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Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania

INTRODUCTION: 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. OBJECTIVE: To implement point-of-contact interactive rec...

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Autores principales: Rentsch, Christopher T., Reniers, Georges, Kabudula, Chodziwadziwa, Machemba, Richard, Mtenga, Baltazar, Harron, Katie, Mee, Paul, Michael, Denna, Natalis, Redempta, Urassa, Mark, Todd, Jim, Zaba, Basia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314455/
https://www.ncbi.nlm.nih.gov/pubmed/30613799
http://dx.doi.org/10.23889/ijpds.v2i1.408
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author Rentsch, Christopher T.
Reniers, Georges
Kabudula, Chodziwadziwa
Machemba, Richard
Mtenga, Baltazar
Harron, Katie
Mee, Paul
Michael, Denna
Natalis, Redempta
Urassa, Mark
Todd, Jim
Zaba, Basia
author_facet Rentsch, Christopher T.
Reniers, Georges
Kabudula, Chodziwadziwa
Machemba, Richard
Mtenga, Baltazar
Harron, Katie
Mee, Paul
Michael, Denna
Natalis, Redempta
Urassa, Mark
Todd, Jim
Zaba, Basia
author_sort Rentsch, Christopher T.
collection PubMed
description INTRODUCTION: 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. OBJECTIVE: To 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. METHODS: Individuals 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). RESULTS: Among 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). CONCLUSION: Where 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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spelling pubmed-63144552019-01-02 Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania Rentsch, Christopher T. Reniers, Georges Kabudula, Chodziwadziwa Machemba, Richard Mtenga, Baltazar Harron, Katie Mee, Paul Michael, Denna Natalis, Redempta Urassa, Mark Todd, Jim Zaba, Basia Int J Popul Data Sci Population Data Science INTRODUCTION: 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. OBJECTIVE: To 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. METHODS: Individuals 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). RESULTS: Among 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). CONCLUSION: Where resources are available, PIRL is a viable approach to link HDSS and other administrative data sources that outperforms purely retrospective approaches. Swansea University 2017-12-15 /pmc/articles/PMC6314455/ /pubmed/30613799 http://dx.doi.org/10.23889/ijpds.v2i1.408 Text en https://www.ijpds.org/article-xml-files/408/https://creativecommons.org/licenses/by-nc-nd/4.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
spellingShingle Population Data Science
Rentsch, Christopher T.
Reniers, Georges
Kabudula, Chodziwadziwa
Machemba, Richard
Mtenga, Baltazar
Harron, Katie
Mee, Paul
Michael, Denna
Natalis, Redempta
Urassa, Mark
Todd, Jim
Zaba, Basia
Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania
title Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania
title_full Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania
title_fullStr Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania
title_full_unstemmed Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania
title_short Point-of-contact interactive record linkage (PIRL) between demographic surveillance and health facility data in rural Tanzania
title_sort point-of-contact interactive record linkage (pirl) between demographic surveillance and health facility data in rural tanzania
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6314455/
https://www.ncbi.nlm.nih.gov/pubmed/30613799
http://dx.doi.org/10.23889/ijpds.v2i1.408
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