Community-Engaged Data Science (CEDS): A Case Study of Working with Communities to Use Data to Inform Change

Overview

Journal J Community Health

Publisher Springer

Specialty Public Health

Date 2024 Jul 3

PMID 38958892

Authors

Ramona G Olvera

Courtney Plagens

Sylvia Ellison

Kesla Klingler

Amy K Kuntz

Rachel P Chase

Affiliations

Soon will be listed here.

Abstract

Data-informed decision making is a critical goal for many community-based public health research initiatives. However, community partners often encounter challenges when interacting with data. The Community-Engaged Data Science (CEDS) model offers a goal-oriented, iterative guide for communities to collaborate with research data scientists through data ambassadors. This study presents a case study of CEDS applied to research on the opioid epidemic in 18 counties in Ohio as part of the HEALing Communities Study (HCS). Data ambassadors provided a pivotal role in empowering community coalitions to translate data into action using key steps of CEDS which included: data landscapes identifying available data in the community; data action plans from logic models based on community data needs and gaps of data; data collection/sharing agreements; and data systems including portals and dashboards. Throughout the CEDS process, data ambassadors emphasized sustainable data workflows, supporting continued data engagement beyond the HCS. The implementation of CEDS in Ohio underscored the importance of relationship building, timing of implementation, understanding communities' data preferences, and flexibility when working with communities. Researchers should consider implementing CEDS and integrating a data ambassador in community-based research to enhance community data engagement and drive data-informed interventions to improve public health outcomes.

References

Wu E, Villani J, Davis A, Fareed N, Harris D, Huerta T . Community dashboards to support data-informed decision-making in the HEALing communities study. Drug Alcohol Depend. 2020; 217:108331. PMC: 7528750. DOI: 10.1016/j.drugalcdep.2020.108331. View

Provost F, Fawcett T . Data Science and its Relationship to Big Data and Data-Driven Decision Making. Big Data. 2016; 1(1):51-9. DOI: 10.1089/big.2013.1508. View

Israel B, Schulz A, Parker E, Becker A . Review of community-based research: assessing partnership approaches to improve public health. Annu Rev Public Health. 1998; 19:173-202. DOI: 10.1146/annurev.publhealth.19.1.173. View

Knudsen H, Drainoni M, Gilbert L, Huerta T, Oser C, Aldrich A . Model and approach for assessing implementation context and fidelity in the HEALing Communities Study. Drug Alcohol Depend. 2020; 217:108330. PMC: 7531282. DOI: 10.1016/j.drugalcdep.2020.108330. View

Sprague Martinez L, Rapkin B, Young A, Freisthler B, Glasgow L, Hunt T . Community engagement to implement evidence-based practices in the HEALing communities study. Drug Alcohol Depend. 2020; 217:108326. PMC: 7537729. DOI: 10.1016/j.drugalcdep.2020.108326. View

Blei D, Smyth P . Science and data science. Proc Natl Acad Sci U S A. 2017; 114(33):8689-8692. PMC: 5565421. DOI: 10.1073/pnas.1702076114. View

Bazzano A, Martin J, Hicks E, Faughnan M, Murphy L . Human-centred design in global health: A scoping review of applications and contexts. PLoS One. 2017; 12(11):e0186744. PMC: 5665524. DOI: 10.1371/journal.pone.0186744. View

Gostin L, Hodge Jr J, Noe S . Reframing the Opioid Epidemic as a National Emergency. JAMA. 2017; 318(16):1539-1540. DOI: 10.1001/jama.2017.13358. View

. The HEALing (Helping to End Addiction Long-term ) Communities Study: Protocol for a cluster randomized trial at the community level to reduce opioid overdose deaths through implementation of an integrated set of evidence-based practices. Drug Alcohol Depend. 2020; 217:108335. PMC: 7568493. DOI: 10.1016/j.drugalcdep.2020.108335. View

10.

Krzyzanowski B, Manson S . Twenty Years of the Health Insurance Portability and Accountability Act Safe Harbor Provision: Unsolved Challenges and Ways Forward. JMIR Med Inform. 2022; 10(8):e37756. PMC: 9386597. DOI: 10.2196/37756. View

11.

Israel B, Lachance L, Coombe C, Lee S, Jensen M, Wilson-Powers E . Measurement Approaches to Partnership Success: Theory and Methods for Measuring Success in Long-Standing Community-Based Participatory Research Partnerships. Prog Community Health Partnersh. 2020; 14(1):129-140. PMC: 7439287. DOI: 10.1353/cpr.2020.0015. View

12.

Israel B, Schulz A, Parker E, Becker A . Community-based participatory research: policy recommendations for promoting a partnership approach in health research. Educ Health (Abingdon). 2004; 14(2):182-97. DOI: 10.1080/13576280110051055. View

13.

Wong E, Schachter A, Collins H, Song L, Ta M, Dawadi S . Cross-Sector Monitoring and Evaluation Framework: Social, Economic, and Health Conditions Impacted During the COVID-19 Pandemic. Am J Public Health. 2021; 111(S3):S215-S223. PMC: 8561070. DOI: 10.2105/AJPH.2021.306422. View

14.

Hansotte E, Bowman E, Gibson P, Dixon B, Madden V, Caine V . Supporting Health Equity Through Data-Driven Decision-Making: A Local Health Department Response to COVID-19. Am J Public Health. 2021; 111(S3):S197-S200. PMC: 8561071. DOI: 10.2105/AJPH.2021.306421. View

15.

Katapally T . The SMART Framework: Integration of Citizen Science, Community-Based Participatory Research, and Systems Science for Population Health Science in the Digital Age. JMIR Mhealth Uhealth. 2019; 7(8):e14056. PMC: 6743262. DOI: 10.2196/14056. View

16.

El-Bassel N, Gilbert L, Hunt T, Wu E, Oga E, Mukherjee T . Using community engagement to implement evidence-based practices for opioid use disorder: A data-driven paradigm & systems science approach. Drug Alcohol Depend. 2021; 222:108675. PMC: 8058324. DOI: 10.1016/j.drugalcdep.2021.108675. View

17.

Harris P, Taylor R, Minor B, Elliott V, Fernandez M, ONeal L . The REDCap consortium: Building an international community of software platform partners. J Biomed Inform. 2019; 95:103208. PMC: 7254481. DOI: 10.1016/j.jbi.2019.103208. View

18.

Minkler M . Using Participatory Action Research to build Healthy Communities. Public Health Rep. 2000; 115(2-3):191-7. PMC: 1308710. DOI: 10.1093/phr/115.2.191. View

19.

Ryan G, Bloom E, Lowsky D, Linthicum M, Juday T, Rosenblatt L . Data-driven decision-making tools to improve public resource allocation for care and prevention of HIV/AIDS. Health Aff (Millwood). 2014; 33(3):410-7. DOI: 10.1377/hlthaff.2013.1155. View

20.

Salimi Y, Shahandeh K, Malekafzali H, Loori N, Kheiltash A, Jamshidi E . Is Community-based Participatory Research (CBPR) Useful? A Systematic Review on Papers in a Decade. Int J Prev Med. 2012; 3(6):386-93. PMC: 3389435. View