Dataset Information

Analysis of dementia in the US population using Medicare claims: Insights from linked survey and administrative claims data.

ABSTRACT: Introduction:Medicare claims data may be a rich data source for tracking population dementia rates. Insufficient understanding of completeness of diagnosis, and for whom, limits their use. Methods:We analyzed agreement in prevalent and incident dementia based on cognitive assessment from the Health and Retirement Study for persons with linked Medicare claims from 2000 to 2008 (N = 10,450 persons). Multinomial logistic regression identified sociodemographic factors associated with disagreement. Results:Survey-based cognitive tests and claims-based dementia diagnosis yielded equal prevalence estimates, yet only half were identified by both measures. Race and education were associated with disagreement. Eighty-five percent of respondents with incident dementia measured by cognitive decline received a diagnosis or died within the study period, with lower odds among blacks and Hispanics than among whites. Discussions:Claims data are valuable for tracking dementia in the US population and improve over time. Delayed diagnosis may underestimate rates within black and Hispanic populations.

SUBMITTER: Chen Y

PROVIDER: S-EPMC6556828 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Analysis of dementia in the US population using Medicare claims: Insights from linked survey and administrative claims data.

Chen Yi Y Tysinger Bryan B Crimmins Eileen E Zissimopoulos Julie M JM

Alzheimer's & dementia (New York, N. Y.) 20190606

<h4>Introduction</h4>Medicare claims data may be a rich data source for tracking population dementia rates. Insufficient understanding of completeness of diagnosis, and for whom, limits their use.<h4>Methods</h4>We analyzed agreement in prevalent and incident dementia based on cognitive assessment from the Health and Retirement Study for persons with linked Medicare claims from 2000 to 2008 (N = 10,450 persons). Multinomial logistic regression identified sociodemographic factors associated with ...[more]

PMID: 31198838

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Project description:ObjectiveTo determine if a prototype pharmacists' services evaluation programme that uses linked community pharmacy claims and health administrative data to measure pharmacists' performance can be used to identify characteristics of pharmacies providing higher quality of care.DesignPopulation-based cohort study using community pharmacy claims from 1 November 2009 to 30 June 2010.SettingAll community pharmacies in Quebec, Canada.Participants1742 pharmacies dispensing 8 655 348 antihypertensive prescriptions to 760 700 patients.Primary outcome measurePatient adherence to antihypertensive medications.PredictorsPharmacy level: dispensing workload, volume of pharmacist-provided professional services (eg, refusals to dispense, pharmacotherapy recommendations), pharmacy location, banner/chain, pharmacist overlap and within-pharmacy continuity of care. Patient level: sex, age, income, patient prescription cost, new/chronic therapy, single/multiple antihypertensive medications, single/multiple prescribers and single/multiple dispensing pharmacies. Dispensing level: prescription duration, time of day dispensed and antihypertensive class. Multivariate alternating logistic regression estimated predictors of the primary outcome, accounting for patient and pharmacy clustering.Results9.2% of dispensings of antihypertensive medications were provided to non-adherent patients. Male sex, decreasing age, new treatment, multiple prescribers and multiple dispensing pharmacies were risk factors for increased non-adherence. Pharmacies that provided more professional services were less likely to dispense to non-adherent hypertensive patients (OR: 0.60; 95% CI: 0.57 to 0.62) as were those with better scores on the Within-Pharmacy Continuity of Care Index. Neither increased pharmacists' services for improving antihypertensive adherence per se nor increased pharmacist overlap impacted the odds of non-adherence. However, pharmacist overlap was strongly correlated with dispensing workload. There was significant unexplained variability among pharmacies belonging to different banners and chains.ConclusionsPharmacy administrative claims data can be used to calculate pharmacy-level characteristics associated with improved quality of care. This study supports the importance of pharmacist's professional services and continuity of pharmacist's care.

Project description:IntroductionNontraumatic intracranial hemorrhage (ICH) is a neurological emergency of research interest; however, unlike ischemic stroke, has not been well studied in large datasets due to the lack of an established administrative claims-based definition. We aimed to evaluate both explicit diagnosis codes and machine learning methods to create a claims-based definition for this clinical phenotype.MethodsWe examined all patients admitted to our tertiary medical center with a primary or secondary International Classification of Disease version 9 (ICD-9) or 10 (ICD-10) code for ICH in claims from any portion of the hospitalization in 2014-2015. As a gold standard, we defined the nontraumatic ICH phenotype based on manual chart review. We tested explicit definitions based on ICD-9 and ICD-10 that had been previously published in the literature as well as four machine learning classifiers including support vector machine (SVM), logistic regression with LASSO, random forest and xgboost. We report five standard measures of model performance for each approach.ResultsA total of 1830 patients with 2145 unique ICD-10 codes were included in the initial dataset, of which 437 (24%) were true positive based on manual review. The explicit ICD-10 definition performed best (Sensitivity = 0.89 (95% CI 0.85-0.92), Specificity = 0.83 (0.81-0.85), F-score = 0.73 (0.69-0.77)) and improves on an explicit ICD-9 definition (Sensitivity = 0.87 (0.83-0.90), Specificity = 0.77 (0.74-0.79), F-score = 0.67 (0.63-0.71). Among machine learning classifiers, SVM performed best (Sensitivity = 0.78 (0.75-0.82), Specificity = 0.84 (0.81-0.87), AUC = 0.89 (0.87-0.92), F-score = 0.66 (0.62-0.69)).ConclusionsAn explicit ICD-10 definition can be used to accurately identify patients with a nontraumatic ICH phenotype with substantially better performance than ICD-9. An explicit ICD-10 based definition is easier to implement and quantitatively not appreciably improved with the additional application of machine learning classifiers. Future research utilizing large datasets should utilize this definition to address important research gaps.

Dataset Information

Analysis of dementia in the US population using Medicare claims: Insights from linked survey and administrative claims data.

Publications

Analysis of dementia in the US population using Medicare claims: Insights from linked survey and administrative claims data.

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