The overall goal of this project is to develop national and local data sources that allow us to model and mitigate the unintended impact of law enforcement efforts to disrupt the illicit drug supply network. Our analysis will look both at county and neighborhood level effects of the illicit supply network and distinguish across multiple illicit substances (e.g., fentanyl, heroin, methamphetamine, cocaine, benzodiazepines, prescription medications, and cannabis). We will study the effect of network disruptions on the transition to more potent substances and subsequent changes in rates of drug overdoses.

Diabetic retinopathy (DR) is expected to affect over 11 million people in the US by 2030 and is the leading cause of blindness in working age Americans, despite being almost entirely preventable with timely detection, treatment, and adherence to follow-up care. To reach the over 30 million adults living with diabetes in the US, the Retinal Care-DR program is designed to eliminate the deficiencies of the current care delivery model by implementing a first-of-its-kind, end-to-end solution for DR care and blindness prevention. This will be accomplished through the application of machine learning to prioritize patients for care coordination by DR risk, development of patient-specific engagement strategies to identify and modify adherence behaviors, implementation of agent-based simulation to maximize patient health outcomes while minimizing the cost of care coordination, and the identification of care coordination strategies that result in higher rates of screening using a user-centered design process.

Aim 1: Modify our CRC simulation model and population characterization developed in previous years/prior work to project the lifelong CRC impacts of CRCCP clinic-based interventions moving forward, working collaboratively with CDC partners. Given the switch in CRCCP focus, the population should represent Community Health Center/Federally Qualified Health Centers CDC supports CRC intervention in. We will also reconsider whether we should use the natural history model calibration from CDEE data (high prevalence of polyps; potentially identified because they are higher risk) or our current NC population-level model (or something in between). Aim 2: Use the modified model from Aim 1, working with CDC partners, to conduct analyses and manuscript development related to modifications to current screening operations. Aim 3: Experiment with a fuzzy set QCA to explore the necessary and sufficient conditions for investment in clinics having a substantial (versus not, to be defined) impact on the impact of investment on CRC screening. In other words, can we identify conditions associated with investment more substantially impacting clinic performance than not? Then, once results emerge, we will use causal loop diagramming to understand why these findings emerge (i.e., why are certain combinations of approaches most effective ��� what are their joint mechanisms for change; why might other combinations not be as effective)?

Legitimate massage businesses across the United States use online platforms, such as advertisement services, job recruitment ads, and review boards. Illicit massage businesses (IMBs), which are often associated with human trafficking activity, do so as well. To better triage, pivot through, and identify illicit businesses through online reviews, we will examine coexisting features present in business level, spatial, and socio-demographic data. This project will create a robust, diverse data-enrichment methodology applicable by cross-domain researchers in adversarial online environments through two primary methods: 1) developing data collection, cleaning, and joining methods and 2) utilizing external geospatial and socio-demographic data to prioritize and target features most likely present in illicit massage business reviews online.

Human trafficking is the world������������������s fastest growing criminal economy and the third largest overall. Law enforcement, government agencies, nonprofit organizations, technology companies and commercial enterprises all have relevant data and skills that could drastically increase the number of victims identified and rescued as well provide evidence to secure traffickers������������������ convictions. In the digital age, human trafficking has evolved into a complex system that leverages the power of technology to build supply chains, create sophisticated communication techniques, and protect criminals from scrutiny. There are many forms of human trafficking, including sex trafficking and labor trafficking, among others. This proposal focuses on countering human trafficking at illegal massage businesses by enabling the identification of victims and traffickers via statistical learning.

The COVID-19 Simulation Integrated Modeling (COVSIM)CovSim2 team brings deep experience in infectious disease modeling and engagement with public health agencies, including work on COVID-19 (as part of first year of funding from CDC and CSTE 2020-2021) and on influenza pandemic (2007 to 2018). Across the team, there is also previous experience in modeling and decision-making for cholera, malaria, HIV, Hepatitis C, guinea worm disease, obesity, diabetes, sepsis, and more. We pair our deep computational experience with domain expertise in public health, communication, and visualization to elicit feedback and disseminate results. Our team has extensive experience building complex models in a variety of clinical and policy settings and using them to inform decision making.

Aim 1: Complete analysis predicting the impact of a first cycle of CRC screening (including routine and any diagnostic testing) received through the CRCCP between 2009 and 2020 on lifetime CRC diagnoses, stage at diagnosis, CRC death, and life-years lost. We are also simulating the expected cost of future screening/testing for individuals receiving the intervention compared to their usual care counterfactual (under different assumptions about usual and future care). Aim 2: Extend Aim 1 analysis to include long-term costs related to CRC treatment and survivorship care. For this aim, we will need to develop a mini-model to estimate the life expectancy and aggregated cost of CRC survivorship care (including treatment and surveillance), incorporating discounting, based on the patients age, sex, and CRC stage at diagnosis. Aim 3: Estimate the impact (benefits and cost) of CRCCP providing additional cycles of screening for individuals. What was the impact among patients in CCDE who received a second+ cycle? What is the relative benefit to CRCCP of providing a first cycle of CRC screening to patients versus second+? If second+ screening is offered, who should it be targeted at? (In which subgroups of patients is the impact greatest relative to the cost?)

In this project we build on a previously developed colorectal cancer simulation model. Specifically, we will enhance to model to be more realistic as well as more representative of a national population and more usable. We will work on the following aims: Aim 1: Calibrate the model with a larger datset. Aim 2: We will add detail to model to remove previously needed assumptions about (a) prior screening, (b) subsequent impact on screening beyond intervention, and (c) explore the use of different data sources (e.g. census or BRFSS) to create more holistic person-level agent files. Aim 3: Create more useful ways of displaying model outputs for demonstration to stake-holders.

Matriculation and Well-Being Under Emergent Events (MWEE), will harness data from four campuses, engage communities and encourage the development of processes and actions to address this global challenge and answer questions such as: Can we measure and assess our ability to create virtual community through synchronous and asynchronous learning? Can we use data to inform real time risk stratification within campus community? Can we evaluate the effect of social distancing policies enacted on campuses? In doing so, we expect universities to be able to layer the results with data from their campuses to predict retention and graduation rates given emergent events including COVID-19.

The COVID-19 pandemic has intensified interest in health systems������������������ ability to prepare for, and cope with, surges (sudden large-scale escalations) in the treatment needs of critically ill patients. Several studies in the literature and the US Department of Health and Human Services provide recommendations and standard operating procedures for intensive care unit (ICU) and hospital preparations for a mass disaster or pandemic. However, due to the rarity of the events and the lack of a mechanism to gather data in real-time, the implementation of these recommendations in a healthcare organization usually depends on the experience of hospital emergency managers rather than evidence-based approaches. Furthermore, the scale, velocity, and global impact of the COVID-19 pandemic is beyond what any plans had anticipated. In this proposal, we aim to document medical surge capacity preparations and real-time adjustments for the COVID-19 pandemic in two major hospital systems: (1) Prisma Health, the largest not-for-profit health organization in South Carolina, serving more than 1.2 million patients annually; and (2) MedStar, a not-for-profit healthcare organization operating more than 120 entities, including ten hospitals in the Baltimore������������������Washington, DC area.