Using Precision Epidemiology to Identify Racialized Disparities in Overdose Mortality

Using Precision Epidemiology to Identify Racialized Disparities in Overdose Mortality | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Using Precision Epidemiology to Identify Racialized Disparities in Overdose Mortality Amir Forati, Rina Ghose, Fahimeh Mohebbi, John Mantsch This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4013689/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Efforts to understand and respond to the opioid crisis have focused on overdose fatalities. Overdose mortality rates (ratios of overdoses resulting in death) are rarely examined though they are important indicators of harm reduction effectiveness. Factors that vary across urban communities likely determine which community members are receiving the resources needed to reduce fatal overdose risk. Identifying communities with higher risk for overdose mortality and understanding influential factors is critical for guiding responses and saving lives. Using incident reports and mortality data from 2018-2021 we defined overdose mortality ratios across Milwaukee at the census tract level. To identify neighborhoods displaying higher mortality than predicted, we used Integrated Nested Laplace Approximation to define standardized mortality ratios (SMRs) for each tract. Geospatial and spatiotemporal analyses were used to identify emerging hotspots for high mortality risk. Overall, mortality was highest in Hispanic and lowest in White communities. Communities with unfavorable SMRs were predominantly Black or Hispanic, younger, less employed, poorer, less educated, and had higher incarceration rates and worse mental and physical health. Communities identified as hotspots for overdoses were predominantly non-White, poorer, and less employed and educated with worse mental and physical health, higher incarceration rates, and less housing stability. The findings demonstrate that overdose mortality rates vary across urban communities and are influenced by racial disparities. A framework that enables identification of challenged communities and guides community responses is needed. overdose mortality disparities geospatial socioeconomic Figures Figure 1 Figure 2 Figure 3 INTRODUCTION The overdose crisis continues to surge and is having devastating societal effects. Fueled by fentanyl, it is estimated that there were more than 109,000 overdose fatalities in the United States in 2023 [ 1 ]. The need for more effective harm reduction and intervention strategies persists. However, despite recent investments, better outcomes await a better understanding of trends and influential factors at the community/neighborhood level. The application of “one-size-fits-all” solutions to this complex challenge has been problematic, particularly in diverse and segregated metropolitan areas. A framework enabling timely identification of at-risk neighborhoods and guides community responses is needed. Milwaukee County is a diverse and segregated urban area in southeastern Wisconsin ranked eighth nationally in 2022 per capita overdose fatality rates [ 1 ]. Overdose trends have varied in Milwaukee according to racial demographics. Fatality rates among Black community members (106 per 100,000) outpace those in White (64 per 100,000) and Hispanic community members (49 per 100,000). Between 2020–2022 [ 2 ], fatality rates increased by 77% in Black community members in contrast to the 1% increase observed in White community members. These alarming data align with reports that the opioid crisis has disproportionately impacted Black communities nationwide and likely reflects widening racial disparities [ 3 ]. Racial disparities in health in urban areas are spatially determined, as they are directly related to segregation stemming from historical policies such as discriminatory mortgage lending practices (redlining). Residents of segregated neighborhoods experience disproportionate socioeconomic inequality, environmental pollution, food and housing insecurity, and crime, along with limited access to health care services. These factors likely influence overdose deaths. Indeed, we previously reported that Milwaukee neighborhoods with higher overdose rates have lower educational attainment, reduced access to health care, and less housing security and neighborhood stability [ 4 ]. Due in part to challenges associated with timely and accurate reporting of survived overdoses [ 5 ], efforts to understand the opioid crisis have focused on overdose fatalities. Overdose mortality rates (ratios of overdoses resulting in death) are rarely examined though they are important indicators of harm reduction effectiveness. Factors that vary across urban communities likely determine which community members are receiving the resources needed to reduce fatal overdose risk. Identifying communities with higher risk for overdose mortality and understanding influential factors is critical for guiding responses and saving lives. Despite ongoing harm reduction and education efforts, overall overdose mortality rates in Milwaukee remained from 2020–2022 (2020: 9.61%; 2021: 10.43%; 2022: 10.63%; [ 2 ]). However, since 2020, mortality rates have increased by 28% in Black community members but only by 3% in Milwaukee’s White population. This study aimed to define neighborhood characteristics associated with high risk for overdose mortality and to provide a framework that supports the identification of emerging high-risk communities. Using advanced geospatial and spatiotemporal analyses, we demonstrate that overdose mortality rates vary across urban communities and are heavily influenced by racial disparities. METHOD Milwaukee County, Wisconsin Milwaukee County, located in southeastern Wisconsin, is an appropriate case study to investigate drug overdose mortality, considering its high levels of racial and economic segregation. With a population of over 915,000, Milwaukee is one of Wisconsin’s most populous counties. It is diverse, with 66% of the residents identifying as White, 18% as Black or African American, and 17% as Hispanic or Latino (U.S. Census Bureau, 2020). Milwaukee has a lower median household income and higher poverty rate than the national average. High levels of racial and economic segregation in Milwaukee have led to a disproportionate impact of the opioid crisis across communities [ 4 ]. Studying the crisis in Milwaukee should provide valuable insights into relationships among segregation, race, socioeconomic status, and overdose mortality. Data compilation A dataset was compiled combining information on fatal overdoses (1985 total) from the Milwaukee County Medical Examiner and nonfatal overdose events (17476 total) from the Milwaukee County Office of Emergency Management from 2018–2021. Data were obtained from DataShare, a secure, integrated data system that links data across sectors to support research and analysis in public health and safety and data-informed decision making [ 6 ]. Geocoded data were cleaned and joined to the administrative boundary shapefile of census tracts collected from the TIGER/Line database using ArcGIS Desktop 10.7. Leveraging the dataset, we constructed a comprehensive analytical framework to investigate overdose mortality, enabling exploration of variations across communities. Analyzing the data with fine-grained spatial resolution, we identified high-risk areas and populations and explored the contributing factors. Spatial Empirical Bayesian Shrinkage Spatial Empirical Bayesian Shrinkage (SEBS) is a hierarchical Bayesian modeling approach that incorporates spatial correlation between small area estimates and "borrows strength" from neighboring areas to improve estimation of small area rates [ 7 ]. We used SEBS to estimate Overdose Mortality Ratios (OMRs) and relative risks for mortality across census tracts. SEBS combines information from observed data with prior information about the distribution of relative risk for overdose mortality in the population. Small populations and the modifiable areal unit problem (MAUP) are important considerations when estimating small area rates using spatial models. Small population sizes can lead to unstable estimates and increased variability when considering rare events such as drug overdoses. The MAUP is a phenomenon where results and conclusions are problematically influenced based on the geographic aggregation scheme (e.g., census tract vs. zip code) that is applied [ 8 ]. SEBS accounts for spatial correlation and allows for the use of finer geographic resolutions to estimate relative risks and then aggregates estimates to obtain area-level data, reducing the impact of the MAUP. Standardized Mortality Ratio and Integrated Nested Laplace Approximation (INLA) Standardized Mortality Ratios (SMRs) compare the observed number of deaths in a population to the expected number of deaths based on age/gender/race-specific mortality rates of a reference population. SMRs can be used to reveal higher or lower mortality rates than expected from the standard population and to estimate the impact of risk factors on mortality outcomes [ 9 ]. While this approach has been applied to better understand variation in overdose mortality across counties [ 10 ] and larger geographic regions [ 11 ], it has not been implemented at smaller geographic scales. INLA is a Bayesian inference method that allows fitting of complex hierarchical models with latent variables and spatial structures [ 12 ]. INLA provides a computationally efficient alternative for estimating posterior distributions of model parameters and hyperparameters, performing model selection and comparison, and making predictions [ 12 ]. INLA has been widely applied in spatial epidemiology to analyze large spatial datasets with complex dependence structures and model spatial patterns in disease outcomes and is particularly useful for examining drug overdose mortality, which is influenced by socio-demographic characteristics, race, age, and gender [ 4 ]. To account for spatial dependence and random variation in data, INLA can be coupled with the BYM2 model [ 14 ], which includes a spatial random effect term, permitting spatial correlation of mortality rates between neighboring areas. This accounts for areas that are so geographically close to each other that they have similar OMRs due to shared environmental, socio-economic, and demographic factors. Independent and identically distributed normal variables were included as an area random effect to account for variability in OMRs that cannot be explained by spatial dependence or stratification factors. We used R-INLA [ 12 ] to implement the SMR model and calculate OMRs across communities. The expected number of deaths was calculated by applying the age/gender/race-specific mortality rates of the reference population to the distributions in the study population. We used the 2020 US population as the reference to calculate the expected number of deaths for each stratum. The R-INLA package allowed us to estimate parameters of the SMR model and obtain the posterior distribution of SMR values, enabling identification of areas with higher or lower overdose mortality rates than expected. Time-Space Cube Analysis To investigate spatiotemporal patterns of overdoses, we employed Time-Space Cube Analysis [ 15 ] using Esri’s ArcGIS software suite. Within the Time-Space Cube framework, Emerging Hotspot Analysis was conducted to identify spatiotemporal patterns of overdose incidents. This analysis utilized the Getis-Ord Gi* statistics to measure local spatial autocorrelation and pinpointed areas with statistically significant concentrations of overdoses ("hot" and "cold" spots). Maan Kendal tests were performed to assess changes over time [ 13 ]. As the number of fatal overdoses was too low, total overdoses (fatal and non-fatal) based on Milwaukee Medical Examiner and Milwaukee Office of Emergency Management data were used for analysis. Socio-economic and demographic data were obtained from the U.S. Census Bureau's website (census.gov) and integrated into our spatial framework by joining them to census tract boundaries using ArcGIS Pro. This additional layer of data provided context for the identified hot and cold spots. RESULTS Overdose mortality ratio in Milwaukee County After preprocessing and cleaning the data, we were left with a total of 17,476 nonfatal and 1,985 fatal overdoses in Milwaukee County. The Overdose Mortality Ratio (OMR), the percentage of fatal overdoses out of all overdoses, was calculated with an average value of 12.14% across the county. The OMR showed slight yearly fluctuation, with a minimum of 11.05% in 2020 and a maximum of 13.02% in 2019. Identification of high- and low-risk census tracts for overdose mortality We used SEBS and Jenks Natural Breaks Classification to calculate the Excess Risk Factor (ERF) of overdose mortality and create a map based on five classes of SEBS ratios: Very Low, Low, Moderate, High, and Very High. There was significant spatial heterogeneity in mortality in Milwaukee (Fig. 1 ), with census tracts having OMRs as low as 4.61% and as high as 26.48%. Geographic racial disparities in overdose mortality risk Milwaukee has a high level of racial segregation. Thus, we divided Milwaukee into three regions based on the predominant racial population (Black, Hispanic, or White) and calculated average OMRs across these communities. Over the 4-year period, average OMRs in Black-, Hispanic-, and White-majority neighborhoods were 11.96%, 16.15%, and 11.45%, respectively. OMRs also varied across racial demographic majority communities over time. In predominantly Black neighborhoods, OMRs showed little variation over the years, ranging from 12.68% in 2018 to 12.16% in 2021. In contrast, predominantly Hispanic neighborhoods experienced more variation, with the highest OMR of 21.03% in 2019 and the lowest of 13.72% in 2020. Predominantly White neighborhoods exhibited a stable OMR trend, with a slight increase from 11.37% in 2018 to 11.79% in 2021 (Table 1 ). It should not be assumed that differential OMRs in neighborhoods classified according to racial demographic majorities translate to similar OMRs in members of those communities or in community members of the same racial demographics. Indeed, we have previously reported that many overdose deaths, particularly in Hispanic-majority neighborhoods, are geographically discordant (i.e., overdosing individuals are traveling from other communities of residence; [ 24 ]). Nonetheless, the findings are consistent with previous research documenting disparities in access to health care and social determinants of health across racial and ethnic communities [ 16 ]. Table 1 Overdose Mortality Ratios Across Racial Demographic Majority Communities in Milwaukee County. 2018 2019 2020 2021 Black 12.68% 13.00% 9.99% 12.16% Hispanic 14.39% 21.03% 13.72% 15.46% White 11.37% 11.39% 11.25% 11.79% Standardized mortality ratios Next, we applied the INLA Bernardinelli model to calculate relative risks and expected overdoses, accounting for the complex spatial and temporal patterns of drug overdose mortality. We calculated SMRs using 12*4 strata, defined based on age, race, and gender categories. Categories included six age groups (under 18, 18–20, 20–30, 30–40, 40–50, and older than 50), four racial groups (White, Black, Hispanic, and other), and two gender groups (female/male). Results obtained from the model were used to estimate the expected number of overdoses in each stratum. Estimates were compared to the observed number of overdoses to calculate SMRs for each census tract. SMRs provide a measure of relative risk, indicating whether an area has higher, equal, or lower risk than expected from the standard population. An SMR of 1.0 indicates that the observed number of overdoses is the same as expected; an SMR greater than 1.0 suggests higher risk; and an SMR less than 1.0 indicates lower risk. SMRs varied across Milwaukee (Fig. 2 ), demonstrating spatial heterogeneity in overdose mortality risk. Areas with the highest relative risk are concentrated in the central and southern parts of Milwaukee County, including downtown and predominantly Hispanic neighborhoods. By contrast, areas with the lowest relative risk of are generally located in the shoreline and western parts of Milwaukee, including suburban and predominantly White neighborhoods. Overall, the results suggest that overdose mortality risk is not evenly distributed across communities and that some communities and likely subpopulations are at higher risk than others. These findings are consistent with studies identifying disparities in overdose mortality rates across neighborhoods and demographic groups. Socioeconomic and demographic characteristics of high and low SMR communities Table 2 displays socioeconomic and demographic data for areas with higher- and lower-than-expected mortality risk and county-wide data. Areas with higher-than-expected relative risk had a lower proportion of White residents and a higher proportion of Black or Hispanic residents relative to lower-than-expected risk areas or Milwaukee as a whole. Higher-than-expected risk areas also had lower median age, educational attainment, and per capita income and greater unemployment and incarceration rates, as well as poorer mental and physical health and a higher digital divide index, indicating lower access to digital resources. Conversely, lower-than-expected risk areas had a higher proportion of White residents, were older, had higher educational attainment and per capita income, and lower unemployment and incarceration rates, as well as better mental and physical health and a smaller digital divide. Table 2 Socioeconomic, demographic, and health characteristics in areas with higher and lower than expected mortality risk in Milwaukee County. Equal Higher Lower Milwaukee County White 42.31% 47.63% 59.40% 55.67% Black or African American 44.23% 35.12% 28.17% 30.64% Asian 6.75% 3.91% 3.99% 4.09% American Indian and Alaska Native 0.47% 0.70% 0.52% 0.56% Hispanic or Latino (of any race) 10.09% 20.64% 12.74% 14.63% born outside U.S. 2.49% 3.63% 1.87% 2.34% Households with an Internet Subscription 74.88% 75.56% 81.49% 79.70% Single-Parent Households 44.70% 47.17% 38.19% 40.75% Homeownership 37.75% 34.97% 45.82% 42.72% Median Household Income $ 45,017 $ 44,310 $ 55,900 $ 52,486 Per Capita Income $ 23,613 $ 22,687 $ 31,361 $ 28,823 Families Living Below Poverty Level 21.35% 23.19% 14.40% 16.93% Poor Physical Health: 14 + Days 14.65% 15.49% 12.87% 13.61% Poor Mental Health: 14 + Days 17.40% 17.86% 15.39% 16.10% Self-Reported General Health Assessment: Poor or Fair 23.05% 24.93% 19.20% 20.82% Adults without Health Insurance 16.84% 20.08% 14.79% 16.22% Adults Ever Diagnosed with Depression 20.08% 20.65% 20.03% 20.19% Renter-occupied housing units 56.98% 60.32% 50.30% 53.13% Median age (years) 32.79 32.41 35.55 34.63 Full-time_ year-round civilian employed population 16 years and over 44.00% 44.85% 50.69% 48.91% People 25 + with a bachelor’s degree or Higher 24.15% 20.76% 32.30% 29.02% Renters Spending 30% or More of Household Income on Rent 50.78% 51.01% 48.21% 49.03% Spatiotemporal analysis of overdoses While time-aggregated data provide important information regarding spatial distribution, the analysis of spatiotemporal trends is more important for guiding community responses. We used a Time-Space Cube analysis to determine distinct spatiotemporal patterns of overdoses. Due to the small numbers of fatal overdoses within each time-space cube, we were unable to conduct analyses of fatal overdoses or overdose mortality. We instead chose to examine total (fatal and nonfatal) overdoses using this approach. Twelve unique patterns were revealed: Consecutive Cold and Hot Spots, Diminishing Cold and Hot Spots, Intensifying Cold and Hot Spots, New Hot Spots, Oscillating Cold Spots, Persistent Cold and Hot Spots, Sporadic Cold and Hot Spots (Fig. 3 ). Overall, with some exceptions, Hot Spot neighborhoods were localized to areas in central and north-central Milwaukee, while Cold Spot neighborhoods were found along the lakefront and in the western suburban communities in Milwaukee. While many Hot Spots were Consecutive/Persistent, New and Intensifying Hot Spots were identified in north-central and south-central Milwaukee, respectively. Demographic characteristics of hot and cold spot communities There were large differences in demographic composition and socioeconomic indicators across communities defined according to spatiotemporal overdose patterns, indicating racialized disparities. Disparities were particularly evident when comparing Consecutive Cold and Hot Spots (Table 3 ). Consecutive Cold Spot census tracts for overdoses were overwhelmingly (86.81%) White, only 3.52% Black, and 8.62% Hispanic. By contrast, Consecutive Hot Spots census tracts were only 29.87% White, 52.72% Black, and 23,47% Hispanic. In comparison, Milwaukee County as a whole is 55.67% White, 30.64% Black, and 14,63% Hispanic. Communities experiencing sporadic overdoses were disproportionately Black and Hispanic. Intensifying Hot Spots were predominantly (71.48%) Hispanic, while new Hot Spots were primarily Black (30.64%) or White (61.18%). Another notable trend is the disproportionate representation of immigrant populations in communities identified as intensifying hotspots. Table 3 Socioeconomic, demographic, and health characteristics in Hot and Cold Spot Communities for Overdoses in Milwaukee County. Consecutive Hot Spot Sporadic Hot Spot Intensifying Hot Spot New Hot Spot Consecutive Cold Spot Milwaukee County White 29.87% 37.54% 38.81% 61.18% 86.81% 55.67% Black 52.72% 41.77% 9.95% 28.14% 3.53% 30.64% Asian 3.59% 2.71% 1.75% 4.39% 3.80% 4.09% American Indian and Alaska Native 0.43% 0.64% 1.28% 0.56% 0.64% 0.56% Hispanic or Latino (of any race) 23.47% 35.11% 71.48% 6.68% 8.62% 14.63% born outside the U.S. 3.46% 3.52% 13.42% 1.57% 1.14% 2.34% Households with an Internet Subscription 68.33% 69.93% 65.68% 76.45% 87.50% 79.70% Single-Parent Households 52.97% 48.01% 46.59% 66.40% 22.27% 40.75% Homeownership 24.61% 28.72% 23.38% 6.45% 58.57% 42.72% Median Household Income $ 35,812 $ 35,219 $ 30,824 $ 39,001 $ 74,446 $ 52,486 Per Capita Income $ 19,736 $ 16,873 $ 14,233 $ 18,121 $ 40,543 $ 28,823 Families Living Below Poverty Level 29.22% 30.24% 36.02% 40.75% 4.85% 16.93% Poor Physical Health: 14 + Days 17.37% 17.15% 18.63% 10.80% 10.40% 13.61% Self-Reported General Health Assessment: Poor or Fair 29.35% 29.53% 34.74% 17.00% 13.29% 20.82% Adults without Health Insurance 23.09% 25.89% 36.62% 16.40% 9.75% 16.22% Adults Ever Diagnosed with Depression 20.09% 20.38% 21.21% 22.25% 20.17% 20.19% Poor Mental Health: 14 + Days 19.48% 18.90% 20.37% 21.05% 12.60% 16.10% Renter-occupied housing units 68.16% 65.09% 74.28% 93.66% 38.72% 53.13% Median age (years) 30.47 30.29 28.77 24.55 39.84 34.63 Percent of Households Divorced 10.57% 10.52% 8.05% 6.55% 10.69% 10.77% Fulltime Employment 36.06% 40.15% 40.51% 18.46% 60.51% 48.91% Educational Attainment 9.01% 6.23% 4.68% 13.75% 31.44% 19.26% Renters Spending 30% or More of Household Income on Rent 60.63% 63.46% 58.86% 60.40% 41.43% 51.55% Socioeconomic and health characteristics of hot and cold spot communities Compared to Consecutive Hot Spots and County-wide numbers, Consecutive Cold Spots were more economically affluent with more than twice the median household and per capita incomes and approximately eight times fewer families living below the poverty line. Employment in Consecutive Cold Spots was 60.51% and educational attainment (bachelor’s degree or higher) was 31.44% compared to 36.06% and 9.01% for Consecutive Hot Spots. Housing stability (homeownership, low renter occupied units, rent burden) was greater in Consecutive Cold Spots relative to Consecutive Hotspots. Moreover, Consecutive Cold Spot communities had better overall, physical and mental health relative to Consecutive Hotspots. Community members in Consecutive Cold Spots were more likely to have health insurance. Mean age in Consecutive Cold Spots was higher than in Consecutive Hot Spots (39.84 vs. 30.47). Overall, indicators in Sporadic and Intensifying Hotspots were similar to those in Consecutive Hot Spots. New Hot Spots tended to have higher educational attainment but lower fulltime employment, more renter-occupied housing units and less home ownership, and different health burden than other Hot Spot areas. Notably, mean age in New Hot Spot communities was much lower (24.55) than all other communities and 10 years less than the county mean (34.63). These results suggest a recent expansion of overdose risk that may include new subpopulations of community members previously less affected by the opioid crisis. DISCUSSION Racialized disparities in overdose deaths and mortality Despite continued investment in education, harm reduction, and interventions, overdose numbers continue to rise in the US. Alarmingly, our analysis in Milwaukee County, Wisconsin, suggests that the likelihood that community members survived an overdose did not change between 2020 and 2022. As the overdose crisis continues to unfold, it is critical to acknowledge that its impact has not been uniform and has been heavily influenced by widening racialized disparities. As a result, as we have responded to the challenge, some communities and subpopulations of community members have been left behind. For example, in Milwaukee County, fatality rates have increased by 77% in Black community members between 2020 and 2022, in contrast to the 1% increase observed in White community members. Approaches aimed at understanding the demographic and socioeconomic context associated with high and worsening risk for overdose mortality at the neighborhood level are desperately needed to guide our responses to this worsening crisis. Characteristics of neighborhoods with higher than predicted overdose mortality GIS-based approaches are powerful tools for understanding community-level factors that influence health outcomes. We previously used a GIS-based approach (Multiscale Geographically Weighted Regression; MGWR) to demonstrate differences in factors that influence overdose deaths across communities defined according to racial composition and revealed racialized health disparities that included differential influences of factors such as incarceration rates and naloxone availability on overdose deaths in non-White vs. White communities [ 4 ]. Here we demonstrate that overdose mortality risk varies widely across the diverse and hyper-segregated communities in Milwaukee and find that higher-than-predicted risk communities, defined based on SMRs, have higher proportions of Black or Hispanic residents, lower median age, lower educational attainment, lower per capita income, greater unemployment and incarceration rates, poorer mental and physical health, and a higher digital divide index relative to lower-than-expected risk areas or Milwaukee County as a whole. These findings extend observations that high incidences of overdose deaths are associated with socioeconomic hardship as a result of racial disparities by demonstrating similar relationships with OMRs. Moreover, they are consistent with prior findings of disparities in access to healthcare and social determinants of health across different racial and ethnic communities [ 16 ]. Using Time-Space Cube analysis to identify overdose trends While defining characteristics of communities historically struggling with overdoses is important for understanding contributing factors, approaches that enable the identification overdose trends/patterns over time are needed to effectively guide community responses. Time-Space Cube analysis is a powerful approach for determining spatiotemporal patterns. This approach has been previously used to identify emerging hotspots for infectious disease, including the spread of COVID-19 [ 17 ] and has been extended to include examination of spatiotemporal trends related to a range of public health issues ranging from cancer to suicide. With few exceptions, analyses have been conducted at larger (i.e., global, national, or state/province) geographic scales. We applied Time-Space Cube analysis to examine overdoses at the census tract scale. While the small number of overdose fatalities in each space-time bin prevented us from analyzing spatiotemporal trends in overdose deaths and mortality rates, we were able to categorize communities according to trends in the total number of overdoses (fatal and nonfatal) over the study period. Hot (Consecutive, Oscillating, Intensifying) and Cold Spots for overdoses were identified. These local patterns indicate persistent, episodic, and emerging factors that increase or protect against overdose risk and highlight the variability across neighborhoods in the impact of the current opioid crisis. This highlights the importance of local community context to overdose risk and argues against one-size-fits-all solutions. Importantly, this approach could be a valuable resource that can be used by public health departments, policymakers, and community organizations to identify struggling communities and target resources and interventions. Characteristics of overdose hot spot communities Examination of characteristics of communities classified according to spatiotemporal trends in overdoses revealed large racial disparities. This was most evident with Consecutive Cold Spot communities which were overwhelmingly White and more affluent, educated, residentially stable, and healthy relative to Hot Spot communities. These findings are consistent with reports of widening racial disparities in overdose deaths [ 3 ] and with findings that socioeconomic hardship [ 18 , 19 ], housing insecurity [ 20 ], low educational attainment [ 21 ], and poor mental health [ 22 ] are associated with overdose risk. Although the characteristics of Consecutive and Sporadic Hot Spots were generally similar, some interesting trends were evident in Intensifying and New Hot Spots. New Hot Spots, located in the downtown area of the City of Milwaukee, seem to be associated with a unique socioeconomic and demographic profile. Community members in these areas are significantly younger, healthier, more insured, and disproportionately White and single. They are also less likely to have full-time employment. It is likely that many of these are college students or recent graduates. This may indicate that overdose risk is expanding into previously less-affected populations. Intensifying Hot Spots had disproportionately high compositions of Hispanic and immigrant community members. This is consistent with local [ 2 ] and national [ 23 ] trends in overdoses in Hispanic community members and is likely exacerbated by our observation that many non-Hispanic community members are traveling to and overdosing in Milwaukee’s Hispanic communities [ 24 ]. While it is possible that overdoses in Hispanic immigrants are increasing, it has been reported that, historically, overdoses are much higher among whites relative to Hispanic immigrant populations and lower in first– versus second- and third-generation Hispanic immigrants [ 25 ]. Determinants of overdose hot spot communities Several factors likely contributed to the observed overdose disparities. First, harm reduction resources are often underutilized by non-White community members [ 26 ], findings that are consistent with our own observation that naloxone availability predicts lower overdose fatalities in White-majority but not Black-majority or Hispanic-majority communities in Milwaukee [ 4 ]. Contributing factors likely include access to harm-reduction resources [ 27 ] and interventions [ 28 ], stigma [ 29 ] and education regarding the utilization and need for harm-reduction resources [ 30 ], which are compounded by experiences of racism and discrimination [ 31 ] and the lack of availability of culturally congruent resources [ 32 ]. Other factors may include variation within the drug supply, differences in drug use patterns (e.g., alone or with partners), and the disproportionate influence of factors that are antecedents to use (e.g., stress/trauma). Study limitations Our study has several limitations. First, the accuracy of the analysis depends on the reliability of the locational data and incidence reports. We used administrative data sources, which may be subject to biases and limitations, such as underreporting or misclassification of overdose deaths. Furthermore, the use of census tract-level data may mask within-tract variations in OMRs, particularly in areas with high population density or heterogeneity. Second, our examination of the community context associated with overdose risk is limited by available datasets. More meaningful analysis awaits availability of key datasets, including robust resource maps and surveillance testing of testing of local drug supplies. Third, a more precise/disaggregated demographic analysis needs to be applied. For example, it has been found that overdose mortality in Hispanic community members of Puerto Rican heritage is more than three times higher than in those of Mexican heritage [ 33 ]. Likewise, age and sex-specific increases in overdose fatalities have been found among Black community members, with middle-aged Black men showing alarming trends [ 34 , 35 ]. Other demographic groups (e.g., LGBTQ+) are rarely included in analyses. Fourth, future studies need to apply approaches (e.g., MGWR) that permit strong inference of influential relationships (see [ 4 ]). Fifth, actionable findings will require more timely data acquisition/analysis and dissemination. Finally, conclusions are limited in the absence of direct engagement of community members and leaders. Conclusions The opioid crisis continues to pose a significant public health threat. This study highlights the importance of examining overdose OMRs to improve our understanding of the crisis. The study also underscores the need for targeted interventions to reduce risk in areas with high OMRs and address underlying social determinants of health that contribute to disparities across racial and ethnic communities. Declarations ACKNOWLEDGMENTS The authors kindly acknowledge Dr. Constance Kostelac, Director of Division of Data Surveillance and Informatics in the Department of Epidemiology & Social Sciences at the Medical College of Wisconsin for her support of the project and access to DataShare, a collaborative integrated data system. Our findings and conclusions do not necessarily reflect the views of the DataShare team. The authors also acknowledge the Milwaukee County Medical Examiner's Office and the Milwaukee County Office of Emergency Management for providing data for analysis. Last the authors acknowledge the collaborative support of Peter Brunzelle and project WisHope. DECLARATION OF COMPETING INTERESTS: The authors declare no competing interests. PRIMARY FUNDING: This work was supported by a grant from the Foundation on Opioid Response Efforts (FORE). References Centers for Disease Control and Prevention, National Center for Health Statistics. (2023). National Vital Statistics System: Overdose Deaths Data. Retrieved January 3, 2024, from https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm. Milwaukee Overdose Dashboard (https://county.milwaukee.gov/EN/Vision/Strategy-Dashboard/Overdose-Data). Accessed January 3, 2024. Furr-Holden, D., Milam, A.J., Wang, L., Sadler, R., 2021. African Americans now outpace whites in opioid-involved overdose deaths: a comparison of temporal trends from 1999 to 2018. Addiction 116(3), 677-683. Forati, A.M., Ghose, R., Mantsch, J.R., 2021. Examining Opioid Overdose Deaths across Communities Defined by Racial Composition: a Multiscale Geographically Weighted Regression Approach. J Urban Health 98(4), 551-562. Jalali, M.S., Ewing, E., Bannister, C.B., Glos, L., Eggers, S., Lim, T.Y., Stringfellow, E., Stafford, C.A., Pacula, R.L., Jalal, H., Kazemi-Tabriz, R., 2021. Data Needs in Opioid Systems Modeling: Challenges and Future Directions. Am J Prev Med 60(2), e95-e105. DataShare (2023). Milwaukee County Medical Examiner, Fatal Overdose Data 2018-2021. Provided by DataShare, through the Medical College of Wisconsin. Devine, O. J., Louis, T. A., Halloran, M. E., 1994. Empirical Bayes estimators for spatially correlated incidence rates. Environmetrics, 5(4), 381-398. Parenteau, M. P., Sawada, M. C., 2011. The modifiable areal unit problem (MAUP) in the relationship between exposure to NO 2 and respiratory health. International Journal of Health Geographics, 10, 1-15. Wolfe, R. A., 1994. The standardized mortality ratio revisited: improvements, innovations, and limitations. American Journal of Kidney Diseases, 24(2), 290-297. Kline, D., Pan, Y., Hepler, S.A., 2021. Spatiotemporal Trends in Opioid Overdose Deaths by Race for Counties in Ohio. Epidemiology 32(2), 295-302. Cano, M., Agan, A., Bandoian, L., Larochelle, L., 2022. Individual and County-Level Disparities in Drug and Opioid Overdose Mortality for Hispanic Men in Massachusetts and the Northeast United States. Subst Use Misuse 57(7), 1131-1143. Rue, H., Martino, S., Chopin, N., 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (statistical methodology), 71(2), 319-392. Hamed, K. H., 2009. Exact distribution of the Mann–Kendall trend test statistic for persistent data. Journal of Hydrology, 365(1-2), 86-94. Riebler, A., Sørbye, S. H., Simpson, D., Rue, H., (2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4), 1145-1165. Bach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S., 2014 (June). A review of temporal data visualizations based on space-time cube operations. In Eurographics Conference on Visualization. Hammonds, E.M., Reverby, S.M., 2019. Toward a Historically Informed Analysis of Racial Health Disparities Since 1619. Am J Public Health 109(10), 1348-1349. Wang, Y., Liu, Y., Struthers, J., Lian, M., 2021. Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States. Clin Infect Dis 72(4), 643-651. Flores, M.W., B, L.C., Mullin, B., Halperin-Goldstein, G., Nathan, A., Tenso, K., Schuman-Olivier, Z., 2020. Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data. Addiction 115(10), 1878-1889. Ghose, R., Forati, A.M., Mantsch, J.R., 2022. Impact of the COVID-19 Pandemic on Opioid Overdose Deaths: a Spatiotemporal Analysis. J Urban Health 99(2), 316-327. Cano, M., Oh, S., 2023. State-level homelessness and drug overdose mortality: Evidence from US panel data. Drug Alcohol Depend 250, 110910. Xu, J.J., Seamans, M.J., Friedman, J.R., 2023. Drug overdose mortality rates by educational attainment and sex for adults aged 25-64 in the United States before and during the COVID-19 pandemic, 2015-2021. Drug Alcohol Depend 255, 111014. Rosenfield, M.N., Beaudoin, F.L., Gaither, R., Hallowell, B.D., Daly, M.M., Marshall, B.D.L., Chambers, L.C., 2023. Association between comorbid chronic pain or prior hospitalization for mental illness and substance use treatment among a cohort at high risk of opioid overdose. J Subst Use Addict Treat 159, 209273. Romero, R., Friedman, J.R., Goodman-Meza, D., Shover, C.L., 2023. US drug overdose mortality rose faster among hispanics than non-hispanics from 2010 to 2021. Drug Alcohol Depend 246, 109859. Forati, A., Ghose, R., Mohebbi, F., Mantsch, J.R., 2023. The journey to overdose: Using spatial social network analysis as a novel framework to study geographic discordance in overdose deaths. Drug Alcohol Depend 245, 109827. Cano, M., 2019. Prescription opioid misuse among U.S. Hispanics. Addict Behav 98, 106021. Rodriguez, M., McKenzie, M., McKee, H., Ledingham, E.M., Kristen, S.J., Koziol, J., Hallowell, B.D., 2023. Differences in Substance Use and Harm Reduction Practices by Race and Ethnicity: Rhode Island Harm Reduction Surveillance System, 2021-2022. J Public Health Manag Pract. Nolen, S., Zang, X., Chatterjee, A., Behrends, C.N., Green, T.C., Linas, B.P., Morgan, J.R., Murphy, S.M., Walley, A.Y., Schackman, B.R., Marshall, B.D.L., 2022. Evaluating equity in community-based naloxone access among racial/ethnic groups in Massachusetts. Drug Alcohol Depend 241, 109668. Gibbons, J.B., McCullough, J.S., Zivin, K., Brown, Z., Norton, E.C., 2023b. Racial and ethnic disparities in medication for opioid use disorder access, use, and treatment outcomes in Medicare. J Subst Use Addict Treat, 209271. Davis, A., Stringer, K.L., Drainoni, M.L., Oser, C.B., Knudsen, H.K., Aldrich, A., Surratt, H.L., Walker, D.M., Gilbert, L., Downey, D.L., Gardner, S.D., Tan, S., Lines, L.M., Vandergrift, N., Mack, N., Holloway, J., Lunze, K., McAlearney, A.S., Huerta, T.R., Goddard-Eckrich, D.A., El-Bassel, N., 2023. Community-level determinants of stakeholder perceptions of community stigma toward people with opioid use disorders, harm reduction services and treatment in the HEALing Communities Study. Int J Drug Policy 122, 104241. Resko, S.M., Pasman, E., Hicks, D.L., Lee, G., Ellis, J.D., O'Shay, S., Brown, S., Agius, E., 2023. Naloxone Knowledge and Attitudes Towards Overdose Response Among Family Members of People who Misuse Opioids. J Community Health. Seo, D.C., Satterfield, N., Alba-Lopez, L., Lee, S.H., Crabtree, C., Cochran, N., 2023. "That's why we're speaking up today": exploring barriers to overdose fatality prevention in Indianapolis' Black community with semi-structured interviews. Harm Reduct J 20(1), 159. Banks, D.E., Duello, A., Paschke, M.E., Grigsby, S.R., Winograd, R.P., 2023. Identifying drivers of increasing opioid overdose deaths among black individuals: a qualitative model drawing on experience of peers and community health workers. Harm Reduct J 20(1), 5. Gelpí-Acosta, C., Cano, M., Hagan, H., 2022. Racial and ethnic data justice: The urgency of surveillance data disaggregation. Drug Alcohol Depend Rep 4. Friedman, L.S., Abasilim, C., Karch, L., Jasmin, W., Holloway-Beth, A., 2023. Disparities in fatal and non-fatal opioid-involved overdoses among middle-aged non-Hispanic Black Men and Women. J Racial Ethn Health Disparities. Jones, A., Santos-Lozada, A., Perez-Brumer, A., Latkin, C., Shoptaw, S., El-Bassel, N., 2023. Age-specific disparities in fatal drug overdoses highest among older black adults and American Indian/Alaska native individuals of all ages in the United States, 2015-2020. Int J Drug Policy 114, 103977. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4013689","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276315810,"identity":"0f4ae7fd-b15e-4345-8ee4-694c571e7b9d","order_by":0,"name":"Amir Forati","email":"","orcid":"","institution":"University of Wisconsin-Madison","correspondingAuthor":false,"prefix":"","firstName":"Amir","middleName":"","lastName":"Forati","suffix":""},{"id":276315811,"identity":"50cbdccc-4491-411a-a3ac-e95426f12b9e","order_by":1,"name":"Rina Ghose","email":"","orcid":"","institution":"University of Wisconsin-Milwaukee","correspondingAuthor":false,"prefix":"","firstName":"Rina","middleName":"","lastName":"Ghose","suffix":""},{"id":276315812,"identity":"ca59ff9d-6aa7-4948-9825-be37dc8765cd","order_by":2,"name":"Fahimeh Mohebbi","email":"","orcid":"","institution":"University of Wisconsin-Milwaukee","correspondingAuthor":false,"prefix":"","firstName":"Fahimeh","middleName":"","lastName":"Mohebbi","suffix":""},{"id":276315813,"identity":"669e093b-34b2-46de-b0ac-0f9e50ec2066","order_by":3,"name":"John Mantsch","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACxgYow4C9AVWAgJYEoBaeA0RqgQCQFokEIrUwt3cnfi78USdvLvk68TMPg43shgOEHNZzdrP0jITDhjtn526W5mFIMyasZUbuBmmehAMJBrdztzHnMBxOJEbL5t88CXUJBjfPgrT8J0rLNqAtzAkGN3hBWg4QoaXn7DZrnrTDhhvOAP3yxyDZeCYhLYbtvZtv89jUyRscP7vx44wKO9k+gloaULgGBJSDgDwRakbBKBgFo2CkAwAH4kVONw95rQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-0099-1599","institution":"Medical College of Wisconsin","correspondingAuthor":true,"prefix":"","firstName":"John","middleName":"","lastName":"Mantsch","suffix":""}],"badges":[],"createdAt":"2024-03-04 18:03:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4013689/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4013689/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52125606,"identity":"184f97bf-7d0a-4c21-9a89-8686dda3a238","added_by":"auto","created_at":"2024-03-07 06:30:29","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1093740,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGeospatial Analysis of Overdose Mortality Ratios within Milwaukee County Census Tracts.\u003c/strong\u003e We used Spatial Empirical Bayesian Shrinkage (SEBS) coupled with Jenks Natural Breaks Classification to derive the Excess Risk Factor (ERF) associated with overdose mortality. The resultant map provides a visual representation of overdose mortality patterns observed across the census tracts within Milwaukee County, categorized into five distinct SEBS ratio classes: \"Very Low\" (4.61%), \"Low\" (8.40%), \"Moderate\" (14.97%), \"High\" (20.63%), and \"Very High\" (26.48%). The pronounced spatial heterogeneity underscores the substantial variation in overdose mortality rates among different census tracts in Milwaukee County.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4013689/v1/df6c8ce7864f2b137a77e280.png"},{"id":52125607,"identity":"a12ff5a7-201a-4ec5-a8f7-9c450e531335","added_by":"auto","created_at":"2024-03-07 06:30:29","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":963252,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial Variability in Standardized Overdose Mortality Ratios (SMRs) among Milwaukee County Census Tracts.\u003c/strong\u003e The INLA Bernardinelli model was used to calculate SMRs, considering the complex spatial and temporal patterns of drug overdose mortality in Milwaukee County. SMRs were computed across 12*4 strata defined by age, race, and gender categories. These SMRs indicate relative risk: an SMR of 1.0 implies parity, while greater values signify higher risk. The map highlights significant spatial heterogeneity in overdose mortality risk, with darker orange areas indicating elevated risk, primarily in central and southern Milwaukee County, including downtown and Hispanic neighborhoods, while lower risk areas are found along the shoreline and in the western suburbs.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4013689/v1/b1c45daa84f1c6474135d36a.png"},{"id":52125608,"identity":"a8172c86-4e5b-4ebc-a0b8-955fd798f455","added_by":"auto","created_at":"2024-03-07 06:30:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":964374,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentifying Spatiotemporal Overdose Patterns in Milwaukee County Communities.\u003c/strong\u003e We utilized Time-Space Cube analysis to identify twelve distinctive patterns in overdose incidents, encompassing both fatal and nonfatal cases. These patterns included Consecutive Cold and Hot Spots, Diminishing Cold and Hot Spots, Intensifying Cold and Hot Spots, New Hot Spots, Oscillating Cold Spots, Persistent Cold and Hot Spots, and Sporadic Cold and Hot Spots. Notably, Hot Spot neighborhoods were primarily concentrated in central and north-central Milwaukee County, while Cold Spot neighborhoods were situated along the lakefront and in the western suburban areas. Additionally, New and Intensifying Hot Spots emerged in north-central and south-central Milwaukee County, respectively.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4013689/v1/d95a894f97556de520394c13.png"},{"id":53585715,"identity":"513793cf-9581-4e6c-bcef-6e52949e9434","added_by":"auto","created_at":"2024-03-27 18:27:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3448149,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4013689/v1/ddc65e93-2de5-4e3b-8565-dd8ed5858074.pdf"}],"financialInterests":"","formattedTitle":"Using Precision Epidemiology to Identify Racialized Disparities in Overdose Mortality","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eThe overdose crisis continues to surge and is having devastating societal effects. Fueled by fentanyl, it is estimated that there were more than 109,000 overdose fatalities in the United States in 2023 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The need for more effective harm reduction and intervention strategies persists. However, despite recent investments, better outcomes await a better understanding of trends and influential factors at the community/neighborhood level. The application of \u0026ldquo;one-size-fits-all\u0026rdquo; solutions to this complex challenge has been problematic, particularly in diverse and segregated metropolitan areas. A framework enabling timely identification of at-risk neighborhoods and guides community responses is needed.\u003c/p\u003e \u003cp\u003eMilwaukee County is a diverse and segregated urban area in southeastern Wisconsin ranked eighth nationally in 2022 per capita overdose fatality rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Overdose trends have varied in Milwaukee according to racial demographics. Fatality rates among Black community members (106 per 100,000) outpace those in White (64 per 100,000) and Hispanic community members (49 per 100,000). Between 2020\u0026ndash;2022 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], fatality rates increased by 77% in Black community members in contrast to the 1% increase observed in White community members. These alarming data align with reports that the opioid crisis has disproportionately impacted Black communities nationwide and likely reflects widening racial disparities [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRacial disparities in health in urban areas are spatially determined, as they are directly related to segregation stemming from historical policies such as discriminatory mortgage lending practices (redlining). Residents of segregated neighborhoods experience disproportionate socioeconomic inequality, environmental pollution, food and housing insecurity, and crime, along with limited access to health care services. These factors likely influence overdose deaths. Indeed, we previously reported that Milwaukee neighborhoods with higher overdose rates have lower educational attainment, reduced access to health care, and less housing security and neighborhood stability [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDue in part to challenges associated with timely and accurate reporting of survived overdoses [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], efforts to understand the opioid crisis have focused on overdose fatalities. Overdose mortality rates (ratios of overdoses resulting in death) are rarely examined though they are important indicators of harm reduction effectiveness. Factors that vary across urban communities likely determine which community members are receiving the resources needed to reduce fatal overdose risk. Identifying communities with higher risk for overdose mortality and understanding influential factors is critical for guiding responses and saving lives. Despite ongoing harm reduction and education efforts, overall overdose mortality rates in Milwaukee remained from 2020\u0026ndash;2022 (2020: 9.61%; 2021: 10.43%; 2022: 10.63%; [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]). However, since 2020, mortality rates have increased by 28% in Black community members but only by 3% in Milwaukee\u0026rsquo;s White population.\u003c/p\u003e \u003cp\u003eThis study aimed to define neighborhood characteristics associated with high risk for overdose mortality and to provide a framework that supports the identification of emerging high-risk communities. Using advanced geospatial and spatiotemporal analyses, we demonstrate that overdose mortality rates vary across urban communities and are heavily influenced by racial disparities.\u003c/p\u003e"},{"header":"METHOD","content":"\u003cp\u003e \u003cb\u003eMilwaukee County, Wisconsin\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMilwaukee County, located in southeastern Wisconsin, is an appropriate case study to investigate drug overdose mortality, considering its high levels of racial and economic segregation. With a population of over 915,000, Milwaukee is one of Wisconsin\u0026rsquo;s most populous counties. It is diverse, with 66% of the residents identifying as White, 18% as Black or African American, and 17% as Hispanic or Latino (U.S. Census Bureau, 2020). Milwaukee has a lower median household income and higher poverty rate than the national average. High levels of racial and economic segregation in Milwaukee have led to a disproportionate impact of the opioid crisis across communities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studying the crisis in Milwaukee should provide valuable insights into relationships among segregation, race, socioeconomic status, and overdose mortality.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData compilation\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA dataset was compiled combining information on fatal overdoses (1985 total) from the Milwaukee County Medical Examiner and nonfatal overdose events (17476 total) from the Milwaukee County Office of Emergency Management from 2018\u0026ndash;2021. Data were obtained from DataShare, a secure, integrated data system that links data across sectors to support research and analysis in public health and safety and data-informed decision making [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Geocoded data were cleaned and joined to the administrative boundary shapefile of census tracts collected from the TIGER/Line database using ArcGIS Desktop 10.7. Leveraging the dataset, we constructed a comprehensive analytical framework to investigate overdose mortality, enabling exploration of variations across communities. Analyzing the data with fine-grained spatial resolution, we identified high-risk areas and populations and explored the contributing factors.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatial Empirical Bayesian Shrinkage\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSpatial Empirical Bayesian Shrinkage (SEBS) is a hierarchical Bayesian modeling approach that incorporates spatial correlation between small area estimates and \"borrows strength\" from neighboring areas to improve estimation of small area rates [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We used SEBS to estimate Overdose Mortality Ratios (OMRs) and relative risks for mortality across census tracts. SEBS combines information from observed data with prior information about the distribution of relative risk for overdose mortality in the population.\u003c/p\u003e \u003cp\u003eSmall populations and the modifiable areal unit problem (MAUP) are important considerations when estimating small area rates using spatial models. Small population sizes can lead to unstable estimates and increased variability when considering rare events such as drug overdoses. The MAUP is a phenomenon where results and conclusions are problematically influenced based on the geographic aggregation scheme (e.g., census tract vs. zip code) that is applied [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. SEBS accounts for spatial correlation and allows for the use of finer geographic resolutions to estimate relative risks and then aggregates estimates to obtain area-level data, reducing the impact of the MAUP.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStandardized Mortality Ratio and Integrated Nested Laplace Approximation (INLA)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eStandardized Mortality Ratios (SMRs) compare the observed number of deaths in a population to the expected number of deaths based on age/gender/race-specific mortality rates of a reference population. SMRs can be used to reveal higher or lower mortality rates than expected from the standard population and to estimate the impact of risk factors on mortality outcomes [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While this approach has been applied to better understand variation in overdose mortality across counties [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and larger geographic regions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], it has not been implemented at smaller geographic scales.\u003c/p\u003e \u003cp\u003eINLA is a Bayesian inference method that allows fitting of complex hierarchical models with latent variables and spatial structures [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. INLA provides a computationally efficient alternative for estimating posterior distributions of model parameters and hyperparameters, performing model selection and comparison, and making predictions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. INLA has been widely applied in spatial epidemiology to analyze large spatial datasets with complex dependence structures and model spatial patterns in disease outcomes and is particularly useful for examining drug overdose mortality, which is influenced by socio-demographic characteristics, race, age, and gender [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo account for spatial dependence and random variation in data, INLA can be coupled with the BYM2 model [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], which includes a spatial random effect term, permitting spatial correlation of mortality rates between neighboring areas. This accounts for areas that are so geographically close to each other that they have similar OMRs due to shared environmental, socio-economic, and demographic factors. Independent and identically distributed normal variables were included as an area random effect to account for variability in OMRs that cannot be explained by spatial dependence or stratification factors.\u003c/p\u003e \u003cp\u003eWe used R-INLA [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] to implement the SMR model and calculate OMRs across communities. The expected number of deaths was calculated by applying the age/gender/race-specific mortality rates of the reference population to the distributions in the study population. We used the 2020 US population as the reference to calculate the expected number of deaths for each stratum. The R-INLA package allowed us to estimate parameters of the SMR model and obtain the posterior distribution of SMR values, enabling identification of areas with higher or lower overdose mortality rates than expected.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTime-Space Cube Analysis\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo investigate spatiotemporal patterns of overdoses, we employed Time-Space Cube Analysis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] using Esri\u0026rsquo;s ArcGIS software suite. Within the Time-Space Cube framework, Emerging Hotspot Analysis was conducted to identify spatiotemporal patterns of overdose incidents. This analysis utilized the Getis-Ord Gi* statistics to measure local spatial autocorrelation and pinpointed areas with statistically significant concentrations of overdoses (\"hot\" and \"cold\" spots). Maan Kendal tests were performed to assess changes over time [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. As the number of fatal overdoses was too low, total overdoses (fatal and non-fatal) based on Milwaukee Medical Examiner and Milwaukee Office of Emergency Management data were used for analysis. Socio-economic and demographic data were obtained from the U.S. Census Bureau's website (census.gov) and integrated into our spatial framework by joining them to census tract boundaries using ArcGIS Pro. This additional layer of data provided context for the identified hot and cold spots.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eOverdose mortality ratio in Milwaukee County\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAfter preprocessing and cleaning the data, we were left with a total of 17,476 nonfatal and 1,985 fatal overdoses in Milwaukee County. The Overdose Mortality Ratio (OMR), the percentage of fatal overdoses out of all overdoses, was calculated with an average value of 12.14% across the county. The OMR showed slight yearly fluctuation, with a minimum of 11.05% in 2020 and a maximum of 13.02% in 2019.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentification of high- and low-risk census tracts for overdose mortality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWe used SEBS and Jenks Natural Breaks Classification to calculate the Excess Risk Factor (ERF) of overdose mortality and create a map based on five classes of SEBS ratios: Very Low, Low, Moderate, High, and Very High. There was significant spatial heterogeneity in mortality in Milwaukee (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), with census tracts having OMRs as low as 4.61% and as high as 26.48%.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eGeographic racial disparities in overdose mortality risk\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMilwaukee has a high level of racial segregation. Thus, we divided Milwaukee into three regions based on the predominant racial population (Black, Hispanic, or White) and calculated average OMRs across these communities. Over the 4-year period, average OMRs in Black-, Hispanic-, and White-majority neighborhoods were 11.96%, 16.15%, and 11.45%, respectively. OMRs also varied across racial demographic majority communities over time. In predominantly Black neighborhoods, OMRs showed little variation over the years, ranging from 12.68% in 2018 to 12.16% in 2021. In contrast, predominantly Hispanic neighborhoods experienced more variation, with the highest OMR of 21.03% in 2019 and the lowest of 13.72% in 2020. Predominantly White neighborhoods exhibited a stable OMR trend, with a slight increase from 11.37% in 2018 to 11.79% in 2021 (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It should not be assumed that differential OMRs in neighborhoods classified according to racial demographic majorities translate to similar OMRs in members of those communities or in community members of the same racial demographics. Indeed, we have previously reported that many overdose deaths, particularly in Hispanic-majority neighborhoods, are geographically discordant (i.e., overdosing individuals are traveling from other communities of residence; [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]). Nonetheless, the findings are consistent with previous research documenting disparities in access to health care and social determinants of health across racial and ethnic communities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverdose Mortality Ratios Across Racial Demographic Majority Communities in Milwaukee County.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlack\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.16%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHispanic\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.46%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWhite\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11.79%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eStandardized mortality ratios\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNext, we applied the INLA Bernardinelli model to calculate relative risks and expected overdoses, accounting for the complex spatial and temporal patterns of drug overdose mortality. We calculated SMRs using 12*4 strata, defined based on age, race, and gender categories. Categories included six age groups (under 18, 18\u0026ndash;20, 20\u0026ndash;30, 30\u0026ndash;40, 40\u0026ndash;50, and older than 50), four racial groups (White, Black, Hispanic, and other), and two gender groups (female/male). Results obtained from the model were used to estimate the expected number of overdoses in each stratum. Estimates were compared to the observed number of overdoses to calculate SMRs for each census tract. SMRs provide a measure of relative risk, indicating whether an area has higher, equal, or lower risk than expected from the standard population. An SMR of 1.0 indicates that the observed number of overdoses is the same as expected; an SMR greater than 1.0 suggests higher risk; and an SMR less than 1.0 indicates lower risk.\u003c/p\u003e \u003cp\u003eSMRs varied across Milwaukee (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), demonstrating spatial heterogeneity in overdose mortality risk. Areas with the highest relative risk are concentrated in the central and southern parts of Milwaukee County, including downtown and predominantly Hispanic neighborhoods. By contrast, areas with the lowest relative risk of are generally located in the shoreline and western parts of Milwaukee, including suburban and predominantly White neighborhoods. Overall, the results suggest that overdose mortality risk is not evenly distributed across communities and that some communities and likely subpopulations are at higher risk than others. These findings are consistent with studies identifying disparities in overdose mortality rates across neighborhoods and demographic groups.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSocioeconomic and demographic characteristics of high and low SMR communities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays socioeconomic and demographic data for areas with higher- and lower-than-expected mortality risk and county-wide data. Areas with higher-than-expected relative risk had a lower proportion of White residents and a higher proportion of Black or Hispanic residents relative to lower-than-expected risk areas or Milwaukee as a whole. Higher-than-expected risk areas also had lower median age, educational attainment, and per capita income and greater unemployment and incarceration rates, as well as poorer mental and physical health and a higher digital divide index, indicating lower access to digital resources. Conversely, lower-than-expected risk areas had a higher proportion of White residents, were older, had higher educational attainment and per capita income, and lower unemployment and incarceration rates, as well as better mental and physical health and a smaller digital divide.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic, demographic, and health characteristics in areas with higher and lower than expected mortality risk in Milwaukee County.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEqual\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMilwaukee County\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42.31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e59.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or African American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.12%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.91%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.99%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian and Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino (of any race)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eborn outside U.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds with an Internet Subscription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e74.88%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e79.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-Parent Households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45.82%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Household Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e45,017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e44,310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e55,900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e52,486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer Capita Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e23,613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e22,687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e31,361\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e28,823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamilies Living Below Poverty Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.19%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Physical Health: 14\u0026thinsp;+\u0026thinsp;Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.49%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Mental Health: 14\u0026thinsp;+\u0026thinsp;Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Reported General Health Assessment: Poor or Fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults without Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.84%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.79%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults Ever Diagnosed with Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.08%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.65%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.03%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenter-occupied housing units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.98%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60.32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e53.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-time_ year-round civilian employed population 16 years and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e48.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePeople 25\u0026thinsp;+\u0026thinsp;with a bachelor\u0026rsquo;s degree or Higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.76%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.30%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.02%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenters Spending 30% or More of Household Income on Rent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e48.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.03%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSpatiotemporal analysis of overdoses\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile time-aggregated data provide important information regarding spatial distribution, the analysis of spatiotemporal trends is more important for guiding community responses. We used a Time-Space Cube analysis to determine distinct spatiotemporal patterns of overdoses. Due to the small numbers of fatal overdoses within each time-space cube, we were unable to conduct analyses of fatal overdoses or overdose mortality. We instead chose to examine total (fatal and nonfatal) overdoses using this approach. Twelve unique patterns were revealed: Consecutive Cold and Hot Spots, Diminishing Cold and Hot Spots, Intensifying Cold and Hot Spots, New Hot Spots, Oscillating Cold Spots, Persistent Cold and Hot Spots, Sporadic Cold and Hot Spots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Overall, with some exceptions, Hot Spot neighborhoods were localized to areas in central and north-central Milwaukee, while Cold Spot neighborhoods were found along the lakefront and in the western suburban communities in Milwaukee. While many Hot Spots were Consecutive/Persistent, New and Intensifying Hot Spots were identified in north-central and south-central Milwaukee, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eDemographic characteristics of hot and cold spot communities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThere were large differences in demographic composition and socioeconomic indicators across communities defined according to spatiotemporal overdose patterns, indicating racialized disparities. Disparities were particularly evident when comparing Consecutive Cold and Hot Spots (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Consecutive Cold Spot census tracts for overdoses were overwhelmingly (86.81%) White, only 3.52% Black, and 8.62% Hispanic. By contrast, Consecutive Hot Spots census tracts were only 29.87% White, 52.72% Black, and 23,47% Hispanic. In comparison, Milwaukee County as a whole is 55.67% White, 30.64% Black, and 14,63% Hispanic. Communities experiencing sporadic overdoses were disproportionately Black and Hispanic. Intensifying Hot Spots were predominantly (71.48%) Hispanic, while new Hot Spots were primarily Black (30.64%) or White (61.18%). Another notable trend is the disproportionate representation of immigrant populations in communities identified as intensifying hotspots.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSocioeconomic, demographic, and health characteristics in Hot and Cold Spot Communities for Overdoses in Milwaukee County.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConsecutive Hot Spot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSporadic Hot Spot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIntensifying Hot Spot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNew Hot Spot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eConsecutive Cold Spot\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMilwaukee County\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.87%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37.54%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e38.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e61.18%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.81%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e55.67%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.77%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.95%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e30.64%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.71%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.39%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.09%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmerican Indian and Alaska Native\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.56%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.64%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.56%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic or Latino (of any race)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.47%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.11%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e71.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.63%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eborn outside the U.S.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.14%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.34%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHouseholds with an Internet Subscription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.33%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.93%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e76.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e87.50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.70%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle-Parent Households\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.97%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.59%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e66.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.27%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e40.75%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHomeownership\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.61%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.45%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e58.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.72%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian Household Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,812\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e35,219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e30,824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e39,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e74,446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e52,486\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer Capita Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e19,736\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e16,873\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e14,233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e18,121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e40,543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan\u003e$\u003c/span\u003e28,823\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamilies Living Below Poverty Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.22%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.24%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.02%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e40.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.85%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.93%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Physical Health: 14\u0026thinsp;+\u0026thinsp;Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.80%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.61%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Reported General Health Assessment: Poor or Fair\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29.35%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.53%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.74%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.00%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.82%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults without Health Insurance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.89%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36.62%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.22%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdults Ever Diagnosed with Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.38%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.21%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22.25%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.17%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e20.19%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor Mental Health: 14\u0026thinsp;+\u0026thinsp;Days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19.48%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.90%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.37%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.10%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenter-occupied housing units\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.16%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65.09%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.28%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e93.66%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.72%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e53.13%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e39.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e34.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent of Households Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10.57%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.52%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.05%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.55%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.69%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.77%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFulltime Employment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36.06%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40.15%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60.51%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e48.91%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducational Attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.01%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.23%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.68%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e31.44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e19.26%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRenters Spending 30% or More of Household Income on Rent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60.63%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63.46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.86%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.40%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e41.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e51.55%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eSocioeconomic and health characteristics of hot and cold spot communities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCompared to Consecutive Hot Spots and County-wide numbers, Consecutive Cold Spots were more economically affluent with more than twice the median household and per capita incomes and approximately eight times fewer families living below the poverty line. Employment in Consecutive Cold Spots was 60.51% and educational attainment (bachelor\u0026rsquo;s degree or higher) was 31.44% compared to 36.06% and 9.01% for Consecutive Hot Spots. Housing stability (homeownership, low renter occupied units, rent burden) was greater in Consecutive Cold Spots relative to Consecutive Hotspots. Moreover, Consecutive Cold Spot communities had better overall, physical and mental health relative to Consecutive Hotspots. Community members in Consecutive Cold Spots were more likely to have health insurance. Mean age in Consecutive Cold Spots was higher than in Consecutive Hot Spots (39.84 vs. 30.47). Overall, indicators in Sporadic and Intensifying Hotspots were similar to those in Consecutive Hot Spots. New Hot Spots tended to have higher educational attainment but lower fulltime employment, more renter-occupied housing units and less home ownership, and different health burden than other Hot Spot areas. Notably, mean age in New Hot Spot communities was much lower (24.55) than all other communities and 10 years less than the county mean (34.63). These results suggest a recent expansion of overdose risk that may include new subpopulations of community members previously less affected by the opioid crisis.\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003e \u003cb\u003eRacialized disparities in overdose deaths and mortality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eDespite continued investment in education, harm reduction, and interventions, overdose numbers continue to rise in the US. Alarmingly, our analysis in Milwaukee County, Wisconsin, suggests that the likelihood that community members survived an overdose did not change between 2020 and 2022. As the overdose crisis continues to unfold, it is critical to acknowledge that its impact has not been uniform and has been heavily influenced by widening racialized disparities. As a result, as we have responded to the challenge, some communities and subpopulations of community members have been left behind. For example, in Milwaukee County, fatality rates have increased by 77% in Black community members between 2020 and 2022, in contrast to the 1% increase observed in White community members. Approaches aimed at understanding the demographic and socioeconomic context associated with high and worsening risk for overdose mortality at the neighborhood level are desperately needed to guide our responses to this worsening crisis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacteristics of neighborhoods with higher than predicted overdose mortality\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGIS-based approaches are powerful tools for understanding community-level factors that influence health outcomes. We previously used a GIS-based approach (Multiscale Geographically Weighted Regression; MGWR) to demonstrate differences in factors that influence overdose deaths across communities defined according to racial composition and revealed racialized health disparities that included differential influences of factors such as incarceration rates and naloxone availability on overdose deaths in non-White vs. White communities [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Here we demonstrate that overdose mortality risk varies widely across the diverse and hyper-segregated communities in Milwaukee and find that higher-than-predicted risk communities, defined based on SMRs, have higher proportions of Black or Hispanic residents, lower median age, lower educational attainment, lower per capita income, greater unemployment and incarceration rates, poorer mental and physical health, and a higher digital divide index relative to lower-than-expected risk areas or Milwaukee County as a whole. These findings extend observations that high incidences of overdose deaths are associated with socioeconomic hardship as a result of racial disparities by demonstrating similar relationships with OMRs. Moreover, they are consistent with prior findings of disparities in access to healthcare and social determinants of health across different racial and ethnic communities [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eUsing Time-Space Cube analysis to identify overdose trends\u003c/b\u003e \u003c/p\u003e \u003cp\u003eWhile defining characteristics of communities historically struggling with overdoses is important for understanding contributing factors, approaches that enable the identification overdose trends/patterns over time are needed to effectively guide community responses. Time-Space Cube analysis is a powerful approach for determining spatiotemporal patterns. This approach has been previously used to identify emerging hotspots for infectious disease, including the spread of COVID-19 [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] and has been extended to include examination of spatiotemporal trends related to a range of public health issues ranging from cancer to suicide. With few exceptions, analyses have been conducted at larger (i.e., global, national, or state/province) geographic scales.\u003c/p\u003e \u003cp\u003eWe applied Time-Space Cube analysis to examine overdoses at the census tract scale. While the small number of overdose fatalities in each space-time bin prevented us from analyzing spatiotemporal trends in overdose deaths and mortality rates, we were able to categorize communities according to trends in the total number of overdoses (fatal and nonfatal) over the study period. Hot (Consecutive, Oscillating, Intensifying) and Cold Spots for overdoses were identified. These local patterns indicate persistent, episodic, and emerging factors that increase or protect against overdose risk and highlight the variability across neighborhoods in the impact of the current opioid crisis. This highlights the importance of local community context to overdose risk and argues against one-size-fits-all solutions. Importantly, this approach could be a valuable resource that can be used by public health departments, policymakers, and community organizations to identify struggling communities and target resources and interventions.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCharacteristics of overdose hot spot communities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExamination of characteristics of communities classified according to spatiotemporal trends in overdoses revealed large racial disparities. This was most evident with Consecutive Cold Spot communities which were overwhelmingly White and more affluent, educated, residentially stable, and healthy relative to Hot Spot communities. These findings are consistent with reports of widening racial disparities in overdose deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and with findings that socioeconomic hardship [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], housing insecurity [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], low educational attainment [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and poor mental health [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] are associated with overdose risk.\u003c/p\u003e \u003cp\u003eAlthough the characteristics of Consecutive and Sporadic Hot Spots were generally similar, some interesting trends were evident in Intensifying and New Hot Spots. New Hot Spots, located in the downtown area of the City of Milwaukee, seem to be associated with a unique socioeconomic and demographic profile. Community members in these areas are significantly younger, healthier, more insured, and disproportionately White and single. They are also less likely to have full-time employment. It is likely that many of these are college students or recent graduates. This may indicate that overdose risk is expanding into previously less-affected populations. Intensifying Hot Spots had disproportionately high compositions of Hispanic and immigrant community members. This is consistent with local [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] and national [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] trends in overdoses in Hispanic community members and is likely exacerbated by our observation that many non-Hispanic community members are traveling to and overdosing in Milwaukee\u0026rsquo;s Hispanic communities [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. While it is possible that overdoses in Hispanic immigrants are increasing, it has been reported that, historically, overdoses are much higher among whites relative to Hispanic immigrant populations and lower in first\u0026ndash; versus second- and third-generation Hispanic immigrants [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eDeterminants of overdose hot spot communities\u003c/b\u003e \u003c/p\u003e \u003cp\u003eSeveral factors likely contributed to the observed overdose disparities. First, harm reduction resources are often underutilized by non-White community members [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], findings that are consistent with our own observation that naloxone availability predicts lower overdose fatalities in White-majority but not Black-majority or Hispanic-majority communities in Milwaukee [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Contributing factors likely include access to harm-reduction resources [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] and interventions [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], stigma [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] and education regarding the utilization and need for harm-reduction resources [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which are compounded by experiences of racism and discrimination [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and the lack of availability of culturally congruent resources [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Other factors may include variation within the drug supply, differences in drug use patterns (e.g., alone or with partners), and the disproportionate influence of factors that are antecedents to use (e.g., stress/trauma).\u003c/p\u003e \u003cp\u003e \u003cb\u003eStudy limitations\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOur study has several limitations. First, the accuracy of the analysis depends on the reliability of the locational data and incidence reports. We used administrative data sources, which may be subject to biases and limitations, such as underreporting or misclassification of overdose deaths. Furthermore, the use of census tract-level data may mask within-tract variations in OMRs, particularly in areas with high population density or heterogeneity. Second, our examination of the community context associated with overdose risk is limited by available datasets. More meaningful analysis awaits availability of key datasets, including robust resource maps and surveillance testing of testing of local drug supplies. Third, a more precise/disaggregated demographic analysis needs to be applied. For example, it has been found that overdose mortality in Hispanic community members of Puerto Rican heritage is more than three times higher than in those of Mexican heritage [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Likewise, age and sex-specific increases in overdose fatalities have been found among Black community members, with middle-aged Black men showing alarming trends [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Other demographic groups (e.g., LGBTQ+) are rarely included in analyses. Fourth, future studies need to apply approaches (e.g., MGWR) that permit strong inference of influential relationships (see [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]). Fifth, actionable findings will require more timely data acquisition/analysis and dissemination. Finally, conclusions are limited in the absence of direct engagement of community members and leaders.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThe opioid crisis continues to pose a significant public health threat. This study highlights the importance of examining overdose OMRs to improve our understanding of the crisis. The study also underscores the need for targeted interventions to reduce risk in areas with high OMRs and address underlying social determinants of health that contribute to disparities across racial and ethnic communities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eACKNOWLEDGMENTS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors kindly acknowledge Dr. Constance Kostelac, Director of Division of Data Surveillance and Informatics in the Department of Epidemiology \u0026amp; Social Sciences at the Medical College of Wisconsin for her support of the project and access to DataShare, a collaborative integrated data system. Our findings and conclusions do not necessarily reflect the views of the DataShare team. The authors also acknowledge the Milwaukee County Medical Examiner's Office and the Milwaukee County Office of Emergency Management for providing data for analysis. Last the authors acknowledge the collaborative support of Peter Brunzelle and project WisHope.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDECLARATION OF COMPETING INTERESTS:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePRIMARY FUNDING:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a grant from the Foundation on Opioid Response Efforts (FORE).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eCenters for Disease Control and Prevention, National Center for Health Statistics. (2023). National Vital Statistics System: Overdose Deaths Data. Retrieved January 3, 2024, from https://www.cdc.gov/nchs/nvss/vsrr/drug-overdose-data.htm.\u003c/li\u003e\n \u003cli\u003eMilwaukee Overdose Dashboard (https://county.milwaukee.gov/EN/Vision/Strategy-Dashboard/Overdose-Data). 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Individual and County-Level Disparities in Drug and Opioid Overdose Mortality for Hispanic Men in Massachusetts and the Northeast United States. Subst Use Misuse 57(7), 1131-1143.\u003c/li\u003e\n \u003cli\u003eRue, H., Martino, S., Chopin, N., 2009. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society: Series B (statistical methodology), 71(2), 319-392.\u003c/li\u003e\n \u003cli\u003eHamed, K. H., 2009. Exact distribution of the Mann\u0026ndash;Kendall trend test statistic for persistent data. Journal of Hydrology, 365(1-2), 86-94.\u003c/li\u003e\n \u003cli\u003eRiebler, A., S\u0026oslash;rbye, S. H., Simpson, D., Rue, H., (2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical Methods in Medical Research, 25(4), 1145-1165.\u003c/li\u003e\n \u003cli\u003eBach, B., Dragicevic, P., Archambault, D., Hurter, C., Carpendale, S., 2014 (June). A review of temporal data visualizations based on space-time cube operations. In Eurographics Conference on Visualization.\u003c/li\u003e\n \u003cli\u003eHammonds, E.M., Reverby, S.M., 2019. Toward a Historically Informed Analysis of Racial Health Disparities Since 1619. Am J Public Health 109(10), 1348-1349.\u003c/li\u003e\n \u003cli\u003eWang, Y., Liu, Y., Struthers, J., Lian, M., 2021. Spatiotemporal Characteristics of the COVID-19 Epidemic in the United States. Clin Infect Dis 72(4), 643-651.\u003c/li\u003e\n \u003cli\u003eFlores,\u0026nbsp;M.W., B, L.C., Mullin, B., Halperin-Goldstein, G., Nathan, A., Tenso, K., Schuman-Olivier, Z., 2020. Associations between neighborhood-level factors and opioid-related mortality: A multi-level analysis using death certificate data. Addiction 115(10), 1878-1889.\u003c/li\u003e\n \u003cli\u003eGhose, R., Forati, A.M., Mantsch, J.R., 2022. Impact of the COVID-19 Pandemic on Opioid Overdose Deaths: a Spatiotemporal Analysis. J Urban Health 99(2), 316-327.\u003c/li\u003e\n \u003cli\u003eCano, M., Oh, S., 2023. State-level homelessness and drug overdose mortality: Evidence from US panel data. Drug Alcohol Depend 250, 110910.\u003c/li\u003e\n \u003cli\u003eXu, J.J., Seamans, M.J., Friedman, J.R., 2023. Drug overdose mortality rates by educational attainment and sex for adults aged 25-64 in the United States before and during the COVID-19 pandemic, 2015-2021. Drug Alcohol Depend 255, 111014.\u003c/li\u003e\n \u003cli\u003eRosenfield, M.N., Beaudoin, F.L., Gaither, R., Hallowell, B.D., Daly, M.M., Marshall, B.D.L., Chambers, L.C., 2023. Association between comorbid chronic pain or prior hospitalization for mental illness and substance use treatment among a cohort at high risk of opioid overdose. J Subst Use Addict Treat 159, 209273.\u003c/li\u003e\n \u003cli\u003eRomero, R., Friedman, J.R., Goodman-Meza, D., Shover, C.L., 2023. US drug overdose mortality rose faster among hispanics than non-hispanics from 2010 to 2021. Drug Alcohol Depend 246, 109859.\u003c/li\u003e\n \u003cli\u003eForati, A., Ghose, R., Mohebbi, F., Mantsch, J.R., 2023. The journey to overdose: Using spatial social network analysis as a novel framework to study geographic discordance in overdose deaths. Drug Alcohol Depend 245, 109827.\u003c/li\u003e\n \u003cli\u003eCano, M., 2019. Prescription opioid misuse among U.S. Hispanics. Addict Behav 98, 106021.\u003c/li\u003e\n \u003cli\u003eRodriguez, M., McKenzie, M., McKee, H., Ledingham, E.M., Kristen, S.J., Koziol, J., Hallowell, B.D., 2023. Differences in Substance Use and Harm Reduction Practices by Race and Ethnicity: Rhode Island Harm Reduction Surveillance System, 2021-2022. J Public Health Manag Pract.\u003c/li\u003e\n \u003cli\u003eNolen,\u0026nbsp;S., Zang, X., Chatterjee, A., Behrends, C.N., Green, T.C., Linas, B.P., Morgan, J.R., Murphy, S.M., Walley, A.Y., Schackman, B.R., Marshall, B.D.L., 2022. Evaluating equity in community-based naloxone access among racial/ethnic groups in Massachusetts. Drug Alcohol Depend 241, 109668.\u003c/li\u003e\n \u003cli\u003eGibbons, J.B., McCullough, J.S., Zivin, K., Brown, Z., Norton, E.C., 2023b. Racial and ethnic disparities in medication for opioid use disorder access, use, and treatment outcomes in Medicare. J Subst Use Addict Treat, 209271.\u003c/li\u003e\n \u003cli\u003eDavis,\u0026nbsp;A., Stringer, K.L., Drainoni, M.L., Oser, C.B., Knudsen, H.K., Aldrich, A., Surratt, H.L., Walker, D.M., Gilbert, L., Downey, D.L., Gardner, S.D., Tan, S., Lines, L.M., Vandergrift, N., Mack, N., Holloway, J., Lunze, K., McAlearney, A.S., Huerta, T.R., Goddard-Eckrich, D.A., El-Bassel, N., 2023. Community-level determinants of stakeholder perceptions of community stigma toward people with opioid use disorders, harm reduction services and treatment in the HEALing Communities Study. Int J Drug Policy 122, 104241.\u003c/li\u003e\n \u003cli\u003eResko, S.M., Pasman, E., Hicks, D.L., Lee, G., Ellis, J.D., O\u0026apos;Shay, S., Brown, S., Agius, E., 2023. Naloxone Knowledge and Attitudes Towards Overdose Response Among Family Members of People who Misuse Opioids. J Community Health.\u003c/li\u003e\n \u003cli\u003eSeo, D.C., Satterfield, N., Alba-Lopez, L., Lee, S.H., Crabtree, C., Cochran, N., 2023. \u0026quot;That\u0026apos;s why we\u0026apos;re speaking up today\u0026quot;: exploring barriers to overdose fatality prevention in Indianapolis\u0026apos; Black community with semi-structured interviews. Harm Reduct J 20(1), 159.\u003c/li\u003e\n \u003cli\u003eBanks, D.E., Duello, A., Paschke, M.E., Grigsby, S.R., Winograd, R.P., 2023. Identifying drivers of increasing opioid overdose deaths among black individuals: a qualitative model drawing on experience of peers and community health workers. Harm Reduct J 20(1), 5.\u003c/li\u003e\n \u003cli\u003eGelp\u0026iacute;-Acosta, C., Cano, M., Hagan, H., 2022. Racial and ethnic data justice: The urgency of surveillance data disaggregation. Drug Alcohol Depend Rep 4.\u003c/li\u003e\n \u003cli\u003eFriedman, L.S., Abasilim, C., Karch, L., Jasmin, W., Holloway-Beth, A., 2023. Disparities in fatal and non-fatal opioid-involved overdoses among middle-aged non-Hispanic Black Men and Women. J Racial Ethn Health Disparities.\u003c/li\u003e\n \u003cli\u003eJones, A., Santos-Lozada, A., Perez-Brumer, A., Latkin, C., Shoptaw, S., El-Bassel, N., 2023. Age-specific disparities in fatal drug overdoses highest among older black adults and American Indian/Alaska native individuals of all ages in the United States, 2015-2020. Int J Drug Policy 114, 103977.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"overdose, mortality, disparities, geospatial, socioeconomic","lastPublishedDoi":"10.21203/rs.3.rs-4013689/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4013689/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEfforts to understand and respond to the opioid crisis have focused on overdose fatalities. Overdose mortality rates (ratios of overdoses resulting in death) are rarely examined though they are important indicators of harm reduction effectiveness. Factors that vary across urban communities likely determine which community members are receiving the resources needed to reduce fatal overdose risk. Identifying communities with higher risk for overdose mortality and understanding influential factors is critical for guiding responses and saving lives. Using incident reports and mortality data from 2018-2021 we defined overdose mortality ratios across Milwaukee at the census tract level. To identify neighborhoods displaying higher mortality than predicted, we used Integrated Nested Laplace Approximation to define standardized mortality ratios (SMRs) for each tract. Geospatial and spatiotemporal analyses were used to identify emerging hotspots for high mortality risk. Overall, mortality was highest in Hispanic and lowest in White communities. Communities with unfavorable SMRs were predominantly Black or Hispanic, younger, less employed, poorer, less educated, and had higher incarceration rates and worse mental and physical health. Communities identified as hotspots for overdoses were predominantly non-White, poorer, and less employed and educated with worse mental and physical health, higher incarceration rates, and less housing stability. The findings demonstrate that overdose mortality rates vary across urban communities and are influenced by racial disparities. A framework that enables identification of challenged communities and guides community responses is needed.\u003c/p\u003e","manuscriptTitle":"Using Precision Epidemiology to Identify Racialized Disparities in Overdose Mortality","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-07 06:30:24","doi":"10.21203/rs.3.rs-4013689/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0d86df0c-a83d-424a-98c2-537eb127639a","owner":[],"postedDate":"March 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-27T18:19:29+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-07 06:30:24","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4013689","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4013689","identity":"rs-4013689","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}