New Survey Measures of Mortality at Older Ages in Low and Middle-Income Countries

项目总结 在儿童死亡率大幅下降和在降低死因死亡率方面取得进展之后， 影响年轻人的死亡率正在迅速转移到50岁以上的低收入和中等偏低收入人群 国家(LLMIC)。全球卫生目标现在强调预防这些老年群体的死亡。 因此，美国联邦机构、国际组织和政府的LLMIC预计将增加 他们在针对非传染性疾病和其他行为因素的干预措施方面的投资 影响LLMIC中50岁以上的死亡风险。设计、确定目标和评估这些干预措施 这需要对老年人死亡率的准确估计。然而，死亡数据的标准来源 (即，民事登记和生命统计系统)在大多数LLMIC中都有缺陷。我们建议对一项调查进行测试 测量LLMICs中50岁以上死亡率的方法，即“父母存活史”。这 方法扩展了用于生成年轻年龄组死亡率调查数据的方法，通过询问 应答者报告其亲生父母的重要状况，以及他们的年龄(如果活着)或死亡年龄 及死亡后的时间(如已去世)。它的收集只需要最短的时间(&lt；每次调查2分钟 答辩人)。因此，它可以很容易地纳入在LLMIC进行的大型调查的问卷中， 例如人口与健康调查。我们研究了亲本中存在选择偏向的可能性 生存史，使用来自几个健康和人口统计学的关于家庭关系和死亡率的长期数据 非洲和亚洲的监视系统。然后，我们测量了父母报告错误的流行率 存活史，包括对生命状态的错误分类，以及年龄和日期错误。最后，我们使用 用于识别a)父母存活率历史可能产生准确结果的环境 老年人死亡率的估计，以及b)这些估计所需的样本量 很精确。如果准确并大规模收集，来自父母生存历史的数据将有助于更好地跟踪 在小岛屿发展中国家实现新的全球卫生目标方面取得进展，这些目标主要与50岁及以上的人有关。

PROJECT SUMMARY Following large declines in childhood mortality and progress in lowering mortality from causes of death that affect young adults, mortality is rapidly shifting to ages above 50 years old in low- and lower middle-income countries (LLMICs). Global health objectives now emphasize preventing deaths in those older age groups. US federal agencies, international organizations and governments of LLMICs are thus expected to increase their investments in interventions addressing non-communicable diseases and other behavioral factors that affect mortality risks above age 50 in LLMICs. Designing, targeting, and evaluating these interventions requires accurate estimates of death rates at older ages. However, the standard sources of mortality data (i.e., civil registration and vital statistics systems) are deficient in most LLMICs. We propose to test a survey method for the measurement of mortality above age 50 in LLMICs, the “parental survival history”. This method extends the approach used to generate survey data on mortality in younger age groups, by asking respondents to report the vital status of their biological parents, as well as their age (if alive) or age at death and time since death (if deceased). Its collection only requires minimal time (<2 minutes per survey respondent). It can thus readily be integrated into the questionnaires of large surveys conducted in LLMICs, such as the Demographic and Health Surveys. We examine the possibility of selection biases in parental survival histories, using long-term data on family ties and mortality from several health and demographic surveillance systems in Africa and Asia. Then, we measure the prevalence of reporting errors in parental survival histories, including misclassification of vital status, as well as age and date errors. Finally, we use micro-simulations to identify a) the settings in which parental survival histories might yield accurate estimates of mortality at older ages, and b) the sample sizes required for these estimates to be sufficiently precise. If accurate and collected on a large scale, data from parental survival histories will help better track progress towards new global health goals in LLMICs that pertain primarily to ages 50 and older.