Reliability of the recording of hysterectomy in the Saskatchewan health care system

Edouard, Lindsay; Rawson, Nigel S. B.

doi:10.1111/j.1471-0528.1996.tb09908.x

Cited by 33 publications

(18 citation statements)

References 23 publications

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“…The quality of Saskatchewan’s administrative health data for research has been documented in multiple studies [9-12]. Ethics approval for database access was received from the University of Saskatchewan Biomedical Research Ethics Board.…”

Section: Methodsmentioning

confidence: 99%

Validity of the RAI-MDS for ascertaining diabetes and comorbid conditions in long-term care facility residents

Lix

Yan

Blackburn

et al. 2014

BMC Health Serv Res

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BackgroundThis study assessed the validity of the Resident Assessment Instrument Minimum Data Set (RAI-MDS) Version 2.0 for diagnoses of diabetes and comorbid conditions in residents of long-term care facilities (LTCFs).MethodsHospital inpatient, outpatient physician billing, RAI-MDS, and population registry data for 1997 to 2011 from Saskatchewan, Canada were used to ascertain cases of diabetes and 12 comorbid conditions. Prevalence estimates were calculated for both RAI-MDS and administrative health data. Sensitivity, specificity, and positive and negative predictive values (PPV and NPV) were calculated using population-based administrative health data as the validation data source. Cohen’s κ was used to estimate agreement between the two data sources.Results23,217 LTCF residents were in the diabetes case ascertainment cohort. Diabetes prevalence was 25.3% in administrative health data and 21.9% in RAI-MDS data. Overall sensitivity of a RAI-MDS diabetes diagnoses was 0.79 (95% CI: 0.79, 0.80) and the PPV was 0.92 (95% CI: 0.91, 0.92), when compared to administrative health data. Sensitivity of the RAI-MDS for ascertaining comorbid conditions ranged from 0.21 for osteoporosis to 0.92 for multiple sclerosis; specificity was high for most conditions.ConclusionsRAI-MDS clinical assessment data are sensitive to ascertain diabetes cases in LTCF populations when compared to administrative health data. For many comorbid conditions, RAI-MDS data have low validity when compared to administrative data. Risk-adjustment measures based on these comorbidities might not produce consistent results for RAI-MDS and administrative health data, which could affect the conclusions of studies about health outcomes and quality of care across facilities.

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Section: Methodsmentioning

confidence: 99%

Validity of the RAI-MDS for ascertaining diabetes and comorbid conditions in long-term care facility residents

Lix

Yan

Blackburn

et al. 2014

BMC Health Serv Res

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show abstract

“…The accuracy and completeness of Saskatchewan's administrative databases have made them popular data sources for numerous studies of population health and health services utilization [21-23]. Ethical approval for this research was received from the University of Saskatchewan Biomedical Research Ethics Board .…”

Section: Methodsmentioning

confidence: 99%

Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts

Quail

Lix

Osman

et al. 2011

BMC Health Serv Res

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BackgroundMultiple comorbidity measures have been developed for risk-adjustment in studies using administrative data, but it is unclear which measure is optimal for specific outcomes and if the measures are equally valid in different populations. This research examined the predictive performance of five comorbidity measures in three population-based cohorts.MethodsAdministrative data from the province of Saskatchewan, Canada, were used to create the cohorts. The general population cohort included all Saskatchewan residents 20+ years, the diabetes cohort included individuals 20+ years with a diabetes diagnosis in hospital and/or physician data, and the osteoporosis cohort included individuals 50+ years with diagnosed or treated osteoporosis. Five comorbidity measures based on health services utilization, number of different diagnoses, and prescription drugs over one year were defined. Predictive performance was assessed for death and hospitalization outcomes using measures of discrimination (c-statistic) and calibration (Brier score) for multiple logistic regression models.ResultsThe comorbidity measures with optimal performance were the same in the general population (n = 662,423), diabetes (n = 41,925), and osteoporosis (n = 28,068) cohorts. For mortality, the Elixhauser index resulted in the highest c-statistic and lowest Brier score, followed by the Charlson index. For hospitalization, the number of diagnoses had the best predictive performance. Consistent results were obtained when we restricted attention to the population 65+ years in each cohort.ConclusionsThe optimal comorbidity measure depends on the health outcome and not on the disease characteristics of the study population.

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“…The accuracy and completeness of Saskatchewan’s administrative health data for research has been well documented [8-12] although not specifically for LTCF populations. Ethics approval for database access was received from the University of Saskatchewan Biomedical Research Ethics Board.…”

Section: Methodsmentioning

confidence: 99%

Agreement between administrative data and the Resident Assessment Instrument Minimum Dataset (RAI-MDS) for medication use in long-term care facilities: a population-based study

et al. 2015

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BackgroundPrescription medication use, which is common among long-term care facility (LTCF) residents, is routinely used to describe quality of care and predict health outcomes. Data sources that capture medication information, which include surveys, medical charts, administrative health databases, and clinical assessment records, may not collect concordant information, which can result in comparable prevalence and effect size estimates. The purpose of this research was to estimate agreement between two population-based electronic data sources for measuring use of several medication classes among LTCF residents: outpatient prescription drug administrative data and the Resident Assessment Instrument Minimum Data Set (RAI-MDS) Version 2.0.MethodsPrescription drug and RAI-MDS data from the province of Saskatchewan, Canada (population 1.1 million) were linked for 2010/11 in this cross-sectional study. Agreement for anti-psychotic, anti-depressant, and anti-anxiety/hypnotic medication classes was examined using prevalence estimates, Cohen’s κ, and positive and negative agreement. Mixed-effects logistic regression models tested resident and facility characteristics associated with disagreement.ResultsThe cohort was comprised of 8,866 LTCF residents. In the RAI-MDS data, prevalence of anti-psychotics was 35.7%, while for anti-depressants it was 37.9% and for hypnotics it was 27.1%. Prevalence was similar in prescription drug data for anti-psychotics and anti-depressants, but lower for hypnotics (18.0%). Cohen’s κ ranged from 0.39 to 0.85 and was highest for the first two medication classes. Diagnosis of a mood disorder and facility affiliation was associated with disagreement for hypnotics.ConclusionsAgreement between prescription drug administrative data and RAI-MDS assessment data was influenced by the type of medication class, as well as selected patient and facility characteristics. Researchers should carefully consider the purpose of their study, whether it is to capture medication that are dispensed or medications that are currently used by residents, when selecting a data source for research on LTCF populations.Electronic supplementary materialThe online version of this article (doi:10.1186/s12877-015-0023-2) contains supplementary material, which is available to authorized users.

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Reliability of the recording of hysterectomy in the Saskatchewan health care system

Cited by 33 publications

References 23 publications

Validity of the RAI-MDS for ascertaining diabetes and comorbid conditions in long-term care facility residents

Validity of the RAI-MDS for ascertaining diabetes and comorbid conditions in long-term care facility residents

Comparing comorbidity measures for predicting mortality and hospitalization in three population-based cohorts

Agreement between administrative data and the Resident Assessment Instrument Minimum Dataset (RAI-MDS) for medication use in long-term care facilities: a population-based study

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