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. 2019 Sep 1;188(9):1674-1681.
doi: 10.1093/aje/kwz121.

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

Ellen C Caniglia  1   2 , Rebecca Zash  3 , Sonja A Swanson  2   4 , Kathleen E Wirth  5 , Modiegi Diseko  6 , Gloria Mayondi  6 , Shahin Lockman  6   7   8 , Mompati Mmalane  6 , Joseph Makhema  6 , Scott Dryden-Peterson  6   7   8 , Kalé Z Kponee-Shovein, Oaitse John  6 , Eleanor J Murray  2 , Roger L Shapiro  3   6   8
Affiliations

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

Ellen C Caniglia et al. Am J Epidemiol. .

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.

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

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Figures

Figure 1.
Figure 1.
Causal diagram including distance, outcome (in our case study, stillbirth), selection, and a shared cause U of selection and outcome. Selection bias might occur via conditioning on selection into the study, a collider on the path following distance, selection, U, and outcome. In our case study, U was measured among those included in the study but not among those not included.
Figure 2.
Figure 2.
Causal diagram including distance, outcome (in our case study, stillbirth), and an unmeasured shared cause U of distance and outcome. Confounding might occur via the path following distance, U, and outcome.
Figure 3.
Figure 3.
Causal diagram including distance, outcome (in our case study, stillbirth), versions of distance Distance(v), and shared causes L(v) of Distance(v) and outcome. Confounding might occur via the path following Distance(v), L(v), and outcome. A) Three versions of distance and 3 shared causes for each version of distance and the outcome. B) A simplified causal diagram including 1 node for versions of distance and 1 node for shared causes of versions of distance and the outcome.
Figure 4.
Figure 4.
Causal diagram including a proposed proxy instrument distance, proposed causal instrument Distance(v), exposure, outcome (in our case study, stillbirth), and unmeasured shared cause U of exposure and outcome. (Note that this U might differ from the U in Figures 1 and 2.)
Figure 5.
Figure 5.
Causal diagram including proposed proxy instrument distance, proposed causal instrument Distance(v), exposure, outcome (in our case study, stillbirth), selection, and unmeasured shared cause U of exposure, outcome, and selection. Selection bias might occur via conditioning on selection into the study, a collider on the path following distance, Distance(v), selection, U, and outcome.
Figure 6.
Figure 6.
Causal diagram including proposed proxy instrument distance, proposed causal instrument Distance(v), exposure, outcome (in our case study, stillbirth), unmeasured shared cause U1 of Distance(v) and outcome, and unmeasured shared cause U2 of exposure and outcome. Confounding might occur via the path following distance, Distance(v), U1, and outcome. Note that U1 And U2 might not be distinct.
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Comment in

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