Literature DB >> 31107529

Methodological Challenges When Studying Distance to Care as an Exposure in Health Research.

Ellen C Caniglia1,2, Rebecca Zash3, Sonja A Swanson2,4, Kathleen E Wirth5, Modiegi Diseko6, Gloria Mayondi6, Shahin Lockman6,7,8, Mompati Mmalane6, Joseph Makhema6, Scott Dryden-Peterson6,7,8, Kalé Z Kponee-Shovein, Oaitse John6, Eleanor J Murray2, Roger L Shapiro3,6,8.   

Abstract

Distance to care is a common exposure and proposed instrumental variable in health research, but it is vulnerable to violations of fundamental identifiability conditions for causal inference. We used data collected from the Botswana Birth Outcomes Surveillance study between 2014 and 2016 to outline 4 challenges and potential biases when using distance to care as an exposure and as a proposed instrument: selection bias, unmeasured confounding, lack of sufficiently well-defined interventions, and measurement error. We describe how these issues can arise, and we propose sensitivity analyses for estimating the degree of bias.
© The Author(s) 2019. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  causal diagrams; causal inference; distance to care; identifiability conditions; instrumental variables; selection bias; unmeasured confounding; well-defined interventions

Year:  2019        PMID: 31107529      PMCID: PMC6735874          DOI: 10.1093/aje/kwz121

Source DB:  PubMed          Journal:  Am J Epidemiol        ISSN: 0002-9262            Impact factor:   4.897


  39 in total

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6.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions.

Authors:  M A Hernán; S L Taubman
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Journal:  Health Serv Res       Date:  2005-04       Impact factor: 3.402

8.  Do pediatric patients with trauma in Florida have reduced mortality rates when treated in designated trauma centers?

Authors:  Etienne E Pracht; Joseph J Tepas; Barbara Langland-Orban; Lisa Simpson; Pam Pieper; Lewis M Flint
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9.  Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships.

Authors:  Jeremy A Rassen; M Alan Brookhart; Robert J Glynn; Murray A Mittleman; Sebastian Schneeweiss
Journal:  J Clin Epidemiol       Date:  2009-04-08       Impact factor: 6.437

Review 10.  Epidemiology and causes of preterm birth.

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  2 in total

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2.  Changes in Behavior with Increasing Pregnancy Attempt Time: A Prospective Cohort Study.

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