Systematic Review and Critical Appraisal of Validation Studies to Identify Rheumatic Diseases in Health Administrative Databases

Widdifield, Jessica; Labrecque, Jeremy; Lix, Lisa M.; Paterson, J. Michael; Bernatsky, Sasha; Tu, Karen; Ivers, Noah; Bombardier, Claire

doi:10.1002/acr.21993

Cited by 61 publications

(92 citation statements)

References 39 publications

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“…Multiple administrative data algorithms using combinations of physician billing claims, hospitalization diagnoses and/or pharmacy prescription data were tested and validated for PKM within the administrative data. We used the modified Standards for Reporting of Diagnostic Accuracy (STARD) criteria to report our findings [7] and our methodology follows recent recommendations on the reporting and study design of administrative data validation studies [6,12,13]. …”

Section: Methodsmentioning

confidence: 99%

“…A discriminating test would have sensitivity, specificity, PPV and NPV close to 100%, and Youden's index and kappa close to 1.0. We selected the optimal algorithms according to high PPV while also maximizing sensitivity and specificity in order to create a more homogeneous sample and avoid detecting false PKM cases [13]. EMR disease prevalence was calculated as the number of patients with PKM divided by the total number of eligible patients.…”

Section: Methodsmentioning

confidence: 99%

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A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

Butt

Young

et al. 2014

Neuroepidemiology

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Background: Epidemiological studies for identifying patients with Parkinson's disease (PD) or Parkinsonism (PKM) have been limited by their nonrandom sampling techniques and mainly veteran populations. This reduces their use for health services planning. The purpose of this study was to validate algorithms for the case ascertainment of PKM from administrative databases using primary care patients as the reference standard. Methods: We conducted a retrospective chart abstraction using a random sample of 73,003 adults aged ≥20 years from a primary care Electronic Medical Record Administrative data Linked Database (EMRALD) in Ontario, Canada. Physician diagnosis in the EMR was used as the reference standard and population-based administrative databases were used to identify patients with PKM from the derivation of algorithms. We calculated algorithm performance using sensitivity, specificity, and predictive values and then determined the population-level prevalence and incidence trends with the most accurate algorithms. Results: We selected, ‘2 physician billing codes in 1 year' as the optimal administrative data algorithm in adults and seniors (≥65 years) due to its sensitivity (70.6-72.3%), specificity (99.9-99.8%), positive predictive value (79.5-82.8%), negative predictive value (99.9-99.7%), and prevalence (0.28-1.20%), respectively. Conclusions: Algorithms using administrative databases can reliably identify patients with PKM with a high degree of accuracy.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

Butt

Young

et al. 2014

Neuroepidemiology

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“…We applied the most current and validated OA case definition for administrative data as our base case (3,8,14,19) based on the following criteria: at least 1 OA-related hospitalization (DAD), or at least 2 OA-related physician visits within 2 years (claims), or at least 2 OA-related ambulatory care visits within 2 years…”

Section: Methodsmentioning

confidence: 99%

“…We explored alternative case definitions (Table 1) based on previously published research (3,8,14,19) and their impact on OA estimates. OA visits to providers and services that were unlikely to confirm an OA diagnosis (e.g., mammography visits) were excluded based on direct input from experienced clinicians and health services researchers with expertise in administrative data analysis (CB and DM).…”

Section: Significance and Innovationsmentioning

confidence: 99%

“…Accurate estimates of disease incidence and prevalence are needed for developing accurate high-quality forecasts of future health resource requirements. Administrative health databases are an efficient data source for population-based estimates and are increasingly being used to obtain estimates of disease burden, quality of care, and resource use (14,15). The work of the Canadian Rheumatology Administrative Data (CAN-RAD) Network to advance the science of administrative health data research in arthritis applications offers best practices and algorithms for accurate case identification (13).…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Burden of Osteoarthritis to Plan for the Future

Marshall

Vanderby

Barnabé

et al. 2015

Arthritis Care & Research

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Objective. With aging and obesity trends, the incidence and prevalence of osteoarthritis (OA) is expected to rise in Canada, increasing the demand for health resources. Resource planning to meet this increasing need requires estimates of the anticipated number of OA patients. Using administrative data from Alberta, we estimated OA incidence and prevalence rates and examined their sensitivity to alternative case definitions. Methods. We identified cases in a linked data set spanning 1993 to 2010 (population registry, Discharge Abstract Database, physician claims, Ambulatory Care Classification System, and prescription drug data) using diagnostic codes and drug identification numbers. In the base case, incident cases were captured for patients with an OA diagnostic code for at least 2 physician visits within 2 years or any hospital admission. Seven alternative case definitions were applied and compared. Results. Age-and sex-standardized incidence and prevalence rates were estimated to be 8.6 and 80.3 cases per 1,000 population, respectively, in the base case. Physician claims data alone captured 88% of OA cases. Prevalence rate estimates required 15 years of longitudinal data to plateau. Compared to the base case, estimates are sensitive to alternative case definitions. Conclusion. Administrative databases are a key source for estimating the burden and epidemiologic trends of chronic diseases such as OA in Canada. Despite their limitations, these data provide valuable information for estimating disease burden and planning health services. Estimates of OA are mostly defined through physician claims data and require a long period of longitudinal data.

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Accuracy of Canadian Health Administrative Databases in Identifying Patients With Rheumatoid Arthritis: A Validation Study Using the Medical Records of Rheumatologists

Widdifield

Bernatsky

Paterson

et al. 2013

Arthritis Care & Research

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129

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Objective. Health administrative data can be a valuable tool for disease surveillance and research. Few studies have rigorously evaluated the accuracy of administrative databases for identifying rheumatoid arthritis (RA) patients. Our aim was to validate administrative data algorithms to identify RA patients in Ontario, Canada. Methods. We performed a retrospective review of a random sample of 450 patients from 18 rheumatology clinics. Using rheumatologist-reported diagnosis as the reference standard, we tested and validated different combinations of physician billing, hospitalization, and pharmacy data. Results. One hundred forty-nine rheumatology patients were classified as having RA and 301 were classified as not having RA based on our reference standard definition (study RA prevalence 33%). Overall, algorithms that included physician billings had excellent sensitivity (range 94 -100%). Specificity and positive predictive value (PPV) were modest to excellent and increased when algorithms included multiple physician claims or specialist claims. The addition of RA medications did not significantly improve algorithm performance. The algorithm of "(1 hospitalization RA code ever) OR (3 physician RA diagnosis codes [claims] with >1 by a specialist in a 2-year period)" had a sensitivity of 97%, specificity of 85%, PPV of 76%, and negative predictive value of 98%. Most RA patients (84%) had an RA diagnosis code present in the administrative data within ؎1 year of a rheumatologist's documented diagnosis date. Conclusion. We demonstrated that administrative data can be used to identify RA patients with a high degree of accuracy. RA diagnosis date and disease duration are fairly well estimated from administrative data in jurisdictions of universal health care insurance.

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Systematic Review and Critical Appraisal of Validation Studies to Identify Rheumatic Diseases in Health Administrative Databases

Cited by 61 publications

References 39 publications

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

A Validation Study of Administrative Data Algorithms to Identify Patients with Parkinsonism with Prevalence and Incidence Trends

Estimating the Burden of Osteoarthritis to Plan for the Future

Accuracy of Canadian Health Administrative Databases in Identifying Patients With Rheumatoid Arthritis: A Validation Study Using the Medical Records of Rheumatologists

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