COVID Community-Engaged Testing in Alabama: Reaching Underserved Rural Populations Through Collaboration

McCollum, Christopher Greer; Creger, Thomas; Rana, Aadia; Matthews, Lynn T.; Baral, Stefan; Burkholder, Greer A.; Curry, William A.; Elopre, Latesha; Fletcher, Faith E.; Grooms, Sydney; Levitan, Emily B; Michael, Max; Pol, Barbara Van Der; Mugavero, Michael J.

doi:10.2105/ajph.2022.306985

Cited by 2 publications

(2 citation statements)

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“…The presentation of these data in tabular and geospatial fashion was easily interpreted by the community and scientific advisory boards, and community testing implementation partners, with prioritization of counties for testing further benefitting from an understanding of the local community context in identified high-severity counties. Equipped with this information, as depicted in Fig 1, community testing partners were able to prioritize rural counties for outreach and the identification of venues and local partners for testing delivery [35]. As our data sources and analytic capacities expand in scope and sophistication, leveraging traditional epidemiological data and analysis retains a vital role in engaging communities to address communicable and non-communicable conditions in their local context, particularly when response to emerging diseases when time for sophisticated analyses is limited.…”

Section: Discussionmentioning

confidence: 99%

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Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state

Matthews

Long

Pratt

et al. 2023

Preprint

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Background: The U.S. Southeast has a high burden of SARS-CoV-2 infections and COVID-19 disease. We used public data sources and community engagement to prioritize county selections for a precision population health intervention to promote a SARS-CoV-2 testing intervention in rural Alabama during October 2020 and March 2021. Methods: We modeled factors associated with county-level SARS-CoV-2 percent positivity using covariates thought to associate with SARS-CoV-2 acquisition risk, disease severity, and risk mitigation practices. Descriptive epidemiologic data were presented to scientific and community advisory boards to prioritize counties for a testing intervention. Results: In October 2020, SARS-CoV-2 percent positivity was not associated with any modeled factors. In March 2021, premature death rate (aRR 1.16, 95% CI 1.07, 1.25), percent Black residents (aRR 1.00, 95% CI 1.00, 1.01), preventable hospitalizations (aRR 1.03, 95% CI 1.00, 1.06), and proportion of smokers (aRR 0.231, 95% CI 0.10, 0.55) were associated with average SARS-CoV-2 percent positivity. We then ranked counties based on percent positivity, case fatality, case rates, and number of testing sites using individual variables and factor scores. Top ranking counties identified through factor analysis and univariate associations were provided to community partners who considered ongoing efforts and strength of community partnerships to promote testing to inform intervention. Conclusions: The dynamic nature of SARS-CoV-2 proved challenging for a modelling approach to inform a precision population health intervention at the county level. Epidemiological data allowed for engagement of community stakeholders implementing testing. As data sources and analytic capacities expand, engaging communities in data interpretation is vital to address diseases locally.

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Section: Discussionmentioning

confidence: 99%

“…The findings are unlikely to generalize to all settings and will be most applicable to other counties or states with strained public health systems in the U.S. healthcare context. Details regarding our testing intervention and findings are published elsewhere [35].…”

Section: Limitationsmentioning

confidence: 99%

Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state

Matthews

Long

Pratt

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

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Using a participatory approach to identify priorities to advance LHS implementation at an academic medical center

Eagleson,

Gibson,

Dobbins

et al. 2024

Learning Health Systems

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IntroductionLike many other academic medical centers, the University of Alabama at Birmingham (UAB) aspires to adopt learning health system (LHS) principles and practices more fully. Applying LHS principles establishes a culture where clinical and operational practices constantly generate questions and leverage information technology (IT) and methodological expertise to facilitate systematic evaluation of care delivery, health outcomes, and the effects of improvement initiatives. Despite the potential benefits, differences in priorities, timelines, and expectations spanning an academic medical center's clinical care, administrative operations, and research arms create barriers to adopting and implementing an LHS.MethodsUAB's Center for Outcomes and Effectiveness Research and Education, in partnership with UAB Medicine's Department of Clinical Practice Transformation, applied part of the Precision Problem Solving methodology to advance the implementation of LHS principles at UAB.ResultsSixty‐two stakeholders across the UAB health system and academic schools noted 131 concerns regarding the development of an LHS at UAB. From the 131 items, eight major themes were identified, named, and prioritized through a series of focus groups. Of the eight major themes, “Creating a Structure for Aligned and Informed Prioritization” and “Right Data, Right Time, Improved Performance” ranked in the top three most important themes across all focus groups and became the critical priorities as UAB enhances its LHS. A task force comprised of diverse constituents from across UAB's academic medical center is taking first steps toward addressing these priority areas. Initial funding supports a prototype for enhanced health system data access and pilot projects conducted by researchers embedded in health system teams.ConclusionWe suggest that our experience conducting a deliberate process with broad engagement across both the health system and academic arms of the university may be informative to others seeking to advance LHS principles at academic health centers across a myriad of settings.

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COVID Community-Engaged Testing in Alabama: Reaching Underserved Rural Populations Through Collaboration

Cited by 2 publications

References 9 publications

Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state

Using publicly available data to identify priority communities for a SARS-CoV-2 testing intervention in a southern U.S. state

Using a participatory approach to identify priorities to advance LHS implementation at an academic medical center

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