BACKGROUND: The World Trade Center (WTC) Health Program, a limited federal healthcare program, provides medical monitoring and treatment for WTC-related conditions to eligible Responders and Survivors of the 9/11 terrorist attacks. Free initial health evaluations (IHE) represent the first step towards the Program's goal of providing equitable and timely member access to healthcare. This study aimed to evaluate equity in IHE utilization among Program members to inform the development of targeted interventions. METHODS: This surveillance study used administrative and surveillance data collected from January 2012 through February 2024. It included Program members newly enrolled during 2012-2022 who completed an IHE or were alive for ≥ 1 year after enrollment. We conducted descriptive and multivariable logistic regression analyses. Outcomes of interest included timely IHE utilization (proportion of members completing an IHE within 6 months of enrollment) and any IHE utilization (proportion completing an IHE by February 2024). Factors of interest included member type, sex, age, race/ethnicity, preferred language, and urban/rural residence. RESULTS: 27,379 Responders and 30,679 Survivors were included. Responders were 89% male, 70% 45-64 years old at enrollment and 76% non-Hispanic White. Survivors were 54% male, 54% 45-64 years old at enrollment and 57% non-Hispanic White. Timely IHE utilization remained stable (~ 65%) among Responders, while for Survivors, it increased from 16% among those enrolled in 2017 to 68% in 2021. Timely IHE utilization was lower for younger members (enrolled < 45 years old vs. ≥ 65 years old, adjusted odds ratio [aOR] = 0.71, p < 0.001), rural residents, female Survivors (44% vs. 47% males, aOR = 0.87, p < 0.001), and Survivors who preferred non-English languages (39% vs. 46% who preferred English, aOR = 0.70, p < 0.001). Compared to non-Hispanic White members, non-Hispanic Black members had higher timely/any IHE utilization, while non-Hispanic Asian/Pacific Islander/Native Hawaiian and Hispanic Survivors had lower timely IHE utilization. CONCLUSIONS: This study highlights Program achievements (e.g. increased timely IHE utilization among Survivors over time and higher timely/any IHE utilization among non-Hispanic Black members compared to non-Hispanic White members) and gaps in providing equitable IHE services to its members. The Program can develop tailored strategies to further improve equity in IHE utilization (e.g. working with providers to adopt/expand flexible IHE scheduling and increase non-English language capacity).

IMPORTANCE: Opioid overdose is a leading public health problem in the United States; however, national data on overdose deaths are delayed by several months or more. OBJECTIVES: To build and validate a statistical model for estimating national opioid overdose deaths in near real time. DESIGN, SETTING, AND PARTICIPANTS: In this cross-sectional study, signals from 5 overdose-related, proxy data sources encompassing health, law enforcement, and online data from 2014 to 2019 in the US were combined using a LASSO (least absolute shrinkage and selection operator) regression model, and weekly predictions of opioid overdose deaths were made for 2018 and 2019 to validate model performance. Results were also compared with those from a baseline SARIMA (seasonal autoregressive integrated moving average) model, one of the most used approaches to forecasting injury mortality. EXPOSURES: Time series data from 2014 to 2019 on emergency department visits for opioid overdose from the National Syndromic Surveillance Program, data on the volume of heroin and synthetic opioids circulating in illicit markets via the National Forensic Laboratory Information System, data on the search volume for heroin and synthetic opioids on Google, and data on post volume on heroin and synthetic opioids on Twitter and Reddit were used to train and validate prediction models of opioid overdose deaths. MAIN OUTCOMES AND MEASURES: Model-based predictions of weekly opioid overdose deaths in the United States were made for 2018 and 2019 and compared with actual observed opioid overdose deaths from the National Vital Statistics System. RESULTS: Statistical models using the 5 real-time proxy data sources estimated the national opioid overdose death rate for 2018 and 2019 with an error of 1.01% and -1.05%, respectively. When considering the accuracy of weekly predictions, the machine learning-based approach possessed a mean error in its weekly estimates (root mean squared error) of 60.3 overdose deaths for 2018 (compared with 310.2 overdose deaths for the SARIMA model) and 67.2 overdose deaths for 2019 (compared with 83.3 overdose deaths for the SARIMA model). CONCLUSIONS AND RELEVANCE: Results of this serial cross-sectional study suggest that proxy administrative data sources can be used to estimate national opioid overdose mortality trends to provide a more timely understanding of this public health problem.

One of the most substantial challenges facing the field of injury and violence prevention is bridging the gap between scientific knowledge and its real-world application to achieve population-level impact. Much synergy is gained when academic and practice communities collaborate; however, a number of barriers prevent better integration of science and practice. This article presents 3 examples of academic-practitioner collaborations, their approaches to working together to address injury and violence issues, and emerging indications of the impact on integrating research and practice. The examples fall along the spectrum of engagement with nonacademic partners as coinvestigators and knowledge producers. They also highlight the benefits of academic-community partnerships and the engaged scholarship model under which Centers for Disease Control and Prevention-funded Injury Control Research Centers operate to address the research-to-practice and practice-to-research gap.