Evidence map›Paper›PMID 39396253›Full record

ArticleInternational journal of epidemiology2024

Probabilistic bias analysis for exposure misclassification of household income by neighbourhood in a cohort of individuals with colorectal cancer.

Laura E Davis, Hailey R Banack, Renzo Calderon-Anyosa, Erin C Strumpf, Alyson L Mahar

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Article in International journal of epidemiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Laura E DavisDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
Hailey R BanackDalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.ORCID 0000-0002-6230-2245
Renzo Calderon-AnyosaDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
Erin C StrumpfDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.
Alyson L MaharSchool of Nursing, Queens University, Kingston, Ontario, Canada.ORCID 0000-0002-6862-3760

Funding

Canadian Cancer SocietyCanadian Centre for Applied Research in Cancer ControlCIHR 457377Fonds de recherche du Québec Santé, Doctoral Training ScholarshipStundship Award
6 · The paper itself

Abstract

introductionDespite poor agreement, neighbourhood income is used as a proxy for household income, due to a lack of data availability. We quantified misclassification between household and neighbourhood income and demonstrate quantitative bias analysis (QBA) in scenarios where only neighbourhood income is available in assessing income inequalities on colorectal cancer mortality.

methodsThis was a retrospective study of adults with colorectal cancer diagnosed 2006-14 from Statistics Canada's Canadian Census Health and Environment Cohort. Neighbourhood income quintiles from Statistics Canada were used. Census household income quintiles were used to determine bias parameters and confirm results of the QBA. We calculated positive and negative predictive values using multinomial models, adjusting for age, sex and rural residence. Probabilistic QBA was conducted to explore the implication of exposure misclassification when estimating the effect of income on 5-year mortality.

resultsWe found poor agreement between neighbourhood and household income: positive predictive values ranged from 21% to 37%. The bias-adjusted risk of neighbourhood income on 5-year mortality was similar to the risk of mortality by household income. The bias-adjusted relative risk of the lowest income quintile compared with the highest was 1.42 [95% simulation interval (SI) 1.32-1.53] compared with 1.46 [95% confidence interval (CI) 1.39-1.54] for household income and 1.18 (95% CI 1.12-1.24) for neighbourhood income.

conclusionQBA can be used to estimate adjusted effects of neighbourhood income on mortality which represent household income. The predictive values from our study can be applied to similar cohorts with only neighbourhood income to estimate the effects of household income on cancer mortality.

Indexed as

BiasColorectal NeoplasmsIncomeAdultAgedAged, 80 and overCanadaFemaleHumansMaleMiddle AgedNeighborhood CharacteristicsResidence CharacteristicsRetrospective StudiesArea-level incomecancercolorectal cancerexposure misclassificationhousehold incomeincome inequalitiesindividual incomeneighbourhood incomequantitative bias analysis

Identifiers

PMID39396253
PMCPMC11471264

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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.