Dataset Information

Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study.

ABSTRACT: BACKGROUND: Administrative data are a valuable source of estimates of diabetes prevalence for groups such as coronary heart disease (CHD) patients. The primary aim of this study was to measure concordance between medical records and linked administrative health data for recording diabetes in CHD patients, and to assess temporal differences in concordance. Secondary aims were to determine the optimal lookback period for identifying diabetes in this patient group, whether concordance differed for Indigenous people, and to identify predictors of false positives and negatives in administrative data. METHODS: A population representative sample of 3943 CHD patients hospitalized in Western Australia in 1998 and 2002-04 were selected, and designated according to the International Classification of Diseases (ICD) version in use at the time (ICD-9 and ICD-10 respectively). Crude prevalence and concordance were compared for the two samples. Concordance measures were estimated from administrative data comparing diabetes status recorded on the selected CHD admission ('index admission') and on any hospitalization in the previous 1, 2, 5, 10 or 15 years, against hospital medical records. Potential modifiers of agreement were determined using chi-square tests and multivariable logistic regression models. RESULTS: Identification of diabetes on the index CHD admission was underestimated more in the ICD-10 than ICD-9 sample (sensitivity 81.5% versus 91.1%, underestimation 15.1% versus 4.4% respectively). Sensitivity increased to 89.6% in the ICD-10 period using at least 10 years of hospitalization history. Sensitivity was higher and specificity lower in Indigenous patients, and followed a similar pattern of improving concordance with increasing lookback period. Characteristics associated with false negatives for diabetes on the index CHD hospital admission were elective admission, in-hospital death, principal diagnosis, and in the ICD-10 period only, fewer recorded comorbidities. CONCLUSIONS: The accuracy of identifying diabetes status in CHD patients is improved in linked administrative health data by using at least 10 years of hospitalization history. Use of this method would reduce bias when measuring temporal trends in diabetes prevalence in this patient group. Concordance measures are as reliable in Indigenous as non-Indigenous patients.

SUBMITTER: Nedkoff L

PROVIDER: S-EPMC3849847 | biostudies-other | 2013

REPOSITORIES: biostudies-other

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Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study.

Nedkoff Lee L Knuiman Matthew M Hung Joseph J Sanfilippo Frank M FM Katzenellenbogen Judith M JM Briffa Tom G TG

BMC medical research methodology 20131001

<h4>Background</h4>Administrative data are a valuable source of estimates of diabetes prevalence for groups such as coronary heart disease (CHD) patients. The primary aim of this study was to measure concordance between medical records and linked administrative health data for recording diabetes in CHD patients, and to assess temporal differences in concordance. Secondary aims were to determine the optimal lookback period for identifying diabetes in this patient group, whether concordance differ ...[more]

PMID: 24079345

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Project description:BACKGROUND:Administrative health records (AHRs) and electronic medical records (EMRs) are two key sources of population-based data for disease surveillance, but misclassification errors in the data can bias disease estimates. Methods that combine information from error-prone data sources can build on the strengths of AHRs and EMRs. We compared bias and error for four data-combining methods and applied them to estimate hypertension prevalence. METHODS:Our study included rule-based OR and AND methods that identify disease cases from either or both data sources, respectively, rule-based sensitivity-specificity adjusted (RSSA) method that corrects for inaccuracies using a deterministic rule, and probabilistic-based sensitivity-specificity adjusted (PSSA) method that corrects for error using a statistical model. Computer simulation was used to estimate relative bias (RB) and mean square error (MSE) under varying conditions of population disease prevalence, correlation amongst data sources, and amount of misclassification error. AHRs and EMRs for Manitoba, Canada were used to estimate hypertension prevalence using validated case definitions and multiple disease markers. RESULTS:The OR method had the lowest RB and MSE when population disease prevalence was 10%, and the RSSA method had the lowest RB and MSE when population prevalence increased to 20%. As the correlation between data sources increased, the OR method resulted in the lowest RB and MSE. Estimates of hypertension prevalence for AHRs and EMRs alone were 30.9% (95% CI: 30.6-31.2) and 24.9% (95% CI: 24.6-25.2), respectively. The estimates were 21.4% (95% CI: 21.1-21.7), for the AND method, 34.4% (95% CI: 34.1-34.8) for the OR method, 32.2% (95% CI: 31.8-32.6) for the RSSA method, and ranged from 34.3% (95% CI: 34.1-34.5) to 35.9% (95% CI, 35.7-36.1) for the PSSA method, depending on the statistical model. CONCLUSIONS:The OR and AND methods are influenced by correlation amongst the data sources, while the RSSA method is dependent on the accuracy of prior sensitivity and specificity estimates. The PSSA method performed well when population prevalence was high and average correlations amongst disease markers was low. This study will guide researchers to select a data-combining method that best suits their data characteristics.

Project description:Between 2012 and 2017, N = 2814 youth between the ages of 4 and 20 were in child protective services (CPS) custody in Hamilton County, Ohio, and placed in out-of-home care. Child welfare administrative records were extracted and linked to electronic health records for all encounters at Cincinnati Children's Hospital Medical Center, with n = 2787 (99.1%) of records successfully linked prior to de-identifying the data for research purposes. Child welfare administrative data fields in the dataset include demographics, dates of entry into and exit from protective custody and out-of-home care, reasons for entry into custody, dates of placement changes, reasons for placement changes, and types of placement (e.g., foster home, kinship home, group home, residential treatment, independent living). Electronic health records (EHR) data fields include demographics, all inpatient and outpatient encounters with medications, diagnoses, screening results, laboratory test results, flowsheet data, and problem list entries. Data have been coded to capture broader categories of health needs and encounter details, medications, and other health concerns. Due to the high representation of children in CPS custody and out-of-home care who are also represented in the EHR data, this dataset provides a comprehensive view of the medical needs and health concerns for school-aged children in CPS custody in an entire county. As a result, these data can be useful for understanding the emergence of global and specific health concerns, frequency of healthcare use, and placement stability for all youth in CPS custody in this community, accounting for variation due to other health and child welfare factors. These data are likely generalizable to other mid-sized urban communities where academic medical centers provide healthcare for children in CPS custody. De-identified data may be made available to other researchers with approved data transfer agreements between academic institutions in place.

Project description:BACKGROUND:Despite the increasing use of medical records to measure quality of care, studies have shown that their validity is suboptimal. The objective of this study is to assess the concordance of cardiovascular care processes evaluated through medical record review and patient self-administered questionnaires (SAQs) using ten quality indicators (TRANSIT indicators). These indicators were developed as part of a participatory research program (TRANSIT study) dedicated to TRANSforming InTerprofessional clinical practices to improve cardiovascular disease (CVD) prevention in primary care. METHODS:For every patient participating in the TRANSIT study, the compliance to each indicator (individual scores) as well as the mean compliance to all indicators of a category (subscale scores) and to the complete set of ten indicators (overall scale score) were established. Concordance between results obtained using medical records and patient SAQs was assessed by prevalence-adjusted bias-adjusted kappa (PABAK) coefficients as well as intraclass correlation coefficients (ICCs) and 95% confidence intervals (95% CI). Generalized linear mixed models (GLMM) were used to identify patients' sociodemographic and clinical characteristics associated with agreement between the two data sources. RESULTS:The TRANSIT study was conducted in a primary care setting among patients (n?=?759) with multimorbidity, at moderate (16%) and high risk (83%) of cardiovascular diseases. Quality of care, as measured by the TRANSIT indicators, varied substantially between medical records and patient SAQ. Concordance between the two data sources, as measured by ICCs (95% CI), was poor for the subscale (0.18 [0.08-0.27] to 0.46 [0.40-0.52]) and overall (0.46 [0.40-0.53]) compliance scale scores. GLMM showed that agreement was not affected by patients' characteristics. CONCLUSIONS:In quality improvement strategies, researchers must acknowledge that care processes may not be consistently recorded in medical records. They must also be aware that the evaluation of the quality of care may vary depending on the source of information, the clinician responsible of documenting the interventions, and the domain of care.

Dataset Information

Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study.

Publications

Concordance between administrative health data and medical records for diabetes status in coronary heart disease patients: a retrospective linked data study.

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