IU Indianapolis ScholarWorks :: Browsing by Subject "Coincidence analysis"

Browsing by Subject "Coincidence analysis"

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Coincidence analysis: a new method for causal inference in implementation science
(BMC, 2020-12-11) Garr Whitaker, Rebecca; Sperber, Nina; Baumgartner, Michael; Thiem, Alrik; Cragun, Deborah; Damschroder, Laura; Miech, Edward J.; Slade, Alecia; Birken, Sarah; Emergency Medicine, School of Medicine
Background: Implementation of multifaceted interventions typically involves many diverse elements working together in interrelated ways, including intervention components, implementation strategies, and features of local context. Given this real-world complexity, implementation researchers may be interested in a new mathematical, cross-case method called Coincidence Analysis (CNA) that has been designed explicitly to support causal inference, answer research questions about combinations of conditions that are minimally necessary or sufficient for an outcome, and identify the possible presence of multiple causal paths to an outcome. CNA can be applied as a standalone method or in conjunction with other approaches and can reveal new empirical findings related to implementation that might otherwise have gone undetected. Methods: We applied CNA to a publicly available dataset from Sweden with county-level data on human papillomavirus (HPV) vaccination campaigns and vaccination uptake in 2012 and 2014 and then compared CNA results to the published regression findings. Results: The original regression analysis found vaccination uptake was positively associated only with the availability of vaccines in schools. CNA produced different findings and uncovered an additional solution path: high vaccination rates were achieved by either (1) offering the vaccine in all schools or (2) a combination of offering the vaccine in some schools and media coverage. Conclusions: CNA offers a new comparative approach for researchers seeking to understand how implementation conditions work together and link to outcomes.
Defining optimal implementation packages for delivering community-wide mass drug administration for soil-transmitted helminths with high coverage
(Springer, 2022-06-18) Gwayi-Chore, Marie-Claire; Aruldas, Kumudha; Avokpaho , Euripide; Chirambo , Chawanangwa Maherebe; Kaliappan , Saravanakumar Puthupalayam; Houngbégnon , Parfait; Togbevi , Comlanvi Innocent; Chabi , Félicien; Nindi, Providence; Simwanza , James; Johnson , Jabaselvi; Miech , Edward J.; Kalua , Khumbo; Ibikounlé , Moudachirou; Ajjampur, Sitara S. R.; Weiner , Bryan J.; Walson, Judd L.; Rubin Means, Arianna; Emergency Medicine, School of Medicine
Background Recent evidence suggests that community-wide mass drug administration (MDA) may interrupt the transmission of soil-transmitted helminths (STH), a group of intestinal worms that infect 1.5 billion individuals globally. Although current operational guidelines provide best practices for effective MDA delivery, they do not describe which activities are most essential for achieving high coverage or how they work together to produce effective intervention delivery. We aimed to identify the various packages of influential intervention delivery activities that result in high coverage of community-wide MDA for STH in Benin, India, and Malawi. Methods We applied coincidence analysis (CNA), a novel cross-case analytical method, to process mapping data as part of the implementation science research of the DeWorm3 Project, a Hybrid Type 1 cluster randomized controlled trial assessing the feasibility of interrupting the transmission of STH using bi-annual community-wide MDA in Benin, India, and Malawi. Our analysis aimed to identify any necessary and/or sufficient combinations of intervention delivery activities (i.e., implementation pathways) that resulted in high MDA coverage. Activities were related to drug supply chain, implementer training, community sensitization strategy, intervention duration, and implementation context. We used pooled implementation data from three sites and six intervention rounds, with study clusters serving as analytical cases (N = 360). Secondary analyses assessed differences in pathways across sites and over intervention rounds. Results Across all three sites and six intervention rounds, efficient duration of MDA delivery (within ten days) singularly emerged as a common and fundamental component for achieving high MDA coverage when combined with other particular activities, including a conducive implementation context, early arrival of albendazole before the planned start of MDA, or a flexible community sensitization strategy. No individual activity proved sufficient by itself for producing high MDA coverage. We observed four possible overall models that could explain effective MDA delivery strategies, all which included efficient duration of MDA delivery as an integral component. Conclusion Efficient duration of MDA delivery uniquely stood out as a highly influential implementation activity for producing high coverage of community-wide MDA for STH. Effective MDA delivery can be achieved with flexible implementation strategies that include various combinations of influential intervention components.
Exploring the Social Determinants of Mental Health by Race and Ethnicity in Army Wives
(Springer, 2024) Dodge, Jessica; Sullivan, Kathrine; Miech, Edward; Clomax, Adriane; Riviere, Lyndon; Castro, Carl; Emergency Medicine, School of Medicine
Objective: To explore the social determinants of mental health (SDoMH) by race/ethnicity in a sample with equal access to healthcare. Using an adaptation of the World Health Organization's SDoMH Framework, this secondary analysis examines the socio-economic factors that make up the SDoMH by race/ethnicity. Method: This paper employed configurational comparative methods (CCMs) to analyze various racial/ethnic subsets from quantitative survey data from (N = 327) active-duty Army wives. Data was collected in 2012 by Walter Reed Army Institute of Research. Results: Initial exploratory analysis revealed the highest-scoring factors for each racial/ethnic subgroup: non-Hispanic Black: employment and a history of adverse childhood events (ACEs); Hispanic: living off post and a recent childbirth; junior enlisted non-Hispanic White: high work-family conflict and ACEs; non-Hispanic other race: high work-family conflict and not having a military history. Final analysis showed four models consistently explained clinically significant depression symptoms and four models consistently explained the absence of clinical depression symptoms, providing a solution for each racial/ethnic minority group (non-Hispanic Black, Hispanic, junior enlisted non-Hispanic White, and non-Hispanic other). Discussion: These findings highlight that Army wives are not a monolithic group, despite their collective exposure to military-specific stressors. These findings also highlight the potential for applying configurational approaches to gain new insights into mental health outcomes for social science and clinical researchers.
Facility-level conditions leading to higher reach: a configurational analysis of national VA weight management programming
(Springer Nature, 2021-08-11) Miech, Edward J.; Freitag, Michelle B.; Evans, Richard R.; Burns, Jennifer A.; Wiitala, Wyndy L.; Annis, Ann; Raffa, Susan D.; Spohr, Stephanie A.; Damschroder, Laura J.; Emergency Medicine, School of Medicine
Background: While the Veterans Health Administration (VHA) MOVE! weight management program is effective in helping patients lose weight and is available at every VHA medical center across the United States, reaching patients to engage them in treatment remains a challenge. Facility-based MOVE! programs vary in structures, processes of programming, and levels of reach, with no single factor explaining variation in reach. Configurational analysis, based on Boolean algebra and set theory, represents a mathematical approach to data analysis well-suited for discerning how conditions interact and identifying multiple pathways leading to the same outcome. We applied configurational analysis to identify facility-level obesity treatment program arrangements that directly linked to higher reach. Methods: A national survey was fielded in March 2017 to elicit information about more than 75 different components of obesity treatment programming in all VHA medical centers. This survey data was linked to reach scores available through administrative data. Reach scores were calculated by dividing the total number of Veterans who are candidates for obesity treatment by the number of "new" MOVE! visits in 2017 for each program and then multiplied by 1000. Programs with the top 40 % highest reach scores (n = 51) were compared to those in the lowest 40 % (n = 51). Configurational analysis was applied to identify specific combinations of conditions linked to reach rates. Results: One hundred twenty-seven MOVE! program representatives responded to the survey and had complete reach data. The final solution consisted of 5 distinct pathways comprising combinations of program components related to pharmacotherapy, bariatric surgery, and comprehensive lifestyle intervention; 3 of the 5 pathways depended on the size/complexity of medical center. The 5 pathways explained 78 % (40/51) of the facilities in the higher-reach group with 85 % consistency (40/47). Conclusions: Specific combinations of facility-level conditions identified through configurational analysis uniquely distinguished facilities with higher reach from those with lower reach. Solutions demonstrated the importance of how local context plus specific program components linked together to account for a key implementation outcome. These findings will guide system recommendations about optimal program structures to maximize reach to patients who would benefit from obesity treatment such as the MOVE!
Facility-level program components leading to population impact: a coincidence analysis of obesity treatment options within the Veterans Health Administration
(Oxford University Press, 2022) Damschroder, Laura J.; Miech, Edward J.; Freitag, Michelle B.; Evans, Richard; Burns, Jennifer A.; Raffa, Susan D.; Goldstein, Michael G.; Annis, Ann; Spohr, Stephanie A.; Wiitala, Wyndy L.; Emergency Medicine, School of Medicine
Obesity is a well-established risk factor for increased morbidity and mortality. Comprehensive lifestyle interventions, pharmacotherapy, and bariatric surgery are three effective treatment approaches for obesity. The Veterans Health Administration (VHA) offers all three domains but in different configurations across medical facilities. Study aim was to explore the relationship between configurations of three types of obesity treatments, context, and population impact across VHA using coincidence analysis. This was a cross-sectional analysis of survey data describing weight management treatment components linked with administrative data to compute population impact for each facility. Coincidence analysis was used to identify combinations of treatment components that led to higher population impact. Facilities with higher impact were in the top two quintiles for (1) reach to eligible patients and (2) weight outcomes. Sixty-nine facilities were included in the analyses. The final model explained 88% (29/33) of the higher-impact facilities with 91% consistency (29/32) and was comprised of five distinct pathways. Each of the five pathways depended on facility complexity-level plus factors from one or more of the three domains of weight management: comprehensive lifestyle interventions, pharmacotherapy, and/or bariatric surgery. Three pathways include components from multiple treatment domains. Combinations of conditions formed "recipes" that lead to higher population impact. Our coincidence analyses highlighted both the importance of local context and how combinations of specific conditions consistently and uniquely distinguished higher impact facilities from lower impact facilities for weight management.
How combinations of state firearm laws link to low firearm suicide and homicide rates: A configurational analysis
(Elsevier, 2022-12) Rich, John A.; Miech, Edward J.; Semenza, Daniel C.; Corbin, Theodore J.; Medicine, School of Medicine
Firearm violence, including both homicide and suicide, is a major public health problem in the United States (US). To decrease firearm mortality, US states have implemented laws to restrict firearm availability. We evaluated ten state firearm laws using configurational comparative methods (CCMs) designed to uncover how multiple factors are linked to a given outcome. We applied coincidence analysis, a novel CCM, to ten firearm laws in US states in 2016, to assess how different combinations of firearm laws distinguished states with low firearm homicide or suicide rates from those states with higher rates. The suicide analysis included all 50 US states; the homicide analysis involved the 47 US states with homicide rates reported by the Centers for Disease Control and Prevention (CDC) in 2016. For low firearm suicide rates, we identified three solution pathways - the presence of universal background checks OR the presence of under 21 firearm possession restrictions OR the presence of junk gun bans - which were sufficient for low firearm suicide rates with high consistency (0.87) and coverage (0.76). For low firearm homicide rates, we identified three solution pathways - presence of under 21 firearm possession restrictions OR the presence of universal background checks together with the absence of trafficking prohibited laws OR membership in the Northern Great Plains -which were sufficient for low firearm homicide rates with high consistency (0.87) and coverage (0.81). We conclude that CCM analysis can add new insights to how multiple firearm laws work together to reduce firearm violence.
How education and racial segregation intersect in neighborhoods with persistently low COVID-19 vaccination rates in Philadelphia
(BMC, 2022-05-25) Rich, John A.; Miech, Edward J.; Bilal, Usama; Corbin, Theodore J.; Emergency Medicine, School of Medicine
Background: COVID-19 infection has disproportionately affected socially disadvantaged neighborhoods. Despite this disproportionate burden of infection, these neighborhoods have also lagged in COVID-19 vaccinations. To date, we have little understanding of the ways that various types of social conditions intersect to explain the complex causes of lower COVID-19 vaccination rates in neighborhoods. Methods: We used configurational comparative methods (CCMs) to study COVID-19 vaccination rates in Philadelphia by neighborhood (proxied by zip code tabulation areas). Specifically, we identified neighborhoods where COVID-19 vaccination rates (per 10,000) were persistently low from March 2021 - May 2021. We then assessed how different combinations of social conditions (pathways) uniquely distinguished neighborhoods with persistently low vaccination rates from the other neighborhoods in the city. Social conditions included measures of economic inequities, racial segregation, education, overcrowding, service employment, public transit use, health insurance and limited English proficiency. Results: Two factors consistently distinguished neighborhoods with persistently low COVID-19 vaccination rates from the others: college education and concentrated racial privilege. Two factor values together - low college education AND low/medium concentrated racial privilege - identified persistently low COVID-19 vaccination rates in neighborhoods, with high consistency (0.92) and high coverage (0.86). Different values for education and concentrated racial privilege - medium/high college education OR high concentrated racial privilege - were each sufficient by themselves to explain neighborhoods where COVID-19 vaccination rates were not persistently low, likewise with high consistency (0.93) and high coverage (0.97). Conclusions: Pairing CCMs with geospatial mapping can help identify complex relationships between social conditions linked to low COVID-19 vaccination rates. Understanding how neighborhood conditions combine to create inequities in communities could inform the design of interventions tailored to address COVID-19 vaccination disparities.
Modeling Contingency in Veteran Community Reintegration: A Mixed Methods Approach
(Sage, 2023) Rattray, Nicholas A.; Miech, Edward J.; True, Gala; Natividad, Diana; Laws, Brian; Frankel, Richard M.; Kukla, Marina; Medicine, School of Medicine
Researchers need approaches for analyzing complex phenomena when assessing contingency relationships where specific conditions explain an outcome only when combined with other conditions. Using a mixed methods design, we paired configurational methods and qualitative thematic analysis to model contingency in veteran community reintegration outcomes, identifying combinations of conditions that led to success or lack of success in community reintegration among US military veterans. This pairing allowed for modeling contingency at a detailed level beyond the capabilities of either approach alone. Our analysis revealed multiple contingent relationships at work in explaining reintegration, including social support, purpose, cultural adjustment, and military separation experiences. This study contributes to the field of mixed methods by pairing a mathematical cross-case method with a qualitative method to model contingency.

Browsing by Subject "Coincidence analysis"

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