Evaluating Linkage Quality of Population-Based Administrative Data for Health Service Research.

BACKGROUND: To overcome the limitations of relying on data from a single institution, many researchers have studied data linkage methodologies. Data linkage includes errors owing to legal issues surrounding personal information and technical issues related to data processing. Linkage errors affect selection bias, and external and internal validity. Therefore, quality verification for each connection method with adherence to personal information protection is an important issue. This study evaluated the linkage quality of linked data and analyzed the potential bias resulting from linkage errors. METHODS: This study analyzed claims data submitted to the Health Insurance Review and Assessment Service (HIRA DATA). The linkage errors of the two deterministic linkage methods were evaluated based on the use of the match key. The first deterministic linkage uses a unique identification number, and the second deterministic linkage uses the name, gender, and date of birth as a set of partial identifiers. The linkage error included in this deterministic linkage method was compared with the absolute standardized difference (ASD) of Cohen's according to the baseline characteristics, and the linkage quality was evaluated through the following indicators: linked rate, false match rate, missed match rate, positive predictive value, sensitivity, specificity, and F1-score. RESULTS: For the deterministic linkage method that used the name, gender, and date of birth as a set of partial identifiers, the true match rate was 83.5 and the missed match rate was 16.5. Although there was bias in some characteristics of the data, most of the ASD values were less than 0.1, with no case greater than 0.5. Therefore, it is difficult to determine whether linked data constructed with deterministic linkages have substantial differences. CONCLUSION: This study confirms the possibility of building health and medical data at the national level as the first data linkage quality verification study using big data from the HIRA. Analyzing the quality of linkages is crucial for comprehending linkage errors and generating reliable analytical outcomes. Linkers should increase the reliability of linked data by providing linkage error-related information to researchers. The results of this study will serve as reference data to increase the reliability of multicenter data linkage studies.

[Interoperability Working Group: core dataset and information systems for data integration and data exchange in the Medical Informatics Initiative]. / Arbeitsgruppe Interoperabilität: Kerndatensatz und Informationssysteme für Integration und Austausch von Daten in der Medizininformatik-Initiative.

The interoperability Working Group of the Medical Informatics Initiative (MII) is the platform for the coordination of overarching procedures, data structures, and interfaces between the data integration centers (DIC) of the university hospitals and national and international interoperability committees. The goal is the joint content-related and technical design of a distributed infrastructure for the secondary use of healthcare data that can be used via the Research Data Portal for Health. Important general conditions are data privacy and IT security for the use of health data in biomedical research. To this end, suitable methods are used in dedicated task forces to enable procedural, syntactic, and semantic interoperability for data use projects. The MII core dataset was developed as several modules with corresponding information models and implemented using the HL7® FHIR® standard to enable content-related and technical specifications for the interoperable provision of healthcare data through the DIC. International terminologies and consented metadata are used to describe these data in more detail. The overall architecture, including overarching interfaces, implements the methodological and legal requirements for a distributed data use infrastructure, for example, by providing pseudonymized data or by federated analyses. With these results of the Interoperability Working Group, the MII is presenting a future-oriented solution for the exchange and use of healthcare data, the applicability of which goes beyond the purpose of research and can play an essential role in the digital transformation of the healthcare system.

[Foundations for the scientific use of extensive health care data in Germany-results of the Data Sharing working group of the Medical Informatics Initiative]. / Grundlagen für die wissenschaftliche Nutzung umfangreicher Versorgungsdaten in Deutschland ­ Ergebnisse der AG Data Sharing der Medizininformatik-Initiative.

Healthcare data are an important resource in applied medical research. They are available multicentrically. However, it remains a challenge to enable standardized data exchange processes between federal states and their individual laws and regulations. The Medical Informatics Initiative (MII) was founded in 2016 to implement processes that enable cross-clinic access to healthcare data in Germany. Several working groups (WGs) have been set up to coordinate standardized data structures (WG Interoperability), patient information and declarations of consent (WG Consent), and regulations on data exchange (WG Data Sharing). Here we present the most important results of the Data Sharing working group, which include agreed terms of use, legal regulations, and data access processes. They are already being implemented by the established Data Integration Centers (DIZ) and Use and Access Committees (UACs). We describe the services that are necessary to provide researchers with standardized data access. They are implemented with the Research Data Portal for Health, among others. Since the pilot phase, the processes of 385 active researchers have been used on this basis, which, as of April 2024, has resulted in 19 registered projects and 31 submitted research applications.

[Digital health progress hubs-data integration beyond university hospitals]. / Die digitalen Fortschrittshubs Gesundheit ­ Gemeinsame Datennutzung über die Universitätsmedizin hinaus.

The digital health progress hubs pilot the extensibility of the concepts and solutions of the Medical Informatics Initiative to improve regional healthcare and research. The six funded projects address different diseases, areas in regional healthcare, and methods of cross-institutional data linking and use. Despite the diversity of the scenarios and regional conditions, the technical, regulatory, and organizational challenges and barriers that the progress hubs encounter in the actual implementation of the solutions are often similar. This results in some common approaches to solutions, but also in political demands that go beyond the Health Data Utilization Act, which is considered a welcome improvement by the progress hubs.In this article, we present the digital progress hubs and discuss achievements, challenges, and approaches to solutions that enable the shared use of data from university hospitals and non-academic institutions in the healthcare system and can make a sustainable contribution to improving medical care and research.

Mainzelliste SecureEpiLinker (MainSEL): privacy-preserving record linkage using secure multi-party computation.

MOTIVATION: Record Linkage has versatile applications in real-world data analysis contexts, where several datasets need to be linked on the record level in the absence of any exact identifier connecting related records. An example are medical databases of patients, spread across institutions, that have to be linked on personally identifiable entries like name, date of birth or ZIP code. At the same time, privacy laws may prohibit the exchange of this personally identifiable information (PII) across institutional boundaries, ruling out the outsourcing of the record linkage task to a trusted third party. We propose to employ privacy-preserving record linkage (PPRL) techniques that prevent, to various degrees, the leakage of PII while still allowing for the linkage of related records. RESULTS: We develop a framework for fault-tolerant PPRL using secure multi-party computation with the medical record keeping software Mainzelliste as the data source. Our solution does not rely on any trusted third party and all PII is guaranteed to not leak under common cryptographic security assumptions. Benchmarks show the feasibility of our approach in realistic networking settings: linkage of a patient record against a database of 10 000 records can be done in 48 s over a heavily delayed (100 ms) network connection, or 3.9 s with a low-latency connection. AVAILABILITY AND IMPLEMENTATION: The source code of the sMPC node is freely available on Github at https://github.com/medicalinformatics/SecureEpilinker subject to the AGPLv3 license. The source code of the modified Mainzelliste is available at https://github.com/medicalinformatics/MainzellisteSEL. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Bayesian record linkage with variables in one file.

In many healthcare and social science applications, information about units is dispersed across multiple data files. Linking records across files is necessary to estimate the associations of interest. Common record linkage algorithms only rely on similarities between linking variables that appear in all the files. Moreover, analysis of linked files often ignores errors that may arise from incorrect or missed links. Bayesian record linking methods allow for natural propagation of linkage error, by jointly sampling the linkage structure and the model parameters. We extend an existing Bayesian record linkage method to integrate associations between variables exclusive to each file being linked. We show analytically, and using simulations, that the proposed method can improve the linking process, and can result in accurate inferences. We apply the method to link Meals on Wheels recipients to Medicare enrollment records.

[Methodology and Attribution Success of a Data Linkage of Clinical Registry Data with Health Insurance Data]. / Methodik und Zuordnungserfolg eines Linkage von Daten klinischer Krebsregister mit Abrechnungsdaten gesetzlicher Krankenkassen.

BACKGROUND: The aim of the project "Effectiveness of care in oncological centres" (WiZen), funded by the innovation fund of the federal joint committee, is to investigate the effectiveness of certification in oncology. The project uses nationwide data from the statuory health insurance AOK and data from clinical cancer registries from three different federal states from 2006-2017. To combine the strengths of both data sources, these will be linked for eight different cancer entities in compliance with data protection regulations. METHODS: Data linkage was performed using indirect identifiers and validated using the health insurance's patient ID ("Krankenversichertennummer") as a direct identifier and gold standard. This enables quantification of the quality of different linkage variants. Sensitivity and specificity as well as hit accuracy and a score addressing the quality of the linkage were used as evaluation criteria. The distributions of relevant variables resulting from the linkage were validated against the original distributions in the individual datasets. RESULTS: Depending on the combination of indirect identifiers, we found a range of 22,125 to 3,092,401 linkage hits. An almost perfect linkage could be achieved by combining information on cancer type, date of birth, gender and postal code. A total of 74,586 one-to-one linkages were achieved with these characteristics. The median hit quality for the different entities was more than 98%. In addition, both the age and sex distributions and the dates of death, if any, showed a high degree of agreement. DISCUSSION AND CONCLUSION: SHI and cancer registry data can be linked with high internal and external validity at the individual level. This robust linkage enables completely new possibilities for analysis through simultaneous access to variables from both data sets ("the best of both worlds"): Information on the UICC stage that stems from the registries can now be combined, for instance, with comorbidities from the SHI data at the individual level. Due to the use of readily available variables and the high success of the linkage, our procedure constitutes a promising method for future linkage processes in health care research.

A blinded evaluation of privacy preserving record linkage with Bloom filters.

BACKGROUND: Privacy preserving record linkage (PPRL) methods using Bloom filters have shown promise for use in operational linkage settings. However real-world evaluations are required to confirm their suitability in practice. METHODS: An extract of records from the Western Australian (WA) Hospital Morbidity Data Collection 2011-2015 and WA Death Registrations 2011-2015 were encoded to Bloom filters, and then linked using privacy-preserving methods. Results were compared to a traditional, un-encoded linkage of the same datasets using the same blocking criteria to enable direct investigation of the comparison step. The encoded linkage was carried out in a blinded setting, where there was no access to un-encoded data or a 'truth set'. RESULTS: The PPRL method using Bloom filters provided similar linkage quality to the traditional un-encoded linkage, with 99.3% of 'groupings' identical between privacy preserving and clear-text linkage. CONCLUSION: The Bloom filter method appears suitable for use in situations where clear-text identifiers cannot be provided for linkage.

FIRLA: a Fast Incremental Record Linkage Algorithm.

Record linkage is an important problem studied widely in many domains including biomedical informatics. A standard version of this problem is to cluster records from several datasets, such that each cluster has records pertinent to just one individual. Typically, datasets are huge in size. Hence, existing record linkage algorithms take a very long time. It is thus essential to develop novel fast algorithms for record linkage. The incremental version of this problem is to link previously clustered records with new records added to the input datasets. A novel algorithm has been created to efficiently perform standard and incremental record linkage. This algorithm leverages a set of efficient techniques that significantly restrict the number of record pair comparisons and distance computations. Our algorithm shows an average speed-up of 2.4x (up to 4x) for the standard linkage problem as compared to the state-of-the-art, without any drop in linkage performance at all. On average, our algorithm can incrementally link records in just 33% of the time required for linking them from scratch. Our algorithms achieve comparable or superior linkage performance and outperform the state-of-the-art in terms of linking time in all cases where the number of comparison attributes is greater than two. In practice, more than two comparison attributes are quite common. The proposed algorithm is very efficient and could be used in practice for record linkage applications especially when records are being added over time and linkage output needs to be updated frequently.

The Data-Adaptive Fellegi-Sunter Model for Probabilistic Record Linkage: Algorithm Development and Validation for Incorporating Missing Data and Field Selection.

BACKGROUND: Quality patient care requires comprehensive health care data from a broad set of sources. However, missing data in medical records and matching field selection are 2 real-world challenges in patient-record linkage. OBJECTIVE: In this study, we aimed to evaluate the extent to which incorporating the missing at random (MAR)-assumption in the Fellegi-Sunter model and using data-driven selected fields improve patient-matching accuracy using real-world use cases. METHODS: We adapted the Fellegi-Sunter model to accommodate missing data using the MAR assumption and compared the adaptation to the common strategy of treating missing values as disagreement with matching fields specified by experts or selected by data-driven methods. We used 4 use cases, each containing a random sample of record pairs with match statuses ascertained by manual reviews. Use cases included health information exchange (HIE) record deduplication, linkage of public health registry records to HIE, linkage of Social Security Death Master File records to HIE, and deduplication of newborn screening records, which represent real-world clinical and public health scenarios. Matching performance was evaluated using the sensitivity, specificity, positive predictive value, negative predictive value, and F1-score. RESULTS: Incorporating the MAR assumption in the Fellegi-Sunter model maintained or improved F1-scores, regardless of whether matching fields were expert-specified or selected by data-driven methods. Combining the MAR assumption and data-driven fields optimized the F1-scores in the 4 use cases. CONCLUSIONS: MAR is a reasonable assumption in real-world record linkage applications: it maintains or improves F1-scores regardless of whether matching fields are expert-specified or data-driven. Data-driven selection of fields coupled with MAR achieves the best overall performance, which can be especially useful in privacy-preserving record linkage.

[Linkage of claims data with data from epidemiological cancer registries: possibilities and limitations in the German federal states]. / Verknüpfung von Abrechnungsdaten gesetzlicher Krankenkassen mit Daten epidemiologischer Krebsregister: länderspezifische Möglichkeiten und Limitationen.

BACKGROUND: In recent years, there has been an increasing demand for the reuse of research data in accordance with the so-called FAIR principles. This would allow researchers to conduct projects on a broader data basis and to investigate new research questions by linking different data sources. OBJECTIVES: We explored if nationwide linking of claims data from statutory health insurances (SHI) with data from population-based cancer registries can be used to obtain additional information on cancer that is missing in claims data and to assess the validity of SHI tumour diagnoses. This paper focuses on describing the specific requirements of German federal states for such data linkage. MATERIALS AND METHODS: The Pharmacoepidemiological Research Database GePaRD at the Leibniz Institute for Prevention Research and Epidemiology - BIPS and six cancer registries were used as data sources. The logistically complex direct linkage was compared with a less complex indirect linkage. For this purpose, permission had to be obtained for GePaRD and for each cancer registry from the respective responsible authority. RESULTS: Regarding the linkage of cancer registry data with GePaRD, the cancer registries showed profound differences in the modalities for data provision, ranging from a complete rejection to an uncomplicated implementation of linkage procedures. DISCUSSION: In Germany, a consistent legal framework is needed to adequately enable the reuse and record linkage of personal health data for research purposes according to the FAIR principles. The new law on the consolidation of cancer registry data could provide a remedy regarding the linkage of cancer registry data with other data sources.

Comparing cancer risk estimates using occupational record linkage approaches in male Florida firefighters.

BACKGROUND: Firefighters have an increased risk of cancer, but variations in reported results could be due to differences in occupational case ascertainment. This study compares cancer risk estimates generated by identifying firefighters from their occupational title available in the Florida Cancer Data System (FCDS) versus identification by a linkage method between the FCDS and the Florida State Fire Marshal's Office. METHODS: Florida firefighter employment records (1972-2012; n = 109,009) were linked with FCDS data (1981-2014; ~3.3 million records), identifying 3760 primary cancers in male firefighters. Using the FCDS occupational data field we identified 1831 male cancer cases in those classified as firefighters, first-line supervisors of firefighting and prevention workers, fire inspectors, emergency medical technicians, or paramedics. Age and calendar year-adjusted odds ratios (aOR) and 95% confidence intervals for firefighters versus non-firefighters were calculated for both groups. RESULTS: For skin cancers the risk estimate for FCDS-indentified firefighters was substantially lower than in the employment-record-linked firefighters (aOR = 1.06; 0.87-1.29 vs. 1.54; 1.37-1.73), but for endocrine system cancers it was greater (aOR = 2.36; 1.77-3.14 vs. 2.08; 1.71-2.53). Remaining cancer risk estimates were in the same direction for the two samples except for lymphoma (aOR = 1.10; 0.90-1.34 vs. 0.86; 0.75-0.99). CONCLUSION: Reliance on occupational title in cancer registry records to characterize firefighter cancer risk may result in estimates that are over- or underestimated depending on cancer site. The authors recommend moving toward national linkages between cancer registries and certification or other administrative records, which are a vital resource for firefighter cancer research.

One-year mortality of colorectal cancer patients: development and validation of a prediction model using linked national electronic data.

BACKGROUND: The existing literature does not provide a prediction model for mortality of all colorectal cancer patients using contemporary national hospital data. We developed and validated such a model to predict colorectal cancer death within 90, 180 and 365 days after diagnosis. METHODS: Cohort study using linked national cancer and death records. The development population included 27,480 patients diagnosed in England in 2015. The test populations were diagnosed in England in 2016 (n = 26,411) and Wales in 2015-2016 (n = 3814). Predictors were age, gender, socioeconomic status, referral source, performance status, tumour site, TNM stage and treatment intent. Cox regression models were assessed using Brier scores, c-indices and calibration plots. RESULTS: In the development population, 7.4, 11.7 and 17.9% of patients died from colorectal cancer within 90, 180 and 365 days after diagnosis. T4 versus T1 tumour stage had the largest adjusted association with the outcome (HR 4.67; 95% CI: 3.59-6.09). C-indices were 0.873-0.890 (England) and 0.856-0.873 (Wales) in the test populations, indicating excellent separation of predicted risks by outcome status. Models were generally well calibrated. CONCLUSIONS: The model was valid for predicting short-term colorectal cancer mortality. It can provide personalised information to support clinical practice and research.

Record Linkage Approaches Using Prescription Drug Monitoring Program and Mortality Data for Public Health Analyses and Epidemiologic Studies.

BACKGROUND: The use of Prescription Drug Monitoring Program (PDMP) data has greatly increased in recent years as these data have accumulated as part of the response to the opioid epidemic in the United States. We evaluated the accuracy of record linkage approaches using the Controlled Substance Monitoring Database (Tennessee's [TN] PDMP, 2012-2016) and mortality data on all drug overdose decedents in Tennessee (2013-2016). METHODS: We compared total, missed, and false positive (FP) matches (with manual verification of all FPs) across approaches that included a variety of data cleaning and matching methods (probabilistic/fuzzy vs. deterministic) for patient and death linkages, and prescription history. We evaluated the influence of linkage approaches on key prescription measures used in public health analyses. We evaluated characteristics (e.g., age, education, sex) of missed matches and incorrect matches to consider potential bias. RESULTS: The most accurate probabilistic/fuzzy matching approach identified 4,714 overdose deaths (vs. the deterministic approach, n = 4,572), with a low FP linkage error (<1%) and high correct match proportion (95% vs. 92% and ~90% for probabilistic approaches not using comprehensive data cleaning). Estimation of all prescription measures improved (vs. deterministic approach). For example, frequency (%) of decedents filling an oxycodone prescription in the last 60 days (n = 1,371 [32%] vs. n = 1,443 [33%]). Missed overdose decedents were more likely to be younger, male, nonwhite, and of higher education. CONCLUSION: Implications of study findings include underreporting, prescribing and outcome misclassification, and reduced generalizability to population risk groups, information of importance to epidemiologists and researchers using PDMP data.

Linking disease registries and nationwide healthcare administrative databases: the French renal epidemiology and information network (REIN) insight.

BACKGROUND: Record linkage is increasingly used in health research worldwide. Combining the patient information available in healthcare, administrative and clinical databases broadens the research perspectives, particularly for chronic diseases. Recent guidelines highlight the need for transparency on the used record linkage processes and the extracted data to be used by researchers. METHODS: Therefore, the aim of this study was to describe the deterministic iterative approach used to link the French Epidemiology and Information Network (REIN), a French national End-Stage Renal Disease registry, with the Système National des Données de Santé (SNDS), a French nationwide medico-administrative healthcare database. RESULTS: Among the 22,073 patients included in the REIN registry who started renal replacement therapy between 2014 and 2015 in France, 19,223 (87.1%) were matched with patients in the SNDS database. Comparison of matched and unmatched patients confirmed the absence of any major selection bias. Then, the record linkage was evaluated using the comorbidity status (diabetes). CONCLUSIONS: This fast and efficient method of record linkage with pseudonymized data and without unique and direct identifier might inspire other research teams. It also opens the path for new research on chronic kidney disease.

Claims-based cardiovascular outcome identification for clinical research: Results from 7 large randomized cardiovascular clinical trials.

BACKGROUND: Medicare insurance claims may provide an efficient means to ascertain follow-up of older participants in clinical research. We sought to determine the accuracy and completeness of claims- versus site-based follow-up with clinical event committee (+CEC) adjudication of cardiovascular outcomes. METHODS: We performed a retrospective study using linked Medicare and Duke Database of Clinical Trials data. Medicare claims were linked to clinical data from 7 randomized cardiovascular clinical trials. Of 52,476 trial participants, linking resulted in 5,839 (of 10,497 linkage-eligible) Medicare-linked trial participants with fee-for-service A and B coverage. Death, myocardial infarction (MI), stroke, and revascularization incidences were compared using Medicare inpatient claims only, site-reported events (+CEC) only, or a combination of the 2. Randomized treatment effects were compared as a function of whether claims-based, site-based (+CEC), or a combined system was used for event detection. RESULTS: Among the 5,839 study participants, the annual event rates were similar between claims- and site-based (+CEC) follow-up: death (overall rate 5.2% vs 5.2%; adjusted κ 0.99), MI (2.2% vs 2.3%; adjusted κ 0.96), stroke (0.7% vs 0.7%; adjusted κ 0.99), and any revascularization (7.4% vs 7.9%; adjusted κ 0.95). Of events detected by claims yet not reported by CEC, a minority were reported by sites but negatively adjudicated by CEC (39% of MIs and 18% of strokes). Differences in individual case concordance led to higher event rates when claims- and site-based (+CEC) systems were combined. Randomized treatment effects were similar among the 3 approaches for each outcome of interest. CONCLUSIONS: Claims- versus site-based (+CEC) follow-up identified similar overall cardiovascular event rates despite meaningful differences in the events detected. Randomized treatment effects were similar using the 2 methods, suggesting claims data could be used to support clinical research leveraging routinely collected data. This approach may lead to more effective evidence generation, synthesis, and appraisal of medical products and inform the strategic approaches toward the National Evaluation System for Health Technology.

Use of sensor-integrated pump therapy to reduce hypoglycaemia in people with Type 1 diabetes: a real-world study in the UK.

AIMS: To assess the efficacy of insulin pumps with automated insulin suspension systems in a real-world setting. METHODS: We analysed anonymized data uploaded to CareLink™ by people (n=920) with Type 1 diabetes using the MiniMed Paradigm Veo system and the MiniMed 640G system (Medtronic International Trading Sàrl, Tolochanez, Switzerland) with SmartGuard technology, with or without automated insulin suspension enabled, between February 2016 and June 2018. Users with ≥15 days of sensor data and ≥70% sensor-wear time were classified as sensor-augmented pump alone, sensor-integrated pump with low glucose suspend enabled or sensor-integrated pump with predictive low glucose management enabled. RESULTS: The median (25th -75th percentile) system use was 161 (58-348) days. The median time spent with sensor glucose values ≤3 mmol/l was 0.8 (0.3-1.7)% in the sensor-augmented pump group, 0.3 (0.1-0.7)% in the sensor-integrated pump with low glucose suspend group, and 0.3 (0.1-0.5)% in the sensor-integrated pump with predictive low glucose management group. In individuals switching from sensor-augmented pump to sensor-integrated pump with low glucose suspend (n=31), there were significant reductions in the monthly rate of hypoglycaemic events <3 mmol/l (rate ratio 0.63, 95% CI 0.45-0.89; P=0.009) and in the percentage of time with glucose values ≤3 mmol/l [sensor-augmented pump: 0.63% (95% CI 0.34-1.29), sensor-integrated pump with low glucose suspend: 0.33% (95% CI 0.16-0.64); P=0.001]. The monthly rate of hypoglycaemic events decreased further in individuals (n=139) switching from sensor-integrated pump with low glucose suspend to sensor-integrated pump with predictive low glucose management [rate ratio 0.82 (95% CI 0.69-0.98); P<0.0274]. Similar results were seen for events <3.9 mmol/l. There was no difference in median time spent in target glucose range. CONCLUSION: Real-world UK data show that increasing automation of insulin suspension reduces hypoglycaemia exposure in people with Type 1 diabetes.

Creating the first national linked dataset on perinatal and maternal outcomes in Australia: Methods and challenges.

BACKGROUND: Data linkage offers a powerful mechanism for examining healthcare outcomes across populations and can generate substantial robust datasets using routinely collected electronic data. However, it presents methodological challenges, especially in Australia where eight separate states and territories maintain health datasets. This study used linked data to investigate perinatal and maternal outcomes in relation to place of birth. It examined data from all eight jurisdictions regarding births planned in hospitals, birth centres and at home. Data linkage enabled the first Australia-wide dataset on birth outcomes. However, jurisdictional differences in data collection created challenges in obtaining comparable cohorts of women with similar low-risk pregnancies in all birth settings. The objective of this paper is to describe the techniques for managing previously linked data, and specifically for ensuring the resulting dataset contained only low-risk pregnancies. METHODS: This paper indicates the procedures for preparing and merging linked perinatal, inpatient and mortality data from different sources, providing technical guidance to address challenges arising in linked data study designs. RESULTS: We combined data from eight jurisdictions linking four collections of administrative healthcare and civil registration data. The merging process ensured that variables were consistent, compatible and relevant to study aims. To generate comparable cohorts for all three birth settings, we developed increasingly complex strategies to ensure that the dataset eliminated women with pregnancies at risk of complications during labour and birth. It was then possible to compare birth outcomes for comparable samples, enabling specific examination of the impact of birth setting on maternal and infant safety across Australia. CONCLUSIONS: Data linkage is a valuable resource to enhance knowledge about birth outcomes from different settings, notwithstanding methodological challenges. Researchers can develop and share practical techniques to address these challenges. Study findings suggest that jurisdictions develop more consistent data collections to facilitate future data linkage.

Statistical supervised meta-ensemble algorithm for medical record linkage.

Identifying unique patients across multiple care facilities or services is a major challenge in providing continuous care and undertaking health research. Identifying and linking patients without compromising privacy and security is an emerging issue in the big data era. The large quantity and complexity of the patient data emphasize the need for effective linkage methods that are both scalable and accurate. In this study, we aim to develop and evaluate an ensemble classification method using the three most typically used supervised learning methods, namely support vector machines, logistic regression and standard feed-forward neural networks, to link records that belong to the same patient across multiple service locations. Our ensemble method is the combination of bagging and stacking. Each base learner's critical hyperparameters were selected through grid search technique. Two synthetic datasets were used in this study namely FEBRL and ePBRN. ePBRN linkage dataset was based on linkage errors noticed in the Australian primary care setting. The overall linkage performance was determined by assessing the blocking performance and classification performance. Our ensemble method outperformed the base learners in all evaluation metrics on one dataset. More specifically, the precision, which is average of individual precision scores in case of base learners increased from 90.70% to 94.85% in FEBRL, and from 62.17% to 99.28% in ePBRN. Similarly, the F-score increased from 94.92% to 98.18% in FEBRL, and from 72.99% to 91.72% in ePBRN. Our experiments suggest that we can significantly improve the linkage performance of individual algorithms by employing ensemble strategies.

Validation of mother-infant linkage using Medicaid Case ID variable within the Medicaid Analytic eXtract (MAX) database.

PURPOSE: The state-assigned Case ID number in the Medicaid Analytic eXtract (MAX) allows for potential linkage of mothers to infants. No validation of respective linkage algorithms is available. We established and validated an algorithm within MAX that links mothers to infants and to identify factors influencing successful mother-infant linkage. METHODS: We identified all mother-infant pairs in FL and TX birth certificates records (BCR) that could be linked individually to MAX records (1999-2005 for FL and 1999-2010 for TX) based on Social Security Number (gold standard pairs). Case ID linkage performance was evaluated as the proportion of gold standard mother-infant pairs that were identified by the algorithm (sensitivity) and the proportion of algorithm defined mother-infant pairs that were correctly linked. Generalized estimating equations were used to calculate the probability for successful Case ID algorithm linkage versus non-linkage using maternal and infant characteristics. RESULTS: We identified 323,160 gold standard pairs in FL BCR and MAX and 1,025,350 in TX BCR and MAX. Depending on Medicaid enrollment the algorithm sensitivity ranged from 85.51% to 87.96% in FL and 19.60% to 35.75% in TX. In both states, positive predictive value exceeded 99%, regardless of enrollment periods. Determinants for successful linkage varied across states, but suggested better results for younger mothers, minority women, and those with lower educational achievement. CONCLUSIONS: Our algorithm can correctly link liveborn infants to their mothers. The algorithm's sensitivity in identifying pairs varied across states, but PPV was consistently high. Linkage performance was associated with certain characteristics that may affect representativeness of successfully linked pairs.