1. How well does the minimum data set measure healthcare use? a validation study
- Author
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Malcolm Doupe, Allan Garland, Shauna Zinnick, Natalia Dik, Lisa M. Lix, Jeff Poss, Peter G. Norton, and University of Manitoba
- Subjects
Male ,Transition to Adult Care ,Healthcare use ,medicine.medical_specialty ,Datasets as Topic ,Nursing homes ,MDS records ,Logistic regression ,Sensitivity and Specificity ,Health administration ,03 medical and health sciences ,0302 clinical medicine ,Positive predicative value ,Validation ,Health care ,medicine ,Humans ,030212 general & internal medicine ,Aged ,Retrospective Studies ,Aged, 80 and over ,Minimum Data Set ,business.industry ,lcsh:Public aspects of medicine ,030503 health policy & services ,Health Policy ,Manitoba ,lcsh:RA1-1270 ,Retrospective cohort study ,Emergency department ,Patient Acceptance of Health Care ,Hospitalization ,Emergency medicine ,Female ,Emergency Service, Hospital ,0305 other medical science ,business ,Research Article ,Cohort study - Abstract
Background To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. Methods This retrospective observational cohort study was conducted on all NH residents (N = 8832) from Winnipeg, Manitoba, Canada, between April 1, 2011 and March 31, 2013. Six study measures exist. NH user status (newly admitted NH residents, those who transferred from one NH to another, and those who died) was measured using both MDS and administrative data. Rates of in-patient hospitalizations, emergency department (ED) visits without subsequent hospitalization, and physician examinations were also measured in each data source. We calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV), and overall agreement (kappa, κ) of each measure as captured by MDS using administrative data as the reference source. Also for each measure, logistic regression tested if the level of disagreement between data systems was associated with resident age and sex plus NH owner-operator status. Results MDS accurately identified newly admitted residents (κ = 0.97), those who transferred between NHs (κ = 0.90), and those who died (κ = 0.95). Measures of healthcare use were captured less accurately by MDS, with high levels of both under-reporting and false positives (e.g., for in-patient hospitalizations sensitivity = 0.58, PPV = 0.45), and moderate overall agreement levels (e.g., κ = 0.39 for ED visits). Disagreement was sometimes greater for younger males, and for residents living in for-profit NHs. Conclusions MDS can be used as a stand-alone tool to accurately capture basic measures of NH use (admission, transfer, and death), and by proxy NH length of stay. As compared to administrative data, MDS does not accurately capture NH resident healthcare use. Research investigating these and other healthcare transitions by NH residents requires a combination of the MDS and administrative data systems. Electronic supplementary material The online version of this article (10.1186/s12913-018-3089-7) contains supplementary material, which is available to authorized users.
- Published
- 2018
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