Literature DB >> 12458308

Measuring medication: do interviews agree with medical record and pharmacy data?

P Todd Korthuis1, Steven Asch, Martha Mancewicz, Martin F Shapiro, W Christopher Mathews, William E Cunningham, J Allen McCutchan, Allen Gifford, Martin L Lee, Samuel A Bozzette.   

Abstract

BACKGROUND: Medication measurement is crucial in assessing quality for chronic conditions yet agreement of alternate data sources remains uncertain.
OBJECTIVES: To evaluate medication agreement between interviews, medical records, and pharmacy data; to assess data source contribution to attributing medication exposure; and to describe the impact of combining data sources on models that predict medication use. RESEARCH
DESIGN: Prospective cohort study.
SUBJECTS: Probability sample of HIV-infected participants in the HIV Cost and Services Utilization Study. MEASURES: Medications reported in 2267 interviews, 1936 medical records, and 457 pharmacy records were compared using crude agreement, kappa, and the proportion of average positive and negative agreement. The percent of medications reported in each source alone was used to assess their relative contribution to attributing exposure status. We performed weighted logistic regression in alternate data sources.
RESULTS: Kappa varied from 0.38 for nucleoside reverse transcriptase inhibitors to 0.70 for protease inhibitors, when comparing drug classes in interview versus medical record, interview versus pharmacy data, and medical record versus pharmacy data. The percentage of medications reported in medical records was greater than that reported in interviews or pharmacy data. Pharmacy data contributed little to the attribution of medication exposure. Adding medication data to interview data did not appreciably change analytic models predicting medication use.
CONCLUSIONS: For specific medications, agreement between alternative data sources is fair to substantial, but is lower for key drug classes. Relying on one data source may lead to misclassification of drug exposure status, but combining data sources does not change the results of analytic models predicting appropriate medication use.

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Year:  2002        PMID: 12458308     DOI: 10.1097/01.MLR.0000036410.86742.27

Source DB:  PubMed          Journal:  Med Care        ISSN: 0025-7079            Impact factor:   2.983


  8 in total

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3.  Does self-report data on HIV primary care utilization agree with medical record data for socially marginalized populations in the United States?

Authors:  Nancy L Sohler; Sharon M Coleman; Howard Cabral; Sylvie Naar-King; Carol Tobias; Chinazo O Cunningham
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Authors:  Gabriela Schmajuk; Jinoos Yazdany; Laura Trupin; Edward Yelin
Journal:  Arthritis Care Res (Hoboken)       Date:  2010-03       Impact factor: 4.794

5.  Osteoporosis screening, prevention, and treatment in systemic lupus erythematosus: application of the systemic lupus erythematosus quality indicators.

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6.  Does racial concordance between HIV-positive patients and their physicians affect the time to receipt of protease inhibitors?

Authors:  William D King; Mitchell D Wong; Martin F Shapiro; Bruce E Landon; William E Cunningham
Journal:  J Gen Intern Med       Date:  2004-11       Impact factor: 5.128

7.  Entry into primary care-based buprenorphine treatment is associated with identification and treatment of other chronic medical problems.

Authors:  Theresa A Rowe; Janet S Jacapraro; Darius A Rastegar
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Review 8.  Aspects and Challenges of Resource Use Measurement in Health Economics: Towards a Comprehensive Measurement Framework.

Authors:  Luca M M Janssen; Ruben M W A Drost; Aggie T G Paulus; Kirsty Garfield; William Hollingworth; Sian Noble; Joanna C Thorn; Irina Pokhilenko; Silvia M A A Evers
Journal:  Pharmacoeconomics       Date:  2021-06-25       Impact factor: 4.981

  8 in total

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