Deterministic Record Linkage Research Articles

Exploring Multimorbidity Through the Use of Deterministic and Probabilistic Record Linkage of Data in Five Health Facilities in Nairobi

IntroductionData collected by Health Demographic Surveillance Systems (HDSSs) at the community level can support very powerful research questions if it was linked to records at health facilities. In particular, understanding multimorbidity and building predictive models for key health outcomes requires linked data collected both in the community and health facilities. Additionally, the linked data can assist health management teams at the national, county, and local levels in making evidence-based choices. Problemthe inability to link population level data with records at health facilities affects the ability to use data to solve key health research questions. lack of common identifiers affects the possibility of integrating clinic records with population-based data. MethodologyWe intend to link data from five health facilities in Nairobi County serving the population in the Nairobi Urban Health Demographic Surveillance System (NUHDSS) where longitudinal data was collected between 2001 and 2018. The longitudinal data is from a database of about 260,000 individuals. This will result in a merged dataset and will also provide an opportunity to assess the accuracy of the deterministic and probabilistic record linkage algorithm compared with other methods. Note: We will have results by the time the conference is happening as groundwork has been put. ConclusionThe growing burden of multimorbidity on the African continent requires creative and effective strategies of addressing the problem through utilization of existing longitudinal data. By merging clinic records with population-based data, this initiative will promote the use of data evidence informed decision making for interventions to address multimorbidity.

Capturing emergency dispatch address points as geocoding candidates to quantify delimited confidence in residential geolocation

BackgroundIn response to citizens’ concerns about elevated cancer incidence in their locales, US CDC proposed publishing cancer incidence at sub-county scales. At these scales, confidence in patients’ residential geolocation becomes a key constraint of geospatial analysis. To support monitoring cancer incidence in sub-county areas, we presented summary metrics to numerically delimit confidence in residential geolocation.ResultsWe defined a concept of Residential Address Discriminant Power (RADP) as theoretically perfect within all residential addresses and its practical application, i.e., using Emergency Dispatch (ED) Address Point Candidates of Equivalent Likelihood (CEL) to quantify Residential Geolocation Discriminant Power (RGDP) to approximate RADP. Leveraging different productivity of probabilistic, deterministic, and interactive geocoding record linkage, we simultaneously detected CEL for 5,807 cancer cases reported to North Carolina Central Cancer Registry (NC CCR)- in January 2022. Batch-match probabilistic and deterministic algorithms matched 86.0% cases to their unique ED address point candidates or a CEL, 4.4% to parcel site address, and 1.4% to street centerline. Interactively geocoded cases were 8.2%. To demonstrate differences in residential geolocation confidence between enumeration areas, we calculated sRGDP for cancer cases by county and assessed the existing uncertainty within the ED data, i.e., identified duplicate addresses (as CEL) for each ED address point in the 2014 version of the NC ED data and calculated ED_sRGDP by county. Both summary RGDP (sRGDP) (0.62–1.00) and ED_sRGDP (0.36–1.00) varied across counties and were lower in rural counties (p < 0.05); sRGDP correlated with ED_sRGDP (r = 0.42, p < 0.001). The discussion covered multiple conceptual and economic issues attendant to quantifying confidence in residential geolocation and presented a set of organizing principles for future work.ConclusionsOur methodology produces simple metrics – sRGDP – to capture confidence in residential geolocation via leveraging ED address points as CEL. Two facts demonstrate the usefulness of sRGDP as area-based summary metrics: sRGDP variability between counties and the overall lower quality of residential geolocation in rural vs. urban counties. Low sRGDP for the cancer cases within the area of interest helps manage expectations for the uncertainty in cancer incidence data. By supplementing cancer incidence data with sRGDP and ED_sRGDP, CCRs can demonstrate transparency in geocoding success, which may help win citizen trust.

Trends and perioperative mortality in gastric cancer surgery: a nationwide population‑based cohort study

This study aimed to investigate changes and perioperative mortality over a 6-year period within the Italian Hospital Information System among patients with gastric cancer (GC) who underwent gastrectomies and to identify risk factors associated with 90-day mortality. Additionally, nationwide differences between high and low-volume hospitals were evaluated. A nationwide retrospective study was conducted using patient hospital discharge records (HDRs) based on the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) classification. The HDRs were linked to the National Tax Registry records using deterministic record linkage. The data were obtained from the Italian National Outcomes Evaluation Programme (PNE). Multivariate logistic regression was used to examine risk factors for 90-day mortality among patients with GC who underwent partial or total gastrectomies over the period from 2018 to 2020 with adjustment for comorbidities. Overall, the number of patients with GC who underwent total or partial gastrectomies steadily decreased in Italy from 5765 in 2015 to 4291 in 2020 (p < 0.001). The use of the laparoscopic approach more than doubled from 2015 (10.8%) to 2020 (26.3%), with a concomitant conversion rate from laparoscopy to open surgery decreasing from 7.7 to 5.8%. The 30 and 90-day mortality rates remained stable over time (p > 0.05). Low-volume hospitals had higher inpatient, early, and late mortality compared to high-volume hospitals (5.9% vs 3.8%, 6.3% vs 3.8%, and 11.8% vs 7.9%, respectively; p < 0.001). Multivariate logistic regression analysis showed that an advanced age (adjusted odds ratio: 3.72; 95% [CI]: 3.15–4.39; p < 0.001), an open approach (adjusted-OR: 1.69, 95% CI: 1.43–1.99, p < 0.001) and a total gastrectomy (adjusted-OR: 1.44, 95% CI: 1.27–1.64, p < 0.001) were independent risk factors for 90-day mortality. Additionally, patients with GC who referred to high-volume hospitals were 26% less likely to die within 90 days after a gastrectomy than those who underwent surgery in low-volume hospitals. During the 6-year period, surgeons implemented a minimally invasive approach to reduce the conversion over time. Centralisation was associated with better outcomes while advanced age, an open approach, and total gastrectomy were identified as risk factors for 90-day mortality.

Prediction of Early and Long-Term Hospital Readmission in Patients with Severe Obesity: A Retrospective Cohort Study.

Adults with obesity have a higher risk of hospitalization and high hospitalization-related healthcare costs. However, a predictive model for the risk of readmission in patients with severe obesity is lacking. We conducted a retrospective cohort study enrolling all patients admitted for severe obesity (BMI ≥ 40 kg/m2) between 2009 and 2018 to the Istituto Auxologico Italiano in Piancavallo. For each patient, all subsequent hospitalizations were identified from the regional database by a deterministic record-linkage procedure. A total of 1136 patients were enrolled and followed up for a median of 5.7 years (IQR: 3.1-8.2). The predictive factors associated with hospital readmission were age (HR = 1.02, 95%CI: 1.01-1.03, p < 0.001), BMI (HR = 1.02, 95%CI: 1.01-1.03, p = 0.001), smoking habit (HR = 1.17, 95%CI: 0.99-1.38, p = 0.060), serum creatinine (HR = 1.22, 95%CI: 1.04-1.44, p = 0.016), diabetes (HR = 1.17, 95%CI: 1.00-1.36, p = 0.045), and number of admissions in the previous two years (HR = 1.15, 95%CI: 1.07-1.23, p < 0.001). BMI lost its predictive role when restricting the analysis to readmissions within 90 days. BMI and diabetes lost their predictive roles when further restricting the analysis to readmissions within 30 days. In conclusion, in this study, we identified predictive variables associated with early and long-term hospital readmission in patients with severe obesity. Whether addressing modifiable risk factors could improve the outcome remains to be established.

Record linkage for Routinely Collected Health Data in an African Health Information Exchange

IntroductionThe 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. AimThis 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. MethodsWe 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. ResultsThe 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.

Record linkage for routinely collected health data in an African health information exchange.

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.

Validating a novel deterministic privacy-preserving record linkage between administrative & clinical data: applications in stroke research.

Research 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. To determine the feasibility and validity of a deterministic privacy preserving data linkage protocol using homomorphically encrypted data. Feasibility 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. With 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. As 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.

9New metrics for assessing linkage quality in deterministic record linkage of health databases

Linkage among healthcare databases, claims, and external data collections such as registries, cohorts or clinical trials is becoming increasingly important for conducting epidemiological research. For example, the French National Health Insurance Information System (SNDS) contains claims-based data on dispensed drugs, on out-of-hospital laboratory tests and on outpatient medical consultations, but without tests results, diagnosis or even the symptoms motivating the patient's consultation. On the other hand, registries, cohorts or clinical trials collect medically validated data, which is time-consuming and costly. The linkage of the two sources could therefore overcome lack of information and limit confounding biases. However, processing record linkage on huge health databases and assessing linkage quality can be challenging. This paper introduces a deterministic record linkage strategy that focuses on assessing linkage quality using new quality metrics. We developed a deterministic linkage strategy that systematically considers all combinations of individual identifiers. An exhaustive exploration of all variable combinations makes it possible to compute a new metric, referred to as robustness, and to generate a linkage cartography that precisely summarizes the linked pair characteristics. This cartography is central to our approach and makes it possible for the expert to easily accept/reject groups of linked pairs. The approach was tested on synthetic datasets staging a variety of possible linkage scenarios (datasets size/ratio, overlap, and errors), and on two real-world studies (a registry database and a clinical trial). Dataset simulations demonstrated very good accuracy with a limited impact of different factors tested, scalability, and encouraging runtimes. Minima were greater than 0.95 for recall and greater than 0.99 for precision, whatever the scenario. Feasibility on real datasets was verified with good results: among 3985 patients from the registry, the algorithm found 3850 single linked pairs and 135 proposals with multiple candidates out of 504,795 candidates. After reviewing the linkage cartography, the expert validated 3783 linked pairs and a manual review of multiple candidates added 20 pairs, reaching a linkage rate of 95.4%. For the trial, only 2 records out of 129 were not linked among 22,426 candidates, as a result of early withdrawal (no information in the trial database) giving a linkage rate of 98.4%. In both cases, unlinked records did not seem to show any potential bias. Performance is good since linking a synthetic database holding 30,000 patients versus a synthetic database holding 3,000,000 patients takes a few seconds only. The approach is resilient by design to missing information and therefore well suited for linking the SNDS database to cohorts/registries when information overlap between the two databases cannot be perfect. Finally, our implementation is fast enough to interactively improve the linkage results through successive improved runs. The novelty of our approach is twofold: first, the linkage cartography provides a new way of classifying and comparing deterministic rules from the set of all possible rules and second, the approach is by design resilient to data corruption and can reach better recall than standard deterministic linkage strategies. Finally, good performance and scalability open the door to the linkage of very large datasets. Les auteurs n'ont pas précisé leurs éventuels liens d'intérêts.

Linking the Center for International Blood and Marrow Transplant Research Registry to the California Cancer Registry and California Hospital Patient Discharge Data

Advances in hematopoietic cell transplantation (HCT) have substantially improved patient survival, increasing the importance of studying outcomes and long-term adverse effects in the rapidly growing population of HCT survivors. Large-scale registry data from the Center for International Blood and Marrow Transplant Research (CIBMTR) are a valuable resource for studying mortality and late effects after HCT, providing detailed data reported by HCT centers on transplantation-related factors and key outcomes. This study was conducted to evaluate the robustness of CIBMTR outcome data and assess health-related outcomes and healthcare utilization among HCT recipients. We linked data from the CIBMTR for California residents with data from the population-based California Cancer Registry (CCR) and hospitalization information from the California Patient Discharge Database (PDD). In this retrospective cohort study, probabilistic and deterministic record linkage used key patient identifiers, such as Social Security number, ZIP code, sex, birth date, hematologic malignancy type and diagnosis date, and HCT type and date. Among 22,733 patients registered with the CIBMTR who underwent autologous or allogeneic HCT for hematologic malignancy between 1991 and 2016, 89.0% were matched to the CCR and/or PDD (n=17,707 [77.9%] for both, n=1179 [5.2%] for the CCR only, and n=1342 [5.9%] for the PDD only). Unmatched patients were slightly more likely to have undergone a first autologous HCT than an allogeneic HCT (12.6% versus 9.0%), to have a larger number of missing linkage identifiers, and to have undergone HCT prior to 2010. Among the patients reported to the CIBMTR who matched to the CCR, 85.7% demonstrated concordance of both hematologic malignancy type and diagnosis date across data sources. This linkage presents unparalleled opportunities to advance our understanding of HCT practices and patient outcomes.

Record linkage for Routinely Collected Health Data in an African Health Information Exchange.

Objectives&#x0D; &#x0D; To describe the record linkage system that is currently implemented at the Provincial Health Data Centre (PHDC) in the Western Cape, South Africa&#x0D; To assess its output to date with respect to types of matches and duplicates trends&#x0D; To describe the errors affecting patient matching&#x0D; &#x0D; ApproachWe 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. Matching records are established from sequentially running the data through multiple passes formed by various combinations of linkage variables. Descriptive analyses are used to estimate the extent of mismatches and duplication in the provincial patient master index (PMI).&#x0D; Results&#x0D; &#x0D; The proportion of duplicates dropped from approximately 16.8% in December 2015 to 9.6% in October 2020, indicating improved data linkage over time.&#x0D; Duplicates mainly arise from spelling errors, and surname and first names carry most of the errors, with different first names and surname for the same individual in approximately 22% of duplicates.&#x0D; Linkage is also affected by completeness, with less than 30 % completeness for the South African national identity (RSA ID) number which is mainly because RSA ID is not mandatory when seeking healthcare.&#x0D; &#x0D; ConclusionLinkage improvement could be due to improved registration practices. Further improvements are possible by repeating data linkage where patients register before creating a new patient record following a failed search. This could use the PHDC linkage approach whilst leveraging all data in addition to search terms used by the clerk.

Data linkage of German statutory health insurance claims data and care needs assessments preceding a population-based cohort study on nursing home admission

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...

Occupations and geographical distribution of mesothelioma in Switzerland 1989–2018 – record linkage of an asbestos-exposed population with the Swiss National Cohort

We investigated the possibility of linking the data of the Swiss Laboratory for Particle Analysis (Silag), a valuable but incomplete data source in the field of asbestos-related diseases, to the Swiss National Cohort (SNC). With the resulting comprehensive dataset, we intended to provide a source for further research in the field. We also conducted preliminary analyses of data focusing on occupations and regional distribution of malignant pleural mesothelioma cases. Data of asbestos-exposed individuals available from the Silag were anonymously linked with the SNC by means of deterministic record linkage. From this linkage, data on occupation classified according to the international standard classification of occupations (ISCO) as well as the canton of residence in Switzerland could be retrieved. Of 838 eligible individuals from the Silag data, 788 (94.0%) could be linked to the SNC database, including 476 mesothelioma cases. In 340 cases of the latter, data on occupation and industries were available. Although the majority of them were blue-collar workers, a significant proportion (n = 44, 12.9%) had executive professions. The Canton of residence in 1990 was established in 430 of subjects with mesothelioma. A cluster could be identified in eastern Switzerland, especially in the canton of Glarus. It was possible to link the datasets to a large extent thereby creating a data source for further research. Of note, the linkage provided data on occupation of a selection of mesothelioma cases in Switzerland.

Optimizing the Retrieval of the Vital Status of Cancer Patients for Health Data Warehouses by Using Open Government Data in France.

Electronic Medical Records (EMR) and Electronic Health Records (EHR) are often missing critical information about the death of a patient, although it is an essential metric for medical research in oncology to assess survival outcomes, particularly for evaluating the efficacy of new therapeutic approaches. We used open government data in France from 1970 to September 2021 to identify deceased patients and match them with patient data collected from the Institut de Cancérologie de l’Ouest (ICO) data warehouse (Integrated Center of Oncology—the third largest cancer center in France) between January 2015 and November 2021. To meet our objective, we evaluated algorithms to perform a deterministic record linkage: an exact matching algorithm and a fuzzy matching algorithm. Because we lacked reference data, we needed to assess the algorithms by estimating the number of homonyms that could lead to false links, using the same open dataset of deceased persons in France. The exact matching algorithm allowed us to double the number of dates of death in the ICO data warehouse, and the fuzzy matching algorithm tripled it. Studying homonyms assured us that there was a low risk of misidentification, with precision values of 99.96% for the exact matching and 99.68% for the fuzzy matching. However, estimating the number of false negatives proved more difficult than anticipated. Nevertheless, using open government data can be a highly interesting way to improve the completeness of the date of death variable for oncology patients in data warehouses

Long term mortality and morbidity of Italian soldiers after deployment in Iraq as related to biomarkers assessment: Results of the SIGNUM study

IntroductionThe health profile of military veterans deployed in foreign operative theatres was assessed by several international studies because of potential exposure to depleted uranium and other pollutants. Here we reported results of 15-year epidemiological surveillance assessing long-term health effects in a cohort of Italian soldiers deployed in Iraq in 2004–2005 and participating in a biomonitoring campaign to identify potential genotoxic exposure to environmental xenobiotics before and after deployment (n = 981, SIGNUM cohort). MethodsWe evaluated mortality and hospitalization risks of the SIGNUM cohort retrospectively until 2016 and 2018 respectively. A wide cohort of military personnel never deployed abroad (n = 114,260) and the general Italian population were used as control populations in risk assessment. Causes of death and diagnoses of hospitalization were derived through deterministic record linkage with official national databases of mortality and hospital discharge. Standardized Mortality Ratio (SMR) and Standardized Hospitalization Ratio (SHR) were computed adjusting according to sex, age, area of birth, and calendar year. Differential pre-post deployment in xenobiotics concentrations and early effect biomarkers (oxidative DNA alterations and micronuclei) measured in blood serum were analysed in relation to cancer hospitalization. ResultsMortality risk due to pathologies was more than halved compared to the general population (SMR = 0.41, 95% CI 0.11–1.05) and not significantly different compared to soldiers never deployed abroad (SMR = 0.69, 95% CI 0.19–1.68).Similarly overall hospitalization risk due to pathologies was decreased with respect to the general population (SHR = 0.86, 95% CI 0.80–0.92) and comparable to the control military group (SHR = 0.99, 95% CI: 0.93–1.06). For haematological cancers a decreased hospitalization risk compared to the Italian general population was observed (SHR = 0.38, 95% CI 0–0.92). No statistically significant differences emerged in the patterns of biomarkers in association with cancer hospitalization. ConclusionThe study confirms the so called ‘healthy warrior’ effect for the SIGNUM veterans and showed no correlation between cancer occurrence and biomonitoring markers measured on field.

A retrospective cohort study of outcomes in hospitalised COVID-19 patients during the first pandemic wave in Ireland.

AimDescribe the epidemiology, resource use and adverse outcomes of COVID-19 patients hospitalised during wave 1 of the COVID-19 pandemic in Ireland. Use this data to identify specific cohorts at high risk of adverse outcomes and to inform acute hospital requirements for future COVID-19 waves in Ireland.MethodsThe Health Service Executive’s (HSE) Hospital Inpatient Enquiry (HIPE) system produced a daily database of COVID-19 discharge episodes from the onset of the COVID-19 pandemic in Ireland. This study analysed data on episodes of COVID-19 hospitalisation recorded between February 29 and July 31, 2020. A deterministic record linkage process transformed records from episode to patient level. Logistic regression modelling identified factors associated with long length of stay (LLOS), intensive care unit (ICU) admission and inhospital mortality.ResultsMedian length of stay was 9 days; 12.8% of patients had ICU admission and 16.6% died in hospital. Male patients were more likely to have ICU admission and die in hospital. Likelihood of LLOS and inhospital mortality increased with age. Obesity, hypertension and diabetes were associated with ICU admission while chronic kidney disease and chronic obstructive pulmonary disease were associated with inhospital mortality. Nursing home residents were less likely to be admitted to ICU and more likely to die in hospital compared to patients admitted from home.ConclusionThis study provides patient-level epidemiological characterisation of hospitalisations during the first COVID-19 pandemic wave in Ireland. The higher risk of adverse outcomes in older age groups supports the age-based prioritisation of COVID-19 vaccinations currently used in Ireland.

Abstract 12016: Surgical Mitral Valve Repair for Degenerative Mitral Regurgitation: Defining the Benchmark for Trans-Catheter Options

Introduction: With the increasing trend of percutaneous treatment of mitral regurgitation (MR), defining the standard of care becomes crucial. The optimal treatment of degenerative MR has to provide excellent immediate and long-term outcomes. With this study we aim to present our results of surgical repair of degenerative MR. Methods: From January 2006 to December 2017, 4195 patients (median age 57 years [IQR 46;67], 68.9% men) underwent mitral valve repair (MVr) for degenerative MR at our Institution. The outcomes were retrieved using a deterministic record linkage with the National Health System, the National Tax Register and the clinical database. The follow-up was 100% complete. A Cox regression model was used to identify factors influencing outcomes. Kaplan-Meier estimates were used to assess survival. Competing risk analysis (CIF) with death as competing risk was used to assess the incidence of major adverse cardiovascular events and mitral valve (MV) reoperation. Results: The median EF was 60% [IQR 60;65], 17.8% of patients were in atrial fibrillation and 11.6% were in NYHA ≥3. Prolapse was posterior in 70.7%, bileaflet in 20.7%, and anterior in 8.5%. A resection technique was the most used approach (60.3%), followed by edge-to-edge (26.8%) and chordal implantation (9.2%). Thirty-day mortality was 0.5% (0.24% in isolated MVr cohort). At 13 years, overall survival was 86±13% (isolated MVr 90±10% vs non-isolated MVr 78±22% p &lt;0.0001). At multivariate analysis male sex, age, cancer, previous admission for CHF, NYHA class ≥3, complex surgery, CPB time, MR &gt;1+ at discharge and in-hospital stay were predictors of long-term mortality. At 13 years, CIF of rehospitalization for heart failure was 5.7±0.35% and CIF of MV reoperation was 1.9±0.2%. At multivariate analysis age, prolapse of the anterior leaflet and need of second pump run independently predicted reoperation. Conclusions: Surgical MVr can be performed with very low in-hospital mortality and excellent long-term survival in high-volume centers. At 13-years, in our series the rate of rehospitalization for heart failure was very low and MV reoperation was rare. These results can be used as surgical benchmark for percutaneous treatment of degenerative MR.

Gender-based homophily in research: A large-scale study of man-woman collaboration

We examined the male-female collaboration practices of all internationally visible Polish university professors (N = 25,463) based on their Scopus-indexed publications from 2009-2018 (158,743 journal articles). We merged a national registry of 99,935 scientists (with full administrative and biographical data) with the Scopus publication database, using probabilistic and deterministic record linkage. Our unique biographical, administrative, publication, and citation database (The Polish Science Observatory) included all professors with at least a doctoral degree employed in 85 research-involved universities. We determined what we term an individual publication portfolio for every professor, and we examined the respective impacts of biological age, academic position, academic discipline, average journal prestige, and type of institution on the same-sex collaboration ratio. The gender homophily principle (publishing predominantly with scientists of the same sex) was found to apply to male scientists - but not to females. The majority of male scientists collaborate solely with males; most female scientists, in contrast, do not collaborate with females at all. Across all age groups studied, all-female collaboration is marginal, while all-male collaboration is pervasive. Gender homophily in research-intensive institutions proved stronger for males than for females. Finally, we used a multi-dimensional fractional logit regression model to estimate the impact of gender and other individual-level and institutional-level independent variables on gender homophily in research collaboration.

Record linkage under suboptimal conditions for data-intensive evaluation of primary care in Rio de Janeiro, Brazil

BackgroundLinking Brazilian databases demands the development of algorithms and processes to deal with various challenges including the large size of the databases, the low number and poor quality of personal identifiers available to be compared (national security number not mandatory), and some characteristics of Brazilian names that make the linkage process prone to errors. This study aims to describe and evaluate the quality of the processes used to create an individual-linked database for data-intensive research on the impacts on health indicators of the expansion of primary care in Rio de Janeiro City, Brazil.MethodsWe created an individual-level dataset linking social benefits recipients, primary health care, hospital admission and mortality data. The databases were pre-processed, and we adopted a multiple approach strategy combining deterministic and probabilistic record linkage techniques, and an extensive clerical review of the potential matches. Relying on manual review as the gold standard, we estimated the false match (false-positive) proportion of each approach (deterministic, probabilistic, clerical review) and the missed match proportion (false-negative) of the clerical review approach. To assess the sensitivity (recall) to identifying social benefits recipients’ deaths, we used their vital status registered on the primary care database as the gold standard.ResultsIn all linkage processes, the deterministic approach identified most of the matches. However, the proportion of matches identified in each approach varied. The false match proportion was around 1% or less in almost all approaches. The missed match proportion in the clerical review approach of all linkage processes were under 3%. We estimated a recall of 93.6% (95% CI 92.8–94.3) for the linkage between social benefits recipients and mortality data.ConclusionThe adoption of a linkage strategy combining pre-processing routines, deterministic, and probabilistic strategies, as well as an extensive clerical review approach minimized linkage errors in the context of suboptimal data quality.

Exploring the Epidemiology of Cancer after Solid Organ Transplantation (EpCOT): an observational cohort study

IntroductionSolid organ transplant patients are counselled regarding increased risk of cancer (principally due to their need for lifelong immunosuppression) and it ranks as one of their biggest self-reported worries. Post-transplantation...

Hybrid Approach to Estimation of Underreporting of Tuberculosis Case Notification in High-Burden Settings With Weak Surveillance Infrastructure: Design and Implementation of an Inventory Study

BackgroundThe greatest risk of infectious disease undernotification occurs in settings with limited capacity to detect it reliably. World Health Organization guidance on the measurement of misreporting is paradoxical, requiring robust, independent systems to assess surveillance rigor. Methods are needed to estimate undernotification in settings with incomplete, flawed, or weak surveillance systems. This study attempted to design a tuberculosis (TB) inventory study that balanced rigor with feasibility for high-need settings.ObjectiveThis study aims to design a hybrid TB inventory study for contexts without World Health Organization preconditions. We estimated the proportion of TB cases that were not reported to the Ministry of Health in 2015. The study sought to describe TB surveillance coverage and quality at different levels of TB care provision. Finally, we aimed to identify structural-, facility-, and provider-level barriers to notification and reasons for underreporting, nonreporting, and overreporting.MethodsRetrospective partial digitalization of paper-based surveillance and facility records preceded deterministic and probabilistic record linkage; a hybrid of health facilities and laboratory census with a stratified sampling of HFs with no capacity to notify leveraged a priori knowledge. Distinct extrapolation methods were applied to the sampled health facilities to estimate bacteriologically confirmed versus clinical TB. In-depth interviews and focus groups were used to identify causal factors responsible for undernotification and test the acceptability of remedies.ResultsThe hybrid approach proved viable and instructive. High-specificity verification of paper-based records in the field was efficient and had minimal errors. Limiting extrapolation to clinical cases improved precision. Probabilistic record linkage is computationally intensive, and the choice of software influences estimates. Record absence, decay, and overestimation of the private sector TB treatment behavior threaten validity, meriting mitigation. Data management demands were underestimated. Treatment success was modest in all sectors (R=37.9%–72.0%) and did not align with treatment success reported by the state (6665/8770, 75.99%). One-fifth of TB providers (36/178, 20%) were doubtful that the low volume of patients with TB treated in their facility merited mastery of the extensive TB notification forms and procedures.ConclusionsSubnational inventory studies can be rigorous, relevant, and efficient in countries that need them even in the absence of World Health Organization preconditions, if precautions are taken. The use of triangulation techniques, with minimal recourse to sampling and extrapolation, and the privileging of practical information needs of local decision makers yield reasonable misreporting estimates and viable policy recommendations.