Factors associated with enrollment into differentiated service delivery model among adults with HIV in Kenya

Author:

Lavoie Marie-Claude C.123,Koech Emily4,Blanco Natalia23,Wangusi Rebecca4,Jumbe Marline4,Kimonye Francis4,Ndaga Angela4,Ndichu Geofrey4,Makokha Violet4,Awuor Patrick4,Momanyi Emmah4,Oyuga Roseline4,Nzyoka Sarah4,Mutisya Immaculate5,Joseph Rachel5,Miruka Fredrick5,Musingila Paul5,Stafford Kristen A.123,Lascko Taylor23,Ngunu Carol6,Owino Elizabeth7,Kiplangat Anthony6,Kepha Abuya8,Ng’eno Caroline4

Affiliation:

1. Division of Global Health Sciences, Department of Epidemiology and Public Health

2. Institute of Human Virology

3. Center for International Health, Education, and Biosecurity, University of Maryland School of Medicine, Baltimore, Maryland, USA

4. Center for International Health, Education, and Biosecurity, MGIC-an affiliate of the University of Maryland Baltimore, Nairobi

5. Division of Global HIV & TB (DGHT), U.S. Centers for Disease Control and Prevention (CDC), Kisumu

6. Nairobi Metropolitan Services Health Management Team, Nairobi

7. Migori County Health Management Team

8. Kisii County Health Management Team, Western, Kenya.

Abstract

Introduction: Differentiated service delivery (DSD) such as multimonth dispensing (MMD) aims to provide client-centered HIV services, while reducing the workload within health facilities. We assessed individual and facility factors associated with receiving more than three MMD and switching from ≥3MMD back to <3MMD in Kenya. Methods: We conducted a retrospective cohort study of clients eligible for DSD between July 2017 and December 2019. A random sample of clients eligible for DSD was selected from 32 randomly selected facilities located in Nairobi, Kisii, and Migori counties. We used a multilevel Poisson regression model to assess the factors associated with receiving ≥3MMD, and with switching from ≥3MMD back to <3MMD. Results: A total of 3501 clients eligible for ≥3MMD were included in our analysis: 1808 (51.6%) were receiving care in Nairobi County and the remaining 1693 (48.4%) in Kisii and Migori counties. Overall, 65% of clients were enrolled in ≥3MMD at the time of entry into the cohort. In the multivariable model, younger age (20–24; 25–29; 30–34 vs. 50 or more years) and switching ART regimen was significantly associated with a lower likelihood of ≥3MMD uptake. Factors associated with a higher likelihood of enrollment in ≥3MMD included receiving DTG vs. EFV-based ART regimen (aRR: 1.10; 95% confidence interval: 1.05–1.15). Conclusion: Client-level characteristics are associated with being on ≥3MMD and the likelihood of switching from ≥3MMD to <3MMD. Monitoring DSD enrollment across different populations is critical to successfully implementing these models continually.

Publisher

Ovid Technologies (Wolters Kluwer Health)

Subject

Infectious Diseases,Immunology,Immunology and Allergy

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