Evidence map›Paper›PMID 30837593›Full record

ArticleScientific reports2019

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

Peh Joo Ho, Chuen Seng Tan, Shajedur Rahman Shawon, Mikael Eriksson, Li Yan Lim, Hui Miao, Eileen Png, Kee Seng Chia, Mikael Hartman, Jonas F Ludvigsson and 3 more

Abstract read
In one paragraph

Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

21 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors.

Peh Joo HoGenome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore.
Chuen Seng TanSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Shajedur Rahman ShawonCancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Old road campus, OX3 7LF, Oxford, UK.ORCID 0000-0003-4502-1457
Mikael ErikssonKarolinska Institutet, Department of Medical Epidemiology and Biostatistics, Box 281, 171 77, Stockholm, Sweden.
Li Yan LimDepartment of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore.
Hui MiaoGenome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore.
Eileen PngGenome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore.ORCID 0000-0001-5586-6395
Kee Seng ChiaSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Mikael HartmanSaw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore.
Jonas F LudvigssonKarolinska Institutet, Department of Medical Epidemiology and Biostatistics, Box 281, 171 77, Stockholm, Sweden.
Kamila CzeneKarolinska Institutet, Department of Medical Epidemiology and Biostatistics, Box 281, 171 77, Stockholm, Sweden.
Per HallKarolinska Institutet, Department of Medical Epidemiology and Biostatistics, Box 281, 171 77, Stockholm, Sweden.
Jingmei LiGenome Institute of Singapore, 60 Biopolis Street, Genome, #02-01, Singapore, 138672, Singapore. lijm1@gis.a-star.edu.sg.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

ComorbidityDatabases, FactualAdultAgedAged, 80 and overFemaleHeart FailureHumansHypertensionLogistic ModelsMiddle AgedMyocardial InfarctionRegistriesSelf ReportSwedenYoung Adult

Identifiers

PMID30837593
PMCPMC6400937

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.