
Our pooled data contained almost 3.5 million person-years of observation and 25,038 deaths. All-cause mortality rates were higher among people in households with 5-8 deprivations on our poverty index compared to 0-2 deprivations, controlling for age, sex, and year (rate ratios ranged 1.42 to 2.06 across HDSS sites). The poorest group had consistently higher death rates in communicable, maternal, neonatal, and nutritional conditions (rate ratios ranged 1.34-4.05) and for non-communicable diseases in several sites (1.14-1.93). The disparities in mortality between 5-8 deprivation groups and 0-2 deprivation groups led to lower life expectancy in the higher-deprivation groups by six years in all sites and more than 10 years in five sites.
We show large disparities in mortality on the basis of socioeconomic status across seven HDSS in sub-Saharan Africa due to disparities in communicable disease mortality and from non-communicable diseases in some sites. Life expectancy gaps between socioeconomic groups within sites were similar to the gaps between high-income and lower-middle-income countries. Prevention and treatment efforts can benefit from understanding subpopulations facing higher mortality from specific conditions.
了解全因死亡率和特定原因死亡率的社会经济差异有助于为预防和治疗策略提供信息。
在撒哈拉以南非洲的 INDEPTH 网络的七个健康和人口监测系统(HDSS)中,按社会经济地位量化七种疾病的特定原因死亡率。
我们将人口居住数据与包含生活水平和教育信息的家庭调查数据相关联,我们使用这些数据创建了一个贫困指数。2003 年至 2016 年(每个 HDSS 的时间段不同)期间,每个 HDSS 都按年龄、性别、年份和贫困指数上的贫困剥夺数量(0-8)进行了分层。根据口头尸检问卷的回答,使用 InterVA-4 模型为每例死亡分配死因。我们估计了特定死因的社会经济群体(我们的贫困指数上有 2-4 和 5-8 个贫困剥夺与 0-2 个贫困剥夺相比)之间的率比,并计算了贫困剥夺群体的预期寿命。
我们的汇总数据包含近 350 万个人年的观察和 25038 例死亡。在控制年龄、性别和年份后,与 0-2 个贫困剥夺相比,我们的贫困指数上有 5-8 个贫困剥夺的家庭中的人全因死亡率更高(在不同的 HDSS 地点,率比范围为 1.42 至 2.06)。最贫困的群体在传染病、孕产妇、新生儿和营养状况下的死亡率一直较高(率比范围为 1.34-4.05),在一些地点的非传染性疾病中也较高(1.14-1.93)。在死亡率方面,5-8 个贫困剥夺组与 0-2 个贫困剥夺组之间的差异导致所有地点的较高贫困剥夺组的预期寿命降低了六年,在五个地点的预期寿命降低了超过十年。
我们表明,在撒哈拉以南非洲的七个 HDSS 中,由于传染病死亡率的差异以及一些地点的非传染性疾病,社会经济地位的死亡率存在很大差异。各地点内社会经济群体之间的预期寿命差距与高收入和中低收入国家之间的差距相似。了解面临特定疾病死亡率较高的亚人群,有助于开展预防和治疗工作。