Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas.

BACKGROUND: The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used. OBJECTIVES: This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps. METHODS: The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure. RESULTS: 231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies. CONCLUSIONS: Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research.

Characterizing patterns in police stops by race in Minneapolis from 2016 to 2021.

The murder of George Floyd centered Minneapolis, Minnesota, in conversations on racial injustice in the US. We leverage open data from the Minneapolis Police Department to analyze individual, geographic, and temporal patterns in more than 170,000 police stops since 2016. We evaluate person and vehicle searches at the individual level by race using generalized estimating equations with neighborhood clustering, directly addressing neighborhood differences in police activity. Minneapolis exhibits clear patterns of disproportionate policing by race, wherein Black people are searched at higher rates compared to White people. Temporal visualizations indicate that police stops declined following the murder of George Floyd. This analysis provides contemporary evidence on the state of policing for a major metropolitan area in the United States.

A spatio-temporal Bayesian model to estimate risk and evaluate factors related to drug-involved emergency department visits in the greater Baltimore metropolitan area.

The ongoing opioid overdose epidemic in the United States presents a major public health challenge. Opioid-involved morbidity, especially nonfatal emergency department (ED) visits, are a key opportunity to prevent mortality and measure the extent of the problem in the local substance use landscape. Data on the rate of ED visits is normally distributed by federal agencies. However, state- and substate-level rates of ED visit demonstrate significant geographic variation. This study uses an ongoing sample of ED visits from four hospitals in the University of Maryland Medical System from January 2016 to December 2019 to provide locally sensitive information on ED visit rates and risk for drug-related health outcomes. Using exploratory spatial data analysis and spatio-temporal Bayesian models, this study analyzes both the frequency and risk of heroin-, methadone-, and cocaine-involved ED visits across the greater Baltimore Maryland area at the Zip Code Tabulation Area-level (ZCTA). The Global Moran's I for total heroin-, methadone-, and cocaine-involved ED visits in 2019 was 0.44, 0.56, and 0.53, demonstrating strong positive spatial autocorrelation. Spatio-temporal Bayesian models indicated that ZCTA with a higher score in a deprivation index, with a higher share of Centers for Medicare Services claims, and adjacent to a sampled UMMS hospital had an increased risk of ED visits, with variation in the magnitude of this increased risk depending on the drug-demographic strata. Modeled disease risk surfaces - including posterior median risk and posterior exceedance probabilities - showed distinctly different risk surfaces between the substances of interest, probabilistically identifying ZCTA with a lower or higher risk of ED visits. The modeling approach used a sample of ED visits from a larger health system to estimate recent, locally sensitive drug-related morbidity across a large metropolitan area.

An analysis of the nutrition status of neighboring Indigenous and non-Indigenous populations in Kanungu District, southwestern Uganda: Close proximity, distant health realities.

OBJECTIVES: Malnutrition is a persistent health concern throughout the world. Globally, Indigenous peoples experience poorer health outcomes compared to their non-Indigenous neighbours. Despite this, malnutrition among Indigenous populations is poorly understood. This analysis estimated the prevalence, and modeled possible determinants of, moderate acute malnutrition (MAM) and severe acute malnutrition (SAM) for Indigenous Batwa and non-Indigenous Bakiga of Kanungu District in Southwestern Uganda. We then characterize possible mechanisms driving differences in malnutrition. METHODS: Retrospective cross-sectional surveys were administered to 10 Batwa communities and 10 matched Bakiga Local Councils during April of 2014 (n = 1167). Individuals were classified as MAM and SAM based on middle upper-arm circumference (MUAC) for their age-sex strata. Mixed-effects regression models quantified the variation in malnutrition occurrence, considering individual, household, and community-ethnicity level effects. Models controlled for age, sex, number of dependents, education, and relative wealth. RESULTS: Malnutrition is high among Batwa children and adults, with nearly half of Batwa adults (45.34%, 95% CI 34.82 to 55.86 for males; 45.86%, 95% CI 37.39 to 54.33 for females) and nearly a quarter of Batwa children (20.31%, 95% CI 13.07 to 26.93 for males; 25.81%, 95% CI 17.56 to 32.84 for females) meeting MAM criteria. SAM prevalence is lower than MAM prevalence, with SAM highest among adult Batwa males (11.60%, 95% CI 4.83 to 18.37) and adult Batwa females (3.00%, 95% CI 0.10 to 5.90). SAM prevalence among children was higher for Batwa males (7.03%, 95% CI 1.36 to 12.70) compared to Bakiga males (0.57%, 95% CI 0 to 1.69). Models that incorporated community ethnicity explained the greatest variance (>60%) in MUAC values. CONCLUSION: This research demonstrates a malnutrition inequality between the Indigenous Batwa and non-Indigenous Bakiga of Kanungu District, Uganda, with model results suggesting further investigation into the role of ethnicity as an upstream social determinant of health.