Evidence map›Paper›PMID 37679673›Full record

ArticleBMC medical research methodology2023

Ascertaining asthma status in epidemiologic studies: a comparison between administrative health data and self-report.

Marie-Claude Rousseau, Florence Conus, Mariam El-Zein, Andrea Benedetti, Marie-Elise Parent

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.2field-weighted citation impact, top 8% of its field
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

8 citing papers in PubMed, 18 citations in OpenAlex.

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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

5 authors at 5 institutions in 1 country.

Marie-Claude RousseauEpidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique (INRS), Laval, QC, Canada. marie-claude.rousseau@inrs.ca.
Florence ConusEpidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique (INRS), Laval, QC, Canada.
Mariam El-ZeinEpidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique (INRS), Laval, QC, Canada.
Andrea BenedettiRespiratory Epidemiology and Clinical Research Unit, Research Institute of the McGill University Health Centre, Montréal, QC, Canada.
Marie-Elise ParentEpidemiology and Biostatistics Unit, Centre Armand-Frappier Santé Biotechnologie, Institut national de la recherche scientifique (INRS), Laval, QC, Canada.
Institut National de la Recherche Scientifique · CAMcGill University · CAMcGill University Health Centre · CAUniversité de Montréal · CAUniversité du Québec · CA

Funding

CIHR MCH-97593CIHR MOP-97777
6 · The paper itself

Abstract

backgroundStudies have suggested that agreement between administrative health data and self-report for asthma status ranges from fair to good, but few studies benefited from administrative health data over a long period. We aimed to (1) evaluate agreement between asthma status ascertained in administrative health data covering a period of 30 years and from self-report, and (2) identify determinants of agreement between the two sources.

methodsWe used administrative health data (1983-2012) from the Quebec Birth Cohort on Immunity and Health, which included 81,496 individuals born in the province of Quebec, Canada, in 1974. Additional information, including self-reported asthma, was collected by telephone interview with 1643 participants in 2012. By design, half of them had childhood asthma based on health services utilization. Results were weighted according to the inverse of the sampling probabilities. Five algorithms were applied to administrative health data (having ≥ 2 physician claims over a 1-, 2-, 3-, 5-, or 30-year interval or ≥ 1 hospitalization), to enable comparisons with previous studies. We estimated the proportion of overall agreement and Kappa, between asthma status derived from algorithms and self-reports. We used logistic regression to identify factors associated with agreement.

resultsApplying the five algorithms, the prevalence of asthma ranged from 49 to 55% among the 1643 participants. At interview (mean age = 37 years), 49% and 47% of participants respectively reported ever having asthma and asthma diagnosed by a physician. Proportions of agreement between administrative health data and self-report ranged from 88 to 91%, with Kappas ranging from 0.57 (95% CI: 0.52-0.63) to 0.67 (95% CI: 0.62-0.72); the highest values were obtained with the [≥ 2 physician claims over a 30-year interval or ≥ 1 hospitalization] algorithm. Having sought health services for allergic diseases other than asthma was related to lower agreement (Odds ratio = 0.41; 95% CI: 0.25-0.65 comparing ≥ 1 health services to none).

conclusionsThese findings indicate good agreement between asthma status defined from administrative health data and self-report. Agreement was higher than previously observed, which may be due to the 30-year lookback window in administrative data. Our findings support using both administrative health data and self-report in population-based epidemiological studies.

Indexed as

AsthmaAdultAlgorithmsCanadaChildEpidemiologic StudiesHumansSelf ReportAdministrative health dataAgreementAsthmaSelf-report

Identifiers

PMID37679673
PMCPMC10486089
OpenAlexW4386497584

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.