Article

Data Quality in an Information-Rich Environment: Canada as an Example

Manitoba Centre for Health Policy, Department of Community Health Sciences, Faculty of Medicine, University of Manitoba, 4th Floor Brodie Centre, Room 408, 727 McDermot Avenue, Winnipeg, MB, R3E 3P5, Canada.
Canadian journal on aging = La revue canadienne du vieillissement (Impact Factor: 0.92). 02/2005; 24 Suppl 1(S1):153-70. DOI: 10.1353/cja.2005.0055
Source: PubMed

ABSTRACT This review evaluates the quality of available administrative data in the Canadian provinces, emphasizing the information needed to create integrated systems. We explicitly compare approaches to quality measurement, indicating where record linkage can and cannot substitute for more expensive record re-abstraction. Forty-nine original studies evaluating Canadian administrative data (registries, hospital abstracts, physician claims, and prescription drugs) are summarized in a structured manner. Registries, hospital abstracts, and physician files appear to be generally of satisfactory quality, though much work remains to be done. Data quality did not vary systematically among provinces. Primary data collection to check place of residence and longitudinal follow-up in provincial registries is needed. Promising initial checks of pharmaceutical data should be expanded. Because record linkage studies were ''conservative'' in reporting reliability, the reduction of time-consuming record re-abstraction appears feasible in many cases. Finally, expanding the scope of administrative data to study health, as well as health care, seems possible for some chronic conditions. The research potential of the information-rich environments being created highlights the importance of data quality.

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    • "The administrative data contains comprehensive health-related information, and have been linked to other data including census-based socioeconomic information, and vital status. MCHP data has been used and validated extensively to study a wide range of medical outcomes [21,22]. "
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    ABSTRACT: Prior studies of patients leaving hospital against medical advice (AMA) have been limited by not being population-based or assessing only one type of patient. We used administrative data at the Manitoba Centre for Health Policy to evaluate all adult residents of Manitoba, Canada discharged alive from acute care hospitals between April 1, 1990 and February 28, 2009. We identified the rate of leaving AMA, and used multivariable logistic regression to identify socio-demographic and diagnostic variables associated with leaving AMA. Of 1 916 104 live hospital discharges, 21 417 (1.11%) ended with the patient leaving AMA. The cohort contained 610 187 individuals, of whom 12 588 (2.06%) left AMA once and another 2 986 (0.49%) left AMA more than once. The proportion of AMA discharges did not change over time. Alcohol and drug abuse was the diagnostic group with the highest proportion of AMA discharges, at 11.71%. Having left AMA previously had the strongest association with leaving AMA (odds ratio 170, 95% confidence interval 156--185). Leaving AMA was more common among men, those with lower average household incomes, histories of alcohol or drug abuse or HIV/AIDS. Major surgical procedures were associated with a much lower chance of leaving the hospital AMA. The rate of leaving hospital AMA did not systematically change over time, but did vary based on patient and illness characteristics. Having left AMA in the past was highly predictive of subsequent AMA events.
    BMC Health Services Research 10/2013; 13(1):415. DOI:10.1186/1472-6963-13-415 · 1.66 Impact Factor
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    • "When interpreting our data both kappa and the degree of concordance should be considered [31]. With the increasing use of administrative data for primary care performance reporting [19,34,35], remuneration and funding decisions [28], disease registries [27] and public health reporting [36] the comparison of administrative data results to multiple different data sources is especially important for guiding the use and interpretation of administrative data results in future research, policy, and planning [27,37,38]. "
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    ABSTRACT: To evaluate the appropriateness of potential data sources for the population of performance indicators for primary care (PC) practices. This project was a cross sectional study of 7 multidisciplinary primary care teams in Ontario, Canada. Practices were recruited and 5-7 physicians per practice agreed to participate in the study. Patients of participating physicians (20-30) were recruited sequentially as they presented to attend a visit. Data collection included patient, provider and practice surveys, chart abstraction and linkage to administrative data sets. Matched pairs analysis was used to examine the differences in the observed results for each indicator obtained using multiple data sources. Seven teams, 41 physicians, 94 associated staff and 998 patients were recruited. The survey response rate was 81% for patients, 93% for physicians and 83% for associated staff. Chart audits were successfully completed on all but 1 patient and linkage to administrative data was successful for all subjects. There were significant differences noted between the data collection methods for many measures. No single method of data collection was best for all outcomes. For most measures of technical quality of care chart audit was the most accurate method of data collection. Patient surveys were more accurate for immunizations, chronic disease advice/information dispensed, some general health promotion items and possibly for medication use. Administrative data appears useful for indicators including chronic disease diagnosis and osteoporosis/ breast screening. Multiple data collection methods are required for a comprehensive assessment of performance in primary care practices. The choice of which methods are best for any one particular study or quality improvement initiative requires careful consideration of the biases that each method might introduce into the results. In this study, both patients and providers were willing to participate in and consent to, the collection and linkage of information from multiple sources that would be required for such assessments.
    BMC Health Services Research 07/2012; 12(1):214. DOI:10.1186/1472-6963-12-214 · 1.66 Impact Factor
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    • "The population registry in this dataset has been demonstrated to provide excellent ascertainment of cohorts [23] and few records were lost for non linkage. Hospital discharge abstracts have demonstrated agreement with re-abstraction studies for capture of serious comorbidity and primary reason for admission with less ability to capture minor comorbidity, which is often under-captured [22]. Two techniques to overcome this are grouping of diagnoses as well as larger sample sizes. "
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    BMC Pregnancy and Childbirth 06/2012; 12:62. DOI:10.1186/1471-2393-12-62 · 2.15 Impact Factor
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