ArticleInternational journal of population data science2017
Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania.
Article in International journal of population data science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Errors in reported ages and dates in surveys of adult mortality: A record linkage study in Niakhar (Senegal).Population studies · 2021Trial
- Ethical and logistical imperatives for AI-driven cardiovascular risk prediction among older adults in Tanzania: framing a digital health agenda for low-income settings.Frontiers in aging · 2025Article
- The Value Proposition of Coordinated Population Cohorts Across Africa.Annual review of biomedical data science · 2024Review
- Cohort Profile Update: Magu Health and Demographic Surveillance System, Tanzania.International journal of epidemiology · 2024Article
- Article
- Health and demographic surveillance systems in low- and middle-income countries: history, state of the art and future prospects.Global health action · 2021Review
- The impact of HIV status on the distance traveled to health facilities and adherence to care. A record-linkage study from rural South Africa.Journal of global health · 2020Article
- Outcomes of patients lost to follow-up after antiretroviral therapy initiation in rural north-eastern South Africa.Tropical medicine & international health : TM & IH · 2019Article
- Impact of linkage quality on inferences drawn from analyses using data with high rates of linkage errors in rural Tanzania.BMC medical research methodology · 2018Article
- Linkage to care and antiretroviral therapy initiation by testing modality among individuals newly diagnosed with HIV in Tanzania, 2014-2017.Tropical medicine & international health : TM & IH · 2018Observational
- Point-of-contact Interactive Record Linkage (PIRL): A software tool to prospectively link demographic surveillance and health facility data.Gates open research · 2017Article
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Authors and funding
12 authors.
Funding
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.
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