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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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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Existing literature on the benefits of electronic records is largely U.S. or primary care based and reports potential rather than actual benefits, with a large proportion of this evidence reporting the potential for electronic records to improve patient safety.
“Existing literature on the benefits of electronic records is largely U.S or primary care based [18–34] and reports ‘potential’ rather than actual benefits; with a large proportion of this evidence reporting the potential for electronic records to improve patient safety [19, 21, 23, 27–30].”
From Exploring the implementation of an electronic record into a maternity unit: a qualitative study using Normalisation Process Theory (2017) · cites this paper as [22]
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- Authors
- KORA Study Group, Hunger, M., Schwarzkopf, L., Heier, M., Peters, A., & Holle, R.
- Affiliations
- Helmholtz Munich (DE)
- Venue
- BMC Health Services Research
- Published
- 2013-01-03
- Volume
- 13
- Issue
- 1
- Pages
- 1-1
- ISSN
- 1472-6963 (ISSN-L)
- PMC
- PMC3545728
- OpenAlex
- W2032279019
- Cited by
- 261
- References
- 40
- Citations by year
- 2026: 7 · 2025: 20 · 2024: 27 · 2023: 14 · 2022: 20 · 2021: 30 · 2020: 15 · 2019: 23 · 2018: 23 · 2017: 30 · 2016: 23 · 2015: 15 · 2014: 13 · 2013: 1
- FWCI
- 53.40
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Health Services Research (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Site cant be reached (submittedVersion, cc-by)
- Funders
- Bundesministerium für Bildung und Forschung (BMBF FKZ 01ET0713, FKZ 01ET0713, 01ET0713)
- Helmholtz Zentrum München (FKZ 01ET0713)
- Topics
- Social Sciences › Sociology and Political Science › Survey Methodology and Nonresponse
- Health Sciences › Health Professions › General Health Professions › Health and Medical Studies
- Health Sciences › Health Professions › Health Information Management › Medical Coding and Health Information
- Keywords
- health care utilization, older adults, recall bias, nonresponse bias, survey validity, self-report questionnaire, long-term care, inpatient services, physician visits, drug use, health insurance claims, KORA-Age study
- MeSH terms
- Aged, Aged, 80 and over, Chronic Disease, Drug Utilization, Female, Germany, Health Services, Humans, Insurance Claim Review, Male, Mental Recall, Bias, Confidence Intervals, Odds Ratio, Sex Distribution, Health Care Surveys, Self Report
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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