An Efficient Validation Method of Probabilistic Record Linkage Including Readmissions and Twins

Novel record linkage (RL) methods have the potential to enhance clinical informatics by integrating patient data from multiple sources-including electronic health records, insurance claims, and digital health devices-to inform patient-centered care. Engaging patients and other stakeholders in the use of RL methods in patient-centered outcomes research (PCOR) is a key step in ensuring RL methods are viewed as acceptable, appropriate, and useful. The University of Colorado Record Linkage (CURL) platform empowers the use of RL in PCOR. This study aimed to describe the process of engaging patients and other stakeholders in the design of an RL dissemination package to support the use of RL methods in PCOR. Customer discovery, value proposition design, and user experience methods were used to iteratively develop an RL dissemination package that includes animated explainer videos for patients and an RL research planning workbook for researchers. Patients and other stakeholders (researchers, data managers, and regulatory officials) were engaged in the RL dissemination package design. Patient partners emphasized the importance of conveying how RL methods may benefit patients and the rules researchers must follow to protect the privacy and security of patient data. Other stakeholders described accuracy, flexibility, efficiency, and data security compared with other available RL solutions. Dissemination package communication products reflect the value propositions identified by key stakeholders. As prioritized by patients, the animated explainer videos emphasize the data privacy and security processes and procedures employed when performing research using RL. The RL workbook addresses researchers' and data managers' needs to iteratively design RL projects and provides accompanying resources to alleviate leadership and regulatory officials' concerns about data regulation compliance. Dissemination products to promote adoption and use of CURL include materials to facilitate patient engagement in RL research and investigator step-by-step decision-making materials about the integration of RL methods in PCOR.

Record linkage (RL) is the process of identifying pairs of records that correspond to the same entity, for example the same patient. The basic approach assigns to each pair of records a similarity weight, and then determines a certain threshold, above which the two records are considered to be a match. Three different RL methods were applied under privacy-preserving conditions on hospital admission data: deterministic RL (DRL), probabilistic RL (PRL), and Bloom filters. The patient characteristics like names were one-way encrypted (DRL, PRL) or transformed to a cryptographic longterm key (Bloom filters). Based on one year of hospital admissions, the data set was split randomly in 30 thousand new and 1,5 million known patients. With the combination of the three RL-methods, a positive predictive value of 83 % (95 %-confidence interval 65 %-94 %) was attained. Thus, the application of the presented combination of RL-methods seem to be suited for other applications of population-based research.

This thesis presents a methodology of data preparation with probabilistic record linkage and information fusion for improving and enriching information visualizations of biomedical citation data. The problem of record linkage of citation databases where only non-unique identifiers such as author names and document titles are available as common identifiers to be linked was investigated. This problem in citation data parallels problems in clinical data and Knowledge Discovery in Databases (KDD) methods from clinical data mining are evaluated. Probabilistic and deterministic (exact-match) record linkage models were developed and compared through the use of a gold standard or truth dataset. Empirical comparison with ROC analysis of record linkage models showed a significant difference (p=.000) in performance of a probabilistic model over deterministic models. The methodology was evaluated with probabilistic linkage of records from the Web of Science, Medline, and CINAHL citation databases in the knowledge domains of medical informatics, HIV/AIDS, and nursing informatics. Data quality metrics for datasets prepared with probabilistic record linkage and information fusion showed improvement in completeness of key variables and reduction in sample bias. The resulting visualizations offered a richer information space for users through an increase in terms entering the visualization. The significant contributions of this work include the development of a novel model of probabilistic record linkage for biomedical citation databases which improves upon existing deterministic models. In addition a methodology for improving and enriching knowledge domain visualizations though a data preparation approach has been validated with analyses of multiple citation databases and knowledge domains. The data preparation methodology of probabilistic record linkage with information fusion offers a remedy for data quality problems, and the opportunity to enrich visualizations with added content for user exploration, which in turn improves the utility of knowledge domain visualizations as a medium for assessing available evidence and forming hypotheses.

Record linkage analysis, which matches records referring to the same real world entities from different data sets, is an important task in data integration. Uncertainty often exists in record linkages due to incompleteness or ambiguity in data. Fortunately, the state-of-the-art probabilistic record linkage methods are capable of computing the probability that two records referring to the same entity.In this paper, we study the novel aggregate queries on probabilistic record linkages, such as counting the number of matched records. We address several fundamental issues. First, we advocate that the answer to an aggregate query on probabilistic record linkages is a probability distribution of possible answers derived from possible worlds. Second, we identify the category of compatible linkages only on which the answers to aggregate queries can be determined properly when the probabilities of individual linkages are available but the joint distributions of multiple linkages are unavailable. Third, we give a quadratic exact algorithm and two approximation algorithms to answer aggregate queries.

BackgroundData on newborns is regularly linked for epidemiological research. However, hospital data often suffers from incomplete data. We report on a linkage of two population-covering administrative health databases containing neonatal and perinatal data without unique personal identifiers and with incomplete information in standard patient identifiers.
 GoalTo study the effects of a policy-induced change from linking a national database without standard patient identifiers to a privacy-preserving Record Linkage method, we compare the linkage system in use to clear-text and privacy-preserving Record Linkage techniques. We expected large proportions of missing identifiers since they are not needed for clinical practice. Therefore, we expected missing links caused by missing identifiers. To study the impact of these missing identifiers on these successful links, we compared several linkage methods. Furthermore, we study the variations of linkage success between hospitals.
 MethodsPerinatal and neonatal data from population-covering real-world administrative databases was linked using several variants of state of the art methods, including Privacy-preserving Record Linkage (PPRL) techniques such as multiple match keys and Bloom filter methods.
 Results We report on the variation of linkage results between the hospitals and give possible explanations for the differences.
 The resulting linkage success is reported for each method. The impact of incomplete data on linkage success for each method is documented.
 Finally, we report on the relative performance of the modified techniques compared to standard linkage procedures used in practice.
 ConclusionImplementing a record linkage system based on identifiers not required for clinical practice caused a large number of missing identifiers. Since this information is essential for successful clear-text and private linkage methods, emphasizing the need for documenting patient identifiers, especially in cases where auxiliary information (such as stable addresses, date of birth or health insurance numbers) are missing, is of central importance for implementing a privacy-preserving Record Linkage system.

<p>We consider probabilistic modelling for accomplishing record linkage across two large scale publicly available data sources: New Zealand Births Deaths and Marriages (BDM), and Māori Land Online (MLO). We undertake this project in the context of te ao Māori, integrating mātauranga Māori principles into the work. We present several methods for record linkage and several novel ways to reject false linkages.</p>

The process of merging data of different data sources is referred to as record linkage. A medical environment with increased preconditions on privacy protection demands the transformation of clear-text attributes like first name or date of birth into one-way encrypted pseudonyms. When performing an automated or privacy preserving record linkage there might be the need of a binary classification deciding whether two records should be classified as the same entity. The classification is the final of the four main phases of the record linkage process: Preprocessing, indexing, matching and classification. The choice of binary classification techniques in dependence of project specifications in particular data quality has not extensively been studied yet. The aim of this work is the introduction and evaluation of an automatable semi-supervised binary classification system applied within the field of record linkage capable of competing or even surpassing advanced automated techniques of the domain of unsupervised classification. This work describes the rationale leading to the model and the final implementation of an automatable semi-supervised binary classification system and the comparison of its classification performance to an advanced active learning approach out of the domain of unsupervised learning. The performance of both systems has been measured on a broad variety of artificial test sets (n = 400), based on real patient data, with distinct and unique characteristics. While the classification performance for both methods measured as F-measure was relatively close on test sets with maximum defined data quality, 0.996 for semi-supervised classification, 0.993 for unsupervised classification, it incrementally diverged for test sets of worse data quality dropping to 0.964 for semi-supervised classification and 0.803 for unsupervised classification. Aside from supplying a viable model for semi-supervised classification for automated probabilistic record linkage, the tests conducted on a large amount of test sets suggest that semi-supervised techniques might generally be capable of outperforming unsupervised techniques especially on data with lower levels of data quality.

A Conceptual Algorithm to Link Police and Hospital Records Based on Occurrence of Values

Results from simulated data sets: probabilistic record linkage outperforms deterministic record linkage

Linking information across databases fosters new research in the medical sciences. Recent European privacy regulations recommend encrypting personal identifiers used for linking. Bloom filter based methods are an increasingly popular Record Linkage method. However, basic Bloom filter encodings are prone to cryptographic attacks. Therefore, hardening methods against these attacks are required. In this paper, a new method for such a hardening method for Privacy-preserving Record Linkage (PPRL) technique is presented. By using a Markov chain-based language model of bigrams of identifiers during the encryption, protection against attacks is increased. Based on real-world mortality data, we compare unencrypted and state of the art PPRL methods with the results of the proposed hardening method.

To evaluate the impact on tuberculosis (TB) incidence rates of removal of improper duplicate records from the notification system. Data from the Sistema de Informação de Agravos de Notificação (Brazilian Information System for Tuberculosis Notification) from 2000 to 2004 were analyzed. Repeat records were identified through probabilistic record linkage and classified into six mutually exclusive categories and then kept, combined or removed from database. Of all TB records, 73.7% had no duplicate, 18.9% were duplicate, 4.7% were triplicate, and 2.7% were quadruplicate or more. Of all repeat records, 47.3% were classified as transfer in/out; 23.6% return after default, 16.4% true duplicates, 10% relapse, 2.5% inconclusive and 0.2% had missing data. These proportions were different in Brazilian states. Removal of improper duplicate records reduced TB incidence rate per 100.000 inhabitants by 6.1% in the year 2000 (from 44 to 41.3), 8.3% in 2001 (from 44.5 to 40.8), 9.4% in 2002 (from 45.8 to 41.5), 9.2% in 2003 (from 46.9 to 42.6) and 8.4% in 2004 (from 45.4 to 41.6). The study results indicate that the observed tuberculosis incidence rates represent estimates that would be closer to the actual rates than those obtained from the raw database at state and country level. The use of record linkage approach should be promoted for better quality of notification system data.

Extending the Fellegi-Sunter record linkage model for mixed-type data with application to the French national health data system

IntroductionResearch data combined with administrative data provides a robust resource capable of answering unique research questions. However, in cases where personal health data are encrypted, due to ethics requirements or institutional restrictions, traditional methods of deterministic and probabilistic record linkages are not feasible. Instead, privacy-preserving record linkages must be used to protect patients’ personal data during data linkage.ObjectivesTo determine the feasibility and validity of a deterministic privacy preserving data linkage protocol using homomorphically encrypted data.MethodsFeasibility was measured by the number of records that successfully matched via direct identifiers. Validity was measured by the number of records that matched with multiple indirect identifiers. The threshold for feasibility and validity were both set at 95%. The datasets shared a single, direct identifier (health card number) and multiple indirect identifiers (sex and date of birth). Direct identifiers were encrypted in both datasets and then transferred to a third-party server capable of linking the encrypted identifiers without decrypting individual records. Once linked, the study team used indirect identifiers to verify the accuracy of the linkage in the final dataset.ResultsWith a combination of manual and automated data transfer in a sample of 8,128 individuals, the privacy-preserving data linkage took 36 days to match to a population sample of over 3.2 million records. 99.9% of the records were successfully matched with direct identifiers, and 99.8% successfully matched with multiple indirect identifiers. We deemed the linkage both feasible and valid.ConclusionsAs combining administrative and research data becomes increasingly common, it is imperative to understand options for linking data when direct linkage is not feasible. The current linkage process ensured the privacy and security of patient data and improved data quality. While the initial implementations required significant computational and human resources, increased automation keeps the requirements within feasible bounds.

IntroductionHepatitis C virus (HCV) infection is a major health problem associated with considerable risk of mortality in different regions of the world. The purpose of this study was to investigate the contribution of HCV infection on all-cause and liver-related mortality, in a large cohort of blood donors in Brazil.MethodsThis is a retrospective cohort study of blood donors from 1994 to 2013, at Fundação Pró-Sangue—Hemocentro de São Paulo (FPS). This cohort included 2,892 and 5,784 HCV antibody seropositive and seronegative donors, respectively. Records from the FPS database and the Mortality Information System (SIM: a national database in Brazil) were linked through a probabilistic record linkage (RL). Mortality outcomes were defined based on ICD-10 (10th International Statistical Classification of Diseases and Related Health Problems) codes listed as the cause of death on the death certificate. Hazard ratios (HRs) were estimated for outcomes using Cox multiple regression models.ResultsWhen all causes of death were considered, RL identified 209 deaths (7.2%) among seropositive blood donors and 190 (3.3%) among seronegative blood donors. Donors seropositive for HCV infection had a 2.5 times higher risk of death due to all causes (95% CI: 1.76–2.62; p<0.001). When only liver-related causes of death were considered, RL identified 73 deaths among seropositive blood donors and only 6 among seronegative blood donors. Donors seropositive for HCV infection had a 23.4 times higher risk of death due to liver related causes (95% CI: 10.2–53.9; p<0.001). Donors seropositive for HCV had a 29.5 (95%CI: 3.9–221.7), 2.8 (95% CI: 1.4–5.5) and a 1.9 (95% CI: 1.2–3.0) times higher risk of death due to hepatocellular carcinoma, infection or trauma, respectively, compared to seronegative donors.ConclusionsAll-cause and liver-related mortality rate was increased among blood donors seropositive for HCV compared with the mortality rate among seronegative blood donors. Our data confirms HCV as a relevant cause of death in Brazil and also suggest that interventions directed at following patients even after access to specific drug treatment are urgent and necessary.

This article presents a scenario for the future of research access to federally collected microdata. Many researchers find access to government databases increasingly desirable. The databases themselves are more comprehensive, of better quality and-with improved database management techniques--better structured. Advances in computer communications enable remote access to these databases. Substantial gains in the performance/cost ratio of computers permit more sophisticated analyses--including ones based on statistical graphics, identification of extreme or influential values, record linkage and Bayesian regression methods. At the same time, the individuals and institutions that provide the data residing on government databases--as well as the agencies who sponsor the collection of such information--are becoming increasingly aware that the same technologies that extend analytical capabilities also furnish tools that threaten the confidentiality of data records. As the broker between the data provider and the data user, government agencies are under increased pressure to implement policies that both increase data access and ensure confidentiality. In response to these cross-pressures, agencies will more actively pursue statistical, administrative and legal approaches to responsible data dissemination. Recent developments in these approaches are discussed as they relate to improvements in database techniques, computer and analytical methodologies and legal and administrative arrangements for access to and protection of federal statistics.