Health

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis

The latest paper from the Franklin County Opioid Crisis Activity Level (FOCAL) mapping project, led by my former student Dr. Yuchen Li, in collaboration with Dr. Ayaz Hyder from OSU College of Public Health.

Li, Y., Miller, H.J., Hyder, A. and Jia, P. (2023) “Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis.” Social Science & Medicine, p.116188.

Abstract

Background.  Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level.

Methods. This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis.

Results. By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level.

Conclusions. The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

Keywords: Opioid overdose epidemic; Sequential analysis; Neighborhood context; Geographic information science; Spatiotemporal pattern mining

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.