An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries

Asher, Jana; Resnick, Dean; Brite, Jennifer; Brackbill, Robert M.; Cone, James E.

doi:10.3390/ijerph17186937

Cited by 23 publications

(22 citation statements)

References 31 publications

(27 reference statements)

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“…The problem is to identify all the records belonging to each entity, and link them across the data sets. For instance 2 , discuss record linkage with reference to World Trade Center (WTC) registries.…”

Section: Introductionmentioning

confidence: 99%

A fast privacy-preserving patient record linkage of time series data

Soliman

Rajasekaran

Toman

et al. 2023

Sci Rep

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Recent advances in technology have led to an explosion of data in virtually all domains of our lives. Modern biomedical devices can acquire a large number of physical readings from patients. Often, these readings are stored in the form of time series data. Such time series data can form the basis for important research to advance healthcare and well being. Due to several considerations including data size, patient privacy, etc., the original, full data may not be available to secondary parties or researchers. Instead, suppose that a subset of the data is made available. A fast and reliable record linkage algorithm enables us to accurately match patient records in the original and subset databases while maintaining privacy. The problem of record linkage when the attributes include time series has not been studied much in the literature. We introduce two main contributions in this paper. First, we propose a novel, very efficient, and scalable record linkage algorithm that is employed on time series data. This algorithm is 400× faster than the previous work. Second, we introduce a privacy preserving framework that enables health institutions to safely release their raw time series records to researchers with bare minimum amount of identifying information.

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Section: Introductionmentioning

confidence: 99%

A fast privacy-preserving patient record linkage of time series data

Soliman

Rajasekaran

Toman

et al. 2023

Sci Rep

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“…The second option is probabilistic record linkage that is based on a model to assess the discriminative power of each variable used in the record linkage strategy. The result is the probability that an entity in the first database is the same entity in the second database [5,6]. Several studies have demonstrated that in most cases, probabilistic approaches give better results than deterministic methods [7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case

Bannay¹,

Bories²,

Corre³

et al. 2021

JMIR Med Inform

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Background Linking different sources of medical data is a promising approach to analyze care trajectories. The aim of the INSHARE (Integrating and Sharing Health Big Data for Research) project was to provide the blueprint for a technological platform that facilitates integration, sharing, and reuse of data from 2 sources: the clinical data warehouse (CDW) of the Rennes academic hospital, called eHOP (entrepôt Hôpital), and a data set extracted from the French national claim data warehouse (Système National des Données de Santé [SNDS]). Objective This study aims to demonstrate how the INSHARE platform can support big data analytic tasks in the health field using a pharmacovigilance use case based on statin consumption and statin-drug interactions. Methods A Spark distributed cluster-computing framework was used for the record linkage procedure and all analyses. A semideterministic record linkage method based on the common variables between the chosen data sources was developed to identify all patients discharged after at least one hospital stay at the Rennes academic hospital between 2015 and 2017. The use-case study focused on a cohort of patients treated with statins prescribed by their general practitioner or during their hospital stay. Results The whole process (record linkage procedure and use-case analyses) required 88 minutes. Of the 161,532 and 164,316 patients from the SNDS and eHOP CDW data sets, respectively, 159,495 patients were successfully linked (98.74% and 97.07% of patients from SNDS and eHOP CDW, respectively). Of the 16,806 patients with at least one statin delivery, 8293 patients started the consumption before and continued during the hospital stay, 6382 patients stopped statin consumption at hospital admission, and 2131 patients initiated statins in hospital. Statin-drug interactions occurred more frequently during hospitalization than in the community (3800/10,424, 36.45% and 3253/14,675, 22.17%, respectively; P<.001). Only 121 patients had the most severe level of statin-drug interaction. Hospital stay burden (length of stay and in-hospital mortality) was more severe in patients with statin-drug interactions during hospitalization. Conclusions This study demonstrates the added value of combining and reusing clinical and claim data to provide large-scale measures of drug-drug interaction prevalence and care pathways outside hospitals. It builds a path to move the current health care system toward a Learning Health System using knowledge generated from research on real-world health data.

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“…Record linkage uses deterministic or probabilistic approaches. [35][36][37][38] Experience from Brazil (a Portuguesespeaking country like Mozambique) 39 showed a high level of correlation for pair classification, but the probabilistic linkage retrieved links unidentified by the deterministic linkage. In the deterministic approach, a link is made if all fields agree.…”

Section: Methodsmentioning

confidence: 99%

Completeness and Factors Affecting Community Workers’ Reporting of Births and Deaths in the Countrywide Mortality Surveillance for Action in Mozambique

Kanté

Mulungo

Ibraimo³

et al. 2023

The American Journal of Tropical Medicine and Hygiene

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Since March 2018, the Countrywide Mortality Surveillance for Action project, implemented as a national sample registration system by the Mozambique Instituto Nacional de Saude and the Instituto Nacional de Estatistica in 700 geographic clusters randomly distributed across the 11 provinces, has trained and deployed community surveillance agents (CSAs) to report births and deaths in each cluster prospectively. An independent, retrospective data collection was conducted to assess the completeness of surveillance data. Record linkage procedures were used to match households and vital events reported in the two data sources. We calculated birth and death reporting rates and used a regression model to determine factors associated with the likelihood of vital events being reported by the CSAs. Between March 2018 and December 2019, CSAs reported 54% of births (8,787/16,421) and 45% of deaths (1,726/3,867). Births of smaller cluster sizes (< 1,000 people) were more likely to be reported (adjusted odds ratio [aOR] = 1.45; 95% CI = 1.15–1.83) compared with those of larger cluster sizes (> 1,500 people). Deaths of rural clusters were more likely to be reported (aOR = 1.41; 95% CI = 1.07–1.85) than those of urban clusters. Adult deaths were more likely to be reported (aOR = 1.49; 95% CI = 1.10–2.02) than child deaths. Our findings suggest that a fully functioning sample vital registration system must adopt a dual system with high-quality surveys or other ways to estimate underregistration periodically, consider a smaller cluster size manageable by a community worker, and pay special attention to urban clusters as underreporting is larger.

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An Introduction to Probabilistic Record Linkage with a Focus on Linkage Processing for WTC Registries

Cited by 23 publications

References 31 publications

A fast privacy-preserving patient record linkage of time series data

A fast privacy-preserving patient record linkage of time series data

Leveraging National Claims and Hospital Big Data: Cohort Study on a Statin-Drug Interaction Use Case

Completeness and Factors Affecting Community Workers’ Reporting of Births and Deaths in the Countrywide Mortality Surveillance for Action in Mozambique

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