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Wilson-Barthes MG, Park JW, Mugavero MJ, Napravnik S, Carey MP, Fava JL, Dale SK, Earnshaw VA, Agil D, Howe CJ, Dulin AJ. Multilevel Resilience and Appointment Attendance Among African American/Black Adults with HIV: A Prospective Multisite Cohort Study. Epidemiology 2025; 36:99-106. [PMID: 39589016 PMCID: PMC11599769 DOI: 10.1097/ede.0000000000001801] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2024]
Abstract
BACKGROUND Attending clinic appointments supports HIV viral suppression, yet racial disparities are documented. We assessed whether multilevel resilience resources were associated with appointment attendance among African American/Black (AA/B) adults living with HIV in the United States. METHODS We ascertained data from 291 AA/B clinical cohort participants from 2018 to 2021. We assessed resilience using the Multilevel Resilience Resource Measure. Binary outcomes were a nonrepeated indicator of attending ≥87.5% of scheduled HIV appointments over 12 months (i.e., visit adherence) and a repeated measure of attending appointments during two sequential 6-month follow-up windows (i.e., clinic attendance). Modified Poisson models estimated adjusted risk ratios (aRRs). RESULTS The aRR for clinic attendance among participants with greater versus lesser multilevel resilience resource endorsement was 0.95 (95% confidence interval: 0.88, 1.0). The aRR for visit adherence among participants with greater versus lesser multilevel resilience resource endorsement was 1.2 (0.95, 1.4). CONCLUSIONS This analysis is one of the first to assess appointment attendance as a function of resilience. Findings should be confirmed in larger cohorts.
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Affiliation(s)
- Marta G. Wilson-Barthes
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Jee Won Park
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
- Program in Epidemiology, University of Delaware, Newark, Delaware, USA
| | - Michael J. Mugavero
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, University of Alabama at Birmingham, Birmingham AL, USA
| | - Sonia Napravnik
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill NC, USA
| | - Michael P. Carey
- Center for Behavioral and Preventive Medicine, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, The Miriam Hospital, Providence RI, USA
| | - Joseph L. Fava
- Center for Behavioral and Preventive Medicine, Department of Psychiatry and Human Behavior, Warren Alpert Medical School of Brown University, The Miriam Hospital, Providence RI, USA
| | - Sannisha K. Dale
- Department of Psychology, University of Miami, Coral Gables FL, USA
| | - Valerie A. Earnshaw
- Department of Human Development and Family Sciences, University of Delaware, Newark, DE, USA
| | - Deana Agil
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill NC, USA
| | - Chanelle J. Howe
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, USA
| | - Akilah J. Dulin
- Center for Health Promotion and Health Equity, Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, Rhode Island, USA
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Nassel A, Wilson-Barthes MG, Howe CJ, Napravnik S, Mugavero MJ, Agil D, Dulin AJ. Characterizing the neighborhood risk environment in multisite clinic-based cohort studies: A practical geocoding and data linkages protocol for protected health information. PLoS One 2022; 17:e0278672. [PMID: 36580446 PMCID: PMC9799318 DOI: 10.1371/journal.pone.0278672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2021] [Accepted: 11/21/2022] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Maintaining patient privacy when geocoding and linking residential address information with neighborhood-level data can create challenges during research. Challenges may arise when study staff have limited training in geocoding and linking data, or when non-study staff with appropriate expertise have limited availability, are unfamiliar with a study's population or objectives, or are not affordable for the study team. Opportunities for data breaches may also arise when working with non-study staff who are not on-site. We detail a free, user-friendly protocol for constructing indices of the neighborhood risk environment during multisite, clinic-based cohort studies that rely on participants' protected health information. This protocol can be implemented by study staff who do not have prior training in Geographic Information Systems (GIS) and can help minimize the operational costs of integrating geographic data into public health projects. METHODS This protocol demonstrates how to: (1) securely geocode patients' residential addresses in a clinic setting and match geocoded addresses to census tracts using Geographic Information System software (Esri, Redlands, CA); (2) ascertain contextual variables of the risk environment from the American Community Survey and ArcGIS Business Analyst (Esri, Redlands, CA); (3) use geoidentifiers to link neighborhood risk data to census tracts containing geocoded addresses; and (4) assign randomly generated identifiers to census tracts and strip census tracts of their geoidentifiers to maintain patient confidentiality. RESULTS Completion of this protocol generates three neighborhood risk indices (i.e., Neighborhood Disadvantage Index, Murder Rate Index, and Assault Rate Index) for patients' coded census tract locations. CONCLUSIONS This protocol can be used by research personnel without prior GIS experience to easily create objective indices of the neighborhood risk environment while upholding patient confidentiality. Future studies can adapt this protocol to fit their specific patient populations and analytic objectives.
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Affiliation(s)
- Ariann Nassel
- Lister Hill Center for Health Policy, School of Public Health, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Marta G. Wilson-Barthes
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, United States of America
| | - Chanelle J. Howe
- Center for Epidemiologic Research, Department of Epidemiology, Brown University School of Public Health, Providence, Rhode Island, United States of America
| | - Sonia Napravnik
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Michael J. Mugavero
- Division of Infectious Diseases, Department of Medicine, Center for AIDS Research, University of Alabama at Birmingham, Birmingham, Alabama, United States of America
| | - Deana Agil
- Division of Infectious Diseases, Department of Medicine, School of Medicine, Department of Epidemiology, Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America
| | - Akilah J. Dulin
- Center for Health Promotion and Health Equity, Department of Behavioral and Social Sciences, Brown University School of Public Health, Providence, Rhode Island, United States of America
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Dulin AJ, Park JW, Scarpaci MM, Dionne LA, Sims M, Needham BL, Fava JL, Eaton CB, Kanaya AM, Kandula NR, Loucks EB, Howe CJ. Examining relationships between perceived neighborhood social cohesion and ideal cardiovascular health and whether psychosocial stressors modify observed relationships among JHS, MESA, and MASALA participants. BMC Public Health 2022; 22:1890. [PMID: 36221065 PMCID: PMC9552445 DOI: 10.1186/s12889-022-14270-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 09/27/2022] [Accepted: 09/29/2022] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Psychosocial stressors increase the risks for cardiovascular disease across diverse populations. However, neighborhood level resilience resources may protect against poor cardiovascular health (CVH). This study used data from three CVH cohorts to examine longitudinally the associations of a resilience resource, perceived neighborhood social cohesion (hereafter referred to as neighborhood social cohesion), with the American Heart Association's Life's Simple 7 (LS7), and whether psychosocial stressors modify observed relationships. METHODS We examined neighborhood social cohesion (measured in tertiles) and LS7 in the Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, and Mediators of Atherosclerosis in South Asians Living in America study. We used repeated-measures, modified Poisson regression models to estimate the relationship between neighborhood social cohesion and LS7 (primary analysis, n = 6,086) and four biological metrics (body mass index, blood pressure, cholesterol, blood glucose; secondary analysis, n = 7,291). We assessed effect measure modification by each psychosocial stressor (e.g., low educational attainment, discrimination). RESULTS In primary analyses, adjusted prevalence ratios (aPR) and 95% confidence intervals (CIs) for ideal/intermediate versus poor CVH among high or medium (versus low) neighborhood social cohesion were 1.01 (0.97-1.05) and 1.02 (0.98-1.06), respectively. The psychosocial stressors, low education and discrimination, functioned as effect modifiers. Secondary analyses showed similar findings. Also, in the secondary analyses, there was evidence for effect modification by income. CONCLUSION We did not find much support for an association between neighborhood social cohesion and LS7, but did find evidence of effect modification. Some of the effect modification results operated in unexpected directions. Future studies should examine neighborhood social cohesion more comprehensively and assess for effect modification by psychosocial stressors.
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Affiliation(s)
- Akilah J Dulin
- Center for Health Promotion and Health Equity, Brown University, Providence, RI, USA.
- Center for Health Promotion and Health Equity Research, Brown University School of Public Health, Box G-S121-8, 02912, Providence, RI, USA.
| | - Jee Won Park
- Center for Epidemiologic Research, Department of Epidemiology, Brown University, Providence, RI, USA
| | - Matthew M Scarpaci
- Hassenfeld Child Health Innovation Institute, Brown University, Providence, Rhode Island, USA
| | - Laura A Dionne
- Center for Health Promotion and Health Equity, Brown University, Providence, RI, USA
| | - Mario Sims
- Department of Social Medicine, Population and Public Health, University of California Riverside School of Medicine, Riverside, CA, USA
| | - Belinda L Needham
- Department of Epidemiology, University of Michigan, Ann Arbor, MI, USA
| | - Joseph L Fava
- Centers for Behavioral and Preventive Medicine, The Miriam Hospital, Providence, RI, USA
| | - Charles B Eaton
- Center for Epidemiologic Research, Department of Epidemiology, Brown University, Providence, RI, USA
- Department of Family Medicine, Warren Alpert Medical School of Brown University, Providence, RI, USA
- Center for Primary Care and Prevention Kent Memorial Hospital, Warwick, RI, USA
| | - Alka M Kanaya
- Division of General Internal Medicine, University of California, San Francisco, San Francisco, CA, USA
| | - Namratha R Kandula
- Department of Internal Medicine, Northwestern University, Chicago, IL, USA
| | - Eric B Loucks
- Center for Epidemiologic Research, Department of Epidemiology, Brown University, Providence, RI, USA
| | - Chanelle J Howe
- Center for Epidemiologic Research, Department of Epidemiology, Brown University, Providence, RI, USA
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