{"title":"在菲律宾真实世界环境中跟踪首例妊娠期疟疾:疾病监测与医院行政数据之间概率记录关联的概念验证。","authors":"Takuya Kinoshita, Fe Espino, Raymart Bunagan, Dodge Lim, Chona Daga, Sabrina Parungao, Aileen Balderian, Katherine Micu, Rutchel Laborera, Ramon Basilio, Marianette Inobaya, Mario Baquilod, Melecio Dy, Hitoshi Chiba, Takehiro Matsumoto, Takeo Nakayama, Kiyoshi Kita, Kenji Hirayama","doi":"10.1186/s41182-024-00583-7","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Although the Philippines targets malaria elimination by 2030, it remains to be a disease that causes considerable morbidity in provinces that report malaria. Pregnant women residing in endemic areas are a vulnerable population, because in addition to the risk of developing severe malaria, their pregnancy is not followed through, and the outcome of their pregnancy is unknown. This study determined the utility of real-world data integrated with disease surveillance data set as real-world evidence of pregnancy and delivery outcomes in areas endemic for malaria in the Philippines.</p><p><strong>Methods: </strong>For the period of 2015 to 2019, electronic data sets of malaria surveillance data and Ospital ng Palawan hospital admission log of pregnant women residing in the four selected barangays of Rizal, Palawan were merged using probabilistic linkage. The source data for record linkage were first and last names, birth date, and address as the mutual variable. The data used for characteristics of the pregnant women from the hospital data set were admission date, discharge date, admitting and final diagnosis and body weight on admission. From the malaria surveillance data these were date of consultation, and malaria parasite species. The Levenshtein distance formula was used for a fuzzy string-matching algorithm. Chi-square test, and Mann-Whitney U test were used to compare the means of the two data sets.</p><p><strong>Results: </strong>The prevalence of pregnant women admitted to the tertiary referral hospital, Ospital ng Palawan, was estimated to be 8.34/100 overall, and 11.64/100 from the four study barangays; that of malaria during pregnancy patients was 3.45/100 and 2.64/100, respectively. There was only one true-positive matched case from 238 women from the hospital and 54 women from the surveillance data sets. The overall Levenshstein score was 97.7; for non-matched cases, the mean overall score was 36.6 (35.6-37.7). The matched case was a minor who was hospitalized for severe malaria. The outcome of her pregnancy was detected from neither data set but from village-based records.</p><p><strong>Conclusions: </strong>This proof-of-concept study demonstrated that probabilistic record linkage could match real-world data in the Philippines with further validation required. The study underscored the need for more integrated and comprehensive database to monitor disease intervention impact on pregnancy and its outcome in the Philippines.</p>","PeriodicalId":23311,"journal":{"name":"Tropical Medicine and Health","volume":null,"pages":null},"PeriodicalIF":3.6000,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10851569/pdf/","citationCount":"0","resultStr":"{\"title\":\"First malaria in pregnancy followed in Philippine real-world setting: proof-of-concept of probabilistic record linkage between disease surveillance and hospital administrative data.\",\"authors\":\"Takuya Kinoshita, Fe Espino, Raymart Bunagan, Dodge Lim, Chona Daga, Sabrina Parungao, Aileen Balderian, Katherine Micu, Rutchel Laborera, Ramon Basilio, Marianette Inobaya, Mario Baquilod, Melecio Dy, Hitoshi Chiba, Takehiro Matsumoto, Takeo Nakayama, Kiyoshi Kita, Kenji Hirayama\",\"doi\":\"10.1186/s41182-024-00583-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Although the Philippines targets malaria elimination by 2030, it remains to be a disease that causes considerable morbidity in provinces that report malaria. Pregnant women residing in endemic areas are a vulnerable population, because in addition to the risk of developing severe malaria, their pregnancy is not followed through, and the outcome of their pregnancy is unknown. This study determined the utility of real-world data integrated with disease surveillance data set as real-world evidence of pregnancy and delivery outcomes in areas endemic for malaria in the Philippines.</p><p><strong>Methods: </strong>For the period of 2015 to 2019, electronic data sets of malaria surveillance data and Ospital ng Palawan hospital admission log of pregnant women residing in the four selected barangays of Rizal, Palawan were merged using probabilistic linkage. The source data for record linkage were first and last names, birth date, and address as the mutual variable. The data used for characteristics of the pregnant women from the hospital data set were admission date, discharge date, admitting and final diagnosis and body weight on admission. From the malaria surveillance data these were date of consultation, and malaria parasite species. The Levenshtein distance formula was used for a fuzzy string-matching algorithm. Chi-square test, and Mann-Whitney U test were used to compare the means of the two data sets.</p><p><strong>Results: </strong>The prevalence of pregnant women admitted to the tertiary referral hospital, Ospital ng Palawan, was estimated to be 8.34/100 overall, and 11.64/100 from the four study barangays; that of malaria during pregnancy patients was 3.45/100 and 2.64/100, respectively. There was only one true-positive matched case from 238 women from the hospital and 54 women from the surveillance data sets. The overall Levenshstein score was 97.7; for non-matched cases, the mean overall score was 36.6 (35.6-37.7). The matched case was a minor who was hospitalized for severe malaria. The outcome of her pregnancy was detected from neither data set but from village-based records.</p><p><strong>Conclusions: </strong>This proof-of-concept study demonstrated that probabilistic record linkage could match real-world data in the Philippines with further validation required. The study underscored the need for more integrated and comprehensive database to monitor disease intervention impact on pregnancy and its outcome in the Philippines.</p>\",\"PeriodicalId\":23311,\"journal\":{\"name\":\"Tropical Medicine and Health\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.6000,\"publicationDate\":\"2024-02-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10851569/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Tropical Medicine and Health\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1186/s41182-024-00583-7\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"TROPICAL MEDICINE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Tropical Medicine and Health","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1186/s41182-024-00583-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"TROPICAL MEDICINE","Score":null,"Total":0}
引用次数: 0
摘要
背景:尽管菲律宾的目标是到 2030 年消灭疟疾,但在报告有疟疾的省份,疟疾仍然是一种会导致相当高发病率的疾病。居住在疟疾流行地区的孕妇是易感人群,因为她们除了有罹患重症疟疾的风险外,其怀孕过程也没有得到跟踪,怀孕结果也不得而知。本研究确定了与疾病监测数据集相结合的真实世界数据作为菲律宾疟疾流行地区怀孕和分娩结果的真实世界证据的效用:在 2015 年至 2019 年期间,使用概率链接法合并了疟疾监测数据电子数据集和 Ospital ng Palawan 医院对居住在巴拉望省里扎尔市四个选定镇的孕妇的入院记录。记录链接的源数据是姓名、出生日期和地址作为互变量。医院数据集中的孕妇特征数据包括入院日期、出院日期、入院诊断和最终诊断以及入院时的体重。疟疾监测数据包括就诊日期和疟疾寄生虫种类。莱文斯坦距离公式用于模糊字符串匹配算法。采用卡方检验(Chi-square test)和曼-惠特尼U检验(Mann-Whitney U test)比较两组数据的平均值:据估计,在三级转诊医院巴拉望医院(Ospital ng Palawan)住院的孕妇发病率为 8.34/100,在四个研究区的发病率为 11.64/100;孕期疟疾患者的发病率分别为 3.45/100 和 2.64/100。在 238 名来自医院的妇女和 54 名来自监测数据集的妇女中,只有一个匹配的真阳性病例。总的莱文施坦因评分为 97.7;非匹配病例的平均总评分为 36.6(35.6-37.7)。配对病例是一名因严重疟疾住院的未成年人。她的妊娠结果既不是从数据集中检测到的,也不是从村里的记录中检测到的:这项概念验证研究表明,概率记录关联可以匹配菲律宾的实际数据,但还需要进一步验证。该研究强调,有必要建立更综合、更全面的数据库,以监测疾病干预对菲律宾妊娠及其结果的影响。
First malaria in pregnancy followed in Philippine real-world setting: proof-of-concept of probabilistic record linkage between disease surveillance and hospital administrative data.
Background: Although the Philippines targets malaria elimination by 2030, it remains to be a disease that causes considerable morbidity in provinces that report malaria. Pregnant women residing in endemic areas are a vulnerable population, because in addition to the risk of developing severe malaria, their pregnancy is not followed through, and the outcome of their pregnancy is unknown. This study determined the utility of real-world data integrated with disease surveillance data set as real-world evidence of pregnancy and delivery outcomes in areas endemic for malaria in the Philippines.
Methods: For the period of 2015 to 2019, electronic data sets of malaria surveillance data and Ospital ng Palawan hospital admission log of pregnant women residing in the four selected barangays of Rizal, Palawan were merged using probabilistic linkage. The source data for record linkage were first and last names, birth date, and address as the mutual variable. The data used for characteristics of the pregnant women from the hospital data set were admission date, discharge date, admitting and final diagnosis and body weight on admission. From the malaria surveillance data these were date of consultation, and malaria parasite species. The Levenshtein distance formula was used for a fuzzy string-matching algorithm. Chi-square test, and Mann-Whitney U test were used to compare the means of the two data sets.
Results: The prevalence of pregnant women admitted to the tertiary referral hospital, Ospital ng Palawan, was estimated to be 8.34/100 overall, and 11.64/100 from the four study barangays; that of malaria during pregnancy patients was 3.45/100 and 2.64/100, respectively. There was only one true-positive matched case from 238 women from the hospital and 54 women from the surveillance data sets. The overall Levenshstein score was 97.7; for non-matched cases, the mean overall score was 36.6 (35.6-37.7). The matched case was a minor who was hospitalized for severe malaria. The outcome of her pregnancy was detected from neither data set but from village-based records.
Conclusions: This proof-of-concept study demonstrated that probabilistic record linkage could match real-world data in the Philippines with further validation required. The study underscored the need for more integrated and comprehensive database to monitor disease intervention impact on pregnancy and its outcome in the Philippines.