Tien-Tsai Cheng, Han-Ming Lai, and Shan-Fu Yu provided osteoporosis care. Ying-Chou. Chen analyzed data and wrote the paper. Sponsor's Role: None.
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cancer as an outcome, underlying hepatitis must be considered as a confounding factor. Furthermore, individuals with hepatitis are more prone to osteopenia10 and so may be more prone to visit an osteoporotic clinic for possible low bone mass–related fractures. This reinforces the need to consider hepatitis as an associated medical illness in osteoporosis studies. In conclusion, the association between the use of long term anti-osteoporotic drugs and the risk of developing cancer was examined. Although some findings were consistent between databases, no greater risk of any cancer was found in participants receiving anti-osteoporotic treatment. Nevertheless, the sample size in this study was small, and further studies with larger sample sizes are needed to clarify this finding. Ying-Chou Chen, MD Fu-Mei Su, MD Tien-Tsai Cheng, MD Han-Ming Lai, MD Shan-Fu Yu, MD Department of Rheumatology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
ACKNOWLEDGMENTS Conflict of Interest: The authors declare that they have no conflict of interest. Author Contributions: Ying-Chou Chen designed and performed the research. Tien-Tsai Cheng, Han-Ming Lai, and Shan-Fu Yu provided osteoporosis care. Ying-Chou Chen analyzed data and wrote the paper. Sponsor’s Role: None.
REFERENCES 1. Abrahamsen B, Eiken P, Eastell R. More on reports of esophageal cancer with oral bisphosphonate use. N Engl J Med 2009;360:1789; author reply 1791–1782. 2. Wysowski DK. Reports of esophageal cancer with oral bisphosphonate use. N Engl J Med 2009;360:89–90. 3. Cardwell CR, Abnet CC, Cantwell MM et al. Exposure to oral bisphosphonates and risk of esophageal cancer. JAMA 2010;304:657–663. 4. Overman RA, Borse M, Gourlay ML. Salmon calcitonin use and associated cancer risk. Ann Pharmacother 2013;47:1675–1684. 5. Lempicki KA, Borchert JS. Cancer risk associated with calcitonin use. J Am Geriatr Soc 2014;62:2447–2450. 6. Sun LM, Lin MC, Muo CH et al. Calcitonin nasal spray and increased cancer risk: A population-based nested case-control study. J Clin Endocrinol Metab 2014;99:4259–4264. 7. Lee CM, Chen CH, Lu SN et al. Prevalence and clinical implications of hepatitis B virus genotypes in southern Taiwan. Scand J Gastroenterol 2003;38:95–101. 8. Lin CF, Twu SJ, Chen PH et al. Prevalence and determinants of hepatitis B antigenemia in 15,007 inmates in Taiwan. J Epidemiol 2010;20: 231–236. 9. Tanaka M, Katayama F, Kato H et al. Hepatitis B and C virus infection and hepatocellular carcinoma in China: A review of epidemiology and control measures. J Epidemiol 2011;21:401–416. 10. Carey EJ, Balan V, Kremers WK et al. Osteopenia and osteoporosis in patients with end-stage liver disease caused by hepatitis C and alcoholic liver disease: Not just a cholestatic problem. Liver Transpl 2003;9:1166– 1173.
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SURVEY MODE BIASES REPORTING OF ACTIVITIES OF DAILY LIVING AND INSTRUMENTAL ACTIVITIES OF DAILY LIVING To the Editor: Disparities between outcome data generated from interviews and those from written questionnaires is a well-recognized phenomenon in health research.1 Such disparities may be due to various reasons, such as the drive to give socially desirable answers or the fact that respondents may interpret written and spoken questions differently.2 In aging research, scales measuring activities of daily living (ADLs) and instrumental activities of daily living (IADLs) are no exception, with written questionnaires often increasing the likelihood of reported ADL and IADL independence.3 Reporting disparities, also referred to as “differential item functioning” (DIF), are a type of measurement bias whereby participant subgroups (e.g., those completing an interview vs a written questionnaire) have different probabilities of reporting an outcome (e.g., disability in an ADL or IADL item) even when they possess the same ability level.4 To examine DIF across survey modes, researchers frequently perform a comparative analysis against a reference standard, such as clinical assessment, but in the absence of clinical assessment, alternative statistical techniques, such as Rasch analysis, can demonstrate DIF.4 The purpose of the current study was to assess DIF in self-reported ADLs and IADLs according to survey mode.
METHODS Data were derived from The Older Persons and Informal Caregivers Minimum DataSet (TOPICS-MDS; www.topics-mds.eu), a public-access data set designed to capture essential health and well-being information on older persons and informal caregivers in the Netherlands.5 Briefly, TOPICS-MDS consists of prospectively collected data and includes a modified version of the Katz Index of independence of activities in daily living,6 comprising 15 dichotomous ADL and IADL items. The analysis is based on 1,937 participants aged 65 and older residing in residential care facilities in the Netherlands with complete data on ADL and IADL functioning and survey mode. To assess DIF according to survey mode, a Rasch measurement model was applied. This model assumes that the probability of item endorsement is a logistic function of the relative distance between the item’s difficulty and the participant’s ability.7 In other words, persons with similar ability levels are more likely to report the need for assistance with similar ADLs and IADLs (e.g., it is likely that persons requiring assistance with eating also require assistance with dressing). Based on this model, an analysis of variance (ANOVA) was performed of the standardized response residuals of each ADL and IADL item; significant differences suggest DIF.7,8 DIF was visually confirmed by inspecting item characteristic curves according to survey mode.7,8 These curves represent the probability of reporting an ADL or IADL item based on the ability level of the participant completing the scale. If these curves follow different trajectories, DIF is present. Analyses were
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conducted using RUMM2030 (Rumm Laboratory, Perth, Australia).
RESULTS Of the 1,937 participants, 1,421 (73.4%) were female. The average age standard deviation was 85 6 (range 65–102). One thousand three hundred ninety-five (72.0%) participants were interviewed, and 542 (28.0%) completed a written questionnaire. Participants were most likely to require assistance with household tasks (n = 1,729, 89.3%) and least likely to require assistance with eating (n = 72, 3.7%).
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Several ADL and IADL items clearly exhibited DIF according to survey mode (use of incontinence products, walking, meal preparation; ANOVA results not shown). Participants who were interviewed were more likely to report using incontinence products than those completing a written questionnaire (Figure 1A). For participants with poorer functional ability (0 on the logit scale; Figure 1B). The converse pattern was observed for meal preparation (Figure 1C).
Panel A Use of incontinence products
Panel B Walking
Panel C Meal Preparation
Figure 1. Item characteristic curves for select activity of daily living (ADL) or instrumental activity of daily living (IADL) ((A) use of incontinence products, (B) walking, (C) meal preparation) according to survey mode. The x-axis (person location) represents participants’ underlying ability based on a standardized logit scale, ranging from less functional ability (negative values) to greater functional ability (positive values). The y-axis (expected value, possible range 0–1) refers to the probability that participants reported requiring assistance with a specific ADL or IADL.
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DISCUSSION DIF is problematic for health measurement scales and, if serious, likely requires the exclusion of an item. Reassuringly, DIF according to survey mode was not present for all ADL and IADL items. Nonetheless, meal preparation exhibited DIF across survey mode, as well as across sex in previous research;9 removing this item from the scale may be warranted. It was not possible to confirm why DIF occurred for use of incontinence products and walking, although the interaction with the interviewer may have prompted contrasting response patterns in participants with different functional levels. Researchers interested in the construction or refinement of ADL and IADL scales should consider Rasch analysis to identify items prone to reporting disparities to guide item selection. This study demonstrates that reporting disparities according to survey mode are prominent for certain ADL and IADL items and supports a single data collection mode.2 However, a single data collection mode may not always be realistic in aging populations with varying abilities to complete a survey. Careful consideration of how to treat mixed-mode data should be undertaken in the analytical phase and may require the use of subgroup analyses or interaction terms.2 Jennifer E. Lutomski, MS Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, the Netherlands Paul F. M. Krabbe, PhD Department of Epidemiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands Nienke Bleijenberg, PhD Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands Jeanet W. Blom, PhD Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, the Netherlands Bianca M. Buurman, PhD Department of Internal Medicine and Geriatrics, Academic Medical Center, Amsterdam, the Netherlands Gertrudis I. J. M. Kempen, PhD Department of Health Services Research, School for Public Health and Primary Care, Maastricht University, Maastricht, the Netherlands Maaike E. Muntinga, MS Department of General Practice and Elderly Care Medicine, EMGO+ Institute for Health and Care Research, VU Medical Center, Amsterdam, the Netherlands Ewout W. Steyerberg, PhD Department of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands
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Marcel G. M. Olde Rikkert, PhD Rene J. F. Melis, PhD Department of Geriatric Medicine, Radboud University Medical Center, Nijmegen, the Netherlands
ACKNOWLEDGMENTS Funded by the Dutch National Care for the Elderly Programme and The Dutch Organisation for Health Research and Development. Conflict of Interest: The editor in chief has reviewed the conflict of interest checklist provided by the authors and has determined that the authors have no financial or any other kind of personal conflicts with this paper. Author Contributions: Lutomski: data analysis and interpretation. Krabbe: formulating study aims, data interpretation. Bleijenberg, Blom, Buurman, Kempen, Blom, Steyerberg, and Olde Rikkert are on the research consortium for TOPICS-MDS and critically revised the letter for intellectual content. All authors read the final draft of this letter and approved its submission for publication. Sponsor’s Role: The sponsor had no role in the design, methods, subject recruitment, data collection, analysis, or preparation of the letter.
REFERENCES 1. Hood K, Robling M, Ingledew D et al. Mode of data elicitation, acquisition and response to surveys: A systematic review. Health Technol Assess 2012;16:1–162. 2. Martin P, Lynn P. The Effects of Mixed Mode Survey Designs on Simple and Complex Analyses. Centre for Comparative Social Surveys Working Paper Series: Paper No. 04. London, UK: Centre for Comparative Social Surveys, City University London, 2011. 3. Waehrens EE, Bliddal H, Danneskiold-Samsoe B et al. Differences between questionnaire- and interview-based measures of activities of daily living (ADL) ability and their association with observed ADL ability in women with rheumatoid arthritis, knee osteoarthritis, and fibromyalgia. Scand J Rheumatol 2012;41:95–102. 4. Osterlind SJ, Everson HT. Differential Item Functioning, 2nd Ed. Thousand Oaks, CA: Sage Publications, 2009. 5. Lutomski JE, Baars MA, Schalk BW et al. The development of the Older Persons and Informal Caregivers Survey Minimum DataSet (TOPICS-MDS): A large-scale data sharing initiative. PLoS ONE 2013;8:e81673. 6. Weinberger M, Samsa GP, Schmader K et al. Comparing proxy and patients’ perceptions of patients’ functional status: Results from an outpatient geriatric clinic. J Am Geriatr Soc 1992;40:585–588. 7. Tennant A, Conaghan C. The Rasch Measurement Model in rheumatology: What is it and why use it? When should it be applied, and what should one look for in a Rasch Paper? Arthritis Rheum 2007;57:1358–1362. 8. Pallant JF, Miller RL, Tennant A. Evaluation of the Edinburgh Post Natal Depression Scale using Rasch analysis. BMC Psychiatry 2006;6:28. 9. McDowell I. Physical Disability and Handicap. Measuring Health: A Guide to Rating Scales and Questionnaires. New York: Oxford University Press, Inc., 2006.
HEALTHCARE COSTS IN OLDER ADULTS WITH DIABETES MELLITUS: CHALLENGES FOR HEALTH SYSTEMS AND FOR SOCIETY To the Editor: Diabetes mellitus and its complications are a great economic challenge for any health system. Many countries have a growing worldwide diabetes mellitus problem. Projections from 2010 to 2030 estimate that
