Surname lists to identify South Asian and Chinese ethnicity from secondary data in Ontario, Canada: a validation study

<p>Abstract</p> <p>Background</p> <p>Surname lists are useful for identifying cohorts of ethnic minority patients from secondary data sources. This study sought to develop and validate lists to identify people of South Asian and Chinese origin.</p> <p>Methods</p> <p>Comprehensive lists of South Asian and Chinese surnames were reviewed to identify those that uniquely belonged to the ethnic minority group. Surnames that were common in other populations, communities or ethnic groups were specifically excluded. These surname lists were applied to the Registered Persons Database, a registry of the health card numbers assigned to all residents of the Canadian province of Ontario, so that all residents were assigned to South Asian ethnicity, Chinese ethnicity or the General Population. Ethnic assignment was validated against self-identified ethnicity through linkage with responses to the Canadian Community Health Survey.</p> <p>Results</p> <p>The final surname lists included 9,950 South Asian surnames and 1,133 Chinese surnames. All 16,688,384 current and former residents of Ontario were assigned to South Asian ethnicity, Chinese ethnicity or the General Population based on their surnames. Among 69,859 respondents to the Canadian Community Health Survey, both lists performed extremely well when compared against self-identified ethnicity: positive predictive value was 89.3% for the South Asian list, and 91.9% for the Chinese list. Because surnames shared with other ethnic groups were deliberately excluded from the lists, sensitivity was lower (50.4% and 80.2%, respectively).</p> <p>Conclusions</p> <p>These surname lists can be used to identify cohorts of people with South Asian and Chinese origins from secondary data sources with a high degree of accuracy. These cohorts could then be used in epidemiologic and health service research studies of populations with South Asian and Chinese origins.</p

Use of linked electronic health records to evaluate cardiovascular risk prediction models in Ontario, Canada

Introduction Electronic health records (EHR) contain individual-level clinical information not found in traditional administrative databases. As part of the CANHEART-Strategy for Patient Oriented Research (SPOR) initiative, we created a linked EHR-administrative data cohort that enables us to measure the Framingham and ACC/AHA Pooled Cohort cardiovascular risk prediction scores in Ontario, Canada. Objectives and Approach An EHR primary care cohort was created using the Electronic Medical Record Administrative data Linked Database (EMRALD) database, which contains the blood pressure and lipid values, weight and height measures, prescriptions and smoking status of up to 350,000 patients in Ontario, Canada. We enriched the lipid information through linkage to the Ontario Laboratory Information System, which is a repository of 90%+ of all lipid tests in Ontario. Individual-level information on co-morbidities, hospitalizations and mortality attributed to cardiovascular causes (e.g. myocardial infarction, stroke, cardiovascular mortality) were obtained through linkage to provincial health administrative and vital statistics databases using CANHEART methodology (www.canheart.ca). Results Patients were entered into the cohort between 2008 and 2014 if they had measurements for blood pressure and lipids (total cholesterol and high-density lipoprotein) taken within a year of each other during this accrual window. The earliest such group of values was chosen and determined the individual’s index date. Age, sex, smoking, diabetes and anti-hypertensive treatment status were extracted from EHR or administrative data to calculate the two scores. Patients were excluded if not aged 40-75 on the index date or if they had a history of cardiovascular disease. A cohort of 84,628 Ontario residents (mean age 55.0 years) had the elements required to calculate both scores. Follow-up for outcome events were done through record linkage to the end of 2014, with a mean follow-up of 3.62 years. Conclusion/Implications The creation of this cohort will allow for the validation of the Framingham and AHA/ACC Pooled Cohort equations in the diverse Ontario population. It would also enable the possible development of a new ‘made-in-Canada’ cardiovascular risk prediction model

Effect of Cardiac and Noncardiac Conditions on Survival After Defibrillator Implantation

ObjectivesWe sought to examine outcomes in recipients of implantable cardioverter-defibrillators (ICDs) and the effect of age, gender, and comorbidities on survival.BackgroundAge, gender, and comorbidities may significantly affect outcomes in ICD recipients.MethodsWe examined factors associated with mortality in 2,467 ICD recipients in Ontario, Canada, using a province-wide database. Comorbidities were identified retrospectively by examining all diagnosis codes within the 3 years before implant.ResultsMean ages at ICD implant were 63.2 ± 12.5 years (1,944 men) and 59.8 ± 15.9 years (523 women). Mortality rates at one and 2 years were 7.8% and 14.0%. Older age at implant increased the risk of death with hazard ratios (HR) of 2.05 (95% confidence interval [CI] 1.70 to 2.47) and 3.00 (95% CI 2.43 to 3.71) for those 65 to 74 years and ≥75 years, respectively (both p < 0.001), but gender was not a predictor of death. Common noncardiac conditions associated with death included peripheral vascular disease (adjusted HR 1.50, 95% CI 1.18 to 1.91), pulmonary disease (adjusted HR 1.35, 95% CI 1.10 to 1.66), and renal disease (adjusted HR 1.57, 95% CI 1.25 to 1.99). Many ICD recipients had prior heart failure (46.2%) with an increased HR of 2.33 for death (95% CI 1.96 to 2.76; p < 0.001). Greater comorbidity burden conferred increased risk, with HRs adjusted for age, gender, and heart failure of 1.72 (95% CI 1.44 to 2.05), 2.79 (95% CI 2.15 to 3.62), and 2.98 (95% CI 1.74 to 5.10) for those with 1, 2, and 3 or more noncardiac comorbidities, respectively (all p < 0.001).ConclusionsAge, noncardiac comorbidities, and prior heart failure influence survival outcomes in ICD recipients. These factors should be considered in the care of ICD recipients

Internal and External Data Linkage of Complex Relational Database: Results from CorHealth Ontario

Introduction CorHealth Ontario, formerly Cardiac Care Network (CCN), maintains a registry of patients undergoing select cardiac procedures/surgeries in Ontario, Canada. This population-based database contains over 35 datasets with complex structure, linked by unique primary key or multiple keys. Objectives and Approach We aimed to simplify the complex CorHealth database so that research analysts could create study cohorts more efficiently and effectively, and to enrich the study cohort by getting more clinical information through database linkage. Through internal linkage, we could combine clinical fields from multiple CorHealth datasets. While the CorHealth dataset may not have all the clinical information needed for a given study, we may link the CorHealth study cohort externally to other administration databases to obtain additional fields via the probability matching (i.e., identical patient ID, hospital ID and procedure/surgery date). Results After identifying the primary keys on the relational database flowchart, we designed new data structures by combining similar topic datasets. The total number of datasets was reduced from 35 to 13. This simplified CorHealth dataset includes one main CorHealth dataset (including demographic information, referral data, comorbidities) plus 12 other linkable specific datasets (including stent, vessel, TAVI, STEMI). Through internal linkage, we can get the stent numbers, lengths and types of Percutaneous Coronary Interventions from the Stent dataset. Linking to Discharge Abstract Database (DAD), we can get the hospital length of stay and the episode of care of hospital transfer for each procedure; linking to The Ontario Health Insurance Plan database (OHIP), we can find the graft numbers and vessel types of Coronary Artery Bypass Graft. Conclusion/Implications To improve the research capacity and increase the value of the CorHealth database, analysts could create enhanced cardiovascular study cohorts derived from the simplified CorHealth database, plus internal linkage from other CorHealth datasets, and external data linkage from population-based administrative sources. We have accomplished three reports (PCI/CABG/TAVI) accordingly in 2017/18