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Sci Rep. 2019 Mar 5;9(1):3527. doi: 10.1038/s41598-019-40072-0.

Comparison of self-reported and register-based hospital medical data on comorbidities in women.

Author information

1
Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore.
2
Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
3
Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Old road campus, OX3 7LF, Oxford, UK.
4
Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Box 281, 171 77, Stockholm, Sweden.
5
Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore.
6
Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
7
Department of Pediatrics, Örebro University Hospital, Örebro University, Örebro, Sweden.
8
Department of Oncology, Södersjukhuset, 118 84, Stockholm, Sweden.
9
Genome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore. lijm1@gis.a-star.edu.sg.
10
Karolinska Institutet, Department of Medical Epidemiology and Biostatistics, Box 281, 171 77, Stockholm, Sweden. lijm1@gis.a-star.edu.sg.
11
Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore. lijm1@gis.a-star.edu.sg.

Abstract

Breast cancer patients commonly present with comorbidities which are known to influence treatment decisions and survival. We aim to examine agreement between self-reported and register-based medical records (National Patient Register [NPR]). Ascertainment of nine conditions, using individually-linked data from 64,961 women enrolled in the Swedish KARolinska MAmmography Project for Risk Prediction of Breast Cancer (KARMA) study. Agreement was assessed using observed proportion of agreement (overall agreement), expected proportion of agreement, and Cohen's Kappa statistic. Two-stage logistic regression models taking into account chance agreement were used to identify potential predictors of overall agreement. High levels of overall agreement (i.e. ≥86.6%) were observed for all conditions. Substantial agreement (Cohen's Kappa) was observed for myocardial infarction (0.74), diabetes (0.71) and stroke (0.64) between self-reported and NPR data. Moderate agreement was observed for preeclampsia (0.51) and hypertension (0.46). Fair agreement was observed for heart failure (0.40) and polycystic ovaries or ovarian cysts (0.27). For hyperlipidemia (0.14) and angina (0.10), slight agreement was observed. In most subgroups we observed negative specific agreement of >90%. There is no clear reference data source for ascertainment of conditions. Negative specific agreement between NPR and self-reported data is consistently high across all conditions.

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