Background: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. Methods: We used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates. Results: LISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period. Conclusion: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Self-perceived health is an important factor for assessing urban residents’ satisfaction and quality of life. However, few have comprehensively investigated the impact of demographics, lifestyle and health awareness, indoor environment characteristics, and neighborhood features on self-perceived health. To fill this gap, we designed a framework using multivariable regressions to derive odd rations and to analyze the determinants of self-rated health, stratified into different sub-groups divided by gender, age, and neighborhood types. The study area is Chengdu, one of the most populous cities in western China. The results show that: (1) female respondents reported worse health, with household income level and marital status significantly affecting self-rated health; (2) elderly people reported the worst health, while unique factors affected only younger people (18–29 years old), such as gender, smoking, and indoor environment characteristics; and (3) different types of neighborhoods influence their residents’ perception of health differently due to historical establishment, current population composition, and housing conditions. Our study provides new observations on neighborhood types, while agreeing with previous studies on the influences of gender and age. We contribute to the field by providing a more complex understanding of the mechanism by which people rate their own health, which is important for understanding the satisfaction of urban residents and the built environment in which they live.

Transportation uses substantial energy and is a significant household expense in the United States; public transportation and working from home present opportunities to reduce energy use and increase household affordability. However, during COVID-19, transportation systems reduced service, and nationwide, public transportation use has been declining. Focusing on six small-to-medium-sized “Gateway Cities” in Massachusetts—more affordable cities with lower-than-state-average median income and lower-than-state-average education—that have regional transit systems and are within Boston’s commuter rail area, we analyzed the changes in public transit ridership and work from home. We estimated linear and hierarchical linear regression models to understand the association between demographics and built environment and lower emission modes to work between 2011 and 2021. We used GIS to visualize the distribution of public transit ridership and work from home over time and space. We found that the block groups in our sample retained public transit users over the study period and saw increases in working from home. Across all cities, transit ridership was more likely to increase in block groups with higher accessibility to jobs and more frequent transportation to those jobs; work-from-home was more likely to increase in block groups with a lower percentage of Hispanic residents and lower rent burden. We found that most block groups either saw an increase in ridership or working from home, suggesting that work from home and public transit users are spatially segmented groups.

The COVID-19 pandemic has exacerbated the risk of opioid-related harm, and previous studies suggest a connection between opioid overdose risk and industry of employment. We used descriptive and spatial-statistical tests with opioid overdose data from the vital records offices of Kentucky and Massachusetts to examine opioid overdose rates by employment industry before and after COVID-19 emergency declarations. Both states had consistently high rates of opioid-related overdose mortality for individuals employed in the construction and arts, recreation, food services, and accommodation service industries. Additionally in both states, census tracts with a high percentage of renters and non-Hispanic Black residents were more likely to be located in fatal opioid-related overdose hotspots following the initial surge of COVID-19 cases. In Kentucky, census tracts with higher percentages of employment in the transportation and other services were more likely to be located in an overdose hotspot before and after the COVID-19 emergency declaration, while in Massachusetts the same was true for census tracts with high employment in manufacturing, agriculture, forest, and fisheries, and hunting.

Methadone treatment dispensed at opioid treatment programs (OTPs) reduces the risk of fatal opioid-related overdose. Access to OTPs is limited in many regions. We utilized location-allocation models to identify gaps in OTP services across Massachusetts and generated descriptive statistics to further prioritize areas for expansion of services for socially vulnerable communities. We used a geographic information system to generate eight location-allocation models, tools to target the placement of new sites based on the distribution of existing sites and demand for services, with a goal of identifying gaps in geographic access to OTPs. We identified potential new OTP locations using 15- and 30-minute walk- and drive-times, considering the location of existing OTPs. We used opioid-related incident (ORI) locations and overdose decedent residences to measure methadone demand. We ranked suggested OTPs for each model by how many ORIs or decedent residences they could serve as the nearest OTP. We calculated mean Social Vulnerability Index scores, population density, fatal opioid-related overdose rates, and ORI rates for census tracts within a 30-minute walk of each suggested site. We also ran supplementary location-allocation analyses that ignored the locations of existing OTPs. Our models consistently recommended new OTP service locations to address unmet needs in the Roxbury and Dorchester neighborhoods of Boston, Lowell, Pittsfield, New Bedford, Gardner, Quincy, Malden, Peabody, Falmouth, Nantucket, and Martha's Vineyard, Massachusetts. Of all suggested new treatment centers, those in Boston, Gardner, Peabody, and Lowell could serve as the closest OTP for the most ORIs or decedent residences. Suggested new OTPs in Boston, Gardner, Springfield, Quincy, New Bedford, and Lowell were in areas with high social vulnerability. Our walk-time models suggested sites in areas with higher community-level social vulnerability than our drive-time models. Areas with existing OTPs had higher average social vulnerabilities than suggested new OTP locations. Location-allocation models that ignored existing OTPs still suggested OTP locations close to current methadone clinics. Through our location-allocation models and vulnerability assessments, we recommended potential locations for new OTPs to provide increased methadone access within areas of high fatal overdose and ORI burden. Results can inform data-to-action expansion of OTP access.

Background: Fatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022. Methods: We use spatial rate smoothing techniques to identify persistent opioid overdose fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were effective in reducing variance common with zero-inflated rates locations where population counts are low such as rural and suburban areas which were affected by the epidemic in Massachusetts such as Worcester, Fall River, New Bedford, and Wareham. We use Getis-Ord hotspot analyses with the smoothed incidence rates to identify locations of persistent risk from 2011-2021. We constructed measures of the socio-built environment and potentially inappropriate prescribing (PIP) using principal components analysis (PCA). The resulting measures were used as covariates in autologistic, zero-inflated Poisson, negative binomial and Conditional Autoregression (CAR) Bayesian regression models to predict if a ZCTA was part of an opioid-involved smoothed hotspot cluster for fatal overdose rates as well as the number of times that it was part of a hotspot. Results: Persistent hotspot clusters in Massachusetts had higher mean percentages of Black and Hispanic residents, and residents experiencing poverty. PCA helped in identifying unique socio-environmental factors, such as poverty and minority presence by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. Fatal opioid-involved overdose hotspots were found to be significantly more likely to be ZCTA with high poverty levels and high percentages of people from minoritized populations. Regressions models that corrected for spatial autocorrelation were necessary to avoid model misspecification. Conclusion: Conducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths. The results can help inform policy makers and planners about locations of persistent risk.