Automated mortality coding for improved health policy in the Philippines.

IF 3.2 2区 医学 Q2 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Population Health Metrics Pub Date : 2024-09-05 DOI:10.1186/s12963-024-00344-y
U S H Gamage, Carmina Sarmiento, Aurora G Talan-Reolalas, Marjorie B Villaver, Nerissa E Palangyos, Karen Joyce T Baraoidan, Nicola Richards, Rohina Joshi
{"title":"Automated mortality coding for improved health policy in the Philippines.","authors":"U S H Gamage, Carmina Sarmiento, Aurora G Talan-Reolalas, Marjorie B Villaver, Nerissa E Palangyos, Karen Joyce T Baraoidan, Nicola Richards, Rohina Joshi","doi":"10.1186/s12963-024-00344-y","DOIUrl":null,"url":null,"abstract":"<p><p>In 2016, the Bloomberg Philanthropies Data for Health initiative assisted the Philippine Statistical Authority in implementing Iris, an automated coding software program that enables medical death certificates to be coded according to international standards. Iris was implemented to improve the quality, timeliness, and consistency of coded data as part of broader activities to strengthen the country's civil registration and vital statistics system. This study was conducted as part of the routine implementation of Iris to ensure that automatically coded cause of death data was of sufficient quality to be released and disseminated as national mortality statistics. Data from medical death certificates coded with Iris between 2017 and 2019 were analysed and evaluated for apparent errors and inconsistencies, and trends were examined for plausibility. Cause-specific mortality distributions were calculated for each of the 3 years and compared for consistency, and annual numeric and percentage changes were calculated and compared for all age groups. The typology, reasons, and proportions of records that could not be coded (Iris 'rejects') were also studied. Overall, the study found that the Philippine Statistical Authority successfully operates Iris. The cause-specific mortality fractions for the 20 leading causes of death showed reassuring stability after the introduction of Iris, and the type and proportion of rejects were similar to international experience. Broadly, this study demonstrates how an automated coding system can improve the accuracy and timeliness of cause of death data-providing critical country experiences to help build the evidence base on the topic.</p>","PeriodicalId":51476,"journal":{"name":"Population Health Metrics","volume":"22 1","pages":"24"},"PeriodicalIF":3.2000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11375827/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Population Health Metrics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s12963-024-00344-y","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
引用次数: 0

Abstract

In 2016, the Bloomberg Philanthropies Data for Health initiative assisted the Philippine Statistical Authority in implementing Iris, an automated coding software program that enables medical death certificates to be coded according to international standards. Iris was implemented to improve the quality, timeliness, and consistency of coded data as part of broader activities to strengthen the country's civil registration and vital statistics system. This study was conducted as part of the routine implementation of Iris to ensure that automatically coded cause of death data was of sufficient quality to be released and disseminated as national mortality statistics. Data from medical death certificates coded with Iris between 2017 and 2019 were analysed and evaluated for apparent errors and inconsistencies, and trends were examined for plausibility. Cause-specific mortality distributions were calculated for each of the 3 years and compared for consistency, and annual numeric and percentage changes were calculated and compared for all age groups. The typology, reasons, and proportions of records that could not be coded (Iris 'rejects') were also studied. Overall, the study found that the Philippine Statistical Authority successfully operates Iris. The cause-specific mortality fractions for the 20 leading causes of death showed reassuring stability after the introduction of Iris, and the type and proportion of rejects were similar to international experience. Broadly, this study demonstrates how an automated coding system can improve the accuracy and timeliness of cause of death data-providing critical country experiences to help build the evidence base on the topic.

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
菲律宾为改进卫生政策而进行的死亡率自动编码。
2016 年,彭博慈善基金会 "数据促进健康 "倡议协助菲律宾统计局实施了自动编码软件程序 Iris,使医疗死亡证明能够按照国际标准进行编码。实施 Iris 是为了提高编码数据的质量、及时性和一致性,这也是加强菲律宾民事登记和生命统计系统的广泛活动的一部分。这项研究是 Iris 常规实施工作的一部分,目的是确保自动编码死因数据的质量足以作为国家死亡率统计数据发布和传播。对 2017 年至 2019 年期间使用 Iris 编码的医学死亡证明书数据进行了分析,评估了明显的错误和不一致之处,并研究了趋势的合理性。计算了 3 年中每一年的特定原因死亡率分布,并对其一致性进行了比较,还计算了所有年龄组的年度数字和百分比变化,并进行了比较。此外,还研究了无法编码的记录的类型、原因和比例(Iris "拒绝")。总体而言,研究发现菲律宾统计局成功地运行了 Iris。在引入 Iris 后,20 种主要死因的特定死亡比例显示出令人欣慰的稳定性,拒收记录的类型和比例与国际经验相似。总的来说,这项研究展示了自动编码系统如何提高死因数据的准确性和及时性--提供了重要的国家经验,有助于建立该主题的证据基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Population Health Metrics
Population Health Metrics PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-
CiteScore
6.50
自引率
0.00%
发文量
21
审稿时长
29 weeks
期刊介绍: Population Health Metrics aims to advance the science of population health assessment, and welcomes papers relating to concepts, methods, ethics, applications, and summary measures of population health. The journal provides a unique platform for population health researchers to share their findings with the global community. We seek research that addresses the communication of population health measures and policy implications to stakeholders; this includes papers related to burden estimation and risk assessment, and research addressing population health across the full range of development. Population Health Metrics covers a broad range of topics encompassing health state measurement and valuation, summary measures of population health, descriptive epidemiology at the population level, burden of disease and injury analysis, disease and risk factor modeling for populations, and comparative assessment of risks to health at the population level. The journal is also interested in how to use and communicate indicators of population health to reduce disease burden, and the approaches for translating from indicators of population health to health-advancing actions. As a cross-cutting topic of importance, we are particularly interested in inequalities in population health and their measurement.
期刊最新文献
The joint distribution of years lived in good and poor health. A new method for estimating recent adult mortality from summary sibling histories. Investigating the impact of the COVID-19 pandemic on the nutritional status of infants and toddlers: insights from China. Harmonizing measurements: establishing a common metric via shared items across instruments. Examining select sociodemographic characteristics of sub-county geographies for public health surveillance.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1