Accuracy of Telephone Self-Report of Drug Use in Older People and Agreement with Pharmaceutical Claims Data
- 104 Downloads
To determine the agreement between two measures of medication use, namely telephone interview self-report and pharmaceutical claims data, in an elderly population.
An agreement study of 566 community-dwelling, general practice patients aged ≥65 years was conducted to compare self-reported use of medicines with pharmaceutical claims data for different retrieval periods. Classes of drugs commonly used in the elderly were selected for comparison.
1094 people were eligible for the main study. Of these, 697 people completed a follow-up survey and 625 of these patients consented to the release of pharmaceutical claims data. A further 59 participants were excluded from the analysis because they had a home visit instead of a telephone interview. The proportion of observed agreement between the telephone self-report and the various retrieval periods was consistently high. Kappa coefficients showed good to very good agreement (≥0.75) with retrieval periods of 30, 60 and 90 days for benzodiazepines, low-risk NSAIDs, thiazide diuretics and most other drugs. The specificity of self-reported medication use compared with claims data was consistently high across all drug classes, suggesting that people usually did not mention drugs that were not included in the claims data. Sensitivity values varied according to drug class and retrieval period, and were lower for NSAIDs than for benzodiazepines and thiazide diuretics. Decline in sensitivity with increased retrieval periods was most marked for benzodiazepines, NSAIDs and low-risk NSAIDs, which are often used on an as-needed basis. Positive predictive values increased with longer retrieval periods
High agreement and accuracy were demonstrated for self-reported use of medicines when patients were interviewed over the telephone compared with pharmaceutical claims data. The telephone inventory method can be used in future studies for accurately measuring drug use in older people when claims data are not available.
KeywordsClaim Data Thiazide Diuretic Pharmaceutical Benefit Scheme High Sensitivity Rate Anatomical Therapeutic Chemical Class
The National and Health Medical Research Council, Australia funded the study but played no role in the formulation of the design, methods, subject recruitment, data collection, analysis or preparation of this paper. SWP received a University of Newcastle Postgraduate Scholarship as a PhD student. The researchers are independent from the funding body. The researchers in the Faculty of Health are members of the Hunter Medical Research Institute.
We thank all the general practitioners (in particular Dr Parker Magin and Dr Peter Hopkins) and their patients for participating in this study; Vibeke Hansen for managing and assisting in data collection; Deborah Bowman for research assistance; Professor Tony Smith, Professor David Henry, Lucy Holt and participating general practitioners for providing extensive advice on development of the content of the medication review checklist; Chris Bonner for advice and providing the Therapeutic Flags manual; Professor Paul Glasziou for developing the grant; the peer reviewers of the thesis for their comments; the Royal Australian College of General Practitioners for approving parts of the research as a clinical audit and as a Continuing Professional Development activity; the National Prescribing Service for approving Practice Incentive Payments; Medicare Australia for providing data; all advisory group members (in particular Judith Mackson [National Prescribing Service] and Professor Kichu Nair) for their advice; all interviewers; and all statisticians involved in this study.
JC (deceased) conceived, designed, analysed and interpreted the results of the study. SWP and JEB designed, analysed and interpreted the results of the study. JC and JB developed the grant. SWP managed and conducted the study and drafted the manuscript. JEB conceived the study, and revised the manuscript. SWP and JEB contributed to the final version. SWP and JEB are guarantors.
- 1.Australian Bureau of Statistics. National Health Survey 1995: use of medications. Canberra (ACT): Australian Bureau of Statistics, 1999Google Scholar
- 4.Bonevski B, Sanson-Fisher RW, Campbell EM, et al. Do general practice patients find computer health risk surveys acceptable? A comparison with pen and paper method. Health Promot J Austr 1997; 7(2): 100–6Google Scholar
- 5.Silman A. Epidemiological studies: a practical guide. Cambridge: Cambridge University Press, 1995Google Scholar
- 6.Bonevski BA. Increasing preventive care in general practice: an examination [doctoral dissertation]. Newcastle (NSW): University of Newcastle, 1996Google Scholar
- 8.Cockburn J. Variables related to antibiotic compliance in general practice patients: the application of behavioural science methodologies [doctoral dissertation]. Newcastle (NSW): University of Newcastle, 1986Google Scholar
- 10.Hancock L. Drug use in the Australian community: prevalence, sociodemographic characteristics of users, and context of use [doctoral dissertation]. Newcastle (NSW): University of Newcastle, 1991Google Scholar
- 24.WHO Collaborating Centre for Drug Statistics Methodology. Anatomical therapeutic chemical (ATC) classification index with defined daily doses (DDDs). Oslo: WHO Collaborating Centre for Drug Statistics Methodology, 2001Google Scholar
- 29.Kable S. An application of interactive computer programs to promote adherence to clinical practice guidelines for childhood asthma in general practice [doctoral dissertation]. Newcastle (NSW): University of Newcastle, 2004Google Scholar
- 31.Department of Health and Ageing. Pharmaceutical Benefits Scheme: Medicare cards and fairer benefits, 8 May 2002 [online]. Available from URL: http://www.health.gov.au/haf/ime/ [Accessed 2003 October 18]