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Purpose: Our aim was to investigate the roles of rurality and distance to care on adverse perinatal outcomes and COVID‐19 seroprevalence at the time of delivery over a 1‐year period. Methods: Data were collected from the electronic medical record on all pregnant patients who delivered at a single, large, Midwest academic medical center over 1 year. Rurality was classified using standard Rural‐Urban Commuting Area codes. Geographic Information System tools were used to map outcomes. Data were analyzed with univariate and multivariate models, controlling for Body Mass Index (BMI), insurance status, and parity. Findings: A total of 2,497 patients delivered during the study period; 20% of patients were rural (n = 499), 18.6% were micropolitan (n = 466), and 61.4% were metropolitan (n = 1,532). 10.4% of patients (n = 259) were COVID‐19 seropositive. Rural patients did not experience higher rates of any measured adverse outcomes than metropolitan patients; micropolitan patients had increased odds of preterm labor (OR = 1.41, P =.022) and pre‐eclampsia (OR = 1.78, P<.001). Patients living 30+ miles away from the medical center had increased odds of preterm labor (OR = 1.94, P<.001), pre‐eclampsia (OR = 1.73, P =.002), and infant admission to the neonatal intensive care unit (OR = 2.12, P<.001), as well as lower gestational age at delivery (β = −9.2 days, P<.001) and birth weight (β = −206 grams, P<.001). Conclusion: Distance to care, rather than rurality, was the key predictor of multiple adverse perinatal outcomes in this cohort of deliveries over a 1‐year period. Our study suggests that rurality should not be used as a standalone indicator of access to care without further knowledge of the specific barriers affecting a given population. [ABSTRACT FROM AUTHOR]

Background: Telehealth has been proposed as one strategy to improve the quality of time‐sensitive sepsis care in rural emergency departments (EDs). The purpose of this study was to measure the association between telehealth‐supplemented ED (tele‐ED) care, health care costs, and clinical outcomes among patients with sepsis in rural EDs. Methods: Cohort study using Medicare fee‐for‐service claims data for beneficiaries treated for sepsis in rural EDs between February 1, 2017, and September 30, 2019. Our primary hospital‐level analysis used multivariable generalized estimating equations to measure the association between treatment in a tele‐ED–capable hospital and 30‐day total costs of care. In our supporting secondary analysis, we conducted a propensity‐matched analysis of patients who used tele‐ED with matched controls from non–tele‐ED–capable hospitals. Our primary outcome was total health care payments among index hospitalized patients between the index ED visit and 30 days after hospital discharge, and our secondary outcomes included hospital mortality, hospital length of stay, 90‐day mortality, 28‐day hospital‐free days, and 30‐day inpatient readmissions. Results: In our primary analysis, sepsis patients in tele‐ED–capable hospitals had 6.7% higher (95% confidence interval [CI] 2.1%–11.5%) total health care costs compared to those in non–tele‐ED–capable hospitals. In our propensity‐matched patient‐level analysis, total health care costs were 23% higher (95% CI 16.5%–30.4%) in tele‐ED cases than matched non–tele‐ED controls. Clinical outcomes were similar. Conclusions: Tele‐ED capability in a mature rural tele‐ED network was not associated with decreased health care costs or improved clinical outcomes. Future work is needed to reduce rural–urban sepsis care disparities and formalize systems of regionalized care. [ABSTRACT FROM AUTHOR]

In naturalistic studies, it is vital to give appropriate context when analyzing driving behaviors. Such contextualization can help address the hypotheses that explore a) how drivers perform within specific types of environment (e.g., road types, speed limits, etc.), and b) how often drivers are exposed to such specific environments. In order to perform this contextualization in an automated fashion, we are using Global Positioning System (GPS) data obtained at 1 Hz and merging this with Geographic Information Systems (GIS) databases maintained by the Iowa Department of Transportation (DOT). In this paper, we demonstrate our methods of doing this based on data from 43 drivers with obstructive sleep apnea (OSA). We also use maps from GIS software to illustrate how information can be displayed at the individual drive or day level, and we provide examples of some of the challenges that still need to be addressed.