First severe malaria in pregnancy followed in Philippine real-world setting: proof-of-concept of probabilistic record linkage between disease surveillance and hospital administrative data

Abstract

Abstract Background Although the Philippines targets malaria elimination by 2030, it is a disease of public health importance in provinces that report malaria. Pregnant women residing in endemic areas are a vulnerable population because 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 dataset 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 datasets 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 datasets. 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 datasets. 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 dataset but from village-based records. Conclusion 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.