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      Official statistics and claims data records indicate non-response and recall bias within survey-based estimates of health care utilization in the older population

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          Abstract

          Background

          The validity of survey-based health care utilization estimates in the older population has been poorly researched. Owing to data protection legislation and a great number of different health care insurance providers, the assessment of recall and non-response bias is challenging to impossible in many countries. The objective of our study was to compare estimates from a population-based study in older German adults with external secondary data.

          Methods

          We used data from the German KORA-Age study, which included 4,127 people aged 65–94 years. Self-report questions covered the utilization of long-term care services, inpatient services, outpatient services, and pharmaceuticals. We calculated age- and sex-standardized mean utilization rates in each domain and compared them with the corresponding estimates derived from official statistics and independent statutory health insurance data.

          Results

          The KORA-Age study underestimated the use of long-term care services (−52%), in-hospital days (−21%) and physician visits (−70%). In contrast, the assessment of drug consumption by postal self-report questionnaires yielded similar estimates to the analysis of insurance claims data (−9%).

          Conclusion

          Survey estimates based on self-report tend to underestimate true health care utilization in the older population. Direct validation studies are needed to disentangle the impact of recall and non-response bias.

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          Most cited references32

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          Non-response and related factors in a nation-wide health survey.

          To analyse selective factors associated with an unexpectedly low response rate. The baseline questionnaire survey of a large prospective follow-up study on the psychosocial health of the Finnish working-aged randomly chosen population resulted in 21,101 responses (40.0%) in 1998. The non-respondent analysis used demographic and health-related population characteristics from the official statistics and behavioural, physical and mental health-related outcome differences between early and late respondents to predict possible non-response bias. Reasons for non-response, indicated by missing responses of late respondents, and factors affecting the giving of consent were also analysed. The probability of not responding was greater for men, older age groups, those with less education, divorced and widowed respondents, and respondents on disability pension. The physical health-related differences between the respondents and the general population were small and could be explained by differences in definitions. The late respondents smoked and used more psychopharmaceutical drugs than the early ones, suggesting similar features in non-respondents. The sensitive issues had a small effect on the response rate. The consent to use a medical register-based follow-up was obtained from 94.5% of the early and 90.9% of the late respondents (odds ratio: 1.70; 95% confidence interval: 1.49-1.93). Consent was more likely among respondents reporting current smoking, heavy alcohol use, panic disorder or use of tranquillisers. The main reasons for non-response may be the predisposing sociodemographic and behavioural factors, the length and sensitive nature of the questionnaire to some extent, and a suspicion of written consent and a connection being made between the individual and the registers mentioned on the consent form.
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            How accurate are self-reports? Analysis of self-reported health care utilization and absence when compared with administrative data.

            To determine the accuracy of self-reported health care utilization and absence reported on health risk assessments against administrative claims and human resource records. Self-reported values of health care utilization and absenteeism were analyzed for concordance to administrative claims values. Percent agreement, Pearson's correlations, and multivariate logistic regression models examined the level of agreement and characteristics of participants with concordance. Self-report and administrative data showed greater concordance for monthly compared with yearly health care utilization metrics. Percent agreement ranged from 30% to 99% with annual doctor visits having the lowest percent agreement. Younger people, males, those with higher education, and healthier individuals more accurately reported their health care utilization and absenteeism. Self-reported health care utilization and absenteeism may be used as a proxy when medical claims and administrative data are unavailable, particularly for shorter recall periods.
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              Self-reports of health care utilization compared to provider records.

              This study compares self-reports of medical utilization with provider records. As part of a chronic disease self-management intervention study, patients completed self-reports of their last six months of health care utilization. A subgroup of patients was selected from the larger study and their self-reports of utilization were compared to computerized utilization records. Consistent with earlier studies, patients tended to report less physician utilization than was recorded in the computerized provider records. However, they also tended to report slightly more emergency room visits than were reported in the computerized utilization records. There was no association between demographic or health variables and the tendency toward discrepancy between self-report and computerized utilization record reports. However, there was a tendency for the discrepancy to increase as the amount of record utilization increased. Thus, the likelihood of bias caused by differing demographic factors is low, but researchers should take into account that underreporting occurs and is likely to increase as utilization increases.
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                Author and article information

                Journal
                BMC Health Serv Res
                BMC Health Serv Res
                BMC Health Services Research
                BioMed Central
                1472-6963
                2013
                3 January 2013
                : 13
                : 1
                Affiliations
                [1 ]Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Health Economics and Health Care Management, Ingolstädter Landstr. 1, Neuherberg, 85764, Germany
                [2 ]Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology II, Ingolstädter Landstr. 1, Neuherberg, 85764, Germany
                Article
                1472-6963-13-1
                10.1186/1472-6963-13-1
                3545728
                23286781
                1b37154a-5cf2-4c62-bc54-cff4087dfd43
                Copyright ©2013 Hunger et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 21 May 2012
                : 28 December 2012
                Categories
                Research Article

                Health & Social care
                health care utilization,self-report,validity,survey,response bias,recall bias,claims data

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