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

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 (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.

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

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.

906 A national cohort of children born after assisted conception in the UK (1992–2009): methodology and birthweight analysis

Poor record linkage sensitivity biased outcomes in a linked cohort analysis

ABSTRACTObjectivesWe compared four different approaches to record linkage, demonstrating its potential in enriching the Netherlands Twin Register (NTR) with healthcare data from a large health insurance provider (AHD), which covers approximately 26% of the population. Challenges included the fact that young twins share most of their linkage variables and overlapping identification numbers were absent. We extensively validated the linkage results and complemented them with an indicator of representativeness.ApproachSubjects born since 1986 were selected in the NTR (N=30,383) and AHD (N=1,532,675). Linkage variables were cleaned and harmonised and linkage keys were chosen that were strong enough to uniquely identify most subjects in either dataset. The four linkages were deterministic, probabilistic, probabilistic with Jaro-Winkler distance, and probabilistic using encrypted identifiers.Validation took place by reviewing linkage variables and information like family relations; linked record pairs were categorised into correct, possible or false links. Furthermore, the consistency of linked data was reviewed focusing on Attention Deficit Hyperactivity Disorder (ADHD), a prevalent condition among children. Finally, we quantified the impact of the linkage on representativeness of the target population.ResultsProbabilistic linkage with distance calculation linked 7944 NTR subjects to the AHD with 82% correct, 17% possible, and 1% false links. It encompassed nearly all links identified by any of the other methods, which were more conservative.Information about medication was available in the NTR dataset for 40% of the linked subjects. Validation of the information about ADHD inside linked records revealed few discrepancies between the two datasets. Linkage had a modest effect on the representativeness of the population. The current linkage has confirmed a number of known ADHD cases and identified 146 new subjects using methylphenidate and 101 receiving psychiatric care for ADHD, who were not previously recognized as ADHD cases within the NTR. ConclusionRecord linkage with sources of data such as a health insurance database can be an efficient way of data collection in cohort research. Our study shows that record linkage is also possible for twin pairs. Although a minority of all NTR subjects were covered by the AHD, the linkage resulted in an enrichment of the NTR dataset. Exact information about the dosage and frequency of drugs was obtained without contacting subjects with detailed questionnaires. With a total of almost eight thousand retrieved records, the size of the linked dataset is sufficiently large for epidemiological research of non-rare conditions.

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.

ObjectivesWe perform and evaluate record linkage of German Care Needs Assessment (CNA) data to Statutory Health Insurance (SHI) claims data. The resulting dataset should enable the identification of factors in...

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.

Building a population-based diabetes register: An Italian experience

ABSTRACT
 ObjectivesWhile record linkage has become a strategic research priority within Australia and internationally, legal and administrative issues prevent data linkage in some situations due to privacy concerns. Even current best practices in record linkage carry some privacy risk as they require the release of personally identifying information to trusted third parties. Application of record linkage systems that do not require the release of personal information can overcome legal and privacy issues surrounding data integration. Current conceptual and experimental privacy-preserving record linkage (PPRL) models show promise in addressing data integration challenges but do not yet address all of the requirements for real-world operations. This paper aims to identify and address some of the challenges of operationalising PPRL frameworks.
 ApproachTraditional linkage processes involve comparing personally identifying information (name, address, date of birth) on pairs of records to determine whether the records belong to the same person. Designing appropriate linkage strategies is an important part of the process. These are typically based on the analysis of data attributes (metadata) such as data completeness, consistency, constancy and field discriminating power. Under a PPRL model, however, these factors cannot be discerned from the encrypted data, so an alternative approach is required. This paper explores methods for data profiling, blocking, weight/threshold estimation and error detection within a PPRL framework.
 ResultsProbabilistic record linkage typically involves the estimation of weights and thresholds to optimise the linkage and ensure highly accurate results. The paper outlines the metadata requirements and automated methods necessary to collect data without compromising privacy. We present work undertaken to develop parameter estimation methods which can help optimise a linkage strategy without the release of personally identifiable information. These are required in all parts of the privacy preserving record linkage process (pre-processing, standardising activities, linkage, grouping and extracting).
 ConclusionsPPRL techniques that operate on encrypted data have the potential for large-scale record linkage, performing both accurately and efficiently under experimental conditions. Our research has advanced the current state of PPRL with a framework for secure record linkage that can be implemented to improve and expand linkage service delivery while protecting an individual’s privacy. However, more research is required to supplement this technique with additional elements to ensure the end-to-end method is practical and can be incorporated into real-world models.

The Patient Master Index (PMI) plays an important role in management of patient information and epidemiological research, and the availability of unique patient identifiers improves the accuracy when linking patient records across disparate datasets. In our environment, however, a unique identifier is seldom present in all datasets containing patient information. Quasi identifiers are used to attempt to link patient records but sometimes present higher risk of over-linking. Data quality and completeness thus affect the ability to make correct linkages. This paper describes the record linkage system that is currently implemented at the Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, and assesses its output to date. We apply a stepwise deterministic record linkage approach to link patient data that are routinely collected from health information systems in the Western Cape province of South Africa. Variables used in the linkage process include South African National Identity number (RSA ID), date of birth, year of birth, month of birth, day of birth, residential address and contact information. Descriptive analyses are used to estimate the level and extent of duplication in the provincial PMI. The percentage of duplicates in the provincial PMI lies between 10% and 20%. Duplicates mainly arise from spelling errors, and surname and first names carry most of the errors, with the first names and surname being different for the same individual in approximately 22% of duplicates. The RSA ID is the variable mostly affected by poor completeness with less than 30% of the records having an RSA ID.The current linkage algorithm requires refinement as it makes use of algorithms that have been developed and validated on anglicised names which might not work well for local names. Linkage is also affected by data quality-related issues that are associated with the routine nature of the data which often make it difficult to validate and enforce integrity at the point of data capture.

This article compares the behaviour of sampling techniques for price indices using a scanner data set as a model population. Indices produced by two purposive deterministic cut-off designs and four probabilistic sampling schemes are compared with each other and with the ‘true’ population index from the whole data set. We found that the two deterministic cut-off sampling schemes show much different behaviour from the probabilistic sampling schemes. This is not unexpected, as the former schemes have a very restricted focus with respect to the variety of products. We also found that the probabilistic schemes are generally closer to each other and the ‘true’ value than the deterministic cut-off designs. The jackknife resampling technique is also explored as a means of estimating the SE of the index and compared with the actual results from repeated sampling.

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.