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生成死因信息以制定卫生政策:所罗门群岛实施自动死因推断系统。

Generating cause of death information to inform health policy: implementation of an automated verbal autopsy system in the Solomon Islands.

机构信息

Bloomberg Philanthropies Data for Health Initiative, University of Melbourne, Melbourne, Australia.

Ministry of Health & Medical Services, Honiara, Solomon Islands.

出版信息

BMC Public Health. 2021 Nov 13;21(1):2080. doi: 10.1186/s12889-021-12180-y.

DOI:10.1186/s12889-021-12180-y
PMID:34774055
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8590305/
Abstract

BACKGROUND

Good quality cause of death (COD) information is fundamental for formulating and evaluating public health policy; yet most deaths in developing countries, including the Solomon Islands, occur at home without medical certification of cause of death (MCCOD). As a result, COD data in such contexts are often of limited use for policy and planning. Verbal autopsies (VAs) are a cost-effective way of generating reliable COD information in populations lacking comprehensive MCCOD coverage, but this method has not previously been applied in the Solomon Islands. This study describes the establishment of a VA system to estimate the cause specific mortality fractions (CSMFs) for community deaths that are not medically certified in the Solomon Islands.

METHODS

Automated VA methods (SmartVA) were introduced into the Solomon Islands in 2016. Trained data collectors (nurses) conducted VAs on eligible deaths to December 2020 using electronic tablet devices and VA responses were analysed using the Tariff 2.0 automated diagnostic algorithm. CSMFs were generated for both non-inpatient deaths in hospitals (i.e. 'dead on/by arrival') and community deaths.

RESULTS

VA was applied to 914 adolescent-and-adult deaths with a median (IQR) age of 62 (45-75) years, 61% of whom were males. A specific COD could be diagnosed for more than 85% of deaths. The leading causes of death for both sexes combined were: ischemic heart disease (16.3%), stroke (13.5%), diabetes (8.1%), pneumonia (5.7%) and chronic-respiratory disease (4.8%). Stroke was the top-ranked cause for females, and ischaemic heart disease the leading cause for males. The CSMFs from the VAs were similar to Global Burden of Disease (GBD) estimates. Overall, non-communicable diseases (NCDs) accounted for 73% of adult deaths; communicable, maternal and nutritional conditions 15%, and injuries 12%. Six of the ten leading causes reported for facility deaths in the Solomon Islands were also identified as leading causes of community deaths based on the VA diagnoses.

CONCLUSIONS

NCDs are the leading cause of adult deaths in the Solomon Islands. Automated VA methods are an effective means of generating reliable COD information for community deaths in the Solomon Islands and should be routinely incorporated into the national mortality surveillance system.

摘要

背景

高质量的死因(COD)信息是制定和评估公共卫生政策的基础;然而,包括所罗门群岛在内的许多发展中国家的大多数死亡都是在家中发生的,没有对死因进行医学认证(MCCOD)。因此,在这种情况下,COD 数据通常对政策和规划的用处有限。 基于死因推断的死因复核(VA)是一种在缺乏全面 MCCOD 覆盖的人群中生成可靠 COD 信息的具有成本效益的方法,但这种方法以前从未在 Solomon 群岛应用过。本研究描述了在 Solomon 群岛建立一个 VA 系统,以估计在 Solomon 群岛未进行医学认证的社区死亡的特定病因死亡率(CSMF)。

方法

2016 年, Solomon 群岛引入了自动化 VA 方法(SmartVA)。经过培训的数据收集员(护士)使用电子平板电脑设备对符合条件的死亡病例进行 VA,截至 2020 年 12 月,VA 反应使用 Tariff 2.0 自动诊断算法进行分析。为医院内(即“死亡/到达时”)和社区死亡的非住院患者分别生成 CSMF。

结果

VA 应用于 914 名青少年和成人死亡病例,中位(IQR)年龄为 62(45-75)岁,其中 61%为男性。超过 85%的死亡病例可以明确特定的 COD。男女合并死因顺位前 5 位的是:缺血性心脏病(16.3%)、中风(13.5%)、糖尿病(8.1%)、肺炎(5.7%)和慢性呼吸疾病(4.8%)。中风是女性的头号死因,而缺血性心脏病是男性的主要死因。VA 得出的 CSMF 与全球疾病负担(GBD)估计值相似。总的来说,非传染性疾病(NCD)占成人死亡的 73%;传染性疾病、孕产妇和营养状况占 15%,伤害占 12%。 Solomon 群岛医疗机构报告的 10 大死因中有 6 种也被确定为基于 VA 诊断的社区死亡的主要死因。

结论

非传染性疾病是 Solomon 群岛成人死亡的主要原因。自动 VA 方法是为 Solomon 群岛社区死亡生成可靠 COD 信息的有效手段,应常规纳入国家死亡率监测系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/fef9c4cd8cc9/12889_2021_12180_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/dcd8cabdee09/12889_2021_12180_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/24943c34d899/12889_2021_12180_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/fef9c4cd8cc9/12889_2021_12180_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/dcd8cabdee09/12889_2021_12180_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/24943c34d899/12889_2021_12180_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8dd9/8590305/fef9c4cd8cc9/12889_2021_12180_Fig3_HTML.jpg

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