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

Distance to care is a common exposure and proposed instrumental variable in health research, but vulnerable to violations of fundamental identifiability conditions for causal inference. In this paper, we use data collected from The Botswana Birth Outcomes Surveillance study between 2014 and 2016 to outline four 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 propose sensitivity analyses for estimating the degree of bias.

[1]  M. Hernán,et al.  The challenging interpretation of instrumental variable estimates under monotonicity. , 2018, International journal of epidemiology.

[2]  P. Lumbiganon,et al.  The global epidemiology of preterm birth. , 2018, Best practice & research. Clinical obstetrics & gynaecology.

[3]  Miguel A Hernán,et al.  The C-Word: Scientific Euphemisms Do Not Improve Causal Inference From Observational Data , 2018, American journal of public health.

[4]  Tyler J. VanderWeele,et al.  Sensitivity Analysis in Observational Research: Introducing the E-Value , 2017, Annals of Internal Medicine.

[5]  M. Hernán.  Invited Commentary: Selection Bias Without Colliders. , 2017, American journal of epidemiology.

[6]  T. VanderWeele.  Commentary: On Causes, Causal Inference, and Potential Outcomes. , 2017, International journal of epidemiology.

[7]  Miguel A Hernán,et al.  Does water kill? A call for less casual causal inferences. , 2016, Annals of epidemiology.

[8]  R. Noor,et al.  Risk factors for small-for-gestational-age and preterm births among 19,269 Tanzanian newborns , 2016, BMC Pregnancy and Childbirth.

[9]  S. Lockman,et al.  Reassuring Birth Outcomes With Tenofovir/Emtricitabine/Efavirenz Used for Prevention of Mother-to-Child Transmission of HIV in Botswana , 2016, Journal of acquired immune deficiency syndromes.

[10]  T. Fenton,et al.  INTERGROWTH-21st very preterm size at birth reference charts , 2016, The Lancet.

[11]  M. Mathai,et al.  Stillbirths: ending preventable deaths by 2030 , 2016, The Lancet.

[12]  J. Hirst,et al.  Stillbirths: rates, risk factors, and acceleration towards 2030 , 2016, The Lancet.

[13]  John W Jackson,et al.  Toward a clearer portrayal of confounding bias in instrumental variable applications. , 2015, Epidemiology.

[14]  J. Cox,et al.  Maternal mortality and distance to facility-based obstetric care in rural southern Tanzania: a secondary analysis of cross-sectional census data in 226 000 households. , 2015, The Lancet. Global health.

[15]  James M. Robins,et al.  Definition and Evaluation of the Monotonicity Condition for Preference-based Instruments , 2015, Epidemiology.

[16]  José Villar,et al.  International standards for newborn weight, length, and head circumference by gestational age and sex: the Newborn Cross-Sectional Study of the INTERGROWTH-21st Project , 2014, The Lancet.

[17]  M. Hernán,et al.  Think globally, act globally: An epidemiologist's perspective on instrumental variable estimation. , 2014, Statistical science : a review journal of the Institute of Mathematical Statistics.

[18]  C. Ronsmans,et al.  Maternal mortality, birth with a health professional and distance to obstetric care in Indonesia and Bangladesh , 2013, Tropical medicine & international health : TM & IH.

[19]  Miguel A Hernán,et al.  Commentary: how to report instrumental variable analyses (suggestions welcome). , 2013, Epidemiology.

[20]  S. Gabrysch,et al.  Distance to Care, Facility Delivery and Early Neonatal Mortality in Malawi and Zambia , 2012, PloS one.

[21]  S. Lockman,et al.  Highly active antiretroviral therapy and adverse birth outcomes among HIV-infected women in Botswana. , 2012, The Journal of infectious diseases.

[22]  B. Sartorius,et al.  The effect of distance to formal health facility on childhood mortality in rural Tanzania, 2005–2007 , 2012, Global health action.

[23]  K. Edmond,et al.  Proximity to health services and child survival in low- and middle-income countries: a systematic review and meta-analysis , 2012, BMJ Open.

[24]  S. Karim,et al.  HIV prevention transformed: the new prevention research agenda , 2011, The Lancet.

[25]  M. Hernán,et al.  Compound Treatments and Transportability of Causal Inference , 2011, Epidemiology.

[26]  Sebastian Schneeweiss,et al.  Instrumental variable methods in comparative safety and effectiveness research , 2010, Pharmacoepidemiology and drug safety.

[27]  M Alan Brookhart,et al.  Instrumental variables I: instrumental variables exploit natural variation in nonexperimental data to estimate causal relationships. , 2009, Journal of clinical epidemiology.

[28]  Tyler J. VanderWeele,et al.  Concerning the consistency assumption in causal inference. , 2009, Epidemiology.

[29]  Timothy L. Lash,et al.  Applying Quantitative Bias Analysis to Epidemiologic Data , 2009, Statistics for Biology and Health.

[30]  M. Hernán,et al.  Does obesity shorten life? The importance of well-defined interventions to answer causal questions , 2008, International Journal of Obesity.

[31]  Roberto Romero,et al.  Epidemiology and causes of preterm birth , 2008, The Lancet.

[32]  J. Pendergast,et al.  Effect of dialysis center profit-status on patient survival: a comparison of risk-adjustment and instrumental variable approaches. , 2006, Health services research.

[33]  J. Robins,et al.  Instruments for Causal Inference: An Epidemiologist's Dream? , 2006, Epidemiology.

[34]  Patrick Royston,et al.  The cost of dichotomising continuous variables , 2006, BMJ : British Medical Journal.

[35]  Donna Spiegelman,et al.  Easy SAS calculations for risk or prevalence ratios and differences. , 2005, American journal of epidemiology.

[36]  B J McNeil,et al.  Does more intensive treatment of acute myocardial infarction in the elderly reduce mortality? Analysis using instrumental variables. , 1994, JAMA.

[37]  P. Spirtes,et al.  Causal inference , 1991, Erkenntnis.

[38]  Stephen R Cole,et al.  The consistency statement in causal inference: a definition or an assumption? , 2009, Epidemiology.

[39]  Etienne E. Pracht,et al.  Do pediatric patients with trauma in Florida have reduced mortality rates when treated in designated trauma centers? , 2008, Journal of pediatric surgery.

[40]  C. Newgard,et al.  Mortality Benefit of Transfer to Level I versus Level II Trauma Centers for Head-Injured Patients: Mortality Benefit of Level I Trauma Centers , 2005 .

[41]  J. Pearl,et al.  Cover Letter: a Cover Letter Must Accompany the Submitted Manuscript Stating That the on the Consistency Rule in Causal Inference , 2022 .