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

项目摘要 在儿童死亡率大幅度下降以及在降低死因死亡率方面取得进展之后， 在低收入和中低收入国家， （LLMIC）。全球卫生目标现在强调预防这些老年群体的死亡。 因此，预计美国联邦机构、国际组织和LLMIC政府将增加 投资于应对非传染性疾病和其他行为因素的干预措施， 影响到50岁以上的死亡风险。设计、确定目标和评估这些干预措施 需要准确估计老年人的死亡率。然而，死亡率数据的标准来源 (i.e.,民事登记和人口动态统计系统）在大多数低收入国家都存在缺陷。我们建议进行一项调查 在LLMIC中，50岁以上死亡率的测量方法是“父母生存史”。这 方法扩展了用于生成较年轻年龄组死亡率调查数据的方法， 被调查者报告其亲生父母的生命状况，以及他们的年龄（如果活着）或死亡年龄 死亡后的时间（如果死亡）。它的收集只需要最少的时间（每次调查不到2分钟 答辩人）。因此，它可以很容易地纳入在内陆发展中国家进行的大型调查的问卷中， 例如人口与健康调查。我们研究了父母选择偏差的可能性， 生存史，使用来自几个健康和人口统计数据库的家庭关系和死亡率的长期数据， 非洲和亚洲的监控系统。然后，我们测量了父母报告错误的患病率， 生存史，包括生命状态的错误分类，以及年龄和日期错误。最后运用 微观模拟，以确定a）父母生存史可能产生准确的环境 老年人死亡率的估计，以及B）这些估计所需的样本量足以 准确如果准确并大规模收集，来自父母生存史的数据将有助于更好地跟踪 内陆发展中国家在实现主要与50岁及以上年龄群体有关的新的全球卫生目标方面取得的进展。

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.