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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. 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. 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. 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.

It is unclear if unique personal identifiers should be requested from participants for health record linkage: this permits high-quality data linkage but at the potential cost of lower consent rates due to privacy concerns. Drawing from a sampling frame based on the FAMILY Cohort, using a 2 × 2 factorial design, we randomly assigned 1,200 participants to (1) request for Hong Kong Identity Card number (HKID) or no request and (2) receiving a souvenir incentive (valued at USD4) or no incentive. The primary outcome was consent to health record linkage. We also investigated associations between demographics, health status, and postal reminders with consent. Overall, we received signed consent forms from 33.3% (95% confidence interval [CI] 30.6–36.0%) of respondents. We did not find an overall effect of requesting HKID (−4.3%, 95% CI −9.8% to 1.2%) or offering souvenir incentives (2.4%, 95% CI −3.1% to 7.9%) on consent to linkage. In subgroup analyses, requesting HKID significantly reduced consent among adults aged 18–44 years (odds ratio [OR] 0.53, 95% CI 0.30–0.94, compared to no request). Souvenir incentives increased consent among women (OR 1.55, 95% CI 1.13–2.11, compared to no souvenirs). Requesting a unique personal identifier or providing a souvenir incentive did not affect overall consent to health record linkage.

When applied in large scale to electronic medical record data, the PheWAS approach replicates GWAS associations and reveals potentially new pleiotropic associations. Candidate gene and genome-wide association studies (GWAS) have identified genetic variants that modulate risk for human disease; many of these associations require further study to replicate the results. Here we report the first large-scale application of the phenome-wide association study (PheWAS) paradigm within electronic medical records (EMRs), an unbiased approach to replication and discovery that interrogates relationships between targeted genotypes and multiple phenotypes. We scanned for associations between 3,144 single-nucleotide polymorphisms (previously implicated by GWAS as mediators of human traits) and 1,358 EMR-derived phenotypes in 13,835 individuals of European ancestry. This PheWAS replicated 66% (51/77) of sufficiently powered prior GWAS associations and revealed 63 potentially pleiotropic associations with P < 4.6 × 10 −6 (false discovery rate < 0.1); the strongest of these novel associations were replicated in an independent cohort ( n = 7,406). These findings validate PheWAS as a tool to allow unbiased interrogation across multiple phenotypes in EMR-based cohorts and to enhance analysis of the genomic basis of human disease.

Privacy-preserving record linkage (PPRL) technology, crucial for linking records across datasets while maintaining privacy, is susceptible to graph-based re-identification attacks. These attacks compromise privacy and pose significant risks, such as identity theft and financial fraud. This study proposes a zero-relationship encoding scheme that minimizes the linkage between source and encoded records to enhance PPRL systems’ resistance to re-identification attacks. Our method’s efficacy was validated through simulations on the Titanic and North Carolina Voter Records (NCVR) datasets, demonstrating a substantial reduction in re-identification rates. Security analysis confirms that our zero-relationship encoding effectively preserves privacy against graph-based re-identification threats, improving PPRL technology’s security.

[...]many countries have relaxed privacy and data protection regulations for video and other communications technologies during the crisis4; the General Data Protection Regulations, which apply in the UK and the European Union, already include a clause excepting work in the overwhelming public interest. [...]change was necessary because governments required that any care that does not require physical interaction must now be provided through remote consultation.5 Remote management is possible for many patients that are seen in primary care and hospital outpatient clinics. [...]AI has the potential to support the treatment of COVID-19 through the development of new drugs and the redeployment of existing drugs. There is also a broad range of urgent research needs, such as studies of virus mutations, patient risk factors, clinical outcomes and drug trials.15 Ultimately, the aim was to have data-driven public policy decisions on testing and tracing strategy, health system management, targeted isolation advice, social distancing rules and freedom of movement.

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.

Linkage of medical databases, including insurer claims and electronic health records (EHRs), is increasingly common. However, few studies have investigated the behavior and output of linkage software. To determine how linkage quality is affected by different algorithms, blocking variables, methods for string matching and weight determination, and decision rules, we compared the performance of 4 nonproprietary linkage software packages linking patient identifiers from noninteroperable inpatient and outpatient EHRs. We linked datasets using first and last name, gender, and date of birth (DOB). We evaluated DOB and year of birth (YOB) as blocking variables and used exact and inexact matching methods. We compared the weights assigned to record pairs and evaluated how matching weights corresponded to a gold standard, medical record number. Deduplicated datasets contained 69,523 inpatient and 176,154 outpatient records, respectively. Linkage runs blocking on DOB produced weights ranging in number from 8 for exact matching to 64,273 for inexact matching. Linkage runs blocking on YOB produced 8 to 916,806 weights. Exact matching matched record pairs with identical test characteristics (sensitivity 90.48%, specificity 99.78%) for the highest ranked group, but algorithms differentially prioritized certain variables. Inexact matching behaved more variably, leading to dramatic differences in sensitivity (range 0.04-93.36%) and positive predictive value (PPV) (range 86.67-97.35%), even for the most highly ranked record pairs. Blocking on DOB led to higher PPV of highly ranked record pairs. An ensemble approach based on averaging scaled matching weights led to modestly improved accuracy. In summary, we found few differences in the rankings of record pairs with the highest matching weights across 4 linkage packages. Performance was more consistent for exact string matching than for inexact string matching. Most methods and software packages performed similarly when comparing matching accuracy with the gold standard. In some settings, an ensemble matching approach may outperform individual linkage algorithms.

Linkage of longitudinal administrative data for mothers and babies supports research and service evaluation in several populations around the world. We established a linked mother-baby cohort using pseudonymised, population-level data for England. Retrospective linkage study using electronic hospital records of mothers and babies admitted to NHS hospitals in England, captured in Hospital Episode Statistics between April 2001 and March 2013. Of 672,955 baby records in 2012/13, 280,470 (42%) linked deterministically to a maternal record using hospital, GP practice, maternal age, birthweight, gestation, birth order and sex. A further 380,164 (56%) records linked using probabilistic methods incorporating additional variables that could differ between mother/baby records (admission dates, ethnicity, 3/4-character postcode district) or that include missing values (delivery variables). The false-match rate was estimated at 0.15% using synthetic data. Data quality improved over time: for 2001/02, 91% of baby records were linked (holding the estimated false-match rate at 0.15%). The linked cohort was representative of national distributions of gender, gestation, birth weight and maternal age, and captured approximately 97% of births in England. Probabilistic linkage of maternal and baby healthcare characteristics offers an efficient way to enrich maternity data, improve data quality, and create longitudinal cohorts for research and service evaluation. This approach could be extended to linkage of other datasets that have non-disclosive characteristics in common.

SummaryThe Forteo Patient Registry (FPR) aims to estimate the incidence of osteosarcoma in US patients treated with teriparatide. Enrollment began in 2009 and will continue through 2019, with linkage planned through 2024. To date, no incident cases of osteosarcoma have been identified among patients registered in the FPR.IntroductionThe Forteo Patient Registry (FPR) was established in 2009 to estimate the incidence of osteosarcoma in US patients treated with teriparatide. The objective of this paper is to describe study methods, challenges encountered, and progress to date.MethodsThe FPR is a prospective US registry designed to link data from participants annually with state cancer registries. Patient enrollment is planned for 10 years (2009–2019) and annual linkage with US state cancer registries for 15 years (2010–2024). All US state cancer registries and DC were invited to participate. Patients are recruited using pre-enrollment materials included in teriparatide device packaging, kits, and brochures distributed by health-care providers; a toll-free number; and a study website. A linkage algorithm is used to match data from enrolled participants with cancer registry data.ResultsFor the eighth annual linkage in 2017, information necessary for linkage with 63,270 patients in the FPR was submitted to each of the 42 participating registries. These patients contributed approximately 242,782 person-years of follow-up. A total of 5268 adult osteosarcoma cases diagnosed since January 1, 2009, were available for linkage from participating state cancer registries. To date, no incident cases of osteosarcoma have been identified among patients registered in the FPR.ConclusionsBased on the estimated 242,782 person-years of observation as of the eighth annual linkage and projecting current enrollment rate to study end in 2024, it is anticipated that the completed study will be able to detect a fourfold increase in the risk of osteosarcoma if one exists.

Background Linkage of electronic healthcare records is becoming increasingly important for research purposes. However, linkage error due to mis-recorded or missing identifiers can lead to biased results. We evaluated the impact of linkage error on estimated infection rates using two different methods for classifying links: highest-weight (HW) classification using probabilistic match weights and prior-informed imputation (PII) using match probabilities. Methods A gold-standard dataset was created through deterministic linkage of unique identifiers in admission data from two hospitals and infection data recorded at the hospital laboratories (original data). Unique identifiers were then removed and data were re-linked by date of birth, sex and Soundex using two classification methods: i) HW classification - accepting the candidate record with the highest weight exceeding a threshold and ii) PII–imputing values from a match probability distribution. To evaluate methods for linking data with different error rates, non-random error and different match rates, we generated simulation data. Each set of simulated files was linked using both classification methods. Infection rates in the linked data were compared with those in the gold-standard data. Results In the original gold-standard data, 1496/20924 admissions linked to an infection. In the linked original data, PII provided least biased results: 1481 and 1457 infections (upper/lower thresholds) compared with 1316 and 1287 (HW upper/lower thresholds). In the simulated data, substantial bias (up to 112%) was introduced when linkage error varied by hospital. Bias was also greater when the match rate was low or the identifier error rate was high and in these cases, PII performed better than HW classification at reducing bias due to false-matches. Conclusions This study highlights the importance of evaluating the potential impact of linkage error on results. PII can help incorporate linkage uncertainty into analysis and reduce bias due to linkage error, without requiring identifiers.