William C. Goedel

Environmental noise is a pervasive exposure known to have effects on human health, yet existing assessments are hindered by limited monitoring data, insufficient spatial resolution, and a focus on A-weighted sound. These constraints reduce our ability to characterize noise environments and integrate noise with other exposures that may co-exist. To address these gaps, we developed statewide, parcel-level prediction models of environmental sound across six sound metrics, including A-, C-, and Z-weighted levels as well as low-frequency, high-frequency, and infrasound bands. Using Rhode Island as a case study, we combined a multi-year monitoring campaign with spatial error modeling to generate fine-scale sound exposure surfaces for approximately 420,000 land parcels. Models showed moderate apparent fit across sound metrics, with RMSE values of 3.26–4.43 dB and pseudo-R² values of 0.56–0.71. Spatial error terms were statistically significant across metrics, while residual Moran’s I values were near zero and non-significant. Spatial cross-validation suggested moderate out-of-sample performance for several metrics and weaker performance for high-frequency sound. We observed disparities in modeled sound exposure, higher exposure across all frequency bands in socially vulnerable communities. Parcels located within state-designated environmental justice focus areas had higher average predicted exposure. This work also demonstrates a scalable framework for environmental exposure assessment. The high-resolution exposure surfaces may support future address-level linkage to health registries and integration with co-occurring environmental data. We built a data visualization dashboard to support community engagement and action. Together, this framework offers a scalable approach for characterizing environmental noise and supporting future environmental health research.

Given substantial reporting delays in overdose deaths, state health departments increasingly use nonfatal overdose data to inform geographically targeted rapid overdose response efforts. We evaluated the extent to which nonfatal overdose events were associated with concurrent and future overdose deaths in Rhode Island. We aggregated nonfatal overdose data from emergency medical services records (2019-2023) and fatal overdose data from the State Unintentional Drug Overdose Reporting System (2020-2023) in 1-, 3-, and 6-month intervals at census block group and census tract levels. Rates of fatal overdose were estimated, relative to nonfatal overdose lagged by 0-12 months, using negative binomial regression, and relative to monthly spikes in nonfatal overdose burden, using zero-inflated Poisson regression. Estimation was implemented using integrated nested Laplace approximation. Each additional nonfatal overdose event per census block group was associated with fatal overdose rates that were 48% higher (95% credible interval, 1.37-1.59) than expected in concurrent months, with smaller associations at the census tract level, in wider time intervals, and when nonfatal overdose data were lagged. Spikes in nonfatal overdose activity were associated with elevated overdose mortality in concurrent periods with fine temporal and geographic granularity, but not in longer time frames and larger geographic areas.

Background: Malaria is a major public health problem, with over half of the world’s population, primarily in sub-Saharan Africa, at risk. Climate change is already affecting the prevalence of malaria, in part by altering the distribution and density of mosquito vectors. This study assessed the relationship between climate variables and mosquito species distribution on malaria prevalence in Kenya from January 2019 to June 2021. Methods: Data from 23 Kenyan counties were analyzed using mixed-effect regression, spatial analysis, correlation analysis, and the Least Absolute Shrinkage and Selection Operator (LASSO) to select variables. Results: Malaria prevalence was highest in the Lake Endemic (19%), followed by the Coastal Endemic (5%), semi-arid seasonal (2%), and low-risk (0.9%) malaria epidemiologic zones in 2020. Mosquito species distribution varied, with distinct ecological preferences observed with Anopheles gambiae dominated coastal and semi-arid areas, while Anopheles funestus was highest in the highland and lake zones. Regression analysis indicated that a combination of environmental factors and public health interventions, including insecticide-treated nets (ITN) coverage, was an important predictor of malaria prevalence. However, the relationship between these factors and malaria prevalence varied across regions and time periods. Conclusion: This study provides insights into the spatial dynamics of mosquito species and their distribution in relation to malaria epidemiological zones in Kenya, thereby informing targeted control strategies.

Context: Predictive modeling can identify neighborhoods at elevated risk of future overdose death and may assist community organizations’ decisions about harm reduction resource allocation. In Rhode Island, PROVIDENT is a research initiative and randomized community intervention trial that developed and validated a machine learning model that predicts future overdose at a census block group (CBG) level. The PROVIDENT model prioritizes the top 20th percentile of CBGs at highest risk of future overdose death over the subsequent 6-month period. In CBGs assigned to the trial intervention arm, these predictions are then displayed for partnering community organizations via an interactive mapping dashboard. Objective: To evaluate whether CBGs prioritized by the PROVIDENT model were associated with increased user engagement via an online dashboard for fatal overdose forecasting and resource planning. Design: We estimated prevalence ratios using modified Poisson regression models, adjusted for CBG-level characteristics that may confound the relationship between model predictions and dashboard engagement. Setting: We used CBG-level data in Rhode Island (N = 809) from November 2021 to July 2024. Intervention: Our exposure of interest was whether each CBG was prioritized by the PROVIDENT model and shown as prioritized on the interactive mapping dashboard. Main Outcome Measure: Our primary outcome was whether a dashboard user from any partnering community organization engaged (eg, clicked, interacted with dashboard elements, or completed assessment or planning surveys) with each CBG on the interactive mapping dashboard. Results: After adjusting for previous model predictions and dashboard engagement, nonfatal overdose counts, and distribution of race and ethnicity, poverty, unemployment, and rent burden, dashboard users were 1.0 to 2.4 times as likely to engage with CBGs prioritized by the PROVIDENT model that were shown as prioritized on the dashboard as compared to CBGs that were prioritized by the PROVIDENT model that were blinded on the dashboard. Conclusions: Interactive mapping tools with predictive modeling may be useful to support community-based harm reduction organizations in the allocation of resources to neighborhoods predicted to be at high risk of future overdose death.

We aimed to identify socioenvironmental determinants of alcohol use among Black sexually minoritized men (SMM) and transgender women (TW). Data were from the Neighborhoods and Networks (N2) cohort study among Black SMM and TW (01/2018-08/2020). We used stepwise negative binomial regression to identify associations with alcohol use. Among our sample of 142 Black SMM and TW (age_mean=24±4 years), 68% reported past month alcohol use. Adverse childhood experiences (IRR=1.10;95%CI=1.03-1.18), GAD-7 (IRR=1.03;95%CI=1.00-1.07), and community violence (IRR=1.06;95%CI=1.02-1.10), were positively associated with alcohol use. Multilevel alcohol reduction interventions can consider reducing community violence and increasing access to mental health services.

Background: Little is known about how best to reach people with social marketing messages promoting use of clinical HIV and sexually transmitted infection (STI) services. Objective: We evaluated a multiplatform, digital social marketing campaign intended to increase use of HIV/STI testing, treatment, and prevention services among gay, bisexual, and other men who have sex with men (MSM) at an LGBTQ+ (lesbian, gay, bisexual, transgender, queer, and/or questioning) community health center. Methods: We evaluated engagement with a social marketing campaign launched by Open Door Health, the only LGBTQ+ community health center in Rhode Island, during the first 8 months of implementation (April to November 2021). Three types of advertisements encouraging use of HIV/STI services were developed and implemented on Google Search, Google Display, Grindr, and Facebook. Platforms tracked the number of times that an advertisement was displayed to a user (impressions), that a user clicked through to a landing page that facilitated scheduling (clicks), and that a user requested a call to schedule an appointment from the landing page (conversions). We calculated the click-through rate (clicks per impression), conversion rate (conversions per click), and the dollar amount spent per 1000 impressions and per click and conversion. Results: Overall, Google Search yielded the highest click-through rate (7.1%) and conversion rate (7.0%) compared to Google Display, Grindr, and Facebook (click-through rates=0.4%‐3.3%; conversion rates=0%‐0.03%). Although the spend per 1000 impressions and per click was higher for Google Search compared to other platforms, the spend per conversion—which measures the number of people intending to attend the clinic for services—was substantially lower for Google Search (US $48.19 vs US $3120.42-US $3436.03). Conclusions: Campaigns using the Google Search platform may yield the greatest return on investment for engaging MSM in HIV/STI services at community health clinics. Future studies are needed to measure clinical outcomes among those who present to the clinic for services after viewing campaign advertisements and to compare the return on investment with use of social marketing campaigns relative to other approaches.

BACKGROUND: Delays in hospital presentation limit access to acute stroke treatments. While prior research has focused on patient-level factors, broader ecological and social determinants have not been well studied. We aimed to create a geospatial map of prehospital delay and examine the role of community-level social vulnerability. METHODS: We studied patients with ischemic stroke who arrived by emergency medical services in 2015 to 2017 from the American Heart Association Get With The Guidelines-Stroke registry. The primary outcome was time to hospital arrival after stroke (in minutes), beginning at last known well in most cases. Using Geographic Information System mapping, we displayed the geography of delay. We then used Cox proportional hazard models to study the relationship between community-level factors and arrival time (adjusted hazard ratios [aHR] <1.0 indicate delay). The primary exposure was the social vulnerability index (SVI), a metric of social vulnerability for every ZIP Code Tabulation Area ranging from 0.0 to 1.0. RESULTS: Of 750 336 patients, 149 145 met inclusion criteria. The mean age was 73 years, and 51% were female. The median time to hospital arrival was 140 minutes (Q1: 60 minutes, Q3: 458 minutes). The geospatial map revealed that many zones of delay overlapped with socially vulnerable areas (https://harvard-cga.maps.arcgis.com/apps/webappviewer/index.html?id=08f6e885c71b457f83cefc71013bcaa7). Cox models (aHR, 95% CI) confirmed that higher SVI, including quartiles 3 (aHR, 0.96 [95% CI, 0.93–0.98]) and 4 (aHR, 0.93 [95% CI, 0.91–0.95]), was associated with delay. Patients from SVI quartile 4 neighborhoods arrived 15.6 minutes [15–16.2] slower than patients from SVI quartile 1. Specific SVI themes associated with delay were a community’s socioeconomic status (aHR, 0.80 [95% CI, 0.74–0.85]) and housing type and transportation (aHR, 0.89 [95% CI, 0.84–0.94]). CONCLUSIONS: This map of acute stroke presentation times shows areas with a high incidence of delay. Increased social vulnerability characterizes these areas. Such places should be systematically targeted to improve population-level stroke presentation times.

As the field of public health rises to the demands of real-time surveillance and rapid data-sharing needs in a postpandemic world, it is time to examine our approaches to the dissemination and accessibility of such data. Distinct challenges exist when working to develop a shared public health language and narratives based on data. It requires that we assess our understanding of public health data literacy, revisit our approach to communication and engagement, and continuously evaluate our impact and relevance. Key stakeholders and cocreators are critical to this process and include people with lived experience, community organizations, governmental partners, and research institutions. In this viewpoint paper, we offer an instructive approach to the tools we used, assessed, and adapted across 3 unique overdose data dashboard projects in Rhode Island, United States. We are calling this model the “Rhode Island Approach to Public Health Data Literacy, Partnerships, and Action.” This approach reflects the iterative lessons learned about the improvement of data dashboards through collaboration and strong partnerships across community members, state agencies, and an academic research team. We will highlight key tools and approaches that are accessible and engaging and allow developers and stakeholders to self-assess their goals for their data dashboards and evaluate engagement with these tools by their desired audiences and users.