Neighborhood social determinants of health and postoperative weight loss

New publication – Pratt, K.J., Hanks, A.S., Miller, H.J., Outrich, M., Breslin, L., Blalock, J., Noria, S., Brethauer, S., Needleman, B. and Focht, B. (2022) “The BARI-hoods Project: Neighborhood social determinants of health and postoperative weight loss using integrated EHR, Census, and county data,” Surgery for Obesity and Related Diseases, online first.

(I may be the first geographer to co-author a paper in Surgery for Obesity and Related Diseases, but I am willing to be corrected.)

HIGHLIGHTS

  • While living in close proximity to foods stores does not ensure utilization, in this study, patients who lived within a 10-minute walk to food stores had better weight loss two years after bariatric surgery.
  • Black patients with access to more food stores within a 10-minute walk and White patients with more access within a 5-minute walk had greater %TWL (percent total weight loss)  over 24 months.
  • Living in areas with lower poverty levels did not negatively affect weight loss for Black patients.
  • There were no significant associations for weight loss based on unemployment rate or proximity to fitness/recreational facilities and percent open area.

ABSTRACT
Background.  While social determinants of health (SDoH) have gained attention for their role in weight loss following bariatric surgery; electronic health record (EHR) data provides limited information beyond demographics associated with disparities in weight loss.

Objective. To integrate EHR, Census, and county data to explore disparities in SDoH and weight loss among patients in the largest populous county of Ohio.

Setting. 772 patients (82.1% female; 37.0% Black) who had primary bariatric surgery (48.7% gastric bypass) from 2015-2019 at The Ohio State University.

Methods. EHR variables included race, insurance, procedure, and %TWL at 2/3, 6, 12, and 24 months. Census variables included poverty and unemployment rates. County variables included food stores, fitness/recreational facilities, and open area within a 5- and 10-minute walk from home. Two mixed multilevel models were conducted with %TWL over 24 months, with visits as the between subjects factor; race, Census, county, insurance, and procedure variables were covariates. Two additional sets of models determined within group differences for Black and White patients.

Results. Access to more food stores within a 10-minute walk was associated with greater %TWL over 24 months (p=0.029). Black patients with access to more food stores within a 10-minute (p=0.017) and White patients with more access within a 5-minute walk (p=0.015) had greater %TWL over 24 months. Black patients who lived in areas with higher poverty rates (p=0.036) experienced greater %TWL over 24 months. No significant differences were found for unemployment rate or proximity to fitness/recreational facilities and open area.

Conclusion. Close proximity to food stores is associated with better weight loss two years after bariatric surgery. Lower poverty levels did not negatively affect Black patient weight loss.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data

New paper:  Li, Y., Miller, H.J., Root, E.D., Hyder, A. and Liu, D. “Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data,” Health and Place, 75, 102792.

Abstract: Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, “found” geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency “311” service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50–64 was positively associated with risk of an OOE but age 35–49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor’s degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

Opioid treatment deserts

The latest outcome from our opioid overdose mapping project: we find disparities across neighborhoods and racial groups in access to opioid treatment providers:

Hyder A, Lee J, Dundon A, Southerland LT, All D, Hammond G, and Miller, H.J. (2021) Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county. PLoS ONE 16(5): e0250324. https://doi.org/10.1371/journal.pone.0250324

Abstract

Objectives.  An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data.
Methods

We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types.

Results. A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26–35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race.

Conclusions.  Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.