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本文引用的文献

1
Leading causes of deaths in the mortality transition in Papua New Guinea: evidence from the Comprehensive Health and Epidemiological Surveillance System.在巴布亚新几内亚的死亡转移过程中的主要死因:来自综合健康和流行病学监测系统的证据。
Int J Epidemiol. 2023 Jun 6;52(3):867-886. doi: 10.1093/ije/dyac232.
2
Strengthening causes of death identification through community-based verbal autopsy during the COVID-19 pandemic.加强基于社区的死因推断在 COVID-19 大流行期间的作用。
BMC Public Health. 2022 Aug 23;22(1):1607. doi: 10.1186/s12889-022-14014-x.
3
Generating cause of death information to inform health policy: implementation of an automated verbal autopsy system in the Solomon Islands.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。
BMC Public Health. 2021 Nov 13;21(1):2080. doi: 10.1186/s12889-021-12180-y.
4
Mortality surveillance and verbal autopsy strategies: experiences, challenges and lessons learnt in Papua New Guinea.死亡率监测和死因推断策略:巴布亚新几内亚的经验、挑战和教训。
BMJ Glob Health. 2020 Dec;5(12). doi: 10.1136/bmjgh-2020-003747.
5
Diarrhoeal disease surveillance in Papua New Guinea: findings and challenges.巴布亚新几内亚的腹泻病监测:研究结果与挑战
Western Pac Surveill Response J. 2020 Mar 30;11(1):7-12. doi: 10.5365/wpsar.2018.9.2.006. eCollection 2020 Jan-Mar.
6
An integrated approach to processing WHO-2016 verbal autopsy data: the InterVA-5 model.一种综合处理世卫组织 2016 年死因推断数据的方法:InterVA-5 模型。
BMC Med. 2019 May 30;17(1):102. doi: 10.1186/s12916-019-1333-6.
7
The epidemiological transition in Papua New Guinea: new evidence from verbal autopsy studies.巴布亚新几内亚的流行病学转变:来自死因推断研究的新证据。
Int J Epidemiol. 2019 Jun 1;48(3):966-977. doi: 10.1093/ije/dyz018.
8
Estimating the completeness of death registration: An empirical method.估算死亡登记的完整性:一种实证方法。
PLoS One. 2018 May 30;13(5):e0197047. doi: 10.1371/journal.pone.0197047. eCollection 2018.
9
The WHO 2016 verbal autopsy instrument: An international standard suitable for automated analysis by InterVA, InSilicoVA, and Tariff 2.0.世界卫生组织 2016 年死因推断工具:适合 InterVA、InSilicoVA 和 Tariff 2.0 自动分析的国际标准。
PLoS Med. 2018 Jan 10;15(1):e1002486. doi: 10.1371/journal.pmed.1002486. eCollection 2018 Jan.
10
Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice.敏感性、特异性和预测值:研究与实践中的基础、灵活性及陷阱
Front Public Health. 2017 Nov 20;5:307. doi: 10.3389/fpubh.2017.00307. eCollection 2017.

验证 InterVA-5 死因分析工具:使用巴布亚新几内亚综合卫生和流行病学监测系统的死亡率数据。

Validating the InterVA-5 cause of death analytical tool: using mortality data from the Comprehensive Health and Epidemiological Surveillance System in Papua New Guinea.

机构信息

Population Health and Demography, Papua New Guinea Institute of Medical Research, Goroka, Eastern Highlands, Papua New Guinea

Population Health and Demography, Papua New Guinea Institute of Medical Research, Goroka, Eastern Highlands, Papua New Guinea.

出版信息

BMJ Open. 2023 May 22;13(5):e066560. doi: 10.1136/bmjopen-2022-066560.

DOI:10.1136/bmjopen-2022-066560
PMID:37217264
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10230342/
Abstract

OBJECTIVE

InterVA-5 is a new version of an analytical tool for cause of death (COD) analysis at the population level. This study validates the InterVA-5 against the medical review method, using mortality data in Papua New Guinea (PNG).

DESIGN AND SETTING

This study used mortality data collected from January 2018 to December 2020 in eight surveillance sites of the Comprehensive Health and Epidemiological Surveillance System (CHESS), established by the PNG Institute of Medical Research in six major provinces.

METHODS

The CHESS demographic team conducted verbal autopsy (VA) interviews with close relatives of the deceased, who died in communities within the catchment areas of CHESS, using the WHO 2016 VA instrument. COD of the deceased was assigned by InterVA-5 tool, and independently certified by the medical team. Consistency, difference and agreement between the InterVA-5 model and medical review were assessed. Sensitivity and positive predictive value (PPV) of the InterVA-5 tool were calculated with reference to the medical review method.

RESULTS

Specific COD of 926 deceased people was included in the validation. Agreement between the InterVA-5 tool and medical review was high (kappa test: 0.72; p<0.01). Sensitivity and PPV of the InterVA-5 were 93% and 72% for cardiovascular diseases, 84% and 86% for neoplasms, 65% and 100% for other chronic non-communicable diseases (NCDs), and 78% and 64% for maternal deaths, respectively. For infectious diseases and external CODs, sensitivity and PPV of the InterVA-5 were 94% and 90%, respectively, while the sensitivity and PPV of the medical review method were both 54% for classifying neonatal CODs.

CONCLUSION

The InterVA-5 tool works well in the PNG context to assign specific CODs of infectious diseases, cardiovascular diseases, neoplasms and injuries. Further improvements with respect to chronic NCDs, maternal deaths and neonatal deaths are needed.

摘要

目的

InterVA-5 是一种新的人群死因分析工具。本研究使用巴布亚新几内亚(PNG)的死亡率数据,验证了 InterVA-5 与医学审查方法的一致性。

设计和设置

本研究使用 PNG 医学研究所于 2018 年 1 月至 2020 年 12 月在六个主要省份的综合健康和流行病学监测系统(CHESS)的 8 个监测点收集的死亡率数据。

方法

CHESS 人口统计小组使用世界卫生组织 2016 年的 VA 工具,对死于 CHESS 集水区内社区的死者的近亲进行口头尸检(VA)访谈。InterVA-5 工具分配死者的死因,并由医疗团队独立确认。评估了 InterVA-5 模型和医学审查之间的一致性、差异和一致性。根据医学审查方法,计算了 InterVA-5 工具的灵敏度和阳性预测值(PPV)。

结果

纳入了 926 名死者的特定死因。InterVA-5 工具和医学审查之间的一致性很高(kappa 检验:0.72;p<0.01)。InterVA-5 的灵敏度和 PPV 分别为心血管疾病 93%和 72%,肿瘤 84%和 86%,其他慢性非传染性疾病(NCDs)65%和 100%,孕产妇死亡 78%和 64%。传染病和外部死因的 InterVA-5 灵敏度和 PPV 分别为 94%和 90%,而医学审查方法的灵敏度和 PPV 分别为 54%,用于分类新生儿死因。

结论

在 PNG 背景下,InterVA-5 工具可用于分配传染病、心血管疾病、肿瘤和损伤的特定死因。需要进一步改进慢性非传染性疾病、孕产妇死亡和新生儿死亡的分类。