Hyak mortality monitoring system: innovative sampling and estimation methods - proof of concept by simulation.

IF 1.1 Q4 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Global Health Epidemiology and Genomics Pub Date : 2018-02-05 eCollection Date: 2018-01-01 DOI:10.1017/gheg.2017.15
S J Clark, J Wakefield, T McCormick, M Ross
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引用次数: 9

Abstract

Traditionally health statistics are derived from civil and/or vital registration. Civil registration in low- to middle-income countries varies from partial coverage to essentially nothing at all. Consequently the state of the art for public health information in low- to middle-income countries is efforts to combine or triangulate data from different sources to produce a more complete picture across both time and space - data amalgamation. Data sources amenable to this approach include sample surveys, sample registration systems, health and demographic surveillance systems, administrative records, census records, health facility records and others. We propose a new statistical framework for gathering health and population data - Hyak - that leverages the benefits of sampling and longitudinal, prospective surveillance to create a cheap, accurate, sustainable monitoring platform. Hyak has three fundamental components: Data amalgamation: A sampling and surveillance component that organizes two or more data collection systems to work together: (1) data from HDSS with frequent, intense, linked, prospective follow-up and (2) data from sample surveys conducted in large areas surrounding the Health and Demographic Surveillance System (HDSS) sites using informed sampling so as to capture as many events as possible;Cause of death: Verbal autopsy to characterize the distribution of deaths by cause at the population level; andSocioeconomic status (SES): Measurement of SES in order to characterize poverty and wealth. We conduct a simulation study of the informed sampling component of Hyak based on the Agincourt HDSS site in South Africa. Compared with traditional cluster sampling, Hyak's informed sampling captures more deaths, and when combined with an estimation model that includes spatial smoothing, produces estimates of both mortality counts and mortality rates that have lower variance and small bias.

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Hyak死亡率监测系统:创新的抽样和估计方法-通过模拟验证概念。
传统上,健康统计数据来源于民事和(或)生命登记。中低收入国家的民事登记从部分覆盖到基本上什么都没有不等。因此,中低收入国家公共卫生信息的最新技术是将来自不同来源的数据进行组合或三角化,以在时间和空间上产生更完整的图像——数据合并。适用这种方法的数据来源包括抽样调查、抽样登记系统、卫生和人口监测系统、行政记录、人口普查记录、卫生设施记录等。我们提出了一个新的收集健康和人口数据的统计框架Hyak,该框架利用采样和纵向前瞻性监测的优势,创建了一个廉价、准确、可持续的监测平台。Hyak有三个基本组成部分:数据合并:一个采样和监测组成部分,它组织两个或多个数据收集系统协同工作:(1)来自HDSS的数据,具有频繁、密集、关联、,前瞻性随访和(2)在健康和人口监测系统(HDSS)站点周围的大区域进行的抽样调查数据,使用知情抽样,以捕捉尽可能多的事件;死亡原因:口头尸检,以描述人口层面按原因划分的死亡分布;社会经济地位(SES):衡量社会经济地位以表征贫困和财富。我们基于南非Agincourt HDSS站点对Hyak的知情采样组件进行了模拟研究。与传统的集群抽样相比,Hyak的知情抽样捕捉到了更多的死亡人数,当与包括空间平滑的估计模型相结合时,可以产生方差较低、偏差较小的死亡率和死亡率估计值。
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来源期刊
Global Health Epidemiology and Genomics
Global Health Epidemiology and Genomics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
1.40
自引率
0.00%
发文量
10
审稿时长
20 weeks
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