Abstract
IntroductionHealth and demographic surveillance systems (HDSS) have been an invaluable resource for monitoring the health status of populations, but often contain self-reported health service utilisation, which are subject to reporting bias.ObjectiveTo implement point-of-contact interactive record linkage (PIRL) between demographic and health facility systems data, characterise attributes associated with (un)successful record linkage, and compare findings with a fully automated retrospective linkage approach.MethodsIndividuals visiting the Kisesa Health Centre were matched to their HDSS records during a short up-take interview in the waiting area of the health facility. The search algorithm was used to rank potential matches, from which the true match(es) were selected after consultation with the patient. Multivariable logistic regression models were used to identify characteristics associated with being matched to an HDSS record. Records matched based on respondent's clarifications were subsequently used as the gold-standard to evaluate fully automated retrospective record linkage by calculating sensitivity and positive predictive value (PPV).ResultsAmong 2,624 individuals who reportedly lived in the HDSS coverage area, we matched 2,206 (84.1%) to their HDSS records. Characteristics associated with a higher odds of being matched were increased age (OR 1.07, 95% CI 1.02, 1.12; per 5-year increment), a later consent into the study (OR 2.07, 95% CI 1.37, 3.12; in the most recent six-month period), and fieldworker level of experience. The main drivers of the linkage algorithm were name, sex, year of birth, village, sub-village, and household member name. At the lowest match score threshold, automated retrospective linkage would have only correctly identified and linked 55% (1440/2612) of the records with a PPV of 55% (1440/2612).ConclusionWhere resources are available, PIRL is a viable approach to link HDSS and other administrative data sources that outperforms purely retrospective approaches.
Highlights
Health and demographic surveillance systems (HDSS) have been an invaluable resource for monitoring the health status of populations, but often contain self-reported health service utilisation, which are subject to reporting bias
The linked clinical data could be used to validate or substitute the self-reported health status and health service use data collected in the health and demographic surveillance systems (HDSS) surveys
We introduced a point-of-contact interactive record linkage (PIRL) system to link HDSS records with a local health facility that serves the HDSS population with the goal of producing a data source that could be used to monitor the utilisation of health services and the outcomes of patients after they have made contact with the health system
Summary
Health and demographic surveillance systems (HDSS) have been an invaluable resource for monitoring the health status of populations, but often contain self-reported health service utilisation, which are subject to reporting bias. Most analyses of health service use are limited to databases of patients enrolled in clinical care. These analyses lack a population perspective on service utilization, clinical outcomes, survival status, and patients who are lost to follow-up. Health and demographic surveillance systems (HDSS) comprehensively measure vital events, but rely on self-reports of health services use. Such reports usually lack detail and accuracy about the clinical events and services received, and their retrospective nature means that they quickly become dated. The linked clinical data could be used to validate or substitute the self-reported health status and health service use data collected in the HDSS surveys
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