Measuring quality of diabetes care by linking health care system administrative databases with laboratory data

<p>Abstract</p> <p>Background</p> <p>Chronic complications of diabetes can be reduced through optimal glycemic and lipid control as evaluated through measurement of glycosylated hemoglobin (A1C) and low-density lipoprotein cholesterol (LDL-C). We aimed to produce measures of quality of diabetes care in Saskatchewan and to identify sub-groups at particular risk of developing complications.</p> <p>Findings</p> <p>Prevalent adult cases of diabetes in 2005/06 were identified from administrative databases and linked with A1C and LDL-C tests measured in centralized laboratories. A1C results were performed in 33,927 of 50,713 (66.9%) diabetes cases identified in Saskatchewan, and LDL-C results were performed in 12,031 of 24,207 (49.7%) cases identified within the province's two largest health regions. The target A1C of <= 7.0% and the target LDL-C of <2.5 mmol/L were achieved in 48.3% and 45.1% of diabetes cases respectively. The proportions were lower among those who were female, First Nations, non-urban, younger and in lower income quintiles. The same groups experienced poorer glycemic control (exception females), and poorer lipid control (exception First Nations people). Among non-Aboriginal people, younger diabetic females were least likely to receive lipid lowering agents.</p> <p>Conclusions</p> <p>Linkage of laboratory with administrative data is an effective method of assessing quality of diabetes care on a population basis and to identify sub-groups requiring particular attention. We found that less than 50% of Saskatchewan people with diabetes achieved optimal glycemic and lipid control. Disparities were most evident among First Nations people and young women. The indicators described can be used to provide standardized information that would support quality improvement initiatives.</p

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

Background: Prescription 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. Methods: Prescription 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. Results: The 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. Conclusions: Agreement 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

An evaluation of data quality in Canada’s Continuing Care Reporting System (CCRS): secondary analyses of Ontario data submitted between 1996 and 2011

Abstract Background Evidence informed decision making in health policy development and clinical practice depends on the availability of valid and reliable data. The introduction of interRAI assessment systems in many countries has provided valuable new information that can be used to support case mix based payment systems, quality monitoring, outcome measurement and care planning. The Continuing Care Reporting System (CCRS) managed by the Canadian Institute for Health Information has served as a data repository supporting national implementation of the Resident Assessment Instrument (RAI 2.0) in Canada for more than 15 years. The present paper aims to evaluate data quality for the CCRS using an approach that may be generalizable to comparable data holdings internationally. Methods Data from the RAI 2.0 implementation in Complex Continuing Care (CCC) hospitals/units and Long Term Care (LTC) homes in Ontario were analyzed using various statistical techniques that provide evidence for trends in validity, reliability, and population attributes. Time series comparisons included evaluations of scale reliability, patterns of associations between items and scales that provide evidence about convergent validity, and measures of changes in population characteristics over time. Results Data quality with respect to reliability, validity, completeness and freedom from logical coding errors was consistently high for the CCRS in both CCC and LTC settings. The addition of logic checks further improved data quality in both settings. The only notable change of concern was a substantial inflation in the percentage of long term care home residents qualifying for the Special Rehabilitation level of the Resource Utilization Groups (RUG-III) case mix system after the adoption of that system as part of the payment system for LTC. Conclusions The CCRS provides a robust, high quality data source that may be used to inform policy, clinical practice and service delivery in Ontario. Only one area of concern was noted, and the statistical techniques employed here may be readily used to target organizations with data quality problems in that (or any other) area. There was also evidence that data quality was good in both CCC and LTC settings from the outset of implementation, meaning data may be used from the entire time series. The methods employed here may continue to be used to monitor data quality in this province over time and they provide a benchmark for comparisons with other jurisdictions implementing the RAI 2.0 in similar populations.http://deepblue.lib.umich.edu/bitstream/2027.42/112338/1/12911_2012_Article_635.pd

Relationship between interRAI HC and the ICF: opportunity for operationalizing the ICF

Background: The International Classification of Functioning, Disability and Health (ICF) is embraced as a framework to conceptualize human functioning and disability. Health professionals choose measures to represent the domains of the framework. The ICF coding classification is an administrative system but multiple studies have linked diverse clinical assessments to ICF codes. InterRAI-HC (home care) is an assessment designed to assist planning of care for patients receiving home care. Examining the relationship between the ICF and the interRAI HC is of particular interest because the interRAI assessments are widely used in clinical practice and research, are computerized, and uploaded to databases that serve multiple purposes including public reporting of quality in Canada and internationally. The objective of this study was to examine the relationship between the interRAI HC (home care) assessment and the ICF. Specifically, the goal was to determine the proportion of interRAI HC items that can be linked to each of the major domains of the ICF (Body Function, Body Structure, Activities and Participation, and the Environmental Factors), the chapters and the specific ICF codes

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

<p>Abstract</p> <p>Background</p> <p>Multiple 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.</p> <p>Methods</p> <p>Administrative 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 (<it>c</it>-statistic) and calibration (Brier score) for multiple logistic regression models.</p> <p>Results</p> <p>The comorbidity measures with optimal performance were the same in the general population (<it>n </it>= 662,423), diabetes (<it>n </it>= 41,925), and osteoporosis (<it>n </it>= 28,068) cohorts. For mortality, the Elixhauser index resulted in the highest <it>c</it>-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.</p> <p>Conclusions</p> <p>The optimal comorbidity measure depends on the health outcome and not on the disease characteristics of the study population.</p

The Resident Assessment Instrument-Minimum Data Set 2.0 quality indicators: a systematic review

BackgroundThe Resident Assessment Instrument-Minimum Data Set (RAI-MDS) 2.0 is designed to collect the minimum amount of data to guide care planning and monitoring for residents in long-term care settings. These data have been used to compute indicators of care quality. Use of the quality indicators to inform quality improvement initiatives is contingent upon the validity and reliability of the indicators. The purpose of this review was to systematically examine published and grey research reports in order to assess the state of the science regarding the validity and reliability of the RAI-MDS 2.0 Quality Indicators (QIs).MethodsWe systematically reviewed the evidence for the validity and reliability of the RAI-MDS 2.0 QIs. A comprehensive literature search identified relevant original research published, in English, prior to December 2008. Fourteen articles and one report examining the validity and/or reliability of the RAI-MDS 2.0 QIs were included.ResultsThe studies fell into two broad categories, those that examined individual quality indicators and those that examined multiple indicators. All studies were conducted in the United States and included from one to a total of 209 facilities. The number of residents included in the studies ranged from 109 to 5758. One study conducted under research conditions examined 38 chronic care QIs, of which strong evidence for the validity of 12 of the QIs was found. In response to these findings, the 12 QIs were recommended for public reporting purposes. However, a number of observational studies (n=13), conducted in &quot;real world&quot; conditions, have tested the validity and/or reliability of individual QIs, with mixed results. Ten QIs have been studied in this manner, including falls, depression, depression without treatment, urinary incontinence, urinary tract infections, weight loss, bedfast, restraint, pressure ulcer, and pain. These studies have revealed the potential for systematic bias in reporting, with under-reporting of some indicators and over-reporting of others.ConclusionEvidence for the reliability and validity of the RAI-MDS QIs remains inconclusive. The QIs provide a useful tool for quality monitoring and to inform quality improvement programs and initiatives. However, caution should be exercised when interpreting the QI results and other sources of evidence of the quality of care processes should be considered in conjunction with QI results.<br /

Incidence and prevalence of dementia in linked administrative health data in Saskatchewan, Canada: a retrospective cohort study.

Determining the epidemiology of dementia among the population as a whole in specific jurisdictions - including the long-term care population-is essential to providing appropriate care. The objectives of this study were to use linked administrative databases in the province of Saskatchewan to determine the 12-month incidence and prevalence of dementia for the 2012/13 period (1) among individuals aged 45 and older in the province of Saskatchewan, (2) according to age group and sex, and (3) according to diagnosis code and other case definition criteria

Immune-mediated genetic pathways resulting in pulmonary function impairment increase lung cancer susceptibility

Impaired lung function is often caused by cigarette smoking, making it challenging to disentangle its role in lung cancer susceptibility. Investigation of the shared genetic basis of these phenotypes in the UK Biobank and International Lung Cancer Consortium (29,266 cases, 56,450 controls) shows that lung cancer is genetically correlated with reduced forced expiratory volume in one second (FEV1: r(g) = 0.098, p = 2.3 x 10(-8)) and the ratio of FEV1 to forced vital capacity (FEV1/FVC: r(g) = 0.137, p = 2.0 x 10(-12)). Mendelian randomization analyses demonstrate that reduced FEV1 increases squamous cell carcinoma risk (odds ratio (OR) = 1.51, 95% confidence intervals: 1.21-1.88), while reduced FEV1/FVC increases the risk of adenocarcinoma (OR = 1.17, 1.01-1.35) and lung cancer in never smokers (OR = 1.56, 1.05-2.30). These findings support a causal role of pulmonary impairment in lung cancer etiology. Integrative analyses reveal that pulmonary function instruments, including 73 novel variants, influence lung tissue gene expression and implicate immune-related pathways in mediating the observed effects on lung carcinogenesis