Frequencies and predictors of missing values as an indicator of data quality in a large population-based sample: an analysis of baseline data from the Hamburg City Health Study
Objective Data quality in epidemiological studies is a basic requirement for good scientific research. The aim of this study was to examine an important indicator of data quality, data completeness, by investigating predictors of missing data. - Methods Baseline data of a cohort study, the populatio...
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| Hauptverfasser: | , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
November 19, 2025
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| In: |
BMJ open
Year: 2025, Jahrgang: 15, Heft: 11, Pages: 1-9 |
| ISSN: | 2044-6055 |
| DOI: | 10.1136/bmjopen-2025-103154 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1136/bmjopen-2025-103154 Verlag, kostenfrei, Volltext: https://bmjopen.bmj.com/content/15/11/e103154 
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| Verfasserangaben: | Christian Wiessner, Janina Freitag, Heiko Becher, Martin Härter, Olaf von dem Knesebeck, Elina L. Petersen, Katharina Stahlmann, Peer Briken, Holger Schulz, Christiane Bleich |
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| 520 | |a Objective Data quality in epidemiological studies is a basic requirement for good scientific research. The aim of this study was to examine an important indicator of data quality, data completeness, by investigating predictors of missing data. - Methods Baseline data of a cohort study, the population-based Hamburg City Health Study, were used. Missingness was investigated at the levels of a whole research unit, on the two segments of health service utilisation and psychosocial variables, and two sensitive items (income and number of sexual partners). Predictors for missingness were sociodemographic variables, cognitive abilities and the mode of data collection. Associations were estimated using binary and multinomial logistic regression models. - Results Of 10 000 participants (mean age=62.4 years; 51.1% women), 32.9% had complete data at the unit level, 66.8% had partially missing data and 0.3% missed all items. The highest proportions of missing values were found for income (27.8%) and the number of sexual partners (36.7%). At both the unit, segment and item level, older age, female sex, low education, a foreign mother language and cognitive impairment were significant predictors for missingness. - Conclusion For analysing population-based data, dealing with missingness is equally important at all levels of analysis. During the design and conduct of the study, the identified groups may be targeted to reach higher levels of data completeness. | ||
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| 700 | 1 | |a Härter, Martin |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Petersen, Elina L. |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Schulz, Holger |e VerfasserIn |4 aut | |
| 700 | 1 | |a Bleich, Christiane |e VerfasserIn |4 aut | |
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