The purpose of this study is to determine whether this multisectoral agricultural and microcredit loan intervention improves food security, prevent antiretroviral treatment failure, and reduce co-morbidities among people living with HIV/AIDS.
Despite major advances in care and treatment for those living with HIV, morbidity and mortality among people living with HIV/AIDS (PLHIV) remains unacceptably high in sub-Saharan Africa (SSA), largely due to the parallel challenges of poverty and food insecurity.\[1\] In the Nyanza Region of Kenya, 15.1% of the adult population is infected by HIV,\[2\] and over 50% of the rural population is food insecure, primarily due to unpredictable rainfall and limited irrigation.\[3,4\] The investigators have previously shown that food insecurity delays antiretroviral therapy (ART) initiation, reduces ART adherence, contributes to worse immunologic and virologic outcomes, and increases morbidity and mortality among PLHIV.\[5-16\] There has been increasing international recognition that improved food security and reduced poverty are essential components for a successful global response to the HIV epidemic.\[17-21\] Yet, to date few studies have systematically evaluated the impacts of sustainable food security interventions on health, economic, and behavioral outcomes among PLHIV. Agricultural interventions, which have potential to raise income and bolster food security, are an important but understudied route through which to sustainably improve nutritional and HIV outcomes in SSA, including Kenya where agriculture accounts for \> 75% of the total workforce, and 51% of the gross domestic product.\[22\] Building on the investigators successful completion of the pilot intervention trial in Kenya and the investigators collective experience studying structural barriers to HIV care in SSA, the investigators plan to test the hypothesis that a multisectoral agricultural and microcredit loan intervention will improve food security, prevent ART treatment failure, and reduce co-morbidities among PLHIV. The investigators' intervention was co-developed with KickStart, a prominent non-governmental organization (NGO) based in SSA that has introduced a human-powered pump, enabling farmers to grow high yield crops year-round. This technology has reduced food insecurity and poverty for 800,000 users in 22 countries in the subcontinent since 1991.\[23\] The investigators' intervention includes: a) a loan (\~$175) from a well-established Kenyan bank for purchasing agricultural implements and commodities; b) agricultural implements to be purchased with the loan including the KickStart treadle pump, seeds, fertilizers and pesticides; and c) education in financial management and sustainable farming practices occurring in the setting of patient support groups. This study is a cluster randomized controlled trial (RCT) of this intervention with the following specific aims: Aim 1: To determine the impact of a multisectoral agricultural intervention among HIV-infected farmers on ART on HIV clinical outcomes. The investigators hypothesize that the intervention will lead to improved viral load suppression (primary outcome) and changes in CD4 cell count, physical health status, WHO stage III/IV disease, and hospitalizations (secondary outcomes) in the intervention arm compared to the control arm. Aim 2: To understand the pathways through which the multisectoral intervention may improve HIV health outcomes. Using the investigator's theoretical model,\[1,24\] the investigators hypothesize that the intervention will improve food security and household wealth, which in turn will contribute to improved outcomes through nutritional (improved nutritional status measured with Body Mass Index), behavioral (improved ART adherence, and retention in care), and mental health (improved mental health/less depression, improved empowerment) pathways (secondary outcomes). Aim 3: To determine the cost-effectiveness of the intervention and obtain the information necessary to inform scale-up in Kenya and similar settings in SSA. The investigators will quantify the cost per disability-adjusted life year averted, and identify lessons to inform successful scale-up. To accomplish Aims 1 \& 2, the investigators will randomize 8 matched pairs of health facilities in the Nyanza Region in a 1:1 ratio to the intervention and control arms, and enroll 44 participants per facility (total n=704). All participants will be followed for 2 years. Impacts of the investigator's intervention on primary health outcomes and mediators will be investigated to provide definitive data of direct and indirect intervention effects. To accomplish Aim 3, the investigators will: a) conduct a cost-effectiveness analysis; b) identify the characteristics of individuals most likely to benefit from the intervention (e.g., gender, educational attainment, family size, wealth, risk tolerance, and entrepreneurial ability); and c) perform a mixed-methods process evaluation with study participants, staff, and various stakeholders to determine what worked and did not work to guide future scale-up efforts of the intervention. The investigator's ultimate goal is to develop and test an intervention to reverse the cycle of food insecurity and HIV/AIDS morbidity and mortality in SSA.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
OTHER
Masking
NONE
Enrollment
746
A) A loan (\~$175) B) Agricultural implements to be purchased with the loan C) Education in financial management and sustainable farming practices
Kitare
Suba, Homa Bay County, Kenya
Sindo
Suba, Homa Bay County, Kenya
Muhuru Bay
Nyatike, Migori County, Kenya
Change in Proportion of Viral Load Suppression (<=200 Copies/mL)
The outcome was the change from baseline to the end of follow-up (2 years) in the proportion of participants in viral load suppression (≤200 copies/mL) compared between study groups using difference-in-differences analyses.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e., Linear Trend) in Proportion of Absolute CD4 Count <=500 Cells/mm^3
The outcome was the change (i.e., linear trend) from baseline to the end of follow-up (2 years) of the proportion of participants with a CD4 cell count \<=500 cells/mm\^3, compared between study groups using difference-in-differences analyses.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in Mean Physical Health Status
The outcome was the change (i.e. linear trend) from baseline to the end of follow-up (2 years) of the mean physical health score compared between study groups using the differences-in-differences analyses. We used the Medical Outcomes Study HIV Health Survey (MOS-HIV), a tool used to assess health-related quality of life that has been validated in resource-limited settings. Scores standardized to a range of 0 to 100. Higher scores mean a better outcome.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e., Linear Trend) in the Proportion of Participants With AIDS-Defining Condition
The outcome was the change (i.e., linear trend) from baseline to the end of follow-up (2 years) of the proportion of participants with an AIDS-defining condition, compared between study groups using difference-in-differences analyses. AIDS-defining conditions including HIV-related illnesses included in the Centers for Disease Control and Prevention's (CDC) list of diagnostic criteria for AIDS. AIDS-defining conditions include opportunistic infections and cancers that are life-threatening in a person with HIV.
Time frame: Baseline and endline (2 years after enrollment)
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Sori Lakeside
Nyatike, Migori County, Kenya
Minyenya
Rongo, Migori County, Kenya
Ngode
Rongo, Migori County, Kenya
Oyani
Rongo, Migori County, Kenya
Nyamasare
Uriri, Migori County, Kenya
Hongo Ogosa
Kisumu, Kenya
Kisumu District Hospital
Kisumu, Kenya
...and 6 more locations
Change (i.e., Linear Trend) in the Proportion of Participants Who Were Hospitalized in the Previous 6 Months
The outcome was the change (i.e. linear trend) from baseline to end of follow-up (2 years) of the proportion of participants hospitalized in the previous 6 months (yes/no), compared between study groups using difference-in-differences analyses.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in the Mean Score of Food Insecurity Score
The outcome was the change (i.e. linear trend) from baseline to the end of follow-up (2 years) of the mean household food insecurity score, compared between study groups using difference-in-differences analyses. using the Household Food Insecurity Access Scale (HFIAS). The HFIAS is a tool to assess household food insecurity (access). The scale scores range from 0 to 27, with higher scores indicating greater food insecurity.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in Mean Nutritional Status (Represented by Body Mass Index (BMI))
The outcome was the change (i.e. linear trend) from baseline to the end of follow-up (2 years) of the mean body mass index (BMI) compared between study grouops using the differences-in-differences analyses.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in Mean Self-reported Adherence to Antiretroviral Therapy
The outcome was the change (i.e. linear trend) from baseline to the end of follow-up (2 years) of the mean self-reported adherence to antiretroviral therapy compared between study groups using the differences-in-differences analyses.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in Mean Self-confidence Score
The outcome was the change (i.e. linear tend) from baseline to the end of follow-up (2 years) in the mean self-confidence score, compared between study groups using difference-in-differences analyses. Self-confidence is measured using the three-item Power Within scale, which has a range of 3 to 9 points where lower scores indicate greater self-confidence.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in Proportion of Probable Depression
The outcome was the change (i.e. linear trend) from baseline to the end of follow-up (2 years) in the proportion with probable depression using the Hopkins Symptom Check-list for Depression, compared between study groups using difference-in-differences analyses.
Time frame: Baseline and endline (2 years after enrollment)
Change (i.e. Linear Trend) in the Mean Internalized Stigma Score
The outcome was the change (i.e. linear trend) from baseline to the end of follow-up (2 years) in the mean internalized stigma score compared between study groups using the differences-in-differences analyses. Internalized HIV stigma arises when someone has accepted and endorsed the negative attitudes towards her/himself due to their HIV status. The internalized HIV stigma sub-scale consisted of six items asking respondents to agree with statements related to how they feel about being HIV positive, such as "having HIV makes me feel like I'm a bad person" and "I feel ashamed of having HIV." Response options ranged from 1 "strongly disagree" to 5 "strongly agree." During the analysis phase, the composite scores of each stigma sub-scale were rescaled to a row average of 1-5, with higher scores indicating greater stigma.
Time frame: Baseline and endline (2 years after enrollment)