Academic Journal
Automated mortality coding for improved health policy in the Philippines
| 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 |
| Source: | Population Health Metrics, Vol 22, Iss 1, Pp 1-7 (2024) |
| Publisher Information: | BMC, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Computer applications to medicine. Medical informatics LCC:Public aspects of medicine |
| Subject Terms: | Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270 |
| More Details: | 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. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1478-7954 |
| Relation: | https://doaj.org/toc/1478-7954 |
| DOI: | 10.1186/s12963-024-00344-y |
| Access URL: | https://doaj.org/article/c787f87760194460a370eaec4cb92cb5 |
| Accession Number: | edsdoj.787f87760194460a370eaec4cb92cb5 |
| Database: | Directory of Open Access Journals |
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| ISSN: | 14787954 |
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| DOI: | 10.1186/s12963-024-00344-y |
| Published in: | Population Health Metrics |
| Language: | English |