Simulating the Simultaneous Impact of Medication for Opioid Use Disorder and Naloxone on Opioid Overdose Death in Eight New York Counties.

BACKGROUND: The United States is in the midst of an opioid overdose epidemic; 28.3 per 100,000 people died of opioid overdose in 2020. Simulation models can help understand and address this complex, dynamic, and nonlinear social phenomenon. Using the HEALing Communities Study, aimed at reducing opioid overdoses, and an agent-based model, Simulation of Community-Level Overdose Prevention Strategy, we simulated increases in buprenorphine initiation and retention and naloxone distribution aimed at reducing overdose deaths by 40% in New York Counties. METHODS: Our simulations covered 2020-2022. The eight counties contrasted urban or rural and high and low baseline rates of opioid use disorder treatment. The model calibrated agent characteristics for opioid use and use disorder, treatments and treatment access, and fatal and nonfatal overdose. Modeled interventions included increased buprenorphine initiation and retention, and naloxone distribution. We predicted a decrease in the rate of fatal opioid overdose 1 year after intervention, given various modeled intervention scenarios. RESULTS: Counties required unique combinations of modeled interventions to achieve a 40% reduction in overdose deaths. Assuming a 200% increase in naloxone from current levels, high baseline treatment counties achieved a 40% reduction in overdose deaths with a simultaneous 150% increase in buprenorphine initiation. In comparison, low baseline treatment counties required 250-300% increases in buprenorphine initiation coupled with 200-1000% increases in naloxone, depending on the county. CONCLUSIONS: Results demonstrate the need for tailored county-level interventions to increase service utilization and reduce overdose deaths, as the modeled impact of interventions depended on the county's experience with past and current interventions.

A Bayesian Spatiotemporal Nowcasting Model for Public Health Decision-Making and Surveillance.

As coronavirus disease 2019 (COVID-19) spread through the United States in 2020, states began to set up alert systems to inform policy decisions and serve as risk communication tools for the general public. Many of these systems included indicators based on an assessment of trends in numbers of reported cases. However, when cases are indexed by date of disease onset, reporting delays complicate the interpretation of trends. Despite a foundation of statistical literature with which to address this problem, these methods have not been widely applied in practice. In this paper, we develop a Bayesian spatiotemporal nowcasting model for assessing trends in county-level COVID-19 cases in Ohio. We compare the performance of our model with the approach used in Ohio and the approach included in decision support materials from the Centers for Disease Control and Prevention. We demonstrate gains in performance while still retaining interpretability using our model. In addition, we are able to fully account for uncertainty in both the time series of cases and the reporting process. While we cannot eliminate all of the uncertainty in public health surveillance and subsequent decision-making, we must use approaches that embrace these challenges and deliver more accurate and honest assessments to policy-makers.

A Systematic Review of Simulation Models to Track and Address the Opioid Crisis.

The opioid overdose crisis is driven by an intersecting set of social, structural, and economic forces. Simulation models are a tool to help us understand and address thiscomplex, dynamic, and nonlinear social phenomenon. We conducted a systematic review of the literature on simulation models of opioid use and overdose up to September 2019. We extracted modeling types, target populations, interventions, and findings; created a database of model parameters used for model calibration; and evaluated study transparency and reproducibility. Of the 1,398 articles screened, we identified 88 eligible articles. The most frequent types of models were compartmental (36%), Markov (20%), system dynamics (16%), and agent-based models (16%). Intervention cost-effectiveness was evaluated in 40% of the studies, and 39% focused on services for people with opioid use disorder (OUD). In 61% of the eligible articles, authors discussed calibrating their models to empirical data, and in 31%, validation approaches used in the modeling process were discussed. From the 63 studies that provided model parameters, we extracted the data sources on opioid use, OUD, OUD treatment, cessation or relapse, emergency medical services, and death parameters. From this database, potential model inputs can be identified and models can be compared with prior work. Simulation models should be used to tackle key methodological challenges, including the potential for bias in the choice of parameter inputs, investment in model calibration and validation, and transparency in the assumptions and mechanics of simulation models to facilitate reproducibility.

Addressing Health Equity Goals for COVID-19 Vaccination Using Integrated Data and Mapping Tools: A Collaboration Between Academia, Public Health, and Health Care Systems in Columbus and Franklin County, Ohio.

CONTEXT: Data sharing between local health departments and health care systems is challenging during public health crises. In early 2021, the supply of COVID-19 vaccine was limited, vaccine appointments were difficult to schedule, and state health departments were using a phased approach to determine who was eligible to get the vaccine. PROGRAM: Multiple local health departments and health care systems with the capacity for mobile and pop-up vaccine clinics came together in Columbus and Franklin County, Ohio, with a common objective to coordinate where, when, and how to set up mobile/pop-up COVID-19 vaccine clinics. To support this objective, the Equity Mapping Tool, which is a set of integrated tools, workflows, and processes, was developed, implemented, and deployed in partnership with an academic institution. IMPLEMENTATION: The Equity Mapping Tool was designed after a rapid community engagement phase. Our analytical approaches were informed by community engagement activities, and we translated the Equity Mapping Tool for stakeholders, who typically do not share timely and granular data, to build capacity for data-enabled decision making. DISCUSSION: We discuss our observations related to the sustainability of the Equity Mapping Tool, lessons learned for public health scientists/practitioners, and future directions for extending the Equity Mapping Tool to other jurisdictions and public health crises.

Economic Evaluation in Opioid Modeling: Systematic Review.

OBJECTIVES: The rapid increase in opioid overdose and opioid use disorder (OUD) over the past 20 years is a complex problem associated with significant economic costs for healthcare systems and society. Simulation models have been developed to capture and identify ways to manage this complexity and to evaluate the potential costs of different strategies to reduce overdoses and OUD. A review of simulation-based economic evaluations is warranted to fully characterize this set of literature. METHODS: A systematic review of simulation-based economic evaluation (SBEE) studies in opioid research was initiated by searches in PubMed, EMBASE, and EbscoHOST. Extraction of a predefined set of items and a quality assessment were performed for each study. RESULTS: The screening process resulted in 23 SBEE studies ranging by year of publication from 1999 to 2019. Methodological quality of the cost analyses was moderately high. The most frequently evaluated strategies were methadone and buprenorphine maintenance treatments; the only harm reduction strategy explored was naloxone distribution. These strategies were consistently found to be cost-effective, especially naloxone distribution and methadone maintenance. Prevention strategies were limited to abuse-deterrent opioid formulations. Less than half (39%) of analyses adopted a societal perspective in their estimation of costs and effects from an opioid-related intervention. Prevention strategies and studies' accounting for patient and physician preference, changing costs, or result stratification were largely ignored in these SBEEs. CONCLUSION: The review shows consistently favorable cost analysis findings for naloxone distribution strategies and opioid agonist treatments and identifies major gaps for future research.

Low Birth Weight and Preterm Birth Among Arab-American Women in Ohio.

OBJECTIVE: There is limited evidence about prevalence and odds of adverse birth outcomes among Arab American women in the United States. We estimated the prevalence of low birth weight (LBW < 2500 g) and preterm birth (PTB < 37 completed weeks' gestation) among Arab American women in Ohio and studied the association between ethnicity, Arab American nativity (foreign or US born) and odds of LBW and PTB. METHODS: We identified Arab American women based on birth certificate data from live singleton births from 2007-2010 to 2013-2015 and a name algorithm. We compared the prevalence of LBW and PTB by ethnicity (Arab American vs. non-Hispanic White) and by nativity (foreign-born Arab American vs. US-born Arab American). Logistic regression models were used to estimate the unadjusted and adjusted effects of ethnicity and mother's nativity on study outcomes. RESULTS: 31,744 Arab American women (2.5% of all births in Ohio) were identified over a 7-year period. 24,129 Arab American women with complete data were included in the analysis after applying exclusion criteria. Prevalence of LBW was 5.2% (non-Hispanic White), 6.1% (Arab American), 6.4% (US-born Arab American) and 5.6% (foreign-born Arab American). Prevalence of PTB was 7.2% (non-Hispanic White), 7.0% (Arab American), 7.3% (US-born Arab American), and 5.4% (foreign-born Arab American). In adjusted models, which controlled for mother demographics, health behaviors, and pregnancy risk factors, Arab Americans had 33% higher odds of LBW (odds ratio [OR] 1.33; 95% Confidence Intervals[CI] 1.26-1.41) than non-Hispanic Whites. Foreign born Arab American women had 15% lower odds of PTB (OR 0.85; 95% CI 0.75-0.95) than US-born Arab Americans. CONCLUSIONS FOR PRACTICE: Our main findings were that LBW is influenced by Arab ethnicity while PTB is influenced by nativity among Arab American women. These findings may be informative for developing and implementing strategies for adverse birth outcomes for a growing US ethnic minority population.

Translational data analytics in exposure science and environmental health: a citizen science approach with high school students.

BACKGROUND: Translational data analytics aims to apply data analytics principles and techniques to bring about broader societal or human impact. Translational data analytics for environmental health is an emerging discipline and the objective of this study is to describe a real-world example of this emerging discipline. METHODS: We implemented a citizen-science project at a local high school. Multiple cohorts of citizen scientists, who were students, fabricated and deployed low-cost air quality sensors. A cloud-computing solution provided real-time air quality data for risk screening purposes, data analytics and curricular activities. RESULTS: The citizen-science project engaged with 14 high school students over a four-year period that is continuing to this day. The project led to the development of a website that displayed sensor-based measurements in local neighborhoods and a GitHub-like repository for open source code and instructions. Preliminary results showed a reasonable comparison between sensor-based and EPA land-based federal reference monitor data for CO and NOx. CONCLUSIONS: Initial sensor-based data collection efforts showed reasonable agreement with land-based federal reference monitors but more work needs to be done to validate these results. Lessons learned were: 1) the need for sustained funding because citizen science-based project timelines are a function of community needs/capacity and building interdisciplinary rapport in academic settings and 2) the need for a dedicated staff to manage academic-community relationships.

Designing an Agent-Based Model Using Group Model Building: Application to Food Insecurity Patterns in a U.S. Midwestern Metropolitan City.

Recent advances in computing resources have increased interest in systems modeling and population health. While group model building (GMB) has been effectively applied in developing system dynamics models (SD), few studies have used GMB for developing an agent-based model (ABM). This article explores the use of a GMB approach to develop an ABM focused on food insecurity. In our GMB workshops, we modified a set of the standard GMB scripts to develop and validate an ABM in collaboration with local experts and stakeholders. Based on this experience, we learned that GMB is a useful collaborative modeling platform for modelers and community experts to address local population health issues. We also provide suggestions for increasing the use of the GMB approach to develop rigorous, useful, and validated ABMs.

Public Funding for Genomics and the Return on Investment: A Public Health Perspective.

This article traces the emergence of lean principles in genomics research and connects this new way of doing science with many of the current pitfalls of precision medicine in its attempts at improving population health outcomes. Precision medicine has a history of public funding, yet the benefits in clinical settings are very slowly being realized due to a variety of factors, such as uncertainty regarding relevant treatments after identifying disease risk, lack of cost-effectiveness studies for general population-level interventions, and letting a culture of "over promise and under deliver" permeate some areas of genomics research. The article concludes with insights into the challenges and opportunities that will need careful consideration and consultation with the wider society in order to decide whether to turn off the "tap" for investment of public funds in research on genomics and other "omics." Ultimately, this article argues for a moderate course correction in how public funds are invested to truly improve the health of all of us, and not just some of us.

PM2.5 exposure and birth outcomes: use of satellite- and monitor-based data.

BACKGROUND: Air pollution may be related to adverse birth outcomes. Exposure information from land-based monitoring stations often suffers from limited spatial coverage. Satellite data offer an alternative data source for exposure assessment. METHODS: We used birth certificate data for births in Connecticut and Massachusetts, United States (2000-2006). Gestational exposure to PM2.5 was estimated from US Environmental Protection Agency monitoring data and from satellite data. Satellite data were processed and modeled by using two methods-denoted satellite (1) and satellite (2)-before exposure assessment. Regression models related PM2.5 exposure to birth outcomes while controlling for several confounders. Birth outcomes were mean birth weight at term birth, low birth weight at term (<2500 g), small for gestational age (SGA, <10th percentile for gestational age and sex), and preterm birth (<37 weeks). RESULTS: Overall, the exposure assessment method modified the magnitude of the effect estimates of PM2.5 on birth outcomes. Change in birth weight per interquartile range (2.41 µg/m) increase in PM2.5 was -6 g (95% confidence interval = -8 to -5), -16 g (-21 to -11), and -19 g (-23 to -15), using the monitor, satellite (1), and satellite (2) methods, respectively. Adjusted odds ratios, based on the same three exposure methods, for term low birth weight were 1.01 (0.98-1.04), 1.06 (0.97-1.16), and 1.08 (1.01-1.16); for SGA, 1.03 (1.01-1.04), 1.06 (1.03-1.10), and 1.08 (1.04-1.11); and for preterm birth, 1.00 (0.99-1.02), 0.98 (0.94-1.03), and 0.99 (0.95-1.03). CONCLUSIONS: Under exposure assessment methods, we found associations between PM2.5 exposure and adverse birth outcomes particularly for birth weight among term births and for SGA. These results add to the growing concerns that air pollution adversely affects infant health and suggest that analysis of health consequences based on satellite-based exposure assessment can provide additional useful information.

A History Of The Impacts Of Discriminatory Policies On Housing And Maternal And Infant Health In An Ohio Neighborhood.

Community-level disinvestment and de facto segregation rooted in decades of discriminatory race-based policies and racism have resulted in unacceptably large infant mortality rates in racial minority neighborhoods across the US. Most community development and housing work, implemented with the goal of addressing health and social inequities, is designed to tackle current challenges in the condition of neighborhoods without a race-conscious lens assessing structural racism and discrimination. Using one historically segregated neighborhood-Linden, in Columbus, Ohio-we detail how state and local policies have affected the neighborhood and shaped neighborhood-level demographics and resources during the past 100 years. We explore how structural racism- and discrimination-informed strategic community reinvestment could provide a solution and yield lasting change.

Understanding the spatiotemporal evolution of opioid overdose events using a regionalized sequence alignment analysis.

BACKGROUND: Opioid overdose events and deaths have become a serious public health crisis in the United States, and understanding the spatiotemporal evolution of the disease occurrences is crucial for developing effective prevention strategies, informing health systems policy and planning, and guiding local responses. However, current research lacks the capability to observe the dynamics of the opioid crisis at a fine spatial-temporal resolution over a long period, leading to ineffective policies and interventions at the local level. METHODS: This paper proposes a novel regionalized sequential alignment analysis using opioid overdose events data to assess the spatiotemporal similarity of opioid overdose evolutionary trajectories within regions that share similar socioeconomic status. The model synthesizes the shape and correlation of space-time trajectories to assist space-time pattern mining in different neighborhoods, identifying trajectories that exhibit similar spatiotemporal characteristics for further analysis. RESULTS: By adopting this methodology, we can better understand the spatiotemporal evolution of opioid overdose events and identify regions with similar patterns of evolution. This enables policymakers and health researchers to develop effective interventions and policies to address the opioid crisis at the local level. CONCLUSIONS: The proposed methodology provides a new framework for understanding the spatiotemporal evolution of opioid overdose events, enabling policymakers and health researchers to develop effective interventions and policies to address this growing public health crisis.

Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision.

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time. In this study, we employ distant supervision utilizing a domain-specific ontology to reduce the need for human labor and train models incorporating domain-specific (e.g., drug use) external knowledge to recognize domain specific entities. We capture entities related the drug use and their trends in government epidemiology reports, with an improvement of 8% in F1-score.

Understanding the role of urban social and physical environment in opioid overdose events using found geospatial data.

Opioid use disorder is a serious public health crisis in the United States. Manifestations such as opioid overdose events (OOEs) vary within and across communities and there is growing evidence that this variation is partially rooted in community-level social and economic conditions. The lack of high spatial resolution, timely data has hampered research into the associations between OOEs and social and physical environments. We explore the use of non-traditional, "found" geospatial data collected for other purposes as indicators of urban social-environmental conditions and their relationships with OOEs at the neighborhood level. We evaluate the use of Google Street View images and non-emergency "311" service requests, along with US Census data as indicators of social and physical conditions in community neighborhoods. We estimate negative binomial regression models with OOE data from first responders in Columbus, Ohio, USA between January 1, 2016, and December 31, 2017. Higher numbers of OOEs were positively associated with service request indicators of neighborhood physical and social disorder and street view imagery rated as boring or depressing based on a pre-trained random forest regression model. Perceived safety, wealth, and liveliness measures from the street view imagery were negatively associated with risk of an OOE. Age group 50-64 was positively associated with risk of an OOE but age 35-49 was negative. White population, percentage of individuals living in poverty, and percentage of vacant housing units were also found significantly positive however, median income and percentage of people with a bachelor's degree or higher were found negative. Our result shows neighborhood social and physical environment characteristics are associated with likelihood of OOEs. Our study adds to the scientific evidence that the opioid epidemic crisis is partially rooted in social inequality, distress and underinvestment. It also shows the previously underutilized data sources hold promise for providing insights into this complex problem to help inform the development of population-level interventions and harm reduction policies.

Community-based Systems Dynamics for Reproductive Health: An Example from Urban Ohio.

BACKGROUND: Health outcomes, risk factors, and policies are complexly related to the reproductive health system. Systems-level frameworks for understanding and acting within communities through community-engaged research are needed to mitigate adverse reproductive health outcomes more effectively within the community. OBJECTIVES: To describe and share lessons learned from an ongoing application of a participatory modeling approach (community-based system dynamics) that aims to eliminate racial inequities in Black-White reproductive health outcomes. METHODS: The community-based system dynamics approach involves conducting complementary activities, workshops, modeling, and dissemination. We organized workshops, co-developed a causal loop diagram of the reproductive health system with participants from the community, and created materials to disseminate workshop findings and preliminary models. LESSONS LEARNED: Many opportunities exist for cross-fertilization of best practices between community-based system dynamics and community-based participatory research. Shared learning environments offer benefits for modelers and domain experts alike. Additionally, identifying local champions from the community helps manage group dynamics. CONCLUSIONS: Community-based system dynamics is well-suited for understanding complexity in the reproductive health system. It allows participants from diverse perspectives to identify strategies to eliminate racial inequities in reproductive health outcomes.

Acute kidney injury in non-critically ill children treated with aminoglycoside antibiotics in a tertiary healthcare centre: a retrospective cohort study.

BACKGROUND: Aminoglycosides (AG) cause acute kidney injury (AKI), but the incidence and severity distribution are unclear, particularly in non-critically ill children. We determined the incidence, severity and risk factors of AG-associated AKI and assessed for associations with longer hospitalization and higher costs. METHODS: At Texas Children's Hospital, we conducted a retrospective cohort study of children treated with AG for ≥ 5 days in 2005, excluding children with admission primary renal diagnoses. AKI was defined by the paediatric Risk, Injury, Failure, Loss, End Stage Kidney Disease (pRIFLE) and Acute Kidney Injury Network (AKIN) definitions. Multiple logistic and linear regression analyses were used to assess independence of associations with outcomes. RESULTS: Five hundred and fifty-seven children [mean ± SD age = 8.0 ± 5.9 years, 286 (51%) male, 489 (88%) gentamicin] were studied. The AKI rate was 33% and 20% by pRIFLE and AKIN definitions, respectively. Longer treatment, higher baseline estimated glomerular filtration rate, being on a medicine (versus surgical) treatment service and prior AG treatment were independent risk factors for AKI development. AKI by pRIFLE or AKIN was independently associated with longer hospital stay and higher total hospital costs. The pRIFLE definition was more sensitive for AKI detection, but the AKIN definition was more strongly related to outcomes. CONCLUSIONS: AKI is common and associated with poorer outcomes in non-critically ill children treated with AG. Future research should attempt to understand how to best define AKI in the non-critical illness paediatric setting.

Acute kidney injury is an independent risk factor for pediatric intensive care unit mortality, longer length of stay and prolonged mechanical ventilation in critically ill children: a two-center retrospective cohort study.

INTRODUCTION: In adults, small (< 50%) serum creatinine (SCr) increases predict mortality. It is unclear whether different baseline serum creatinine (bSCr) estimation methods affect findings of acute kidney injury (AKI)-outcome associations. We characterized pediatric AKI, evaluated the effect of bSCr estimation approaches on AKI-outcome associations and evaluated the use of small SCr increases to predict AKI development. METHODS: We conducted a retrospective cohort database study of children (excluding postoperative cardiac or renal transplant patients) admitted to two pediatric intensive care units (PICUs) for at least one night in Montreal, QC, Canada. The AKI definition was based on the Acute Kidney Injury Network staging system, excluding the requirement of SCr increase within 48 hours, which was impossible to evaluate on the basis of our data set. We estimated bSCr two ways: (1) the lowest SCr level in the three months before admission or the average age- and gender-based norms (the standard method) or (2) by using average norms in all patients. Outcomes were PICU mortality and length of stay as well as required mechanical ventilation. We used multiple logistic regression analysis to evaluate AKI risk factors and the association between AKI and mortality. We used multiple linear regression analysis to evaluate the effect of AKI on other outcomes. We calculated diagnostic characteristics for early SCr increase (< 50%) to predict AKI development. RESULTS: Of 2,106 admissions (mean age ± SD = 5.0 ± 5.5 years; 47% female), 377 patients (17.9%) developed AKI (using the standard bSCr method) during PICU admission. Higher Pediatric Risk of Mortality score, required mechanical ventilation, documented infection and having a bSCr measurement were independent predictors of AKI development. AKI was associated with increased mortality (adjusted odds ratio (OR) = 3.7, 95% confidence interval (95% CI) = 2.1 to 6.4, using the standard bSCr method; OR = 4.5, 95% CI = 2.6 to 7.9, using normative bSCr values in all patients). AKI was independently associated with longer PICU stay and required mechanical ventilation. In children with no admission AKI, the initial percentage SCr increase predicted AKI development (area under the curve = 0.67, 95% CI = 0.60 to 0.74). CONCLUSIONS: AKI is associated with increased mortality and morbidity in critically ill children, regardless of the bSCr used. Paying attention to small early SCr increases may contribute to early AKI diagnosis in conjunction with other new AKI biomarkers.

Opioid Treatment Deserts: Concept development and application in a US Midwestern urban county.

OBJECTIVES: An Opioid Treatment Desert is an area with limited accessibility to medication-assisted treatment and recovery facilities for Opioid Use Disorder. We explored the concept of Opioid Treatment Deserts including racial differences in potential spatial accessibility and applied it to one Midwestern urban county using high resolution spatiotemporal data. METHODS: We obtained individual-level data from one Emergency Medical Services (EMS) agency (Columbus Fire Department) in Franklin County, Ohio. Opioid overdose events were based on EMS runs where naloxone was administered from 1/1/2013 to 12/31/2017. Potential spatial accessibility was measured as the time (in minutes) it would take an individual, who may decide to seek treatment after an opioid overdose, to travel from where they had the overdose event, which was a proxy measure of their residential location, to the nearest opioid use disorder (OUD) treatment provider that provided medically-assisted treatment (MAT). We estimated accessibility measures overall, by race and by four types of treatment providers (any type of MAT for OUD, Buprenorphine, Methadone, or Naltrexone). Areas were classified as an Opioid Treatment Desert if the estimate travel time to treatment provider (any type of MAT for OUD) was greater than a given threshold. We performed sensitivity analysis using a range of threshold values based on multiple modes of transportation (car and public transit) and using only EMS runs to home/residential location types. RESULTS: A total of 6,929 geocoded opioid overdose events based on data from EMS agencies were used in the final analysis. Most events occurred among 26-35 years old (34%), identified as White adults (56%) and male (62%). Median travel times and interquartile range (IQR) to closest treatment provider by car and public transit was 2 minutes (IQR: 3 minutes) and 17 minutes (IQR: 17 minutes), respectively. Several neighborhoods in the study area had limited accessibility to OUD treatment facilities and were classified as Opioid Treatment Deserts. Travel time by public transit for most treatment provider types and by car for Methadone-based treatment was significantly different between individuals who were identified as Black adults and White adults based on their race. CONCLUSIONS: Disparities in access to opioid treatment exist at the sub-county level in specific neighborhoods and across racial groups in Columbus, Ohio and can be quantified and visualized using local public safety data (e.g., EMS runs). Identification of Opioid Treatment Deserts can aid multiple stakeholders better plan and allocate resources for more equitable access to MAT for OUD and, therefore, reduce the burden of the opioid epidemic while making better use of real-time public safety data to address a public health epidemic that has turned into a public safety crisis.

COVID-19 Surveillance for Local Decision Making : An Academic, School District, and Public Health Collaboration.

OBJECTIVE: Data-informed decision making is valued among school districts, but challenges remain for local health departments to provide data, especially during a pandemic. We describe the rapid planning and deployment of a school-based COVID-19 surveillance system in a metropolitan US county. METHODS: In 2020, we used several data sources to construct disease- and school-based indicators for COVID-19 surveillance in Franklin County, an urban county in central Ohio. We collected, processed, analyzed, and visualized data in the COVID-19 Analytics and Targeted Surveillance System for Schools (CATS). CATS included web-based applications (public and secure versions), automated alerts, and weekly reports for the general public and decision makers, including school administrators, school boards, and local health departments. RESULTS: We deployed a pilot version of CATS in less than 2 months (August-September 2020) and added 21 school districts in central Ohio (15 in Franklin County and 6 outside the county) into CATS during the subsequent months. Public-facing web-based applications provided parents and students with local information for data-informed decision making. We created an algorithm to enable local health departments to precisely identify school districts and school buildings at high risk of an outbreak and active SARS-CoV-2 transmission in school settings. PRACTICE IMPLICATIONS: Piloting a surveillance system with diverse school districts helps scale up to other districts. Leveraging past relationships and identifying emerging partner needs were critical to rapid and sustainable collaboration. Valuing diverse skill sets is key to rapid deployment of proactive and innovative public health practices during a global pandemic.