Attrition from HIV treatment after enrollment in a differentiated service delivery model: a cohort analysis of routine care in Zambia
Abstract Background Many sub-Saharan Africa countries are scaling up differentiated service delivery (DSD) models for HIV treatment to increase access and remove barriers to care. We assessed factors associated with attrition after DSD model enrollment in Zambia, focusing on patient-level characteristics.Methods We conducted a retrospective record review using electronic medical records (EMR) of adults (≥15 years) initiated on antiretroviral (ART) between 01 January 2018 and 30 November 2021. Attrition was defined as lost to follow-up (LTFU) or died by November 30, 2021. We categorized DSD models into eight groups: fast-track, adherence groups, community pick-up points, home ART delivery, extended facility hours, facility multi-month dispensing (MMD, 4–6-month ART dispensing), frequent refill care (facility 1-2 month dispensing), and conventional care (facility 3 month dispensing, reference group). We used Fine and Gray competing risk regression to assess patient-level factors associated with attrition, stratified by sex and rural/urban setting.Results Of 547,281 eligible patients, 68% (n=372,409) enrolled in DSD models, most commonly facility MMD (n=306,430, 82%), frequent refill care (n=47,142, 13%), and fast track (n=14,433, 4%), with <2% enrolled in the other DSD groups. Retention was higher in nearly all DSD models for all dispensing intervals, compared to the reference group, except fast track for the ≤2 month dispensing group. Retention benefits were greatest for patients in the extended clinic hours group and least for fast track dispensing.Conclusion Although retention in HIV treatment differed by DSD type, dispensing interval, and patient characteristics, nearly all DSD models out-performed conventional care. Understanding the factors that influence the retention of patients in DSD models could provide an important step towards improving DSD implementation..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 23. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Jo, Youngji [VerfasserIn] |
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| doi: |
10.1101/2022.05.30.22275759 |
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| PPN (Katalog-ID): |
XBI036162868 |
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| 520 | |a Abstract Background Many sub-Saharan Africa countries are scaling up differentiated service delivery (DSD) models for HIV treatment to increase access and remove barriers to care. We assessed factors associated with attrition after DSD model enrollment in Zambia, focusing on patient-level characteristics.Methods We conducted a retrospective record review using electronic medical records (EMR) of adults (≥15 years) initiated on antiretroviral (ART) between 01 January 2018 and 30 November 2021. Attrition was defined as lost to follow-up (LTFU) or died by November 30, 2021. We categorized DSD models into eight groups: fast-track, adherence groups, community pick-up points, home ART delivery, extended facility hours, facility multi-month dispensing (MMD, 4–6-month ART dispensing), frequent refill care (facility 1-2 month dispensing), and conventional care (facility 3 month dispensing, reference group). We used Fine and Gray competing risk regression to assess patient-level factors associated with attrition, stratified by sex and rural/urban setting.Results Of 547,281 eligible patients, 68% (n=372,409) enrolled in DSD models, most commonly facility MMD (n=306,430, 82%), frequent refill care (n=47,142, 13%), and fast track (n=14,433, 4%), with <2% enrolled in the other DSD groups. Retention was higher in nearly all DSD models for all dispensing intervals, compared to the reference group, except fast track for the ≤2 month dispensing group. Retention benefits were greatest for patients in the extended clinic hours group and least for fast track dispensing.Conclusion Although retention in HIV treatment differed by DSD type, dispensing interval, and patient characteristics, nearly all DSD models out-performed conventional care. Understanding the factors that influence the retention of patients in DSD models could provide an important step towards improving DSD implementation. | ||
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