Disparities in Antiemetic Prophylaxis Care Processes Predicted by Patient Neighborhood: Retrospective Cohort and Geospatial Analysis

JMIR Public Health and Surveillance · 2026-02-24

Background: Social determinants of health continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the United States, we examined geospatial clustering of perioperative antiemetic disparities. Objective: The study aimed to determine whether disparities in perioperative antiemetic prophylaxis exhibit geographic clustering based on neighborhood-level disadvantage and whether patients from disadvantaged communities are more likely to be undertreated after adjusting for individual postoperative nausea and vomiting risk. Methods: We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital involving 19,477 patients who met the inclusion criteria. We geocoded patient home addresses and combined them with the census block group-level neighborhood disadvantage, a composite index from the National Neighborhood Data Archive. We stratified our patients by antiemetic risk score and calculated the number of antiemetic interventions. We used Poisson spatial scan statistics, implemented in SaTScan (Information Management Services, Inc), to detect geographic clusters of undertreatment. Results: We identified 1 significant cluster (P<.001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk of the whole cluster was 1.44, implying that patients within the cluster were 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below-median antiemetic prophylaxis after controlling for risk. Conclusions: To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk-adjusted antiemetic prophylaxis.

Spatial Associations of Anti-Asian Hate on Social Media in the USA During COVID-19

Journal of Racial and Ethnic Health Disparities · 2025-03-18

Agent-based travel scheduler: decomposing OD data for predicting individual travel schedules through agent-based modeling

Journal of Geographical Systems · 2025-03-07 · 1 citations

<b>D</b> eveloping and validating an anti-Asian area racism index at the county level in the contiguous United States 2020-2021

AJE Advances Research in Epidemiology · 2025-10-01

Abstract The escalation of prejudice and hate crimes against Asian Americans during the COVID-19 pandemic underscored the need for robust measures to quantify anti-Asian racism. This study proposes a novel county-level index specifically designed to capture the multifaceted nature of anti-Asian racism. The index integrates data sets from diverse sources, including Twitter, the Federal Bureau of Investigation’s Uniform Crime Reporting System, Google Search Trends, and Asian Pacific American Justice. A validation of the index using nationally representative survey data indicates it significantly predicts area racism against Asian respondents. This study offers a nuanced understanding of anti-Asian racism and has the potential to inform targeted interventions, the allocation of resources for community support and educational initiatives, and can be instrumental for policymakers in identifying areas with heightened anti-Asian bias. Additionally, the index serves as a foundation for future research, facilitating the exploration of correlations between anti-Asian racism and various health and social outcomes. While limitations exist regarding data subjectivity and availability, this index represents a significant advancement in measuring anti-Asian racism at the county level. It facilitates a more comprehensive understanding of this critical issue and the development of effective strategies to combat racial injustice and address related geographic disparities.

Spatial Clusters and Predictors of Substance-Related Mortality in Iran: A Geographically Weighted Regression Analysis

medRxiv · 2025-12-09

Background: Substance abuse is a critical public health concern worldwide, causing significant social, economic, and health burdens. In Iran this issue is driven by diverse socioeconomic conditions and its position along regional drug trafficking routes. This study aims to explore spatial variations and identify factors contributing to substance-related mortality across provinces and counties. Methods: An ecological study was conducted using a 5-year data from the Iran Legal Medicine Organization (ILMO) in 31 provinces and 424 counties. Descriptive statistics and univariate regression analysis determined the key variables. Geographically Weighted Poisson Regression (GWPR) was employed to assess local impacts and spatial clustering methods were developed to visualize influential variables. Results: A total of 12,632 substance-related deaths were recorded during the study period, with over 90% of cases among men and 36% aged 30–39 years. Mortality rates ranged from 24.6 per million population in Kohgiluyeh and Boyer-Ahmad to 302.13 per million in Hamedan. Spatial clustering of mortality was significant (Moran's I = 0.015, p = 0.014). The GWPR demonstrated superior model performance than ordinary least squares (AIC: 3351 vs. 6764), revealing substantial spatial heterogeneity in risk factors. Percentages of tenant families, older adults, and substance abuse arrests had the strongest positive association. Conversely, substance smuggling arrests, provincial gross income, and distance from key points demonstrated negative associations (p &lt; 0.05). Conclusion: Substance-related mortality in Iran shows significant spatial clustering with geographically varying socioeconomic determinants, indicating need for region-specific prevention and treatment public health strategies rather than uniform national interventions.

Disparities in Anti-emetic Prophylaxis Care Processes are Predicted by Patient Neighborhood: A Retrospective Cohort and Geospatial Analysis

medRxiv · 2024-11-23

Background: Social Determinants of Health (SDoH) continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the US, we examined geospatial clustering of perioperative antiemetic disparities. Methods: We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital with 19,477 patients meeting inclusion criteria. We geocoded patient home addresses and combined them with the Census Block Group(CBG) level neighborhood disadvantage (ND), a composite index of from the National Neighborhood Data Archive (NaNDA). We stratified our patients by antiemetic risk score and calculated the number of anti-emetic interventions. We utilized Poisson Spatial Scan Statistics, implemented in SaTScan, to detect geographic clusters of under-treatment. Results: We identified one significant cluster (p < .001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk (RR) of the whole cluster is 1.44, implying that patients within the cluster are 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below median antiemetic prophylaxis after controlling for risk. Conclusions: To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk adjusted antiemetic prophylaxis.

Agent-Based Travel Scheduler: Decomposing OD Data for Predicting Individual Travel Schedules through Agent-Based Modeling

Research Square · 2024-11-12

Developing and validating an anti-Asian area racism index at the county level in the contiguous United States 2020 - 2021

2024-08-05

Historical narratives and the "model minority" myth have obscured the realities of anti-Asian racism in the United States. The escalation of prejudice and hate crimes against Asian Americans during the COVID-19 pandemic further underscored the need for robust measures to quantify this phenomenon. This study proposes a novel county-level index specifically designed to capture the multifaceted nature of anti-Asian racism. The index integrates a diverse data set including geotagged Twitter data assessing public attitudes and potential hate speech directed toward Asian Americans, anti-Asian hate crime records from the Federal Bureau of Investigation’s Uniform Crime Reporting System, Google Search Trends data about anti-Asian stereotypes and narratives, and alien land bills denoting context for discriminatory policies against Asian immigrants at the state level. We employed Principal Component Analysis to combine these data sources into a single, composite index. A validation of the index using nationally representative survey data indicates that two of the three identified principal components significantly predict area racism against Asian respondents. This study offers a nuanced understanding of anti-Asian racism and has the potential to inform targeted interventions, the allocation of resources for community support and educational initiatives, and can be instrumental for policymakers in identifying areas with heightened anti-Asian bias. Additionally, the index serves as a foundation for future research, facilitating the exploration of correlations between anti-Asian racism and various health and social outcomes. While limitations exist regarding data subjectivity and availability, this index represents a significant advancement in measuring anti-Asian racism at the county level. It paves the way for a more comprehensive understanding of this critical issue and the development of effective strategies to combat racial injustice and address related geographic disparities.

Addressing equifinality in agent-based modeling: a sequential parameter space search method based on sensitivity analysis

International Journal of Geographical Information Systems · 2024-04-01 · 6 citations

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general.

Spatial analysis of disaster resilience research: A bibliometric study

International Journal of Disaster Risk Reduction · 2024-10-01 · 6 citations