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撒哈拉以南非洲一家三级医院运到即死病例应用自动言语推断和完全尸检确定死因的比较。

Comparison of the Causes of Death Identified Using Automated Verbal Autopsy and Complete Autopsy among Brought-in-Dead Cases at a Tertiary Hospital in Sub-Sahara Africa.

机构信息

National Center for Global Health and Medicine, Shinjuku-ku, Japan.

Department of Public Health, Graduate School of Medicine, Juntendo University, Tokyo, Japan.

出版信息

Appl Clin Inform. 2022 May;13(3):583-591. doi: 10.1055/s-0042-1749118. Epub 2022 Jun 15.

DOI:10.1055/s-0042-1749118
PMID:35705183
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9200488/
Abstract

BACKGROUND

Over one-third of deaths recorded at health facilities in Zambia are brought in dead (BID) and the causes of death (CODs) are not fully analyzed. The use of automated verbal autopsy (VA) has reportedly determined the CODs of more BID cases than the death notification form issued by the hospital. However, the validity of automated VA is yet to be fully investigated.

OBJECTIVES

To compare the CODs identified by automated VA with those by complete autopsy to examine the validity of a VA tool.

METHODS

The study site was the tertiary hospital in the capital city of Zambia. From September 2019 to January 2020, all BID cases aged 13 years and older brought to the hospital during the daytime on weekdays were enrolled in this study. External COD cases were excluded. The deceased's relatives were interviewed using the 2016 World Health Organization VA questionnaire. The data were analyzed using InterVA, an automated VA tool, to determine the CODs, which were compared with the results of complete autopsies.

RESULTS

A total of 63 cases were included. The CODs of 50 BID cases were determined by both InterVA and complete autopsies. The positive predictive value of InterVA was 22%. InterVA determined the CODs correctly in 100% cases of maternal CODs, 27.5% cases of noncommunicable disease CODs, and 5.3% cases of communicable disease CODs. Using the three broader disease groups, 56.0% cases were classified in the same groups by both methods.

CONCLUSION

While the positive predictive value was low, more than half of the cases were categorized into the same broader categories. However, there are several limitations in this study, including small sample size. More research is required to investigate the factors leading to discrepancies between the CODs determined by both methods to optimize the use of automated VA in Zambia.

摘要

背景

在赞比亚,医疗机构记录的死亡人数中有三分之一以上是运到医院时已经死亡(BID),且死因(CODs)并未得到充分分析。据报道,使用自动语音尸检(VA)比医院出具的死亡通知表确定了更多 BID 病例的 CODs。然而,自动 VA 的有效性尚未得到充分研究。

目的

通过比较自动 VA 确定的死因与完全尸检确定的死因,以检验 VA 工具的有效性。

方法

研究地点为赞比亚首都的一家三级医院。2019 年 9 月至 2020 年 1 月,本研究纳入了工作日白天在医院运到的所有年龄在 13 岁及以上的 BID 病例。排除了外部死因病例。通过使用 2016 年世界卫生组织 VA 问卷对死者的亲属进行访谈。使用 InterVA(一种自动 VA 工具)对数据进行分析,以确定 CODs,并将其与完整尸检结果进行比较。

结果

共纳入 63 例病例。50 例 BID 病例的死因由 InterVA 和完整尸检共同确定。InterVA 的阳性预测值为 22%。InterVA 正确确定了 100%的孕产妇死因病例、27.5%的非传染性疾病死因病例和 5.3%的传染性疾病死因病例的死因。使用这三个更广泛的疾病组,两种方法将 56.0%的病例归类到相同的组。

结论

虽然阳性预测值较低,但仍有一半以上的病例被归入相同的更广泛的类别。然而,本研究存在一些局限性,包括样本量小。需要进一步研究导致两种方法确定的死因出现差异的因素,以优化自动 VA 在赞比亚的使用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/dcf2d3c2d41e/10-1055-s-0042-1749118-i210198ra-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/e11c869f701e/10-1055-s-0042-1749118-i210198ra-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/6a34d5981909/10-1055-s-0042-1749118-i210198ra-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/dcf2d3c2d41e/10-1055-s-0042-1749118-i210198ra-3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/e11c869f701e/10-1055-s-0042-1749118-i210198ra-1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/6a34d5981909/10-1055-s-0042-1749118-i210198ra-2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c09/9200488/dcf2d3c2d41e/10-1055-s-0042-1749118-i210198ra-3.jpg

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Distinct mortality patterns at 0-2 days versus the remaining neonatal period: results from population-based assessment in the Indian state of Bihar.0-2 天与新生儿期其余时间死亡率的明显差异:印度比哈尔邦基于人群的评估结果。
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