Browsing by Author "Bershteyn A"

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Assessing regional variations and sociodemographic barriers in the progress toward UNAIDS 95-95-95 targets in Zimbabwe.
(2025-Apr-09) Chowdhury MDT; Bershteyn A; Milali M; Citron DT; Nyimbili S; Musuka G; Cuadros DF; Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA. diego.cuadros@uc.edu.; Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.; Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA.; International Initiative for Impact Evaluation, Harare, Zimbabwe.; Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
BACKGROUND: The HIV/AIDS epidemic remains critical in sub-Saharan Africa, with UNAIDS establishing "95-95-95" targets to optimize HIV care. Using the 2020 Zimbabwe Population-based HIV Impact Assessment (ZIMPHIA) geospatial data, this study aimed to identify patterns in these targets and determinants impacting the HIV care continuum in underserved Zimbabwean communities. METHODS: Analysis techniques, including Gaussian kernel interpolation, optimized hotspot, and multivariate geospatial k-means clustering, were utilized to establish spatial patterns and cluster regional HIV care continuum needs. Further, we investigated healthcare availability, access, and social determinants and scrutinized the association between socio-demographic and behavioral covariates with HIV care outcomes. RESULTS: Disparities in progress toward the "95-95-95" targets were noted across different regions, with each target demonstrating unique geographic patterns, resulting in four distinct clusters with specific HIV care needs. Key factors associated with gaps in achieving targets included younger age, male gender, employment, and minority or no religious affiliation. CONCLUSIONS: Our study uncovers significant spatial heterogeneity in the HIV care continuum in Zimbabwe, with unique regional patterns in "95-95-95" targets. The spatial analysis of the UNAIDS targets presented here could prove instrumental in designing effective control strategies by identifying vulnerable communities that are falling short of these targets and require intensified efforts. We provide insights for designing region-specific interventions and enhancing community-level factors, emphasizing the need to address regional gaps and improve HIV care outcomes in vulnerable communities that lag behind.
Geospatial Patterns of Progress towards UNAIDS "95-95-95" Targets and Community Vulnerability in Zambia.
(2023-Apr-26) Cuadros DF; Chowdhury T; Milali M; Citron D; Nyimbili S; Vlahakis N; Savory T; Mulenga L; Sivile S; Zyambo K; Bershteyn A; National HIV Program, Ministry of Health, Lusaka, Zambia.; Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA.; Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.; Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
In sub-Saharan Africa, HIV/AIDS remains a leading cause of death. The UNAIDS established the "95-95-95" targets to improve HIV care continuum outcomes. Using geospatial data from the Zambia Population-based HIV Impact Assessment (ZAMPHIA), this study aims to investigate geospatial patterns in the "95-95-95" indicators and individual-level determinants that impede HIV care continuum in vulnerable communities, providing insights into the factors associated with gaps. This study used data from the 2016 ZAMPHIA to investigate the geospatial distribution and individual-level determinants of engagement across the HIV care continuum in Zambia. Gaussian kernel interpolation and optimized hotspot analysis were used to identify geospatial patterns in the HIV care continuum, while geospatial k-means clustering was used to partition areas into clusters. The study also assessed healthcare availability, access, and social determinants of healthcare utilization. Multiple logistic regression models were used to examine the association between selected sociodemographic and behavioral covariates and the three main outcomes of study. Varied progress towards the "95-95-95" targets were observed in different regions of Zambia. Each "95" displayed a unique geographic pattern, independent of HIV prevalence, resulting in four distinct geographic clusters. Factors associated with gaps in the "95s" include younger age, male sex, and low wealth, with younger individuals having higher odds of not being on ART and having detectable viral loads. Our study revealed significant spatial heterogeneity in the HIV care continuum in Zambia, with different regions exhibiting unique geographic patterns and levels of performance in the "95-95-95" targets, highlighting the need for geospatial tailored interventions to address the specific needs of different subnational regions. These findings underscore the importance of addressing differential regional gaps in HIV diagnosis, enhancing community-level factors, and developing innovative strategies to improve local HIV care continuum outcomes.
Geospatial patterns of progress towards UNAIDS '95-95-95' targets and community vulnerability in Zambia: insights from population-based HIV impact assessments.
(2023-Oct) Cuadros DF; Chowdhury T; Milali M; Citron DT; Nyimbili S; Vlahakis N; Savory T; Mulenga L; Sivile S; Zyambo KD; Bershteyn A; Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA.; National HIV Program, Ministry of Health, Lusaka, Zambia.; Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA.; Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.; Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA diego.cuadros@uc.edu.
INTRODUCTION: In sub-Saharan Africa, HIV/AIDS remains a leading cause of death. The UNAIDS established the '95-95-95' targets to improve HIV care continuum outcomes. Using geospatial data from the Zambia Population-based HIV Impact Assessment (ZAMPHIA), this study aims to investigate geospatial patterns in the '95-95-95' indicators and individual-level determinants that impede HIV care continuum in vulnerable communities, providing insights into the factors associated with gaps. METHODS: This study used data from the 2016 ZAMPHIA to investigate the geospatial distribution and individual-level determinants of engagement across the HIV care continuum in Zambia. Gaussian kernel interpolation and optimised hotspot analysis were used to identify geospatial patterns in the HIV care continuum, while geospatial k-means clustering was used to partition areas into clusters. The study also assessed healthcare availability, access and social determinants of healthcare utilisation. Multiple logistic regression models were used to examine the association between selected sociodemographic and behavioural covariates and the three main outcomes of study. RESULTS: Varied progress towards the '95-95-95' targets were observed in different regions of Zambia. Each '95' displayed a unique geographical pattern, independent of HIV prevalence, resulting in four distinct geographical clusters. Factors associated with gaps in the '95s' include younger age, male sex, and low wealth, with younger individuals having higher odds of not being on antiretroviral therapy and having detectable viral loads. CONCLUSIONS: Our study revealed significant spatial heterogeneity in the HIV care continuum in Zambia, with different regions exhibiting unique geographical patterns and levels of performance in the '95-95-95' targets, highlighting the need for geospatial tailored interventions to address the specific needs of different subnational regions. These findings underscore the importance of addressing differential regional gaps in HIV diagnosis, enhancing community-level factors and developing innovative strategies to improve local HIV care continuum outcomes.
Progress Towards UNAIDS's 95-95-95 Targets in Zimbabwe: Sociodemographic Constraints and Geospatial Heterogeneity.
(2023-Jul-28) Chowdhury MT; Bershteyn A; Milali M; Citron D; Nyimbili S; Musuka G; Cuadros DF; International Initiative for Impact Evaluation, Harare, Zimbabwe.; Digital Epidemiology Laboratory, Digital Futures, University of Cincinnati, Cincinnati, OH, USA.; Department of Population Health, New York University Grossman School of Medicine, New York, NY, USA.; Centre for Infectious Disease Research in Zambia (CIDRZ), Lusaka, Zambia.
The HIV/AIDS epidemic remains critical in sub-Saharan Africa, with UNAIDS establishing "95-95-95" targets to optimize HIV care. Using the Zimbabwe Population-based HIV Impact Assessment (ZIMPHIA) geospatial data, this study aimed to identify patterns in these targets and determinants impacting the HIV care continuum in underserved Zimbabwean communities. Analysis techniques, including Gaussian kernel interpolation, optimized hotspot, and multivariate geospatial k-means clustering, were utilized to establish spatial patterns and cluster regional HIV care continuum needs. Further, we investigated healthcare availability, access, and social determinants and scrutinized the association between socio-demographic and behavioral covariates with HIV care outcomes. Disparities in progress toward the "95-95-95" targets were noted across different regions, with each target demonstrating unique geographic patterns, resulting in four distinct clusters with specific HIV care needs. Key factors associated with gaps in achieving targets included younger age, male sex, employment, and minority or no religious affiliation. Our study uncovers significant spatial heterogeneity in the HIV care continuum in Zimbabwe, with unique regional patterns in "95-95-95" targets. The spatial analysis of the UNAIDS targets presented here could prove instrumental in designing effective control strategies by identifying vulnerable communities that are falling short of these targets and require intensified efforts. Our result provides insights for designing region-specific interventions and enhancing community-level factors, emphasizing the need to address regional gaps and improve HIV care outcomes in vulnerable communities lagging behind.
Sustainable HIV treatment in Africa through viral-load-informed differentiated care.
(2015-Dec-03) Phillips A; Shroufi A; Vojnov L; Cohn J; Roberts T; Ellman T; Bonner K; Rousseau C; Garnett G; Cambiano V; Nakagawa F; Ford D; Bansi-Matharu L; Miners A; Lundgren JD; Eaton JW; Parkes-Ratanshi R; Katz Z; Maman D; Ford N; Vitoria M; Doherty M; Dowdy D; Nichols B; Murtagh M; Wareham M; Palamountain KM; Chakanyuka Musanhu C; Stevens W; Katzenstein D; Ciaranello A; Barnabas R; Braithwaite RS; Bendavid E; Nathoo KJ; van de Vijver D; Wilson DP; Holmes C; Bershteyn A; Walker S; Raizes E; Jani I; Nelson LJ; Peeling R; Terris-Prestholt F; Murungu J; Mutasa-Apollo T; Hallett TB; Revill P; Instituto Nacional de Saúde (INS), Ministry of Health, PO Box 264, Maputo, Mozambique.; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 615 N. Wolfe Street E6531, Baltimore, Maryland 21205, USA.; Department of Population Health, New York University School of Medicine, 227 East 30th Street Office 615, New York, New York 10016, USA.; Infectious Diseases Institute (IDI), College of Health Sciences, Makerere University, PO Box 22418, Kampala, Uganda.; Kellogg School of Management, Northwestern University, 2001 Sheridan Road Evanston, Illinois 60208, USA.; Medicine, Global Health and Epidemiology, University of Washington (UW), 325 9th Avenue, Seattle, Washington 98104, USA.; Clinical Research Department, London School of Hygiene and Tropical Medicine, Keppel St, London WC1E 7HT, UK.; International Diagnostics Centre, London School of Hygiene &Tropical, Medicine, Keppel Street, London WC1E 7HT, UK.; HIV/AIDS and Global Hepatitis Programme, World Health Organization, 20 Ave Appia 1211, Geneva, Switzerland.; Division of Infectious Disease, Laboratory Grant Building S-146, Office Lane 154, Stanford University Medical Center, 300 Pasteur Drive, Stanford, California 94305-5107, USA.; CHIP, Department of infectious diseases, Rigshospitalet, University of Copenhagen, Blegdamsvej 92100 Copenhagen, Denmark.; Centre for Infectious Disease Research in Zambia, 5032 Great North Road, Lusaka, Zambia.; Health Services Research &Policy, London School of Hygiene and Tropical Medicine, Room 134, 15-17 Tavistock Place, London WC1H 9SY, UK.; The Office of the US Global AIDS Coordinator and Health Diplomacy (S/GAC), U.S. Department of State, SA-22, Suite 10300, 2201 C Street, Washington DC 20520, USA.; Department of Infectious Disease Epidemiology, Imperial College London, St Mary's Campus, Norfolk Place, London W2 1PG, UK.; Massachusetts General Hospital Division of Infectious Diseases, 50 Staniford Street, 936 Boston, Massachusetts 02114, USA.; Institute for Disease Modeling, 3150 139th Avenue SE, Bellevue, Washington 98005, USA.; Department of Infection and Population Health, University College London, Rowland Hill Street, London NW3 2PF, UK.; WHO Country Office 86 Enterprise Road Cnr, Glenara PO Box CY 348, Causeway Harare, Zimbabwe.; University of Zimbabwe, College of Health Sciences, Department of Paediatrics and Child Health, PO Box A178, Avondale, Harare, Zimbabwe.; University of New South Wales, Level 6, Wallace Wurth Building, UNSW Campus, Sydney, New South Wales 2052, Australia.; Care and Treatment Branch Center for Global Health, Division of Global HIV/AIDS (GAP), CDC, MS-E04, 1600 Clifton Road NE, Atlanta, Georgia 30333, USA.; Department of Global Health and Development, London School of Hygiene and Tropical Medicine, 15-17 Tavistock Place, London WC1H 9SH, UK.; Southern Africa Medical Unit (SAMU), Medecins sans Frontieres (MSF) SA, Waverley Business Park, Wyecroft Rd, Mowbray 7700, Cape Town, South Africa.; Médecins Sans Frontières, Access Campaign, rue du Lausanne 82, 1202 Geneva Switzerland.; Médecins Sans Frontières, 78 rue de Lausanne, Case Postale 116, 1211 Geneva 21, Switzerland.; Centre for Health Economics, University of York, Heslington, York YO10 5DD, UK.; Clinton Health Access Initiative, 383 Dorchester Avenue, Boston, Massachusetts 02127, USA.; Department of Viroscience, Erasmus Medical Center, PO Box 20403000CA Rotterdam, the Netherlands.; Division of General Medical Disciplines, Department of Medicine Stanford University, MSOB 1265 Welch Road x332 Stanford, California 94305, USA.; Ministry of Health and Child Care, P. O CY 1122, Causeway, Harare, Zimbabwe.; MRC Clinical Trials Unit at UCL, Institute of Clinical Trials &Methodology, Aviation House, 125 Kingsway, London WC2B 6NH, UK.; Department of Molecular Medicine and Haematology, University of the Witwatersrand, South Africa.; Bill and Melinda Gates Foundation, PO Box 23350, Seattle, Washington 98199, USA.; CIDRZ; Centre for Infectious Disease Research in Zambia (CIDRZ)
There are inefficiencies in current approaches to monitoring patients on antiretroviral therapy in sub-Saharan Africa. Patients typically attend clinics every 1 to 3 months for clinical assessment. The clinic costs are comparable with the costs of the drugs themselves and CD4 counts are measured every 6 months, but patients are rarely switched to second-line therapies. To ensure sustainability of treatment programmes, a transition to more cost-effective delivery of antiretroviral therapy is needed. In contrast to the CD4 count, measurement of the level of HIV RNA in plasma (the viral load) provides a direct measure of the current treatment effect. Viral-load-informed differentiated care is a means of tailoring care so that those with suppressed viral load visit the clinic less frequently and attention is focussed on those with unsuppressed viral load to promote adherence and timely switching to a second-line regimen. The most feasible approach to measuring viral load in many countries is to collect dried blood spot samples for testing in regional laboratories; however, there have been concerns over the sensitivity and specificity of this approach to define treatment failure and the delay in returning results to the clinic. We use modelling to synthesize evidence and evaluate the cost-effectiveness of viral-load-informed differentiated care, accounting for limitations of dried blood sample testing. We find that viral-load-informed differentiated care using dried blood sample testing is cost-effective and is a recommended strategy for patient monitoring, although further empirical evidence as the approach is rolled out would be of value. We also explore the potential benefits of point-of-care viral load tests that may become available in the future.