Methamphetamine Use in Iowa
Funded by the Centers for Disease Control (CDC)
The aim of this project is to provide scientific support to the Iowa Department of Public Health in its substance use prevention, treatment, and recovery efforts by conducting a mixed-method, multi-mode investigation of people in Iowa who have a history of methamphetamine use. Our team is conducting an environmental scan and analysis of quantitative data containing measurements of methamphetamine use (including poly-drug use) to assess time trends, spatial patterns, and group-specific attributes associated with methamphetamine use. Members of this research team engaged in a multi-pronged qualitative investigation among current and former methamphetamine users in Iowa and among a subset of Iowa Provider Network clinicians, staff, and treatment center leadership. These research efforts will enable IDPH to better understand the changing nature of drug supply, access, quality, and price, and the social context of methamphetamine use in Iowa (when, where, why, and how). Key goals of this work are to identify a) risk characteristics of individuals, groups, and communities; b) vulnerable populations; c) social determinants; and d) communities in special need of resources and interventions to prevent and mitigate the harms of methamphetamine use.
You can read the report here.
Iowa's Health Information Platform
Funded by the Centers for Disease Control (CDC)
As part of its award from the CDC (Iowa Opioid Data to Action Program, IA-OD2A) the Iowa Department of Public Health’s Substance Abuse Bureau seeks to develop stronger relationships between critical surveillance and prevention partners and increase the use of data to inform and evaluate prevention initiatives. ISU will partner with IDPH in support of the OD2A goal to decrease licit/illicit opioid and other substance use related to morbidity and mortality. Three aims of this collaboration are 1) Increase use of findings from data sources to inform and target prevention efforts; 2) Increase real-time response to overdose “outbreaks” to offer prevention and treatment services; and 3) Increase stakeholder collaboration.
Big Data in Small Places: Building a Data Science for the Public Good Program to Support Rural Economic Mobility
Funded by the Bill and Melinda Gates Foundation
The interdisciplinary team at ISU will coordinate with the national team (Oregon State University, Virginia Tech University, and University of Virginia) to design and implement a data science training program with a public outreach focus, increase extension professional's awareness of data science principles, and improve data literacy skills in participating communities. Funding supports the development of a data infrastructure for DSPG applications and facilitates engagement with local, regional, and national stakeholders in the evaluation of DSPG expansion efforts. A special focus of the project is to conduct economic mobility research in rural communities, including barriers to mobility, and to strengthen data science communication and accelerate the translation of science into actionable policy.
See here for a recent article discussing our community engagement efforts.
Substance Use among Iowa Families: An Inter-generational Mixed Method Approach for Informing Policy and Practice
Funded by the Centers for Disease Control (CDC)
The purpose of this project is to strengthen the Iowa Department of Public Health’s (IDPH) surveillance of substance use in Iowa, with particular attention to inter-generational impacts, through the deployment of an ethnographic assessment of active and former substance users. The goal of these activities is to help IDPH develop strategies to proactively identify and support children and families who have experienced harmful effects of substance use, either directly or indirectly. Four goals guide this work:
- To better understand multi-generational impacts of substance use, and family risk prediction.
- To conduct a qualitative evaluation of substance use behavior, risk reduction, and socio-demographic and health characteristics in rural communities.
- To better understand the dynamics of opioids and other substance use in Iowa, with special attention to substance use in rural areas.
- To leverage ethnographic interviews to provide information that supports and enhances IDPH efforts to address substance use, with special attention to the two-generational framework (hereafter 2-gen).
Read the final report here.
Data Science for the Public Good (DSPG) Coordinated Innovation Network
Funded by the US Department of Agriculture (USDA)
This project promotes the creation of a data-savvy and community-aware workforce, serving to bridge the gap in the application of data science to public good problems in rural America through the marriage of data science with agricultural, economic, social, and behavioral sciences.
As part of this program, we administer a 10-week summer program for undergraduate and graduate students, faculty, and Cooperative Extension professionals engaged or interested in community development, food, health, nutrition, youth development, forestry, agriculture, and natural resource management in rural communities. Student fellows will partner with faculty advisors to form community-focused research teams and execute a collection of collaboratively constructed data science projects in partnership with local rural community stakeholders.
We elicit community-based research problems by engaging the expertise and community knowledge of Extension professionals in Iowa. The DSPG summer program provides student fellows with an immersive experience in data science, including workshops and training on statistical computing and visualization tools such as R, Python, Tableau, and QGIS; accessing and using local, state, and federal data resources such as Census products and open data portals; and learning about the policy, ethics, and practice of community-driven data science.
To learn more about the summer training program, visit our website.
See here for a recent news article about the program.
To learn about the projects of the 2020 cohort, see here.
Algorithms for Threat Detection (ATD)
Funded by the National Science Foundation (NSF)
Recent advances in the acquisition, storage, and retrieval of big data provide opportunities to develop algorithms for threat detection to help societies prepare and respond to immediate threats, such as local policing, natural disasters, severe weather events, rapid shifts in financial markets, and acts of terrorism. Achieving public trust for such powerful and potentially intrusive algorithms is difficult. Some segments of the public worry about the extent to which ATD inherently designed for favorable outcomes might infringe on individual-level demands for privacy or introduce bias and other harms into the very societies they are designed to protect. To understand public perceptions concerning threat detection algorithms, we design and collect a national survey that measures public opinions about potential threats to public health, local policing, and national security. Results will be used to inform public policy and to educate algorithm creators about public concerns related to ATD.
Attitudes Toward China
In collaboration with the Center on Contemporary China at Princeton University
In this project, we seek to understand attitudes and perceptions concerning China and the US-China trade war. One feature of this project is to identify existing comparative social surveys containing measurements of attitudes, beliefs, and perceptions concerning China. Once identified, we collect, integrate, and harmonize relevant data to facilitate the analysis of attitudes in different countries and time periods. The project gives special attention to attitudes in China and the US concerning the trade war and inter-state relations between the two countries. A second feature of the project involves the collection and analysis of social media data containing measurements of attitudes about China. A third feature of this project is the collection and analysis of experimental data to understand the causal influences on public opinions about China. Research products from this project include datasets, analyses, and scientific publications.