Exavery et al. BMC Public Health
(2020) 20:1251
https://doi.org/10.1186/s12889-020-09361-6
RESEARCH ARTICLE
Open Access
ART use and associated factors among HIV
positive caregivers of orphans and
vulnerable children in Tanzania
Amon Exavery1* , John Charles1, Asheri Barankena1, Erica Kuhlik2, Godfrey M. Mubyazi3, Kassimu Tani1, Amal Ally1,
Epifania Minja1, Alison Koler1, Levina Kikoyo1 and Elizabeth Jere1
Abstract
Background: Utilization of antiretroviral therapy (ART) is crucial for better health outcomes among people living
with the human immunodeficiency virus (PLHIV). Nearly 30% of the 1.6 million PLHIV in Tanzania are not on
treatment. Since HIV positive status is the only eligibility criterion for ART use, it is critical to understand the
obstacles to ART access and uptake to reach universal coverage of ART among PLHIV. For the caregivers of orphans
and vulnerable children (OVC) LHIV and not on ART, attempts to identify them and ensure that they initiate and
continue using ART is critical for their wellbeing and their ability to care for their children.
Methods: Data are from the community-based, United States Agency for International Development (USAID)funded Kizazi Kipya project that aims at scaling up the uptake of HIV/AIDS and other health and social services by
orphans and vulnerable children (OVC) and their caregivers. HIV positive caregivers of OVC who were enrolled in
the USAID Kizazi Kipya project between January 2017 and June 2018 were included in this cross-sectional study.
The caregivers were drawn from 11 regions: Arusha, Iringa, Katavi, Kigoma, Mara, Mbeya, Morogoro, Ruvuma, Simiyu,
Singida, and Tanga. The outcome variable was ART status (either using or not), which was enquired of each OVC
caregiver LHIV at enrollment. Data analysis involved multivariable analysis using random-effects logistic regression
to identify correlates of ART use.
Results: In total, 74,999 caregivers living with HIV with mean age of 44.4 years were analyzed. Of these, 96.4% were
currently on ART at enrollment. In the multivariable analysis, ART use was 30% lower in urban than in rural areas
(adjusted odds ratio (OR) = 0.70, 95% confidence interval (CI) 0.61–0.81). Food security improved the odds of being
on ART (OR = 1.29, 95% CI 1.15–1.45). Disabled caregivers were 42% less likely than non-disabled ones to be on ART
(OR = 0.58, 95% CI 0.45–0.76). Male caregivers with health insurance were 43% more likely than uninsured male
caregivers to be on ART (OR = 1.43, 95% CI 1.11–1.83). Caregivers aged 40–49 years had 18% higher likelihood of
being on ART than the youngest ones. Primary education level was associated with 26% increased odds of being
on ART than no education (OR = 1.26, 95% CI 1.13–1.41).
(Continued on next page)
* Correspondence: aexavery@pactworld.org
1
Pact, P.O. Box 6348, Dar es Salaam, Tanzania
Full list of author information is available at the end of the article
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Exavery et al. BMC Public Health
(2020) 20:1251
Page 2 of 13
(Continued from previous page)
Conclusions: Although nearly all the caregivers LHIV in the current study were on ART (96.4%), more efforts are
needed to achieve universal coverage. The unreached segments of the population LHIV, even if small, may lead to
worse health outcomes, and also spur further spread of the HIV epidemic due to unachieved viral suppression.
Targeting caregivers in urban areas, food insecure households, who are uninsured, and those with mental or
physical disability can improve ART coverage among caregivers LHIV.
Keywords: Utilization, Antiretroviral therapy, HIV, Caregivers of orphans and vulnerable children, Kizazi Kipya,
Tanzania
Background
In 2018, the global estimate of the number of people living with HIV was 37.9 million, 62% (23.3 million) of
whom were accessing antiretroviral therapy (ART) [1].
Corresponding estimates of people living with HIV
(PLHIV) were 20.6 million and 67% (13.8 million) on
ART in Eastern and Southern Africa together [2]. In
Tanzania, 1.6 million people were living with HIV in
2018, 71% of whom were accessing ART [3]. The use of
ART by people living with HIV (PLHIV) is crucial for
better health outcomes: ART decreases HIV-associated
morbidity and mortality, as well as the incidence of new
HIV infections [4].
In 2016, the World Health Organization (WHO) removed all barriers for ART eligibility, and recommended
that all PLHIV should start ART early after undergoing
confirmatory test for HIV (‘Test and Treat’), regardless
of the WHO clinical stage or CD4 count [5]. The early
use of ART keeps PLHIV alive and healthier, and ART
significantly reduces the risk of onward transmission of
HIV, as shown in a real-world setting in sub-Saharan Africa [6]. However, evidence shows that not all PLHV are
on treatment, including those who know that they are
infected with HIV [1–3, 7]. This suggests that barriers to
ART use among PLHIV remain, necessitating further research and programming to address them and achieve
universal ART coverage.
Tanzania introduced an ART program for HIV care and
treatment in 2004. Since then, HIV/AIDS-related morbidity and mortality have been declining, quality of life of
PLHIV improved, and the majority of PLHIV have resumed normal life [8]. The Government of Tanzania
(GoT) in collaboration with development partners and
civil society organizations (CSOs) has continued to scale
up HIV/AIDS care and treatment services. As of 2017, up
to 82.1% of all 7494 health facilities in the country offered
care and treatment services to PLHIV. The GoT has also
been working tirelessly to increase human resources as
well as medical supplies and commodities. Furthermore,
the GoT improves retention and adherence to ART
through community engagement using community volunteers to support care and treatment services and minimizing loss to follow up (LTFU). In this case, more than 350
treatment advocates, 1002 community action teams and
213 cluster coordination teams have been formed and
trained to support PLHIV on care [8].
Accordingly, the goals of the Tanzania Health Sector
HIV and AIDS Strategic Plan IV, 2017–2022 (HSHSP
IV) Monitoring and Evaluation Plan are geared to maximizing efforts and coverage of HIV prevention and treatment services, particularly for key and vulnerable
populations [9]. These populations, according to the national HIV/AIDS response, are Female Sex Workers
(FSW), People who Inject Drugs (PWID), Men who have
Sex with Men (MSM), Adolescents Girls and Young
Women (AGYW), fishers and fishing community,
miners, agricultural plantations workers, long distance
truck drivers, construction workers and students in
higher learning institutions [8]. While individual caregivers of orphans and vulnerable children (OVC) may
fall into one of these categories, as a group they are not
identified as a key and vulnerable population despite
their HIV risk factors and related health, social, and economic challenges in caring for OVC.
In 2014, the UNAIDS proposed the 90–90–90 targets,
which called for a scale–up of HIV testing, so that by
2020, 90% of all PLHIV are aware of their status; 90% of
all people with diagnosed HIV infection receive sustained ART; and 90% of all people receiving ART
achieve viral suppression [4]. This analysis builds off the
second 90, whereby, although data on HIV burden and
ART coverage among OVC caregivers does not yet exist,
in the general adult PLHIV population (age 15 years and
above) in Tanzania, HIV prevalence is 4.7% and ART
use among those who know their HIV positive status is
93.6% [10]. However, looking at ART coverage among
all PLHIV in Tanzania, only 71% were on treatment at
the end of 2018 [3]. Although the country has made
considerable progress towards achievement of the
UNAIDS 90–90-90 targets in adults [10], further efforts
are needed to get all PLHIV on ART because the future
of the HIV epidemic will be driven by those fewer individuals who are not on treatment [11]. Therefore, research efforts are still needed to identify, understand,
and suggest measures to take to reaching universal
coverage of ART among PLHIV.
Exavery et al. BMC Public Health
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Barriers to ART access and use among PLHIV in developing countries have been well documented. The
most commonly cited barriers to ART use among
PLHIV in the developing world are stigma and the distance between place of residence and the health facility
[7, 12–15]. Also, lower ART coverage among men than
women (68% vs. 71%), and among youth under 30 years
than those aged 30–59 years (58% vs. 75%) were observed in KwaZulu–Natal, South Africa, implying that
sex and age are important demographic factors playing a
role in ART coverage [16]. Furthermore, a recent qualitative systematic review in low- and middle-income
countries (LMICs) observed the following reasons for
ART nonuse: feeling healthy, low social support, gender
norms, difficulties translating intentions into actions,
high care–seeking costs, concerns about confidentiality,
low quality health services, recommended lifestyle
changes, and incomplete knowledge of treatment benefits [7]. Similarly, barriers to ART access in terms of fear
to start ART due to drug shortages at health facilities,
staffing shortages, and lack of social and economic support were identified in Uganda [14], as well as fear of
HIV disclosure, women’s lack of support from male
partners, demanding work schedules, and high transport
costs [15]. Male gender, younger age, living alone or in
households with one to two co-residents, and CD4 count
of more than 250 cells per microliter of blood were significantly associated with non-enrollment into free community HIV care in Uganda [17].
In Tanzania, a qualitative study carried out in Iringa
Region found significant barriers inhibiting retention to
care and treatment for PLHIV. The observed barriers include lack of knowledge and general misconception
about treatment, access problems such as difficulties in
reaching distant clinics and pervasive poverty that render PLHIV unable to cope with out–of–pocket financial
costs associated with HIV treatment, persistent
stigmatization of PLHIV, and reliance on alternative
healing [18]. The factors perceived to enhance HIV
treatment with ART were positive perceptions among
the clients of the efficacy of ART, improved ART availability, improved access to care through supplemental
aid, and social support [18]. Despite these observations,
quantitative and large-scale evaluations are needed to
demonstrate statistically representative evidence of conditions and contexts which influence ART use to reach
universal coverage of ART among PLHIV.
In assessing the influence of family and social support
given to PLHIV, it was observed that while OVC are a
well-researched population [19–26], their caregivers
have not been prioritized in studies. To date, HIV burden, treatment coverage and health outcomes are not
well known among the caregivers. Caregivers play a significant role in the lives and wellbeing of OVC and the
entire family, including managing HIV treatment for
children LHIV [27]. Research shows that most caregivers
have to face economic and food insecurity challenges as
a result of additional children to feed and clothe due to
AIDS-related orphanhood [28]. Many HIV-affected caregivers do not earn enough income to adequately care for
their families [28–30]; they struggle to find safe, affordable childcare, and often corrode their incomegenerating activities (IGAs) due to caregiving tensions
[31]. Since poor health of caregivers results in poor
health of their children, strategies to support OVC
should target the caregiver-child dyad [32]. For the caregivers LHIV and not on ART, attempts to identify them
and ensure that they initiate and continue using ART is
critical for their wellbeing and their ability to care for
their children. Thus, an improved understanding of the
health, social, economic, and psychological challenges
faced by the caregivers can inform programs to enhance
their ability to be more responsive to their own needs
and those of their children [33]. This study explored the
extent of and factors associated with ART use among
OVC caregivers LHIV in Tanzania. As the country
strives for universal health coverage (UHC), knowledge
of the factors will alight the high burden subpopulations that should be targeted with additional
support so that all PLHIV are ultimately initiated and
sustained on ART for better health outcomes and accelerating an end to the HIV epidemic [11]. The government, programs, donors, CSOs and other stakeholders
including research communities can benefit from this
knowledge to properly plan or advance interventions.
Ultimately, with improved policies and programs, ART
access and uptake can be scaled among this population
and indirectly benefit their children too.
Methods
Source of data
Data for this study stem from the community–based,
USAID-funded Kizazi Kipya project (2016–2021) in
Tanzania, which aims to increase the uptake of ageappropriate HIV-related and other health and social services for improved health and wellbeing by OVC, adolescents and their families. The study used caregivers’ self–
reported data collected by Community Case Workers
(CCWs) during beneficiary screening and enrollment
into the project using the Family and Child Asset Assessment (FCAA) tool from January 2017 to June 2018.
CCWs are a government cadre of volunteers trained on
the National Integrated Case Management System
(NICMS) to provide services to OVC and their caregivers [34]. The USAID Kizazi Kipya project works
through this cadre to provide doorstep services to OVC
and their caregivers. The project supports the CCWs
with a monthly stipend for their work. The caregivers
Exavery et al. BMC Public Health
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were enrolled into the project if their households met
one or more of the 14 household vulnerabilities related
to HIV presented in Table 1.
Following beneficiary screening and enrollment, the
USAID Kizazi Kipya project develops a care plan for
each caregiver and their OVC in the household. The
project then provides or links caregivers, children and
adolescents to services in the areas of health, nutrition,
education, child protection, social protection, and economic strengthening. No material support is provided.
The project provides psychosocial support, nutrition assessments, counseling and support, referrals and linkages, and care plan monitoring.
To ensure data quality, the USAID Kizazi Kipya project assigned a unique identification number (UID) to
each beneficiary enrolled in the project. The project also
developed electronic data management systems to track
services provided across multiple service delivery points
and merge with assessment and demographic data using
the UID. These systems have enabled the analysis conducted for this study. Enhanced data validation and logic
checks are implemented in the electronic data systems
to ensure that appropriate services are provided to the
appropriate beneficiaries (e.g. based on age, sex, HIV status etc.) [35].
The project provides supportive supervision to the
CCWs through Lead Case Workers (LCWs) and Para
Social Workers (PSWs) who offer them mentorship and
coaching, and also follow up with them to ensure proper
service delivery, timely completeness and correctness of
forms and timely submission of data. Each CCW reports
Table 1 USAID Kizazi Kipya Project household screening and
enrollment criteria
1. Household is headed by child (under 18 years old)
2. Household is headed by an elderly caregiver (60 years or older)
3. Household cares for one or more single or double orphan
4. Caregiver is chronically ill and unable to meet basic needs of children
5. Caregiver is a drug user
6. Caregiver or adolescent aged 10–19 years in the household is a sex
worker
7. One or more adolescent girls aged 10–19 years are sexually active
8. Adolescent girl age 10–19 years in the household is pregnant or has a
child
of her own
9. One or more household members are HIV positive
10. One or more children in the household have tuberculosis
11. One or more children in the household are severely malnourished
12. One or more children in the household have been or are abused or
at risk for abuse
13. One or more children are living and or working on the streets, and
14. One or more children in the household are working in mines.
to a CSO which also provides supportive supervision to
the CCWs through Case Management Coordinators
(CMCs). The CSOs provide refresher trainings and conduct monthly meetings with CCWs to discuss performance, challenges, best practices and way forward. Both
LCWs and CCWs are supervised by an assigned government social welfare officer at ward level to ensure that
services provided and their quality are in accordance
with the government guidelines and standards [36, 37].
The USAID Kizazi Kipya project is further described
elsewhere [38].
Study area
This study is based on new beneficiary enrollment data
from 39 districts (known locally as councils) in 11 regions of Tanzania where the USAID Kizazi Kipya project
conducted beneficiary screening and enrollment activities from January 2017 to June 2018. The regions were:
Arusha, Iringa, Katavi, Kigoma, Mara, Mbeya, Morogoro,
Ruvuma, Simiyu, Singida, and Tanga.
Study design
This is a cross–sectional secondary analysis of existing
program data of the USAID Kizazi Kipya project. FCAA
data were collected during screening and enrollment of
beneficiaries into the project. Beneficiaries in households
meeting at least one of the enrollment criteria listed in
Table 1 and voluntarily consented to participate in the
project were enrolled. After enrollment, beneficiaries
were followed up by the project over time with a variety
of health, education and other social services.
Study population
This study was based on 74,999 caregivers of OVC who
reported that they were living with HIV at the time of
screening and enrollment into the USAID Kizazi Kipya
project. Caregivers who reported that they were not living with HIV as well as those who did not disclose their
HIV status to the project volunteers were excluded. A
caregiver is a guardian who has the greatest responsibility for the daily care and rearing of one or more OVC in
a household [37]. A caregiver is not necessarily a biological parent of the OVC in that household.
Variables
The outcome variable for this study is caregivers’ ART
use status at enrollment as dichotomized to ‘not on ART
(0)’ and ‘on ART (1)’. Caregivers LHIV whose ART status was unknown or not responded, were excluded from
the analysis.
Independent variables included were sex, age, marital
status, education attained, place of residence (rural or
urban), household food security (i.e. whether any household member went 24 h without eating due to lack of
Exavery et al. BMC Public Health
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food three or more times in the last 4 weeks), health insurance status, mental or physical disability status, and
household wealth quintile.
Wealth quintile was constructed using principal component analysis (PCA) of household assets to obtain
household socio–economic status [39]. Five wealth quintiles were constructed, ranging from the lowest quintile
(Q1) for the poorest households, to the highest quintile
(Q5) for the well–off households. The household assets
included in the PCA process were; dwelling materials
(brick, concrete, cement, aluminium and/or other material), livestock (chicken, goats, cows, and others), transportation assets (bicycle, motorcycle/moped, tractor,
motor vehicle, and others), and productive assets (sewing machine, television, couch or sofa, cooking gas, hair
dryer, radio, refrigerator, blender, oven, and others).
Data analysis
The process of data analysis began with exploratory analysis that involved one–way tabulations to obtain distributional features of the data, from which frequency
distribution tables were generated. Then cross–tabulations of ART status by each independent variable were
performed using the Chi-Square test to assess the significance of the association between ART status and
each independent variable.
Multivariable analysis was conducted using multilevel
modelling through random–effects logistic regression to
identify factors associated with ART use. The value of a
multilevel model lies in the fact that it simultaneously
considers data hierarchies (e.g. village-level, householdlevel or individual-level factors), while allowing for nonindependence of observations within groups [40, 41].
For the current study, the multilevel model was fitted
considering two levels: individual-level and village-level.
Higher levels such as district and region were not statistically justified for inclusion (i.e. very low intraclass correlation coefficients [42]), suggesting independence of
observations in those levels. The model was chosen to
account for clustered structure of the data [43]. Clustering of caregivers was assumed at a village-level. Specifically, we assumed that caregivers LHIV who live in the
same village may be correlated with respect to ART use
because they may be likely to share the same sources of,
barriers to, and enablers of ART use. Independent factors associated with ART use were inferred at a significance level of 5% or less.
Furthermore, the multivariable analysis involved the
construction of three models. The first model comprised
the entire study population of 74,999 OVC caregivers.
The remaining models broke down the study population
by sex: the second model was for 54,170 female OVC
caregivers, and the third model was for 20,829 male
OVC caregivers. These approaches (i.e. multivariable
analysis and stratification) played a roles as strategies to
address confounding [44–47] among others. Also, stratification by sex provides a deeper understanding of how
correlates of ART use differ by gender and inform appropriate interventions that are responsive to the unique
needs of each gender [48–50]. For example, one study in
Zambia observed that while poverty-related factors hampered ART uptake in women, side effects and social
pressure attributable to masculinity did so in men [49].
This would not have been observed without stratification
by gender.
Results
Background characteristics
As presented in Table 2, this study was based on 74,999
HIV positive OVC caregivers, 96.4% of whom were on
ART. These caregivers were from 5532 villages in
Tanzania. The average age of the caregivers was 44.4
years (standard deviation [SD] = 11.7) and the majority
were female (72.2%). Nearly half (48.7%) of respondents
were married, and the majority had attained primary
education (78.9%). Nearly one fifth (17.6%) of the caregivers had never attended school. Rural residences
accounted for 63.1% of caregivers, and those from food
insecure households were 14.6%.
ART use by background characteristics
Table 3 presents the percent of caregivers LHIV who
were currently on ART at the time of enrollment in the
USAID Kizazi Kipya project by their background characteristics. The proportion of the HIV positive caregivers
who were on ART varied significantly by some background characteristics. The proportion of caregivers
LHIV who were on ART was 95.2% among the oldest
(60+ years) as compared to 96.7% among those who
were aged 40–49 (p < 0.001). ART use was higher among
those in marital unions (96.7%) than among those
widowed (96.0%) (p = 0.001). ART use was 95.6% among
caregivers who had never been to school, 96.6% among
those who had primary education, and 95.8% among
those who had secondary or higher education (p <
0.001). ART use also varied by wealth quintile, whereby
the lowest level was 95.8% among caregivers in the middle quintile, and the highest level was 97.2% among
those who were in the second quintile (p < 0.001). ART
use was higher in rural areas than in urban areas (96.7%
versus 96.0%) (p < 0.001). Food security was also a significant dimension of ART use, whereby ART use was
higher among caregivers from food secure households
(96.6%) than those in food insecure households (95.3%)
(p < 0.001). ART use was higher among caregivers with
health insurance than those without it (96.9% versus
96.3%) (p = 0.005). Finally, ART use was as low as 93.4%
among caregivers with mental or physical disability and
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Table 2 Frequency distribution of HIV positive caregivers of
orphans and vulnerable children in Tanzania, 2018 (N = 74,999)
Covariate
Number of HIV positive
caregivers (n)
Percent (%)
Overall
74,999
100.0
ART status
Not on ART
2699
3.6
On ART
72,300
96.4
Caregiver sex
Female
Male
54,170
20,829
72.2
Table 3 Percent of HIV positive caregivers of orphans and
vulnerable children who were currently on ART at enrollment
by background characteristics in Tanzania, 2018 (N = 74,999)
Covariate
% of HIV positive caregivers *P - value
currently on ART
Overall
96.4
Caregiver sex
0.348
Female
96.4
Male
96.3
Age group (in Years)
< 0.001
27.8
19–29
30–39
96.6
40–49
96.7
Age group (in Years)
96.2
19–29
5883
7.8
30–39
21,401
28.5
50–59
96.5
60+
95.2
40–49
25,643
34.2
50–59
13,665
18.2
Marital status
0.001
60+
8407
11.2
Married or living together
Mean = 44.4, SD = 11.7
–
–
Divorced or separated
96.4
Never been married
96.2
Widowed
96.0
Marital status
Married or living together
36,525
48.7
96.7
Education attained
< 0.001
Divorced or separated
12,447
16.6
Never been married
5336
7.1
Never been to school
95.6
27.6
Primary
96.6
Secondary or higher
95.8
Widowed
20,691
Education attained
Never been to school
13,191
17.6
Wealth Quintile
Primary
59,182
78.9
Lowest (Q1)
96.1
3.5
Second
97.2
Wealth Quintile
Middle
95.8
Lowest (Q1)
19,763
26.4
Fourth
96.2
Second
12,165
16.2
Highest (Q5)
96.9
Secondary or higher
2626
< 0.001
Place of residence
< 0.001
Middle
13,232
17.6
Fourth
14,437
19.3
Rural
96.7
20.5
Urban
96.0
Highest (Q5)
15,402
Household food security
status
Place of residence
Rural
47,305
63.1
Urban
27,694
36.9
Household food security status
Insecure
10,940
14.6
Secure
64,059
85.4
Family has health insurance (CHF/TIKA)?
No
63,673
84.9
Yes
11,326
15.1
Mentally or physically disabled?
No
73,872
98.5
Yes
1127
1.5
CHF Community Health Fund, TIKA Tiba kwa Kadi
–
< 0.001
Insecure
95.3
Secure
96.6
Family has health insurance
(CHF/TIKA)?
0.005
No
96.3
Yes
96.9
Mentally or physically
disabled?
< 0.001
No
96.5
Yes
93.4
*p—values are based on Pearson’s Chi–Square test
Exavery et al. BMC Public Health
(2020) 20:1251
as high as 96.5% among caregivers without any of the
disabilities (p < 0.001).
Results from multivariable analysis
In Table 4, adjusted odds ratios (OR) and their corresponding 95% confidence intervals (CI) of the factors associated with ART use among caregivers LHIV are
presented, accounting for the random effects. Based on
this context, the following interpretations are made: In
the overall model, age was a significant predictor of ART
use only for the age group 40–49 (OR = 1.18, 95% CI
1.00–1.40). The effect of age on ART use was clearer in
the stratified analysis, whereby ART use was 30% less
likely among the oldest female caregivers than the youngest females (female age 60+: OR = 0.70, 95% CI 0.56–
0.87). On the other hand, the likelihood of ART use increased with age among male caregivers (male age 30–
39: OR = 1.63, 95% CI 1.13–2.35; male age 40–49: OR =
2.04, 95% CI 1.42–2.92; male age 50–59: OR = 2.14, 95%
CI 1.47–3.12; and male age 60+: OR = 1.71, 95% CI 1.16,
95% CI 1.16–2.52).
HIV positive caregivers with primary education were
26% more likely to be on ART than those who had never
attended school (OR = 1.26, 95% CI 1.13–1.41). This effect was maintained for both female caregivers (OR =
1.27, 95% CI 1.12–1.45) and male caregivers (OR = 1.30,
95% CI 1.04–1.62). Similarly, secondary education or
higher was associated with higher likelihood of ART use,
but the effect was not statistically significant.
Wealth quintile was associated with ART use in a
mixed fashion. Generally, caregivers in higher wealth
quintiles were associated with a lower likelihood of ART
use than the lowest quintile, with the exception of the
second quintile in which caregivers were 39% more
likely to be on ART than the lowest quintile (OR = 1.39,
95% CI 1.20–1.61). In the stratified models, women exhibited the same pattern of ART use as the overall
group, but in men, the second, fourth and the highest
wealth quintiles suggested increased likelihood of ART
use, though only the second quintile showed significance
(OR = 1.98, 95% CI 1.42–2.78).
Caregivers living in urban areas were 30% less likely to
be on ART than their rural counterparts (OR = 0.70, 95%
CI 0.61–0.81). This finding was consistent for female caregivers (OR = 0.69, 95% CI 0.59–0.80) and male caregivers
(OR = 0.72, 95% CI 0.56–0.91). Caregivers living in food
secure households were 29% more likely to be on ART
than those in food insecure households (OR = 1.29, 95%
CI 1.15–1.45). While the direction and significance of the
effect remained, the magnitude was lowest (OR = 1.23,
95% CI 1.07–1.41) in females and highest in males (OR =
1.61, 95% CI 1.28–2.03). Caregivers with health insurance
were 19% more likely to be on ART than those without it
(OR = 1.19, 95% CI 1.04–1.36). This effect was statistically
Page 7 of 13
significant among male caregivers (OR = 1.43, 95% CI
1.11–1.83), but not in female caregivers (OR = 1.09, 95%
CI 0.94–1.28). The presence of mental or physical disability was associated with a 42% lower likelihood of being on
ART (OR = 0.58, 95% CI 0.45–0.76). The direction and
significance of the effect remained, but the magnitude was
lowest (32%) in female (OR = 0.68, 95% CI 0.48–0.97) and
highest (63%) in male caregivers (OR = 0.37, 95% CI 0.23–
0.58).
The intraclass correlation coefficient (ICC) suggested
that 40% of the variability in ART use was due to residence of the caregivers in the same village (ICC = 0.40).
This was 40 and 44% in female and male caregivers,
respectively.
Discussion
This study assessed the extent of, and factors associated
with ART utilization among HIV positive caregivers of
OVC in Tanzania. Results showed that 96.4% of all the 74,
999 caregivers who reported that they were LHIV were on
ART at enrollment. This proportion was high and slightly
exceeded the national estimate of 93.6% as the proportion
of adults aged 15 years and older LHIV who are on ART
[10], which reflects the considerable progress the Government of Tanzania has made in linking PLHIV to treatment services. Although this level of ART use nears
universal coverage for people who are aware of their HIV
status, the current efforts need to be accelerated because
the future of the HIV epidemic and its associated morbidities will be driven by the small segments of the infected
populations who are not reached with and sustained on
HIV treatment.
In the multivariable analysis, several caregiver characteristics showed significant association with ART use. Caregivers aged 40–49 years were 18% more likely to be on
ART than the younger ones in the age group 19–29 years.
The higher likelihood of being on ART as age advanced
was clearer for men, since men in all higher age groups
were significantly more likely to be on ART than those in
the youngest age group, 19–29 years. Although this observation was consistent with others in Uganda [17] and
South Africa [16], the underlying mechanism was not
clear, hence a need for further research. On the other
hand, the current study also observed that female HIV
positive caregivers age 60+ years were 30% less likely to be
on ART than their counterparts in the youngest age
group. While this suggests a clear need for targeted strategies to enhance ART uptake among female elderly caregivers, it was also not clear why this was the case. More
studies are required to further explore this association.
Caregivers with primary education level were more
likely than those who had never been to school to be on
ART. This was also the case in both male and female
caregivers in the stratified analysis. However, although
Exavery et al. BMC Public Health
Page 8 of 13
(2020) 20:1251
Table 4 Multivariable random–effects logistic regression of factors associated with ART utilization in HIV positive caregivers of
orphans and vulnerable children in Tanzania, 2018
Covariate
All (N = 74,999)
Odds
ratio
(OR)
Women (N = 54,170)
95% Confidence Interval (CI)
Lower Limit
Upper Limit
Odds
ratio
(OR)
Men (N = 20,829)
95% Confidence Interval (CI)
Lower Limit
Upper Limit
Odds
ratio
(OR)
95% Confidence Interval (CI)
Lower Limit
Upper Limit
Caregiver sex
Female
1.00
–
–
–
–
–
–
–
–
Male
0.92
0.83
1.02
–
–
–
–
–
–
1.00
–
–
1.00
–
–
1.00
–
–
Age group (in Years)
19–29
30–39
1.11
0.94
1.31
1.05
0.87
1.27
**1.63
1.13
2.35
40–49
**1.18
1.00
1.40
1.06
0.88
1.28
***2.04 1.42
2.92
50–59
1.16
0.97
1.39
0.99
0.80
1.22
***2.14 1.47
3.12
60+
0.86
0.71
1.04
**0.70
0.56
0.87
**1.71
1.16
2.52
–
–
1.00
–
–
1.00
–
–
Marital status
Married or living together 1.00
Divorced or separated
0.96
0.85
1.09
0.93
0.81
1.07
1.00
0.77
1.29
Never been married
0.91
0.77
1.08
*0.84
0.70
1.02
1.50
0.87
2.61
Widowed
0.95
0.85
1.06
0.95
0.84
1.08
0.93
0.73
1.17
Never been to school
1.00
–
–
1.00
–
–
1.00
–
–
Primary
***1.26 1.13
1.41
***1.27 1.12
1.45
**1.30
1.04
1.62
Secondary or higher
1.09
0.86
1.38
1.12
0.85
1.48
1.08
0.68
1.72
Lowest (Q1)
1.00
–
–
1.00
–
–
1.00
–
–
Second
***1.39 1.20
1.61
**1.31
1.11
1.54
***1.98 1.42
Middle
**0.84
0.74
0.95
**0.83
0.72
0.96
0.93
0.71
1.21
Fourth
**0.88
0.77
1.00
*0.87
0.75
1.01
1.10
0.86
1.40
Highest (Q5)
0.98
0.85
1.13
0.91
0.77
1.09
*1.26
0.99
1.61
Rural
1.00
–
–
1.00
–
–
1.00
–
–
Urban
***0.70 0.61
0.81
***0.69 0.59
0.80
**0.72
0.56
0.91
–
1.00
–
–
1.00
–
–
1.45
**1.23
1.07
1.41
***1.61 1.28
2.03
Education attained
Wealth Quintile
2.78
Place of residence
Household food security status
Insecure
1.00
Secure
***1.29 1.15
–
Family has health insurance (CHF/TIKA)?
No
1.00
–
–
1.00
–
–
1.00
–
–
Yes
**1.19
1.04
1.36
1.09
0.94
1.28
**1.43
1.11
1.83
–
–
1.00
–
–
1.00
–
–
**0.68
0.48
0.97
Mentally or physically disabled?
No
1.00
Yes
***0.58 0.45
0.76
Number of Caregivers = 74,999;
Number of Villages = 5532;
min = 1, avg. = 13.6, max = 455;
Intraclass correlation
coefficient = 0.40
Significance: ***p < 0.001, **p < 0.050, *p < 0.10
Number of Caregivers = 54,170;
Number of Villages = 5258;
min = 1, avg. = 10.3, max = 358;
Intraclass correlation
coefficient = 0.40
***0.37 0.23
0.58
Number of Caregivers = 20,829;
Number of Villages = 4147;
min = 1, avg. = 5.0, max = 136;
Intraclass correlation
coefficient = 0.44
Exavery et al. BMC Public Health
(2020) 20:1251
there was an increased likelihood of ART use among
caregivers with secondary education or higher, the increase was not statistically significant. It is possible that
people with at least some education are more likely to
comprehend the value of ART, thus use it to stay
healthy. Also, they may be at a better position to understand the instructions for using ART. A recent systematic review concluded that incomplete knowledge of the
benefits ART was a reason for ART nonuse [7]. Another
study in Brazil observed late ART initiation due to low
education levels [51]. Therefore, encouraging formal
education attainment even at the primary level, is likely
to improve ART uptake and possibly other health and
social services. Education can reduce treatment misconceptions as well as the use of alternative healing [18],
which are not scientifically proven to improve health
outcomes in PLHIV.
HIV positive caregivers residing in urban areas were
less likely than their rural counterparts to be on
ART. However, there are many studies showing
higher likelihood of ART utilization in urban areas
than in rural areas [52–54]. Although the infrastructural, health and social service conditions of rural and
urban settings are greatly diverse, with a generally
better condition in urban settings than rural ones, especially in low– and middle–income countries [55],
confounders such as cultural and behavioral factors
associated with rural compared with urban settings
may explain these differences in ART uptake. Rural
populations have been reported to be more communal than urban populations [56]. This may be one of
the likely routes for better social support structures
which are key to ART use [7, 55, 57]. While more
studies may be needed to explain the mechanisms
through which ART use and rural–urban residences
are related, overall, there is a need to tailor interventions to the specific needs of each area [58] to ensure
that all PLHIV are on treatment.
Food security was a significant determinant of ART use
in the HIV positive caregivers, whereby those from food
secure households were more likely to be on ART than
those from food insecure households. This observation
remained significant in the stratified analysis for both female and male caregivers. This observation is consistent
with many others which have established that lack of food
is a significant barrier to ART utilization and adherence
[14, 55, 58–60]. People may be hesitant to start ART if
they do not intend to stay on it due to such reasons as lack
of food. This suggests that integrating food support into
HIV programs may improve coverage of ART in PLHIV,
especially those with limited access to sufficient food.
The results showed an increased likelihood of ART use
among caregivers with health insurance than those without,
especially for male caregivers. It has been observed that
Page 9 of 13
health insurance increases the probability of seeking care
[61], and plays a substantial role in increasing access to facility–based care [62]. The variability in ART use by health
insurance ownership suggests presence of reasons other
than costs because ART is free in Tanzania. One’s decision
to acquire health insurance may be a function of knowledge
and or perceived usefulness, thus a sign of good healthseeking behavior [63]. One possibility for this observation
could be that, as caregivers access their insurance-enabled
health services, they may also have to disclose their HIV
status to the health provider, thus attracting HIV-related
counselling and treatment services for their overall wellbeing. Therefore, while expanding the coverage of health
insurance is required to facilitate access to health services,
this is likely to encourage ART use among PLHIV. This
may be a useful strategy, among others, for enhanced ART
coverage among caregivers LHIV, especially men. Men are
more likely than women to demonstrate poor health behavior, such as late ART initiation [64], thus necessitating targeted support. This is further supported by the results
which demonstrate a stronger relationship between health
insurance and ART use among men than women. This
may be due to less need for insurance among women than
men: women generally demonstrate stronger health seeking
behavior than men, and combined with the free ART distribution, may be less influenced by insurance to take up
ART than their male counterparts.
The current study also found that OVC caregivers LHIV
who were physically or mentally disabled were significantly
less likely to be on ART than those without the disabilities.
These findings held true through the stratified analyses,
among both male and female caregivers. Similar studies have
linked mental health with HIV treatment uptake [65, 66],
but the influence of physical disability is unknown. There
may be physical and logistical challenges to ART access
among disabled PLHIV, thus a need for programs to target
the disabled with interventions to improve ART use among
them. It has been noted that physical or mental disabilities
attract the attention of non-disabled persons, often triggering
prejudices, and discriminatory behaviors. Therefore, the presence of disability and disease can lead nondisabled to avoid
contact with the disabled or even to engage in making antisocial comments or actions [67]. This, in most cases, results
into self-stigma [68], a phenomenon that has been associated
with poor use of ART in PLHIV [7, 14, 15].
Wealth quintile was also a significant predictor of
ART use among the OVC caregivers. Those in the second wealth quintile were more likely to be on ART than
those in the lowest wealth quintile. This was the case
overall, as well as among male and female caregivers.
However, higher wealth quintiles (i.e. middle and fourth)
were significantly associated with lower likelihoods of
ART use in the overall model as well as in women. The
middle and fourth wealth quintiles were not significant
Exavery et al. BMC Public Health
(2020) 20:1251
in the men’s model, but the fifth wealth quintile was associated with higher likelihood of ART use than the lowest quintile. These disparities in ART use between male
and female caregivers in the middle through fifth wealth
quintiles were not clear, thus a need for further research
to clarify the observed relationship. Overall, other studies have observed higher likelihood of ART use with better economic status [7, 14, 57, 58, 69].
Finally, as the intra-class correlation coefficient (ICC) indicated, there was a significant clustering of the HIV positive caregivers at village level, whereby up to 40% of the
variability in ART use among them was due to residence in
the same village. This indicated that there may be closer social interactions and communication among the caregivers
who reside in the same village, thus likely to behave similarly with respect to ART use. This, somehow, can be
shaped by the possibility that these people may be receiving
ART from the same facility. Also, they may be facing similar environmental obstacles or enablers to ART use, such
as distance to facility, which may further explain the relationship. It has been observed that social support from the
family, friends or peers (which is more likely among those
in the same vicinity) is an important dimension of ART use
[7, 55, 57, 70].
Limitations
The data were self-reported by the caregivers, allowing for
the possibility of recall or information bias in the data. To
minimize this, the CCWs who conducted the screening
and enrollment exercise were trained to probe the caregivers for accuracy in responding to the questions.
Conclusions
ART use among OVC caregivers who report their HIV
positive status in Tanzania is high (96.4%) and predicted by
age, formal education attainment, place of residence, food
security, health insurance, mental or physical disability, and
wealth status. Since a few caregivers reporting their HIV
positive status remain unreached, to achieve universal ART
coverage in this population, interventions and policy guidelines should address the observed demand–side barriers
such as older age (60 years and above) for female caregivers,
and below 30 years for male caregivers; education level, particularly those who have never been to school as they may
be having difficulties comprehending ART value for their
health. Health providers should be trained to communicate
about ART use to low-education patients to improve ART
initiation among them. Other barriers that may be integrated in HIV programming activities include urban residences, food insecurity, lack of health insurance particularly
for men, and mental or physical disability. Although there
may be a few caregivers LHIV who are disabled, as well as
those unlikely to be on ART in other subgroups, it is
equally crucial to understand this and plan how to reach
Page 10 of 13
them because global emphasis to ending the HIV/AIDS
epidemic requires leaving no one behind – testing and
treating all [71]. This implies that, high burden subpopulations represent the ultimate future of the HIV epidemic, unless targeted efforts are timely made to ensure
universal coverage of care and treatment services.
Abbreviations
AIDS: Acquired immunodeficiency syndrome; ART: Antiretroviral therapy;
CD4: Cluster of differentiation 4; CHF: Community health fund;
CI: Confidence interval; FCAA: Family and child asset assessment; HIV: Human
immunodeficiency virus; ICC: Intra-class correlation coefficient; LHIV: Living
with HIV; LMICs: Low- and middle-income countries; MRCC: Medical Research
Coordinating Committee; MUAC: Mid-upper arm circumference;
NIMR: National Institute for Medical Research; OR: Adjusted odds ratio;
OVC: Orphans and vulnerable children; PCA: Principal component analysis;
PLHIV: People living with HIV; SD: Standard deviation; TIKA: Tiba kwa Kadi;
UHC: Universal health coverage; UNAIDS: Joint United Nations Programme
on HIV/AIDS; USAID: United States Agency for International Development
Acknowledgements
We acknowledge the project staff, the consortium partners implementing
the USAID Kizazi Kipya Project, Civil Society Organizations (CSOs), Community
Case Workers (CCW) and District Social Welfare Officers (DSWO).
Authors’ contributions
AE conceptualized the problem, conducted statistical analyses, reviewed the
literature, and drafted the manuscript. JC participated in problem refinement,
design, statistical analyses, and critical review of the manuscripts. EK critically
reviewed the manuscript for intellectual content and advised on the
structure. AB, GMM, KT, AA, EM, AK, LK, and EJ critically reviewed the
manuscripts for intellectual content. All authors read and approved the final
draft of the manuscript.
Authors’ information
AE is a professional hands-on Biostatistician with over 10 years of experience
in quantitative data analyses, health research, and program monitoring and
evaluation in Tanzania. He has extensively worked in the areas of HIV/AIDS,
reproductive, maternal, newborn, and child health (RMNCH), and sexual and
reproductive health (SRH). He holds an MSc in Medicine in Population-based
Field Epidemiology from the University of the Witwatersrand, Johannesburg,
and a bachelor’s degree in Statistics from the University of Dar es Salaam in
Tanzania. He currently coordinates Research and Learning activities for Pact’s
USAID Kizazi Kipya (New Generation) Project – the largest OVC program in
Tanzania.
JC is a seasoned Monitoring and Evaluation specialist with extensive
expertise and experience in development planning and results-based monitoring and evaluation that he has been acquired in a career spanning over
10 years of working in international agencies. He has over the years built up
expertise and experience in providing leadership and technical input in the
design and operationalization of the M&E systems for both facility and
community-based programs including HIV prevention, Maternal and Newborn Health, HTC, Family Planning, Pre-Service Education and Supply Chain
Management and Logistics for public health commodities. He has been with
Pact Tanzania since August 2016 as the Monitoring and Evaluation Director
for the USAID Kizazi Kipya Project providing technical leadership in the design and implementation of the project’s monitoring and evaluation and
learning plan and information systems to track progress against targets and
achievement of outcomes and impact.
AB is a Medical Doctor by training, and Public Health specialist with nine
years of experience in HIV/AIDS programing; cutting across clinical and
community interventions in Tanzania. He now serves as Senior Technical
Advisor (STA) for Pact’s USAID Kizazi Kipya project in Tanzania. He has a
passion to serve the least privileged populations, particularly children in the
most vulnerable environment.
EK is a monitoring and evaluation professional who holds a Masters of
Science in Public Health degree from the Johns Hopkins Bloomberg School
of Public Health. She has 10 years of public health and international
development experience with expertise in monitoring and evaluation,
research, HIV/AIDS, OVC programs, malaria, and health education and
Exavery et al. BMC Public Health
Page 11 of 13
(2020) 20:1251
communication. She currently works for Pact headquarters in Washington,
DC and provides M&E support to Pact Tanzania and the USAID Kizazi Kipya
project.
GMM is working with the National Institute for Medical Research as a
Principal Research Scientist, and heading the Department of Health Systems
& Policy Research, with over 40 publications in health matters. He is a Health
Economist, holding a PhD in Health Sciences from the University of
Copenhagen, an MA in Health Management, Planning & Policy from the
University of Leeds, UK, an MBA in Accounting and Finance from the
Kampala International University, Uganda, BA in Economics from the
University of Dar es Salaam, Tanzania, a Diploma in Research Methodology
from the DBL Centre for Health Research and Development in Denmark, and
now continuing with LLB degree course at the Open University of Tanzania.
KT is a Research and Learning Officer, working with Kizazi Kipya Project at
Pact Tanzania, responsible for data analysis, interpretation and research
products dissemination. He has worked on health research for more than 10
years and area of focus was on maternal and child health, health financing,
costing and economic evaluations. He was trained in economics and
specialized in health system and policy research.
AA is an MPH candidate at the Muhimbili University of Health and Allied
Sciences (MUHAS) and a holder of bachelor’s degree in Medical Sociology
from the University of Dar es Salaam (UDSM) in Tanzania. She has 12 years of
work experience in Monitoring and Evaluation in community, household and
facility level interventions, and currently serves as a Senior Monitoring and
Evaluation Officer for Pact’s USAID Kizazi Kipya project in Tanzania. Previously,
she worked in the fields of HIV prevention, Maternal, Newborn and Child
Health, Infection Prevention, HTC, Family Planning, Pre-Service Education,
Cervical Cancer and Control and OVC programs.
EM is a holder of an MSc. degree in Life Sciences and Engineering (Food and
Nutritional Sciences) from The Nelson Mandela African Institution of Science
and Technology (NM-AIST), and a BSc. Home economics and Human
Nutrition from Sokoine University of Agriculture (SUA). She has 14 years of
work experience in management of development projects, and currently
serves as a National level Economic Strengthening Officer for Pact’s USAID
Kizazi Kipya project in Tanzania. Previously, she worked in the fields of
women economic empowerment, health research, and human nutrition. She
is an expert in maternal and child nutrition as well as management of
community savings and lending groups. She is a member of professional
societies including African women in Agriculture research and development
(AWARD), with extensive professional network within and beyond Tanzania.
AK holds a Master of Public Health degree from Columbia University
Mailman School of Public Health. She has over 12 years of technical and
project management experience, including 8 years working oversees in East
and Southern Africa on donor-funded health programs. She has worked on a
range of public health issues including HIV/AIDS, Maternal, Neonatal and
Child Health (MNCH), Orphans and Vulnerable Children (OVC), Water, Sanitation and Hygiene (WASH), and Non-Communicable Diseases (NCDs) and with
a variety of donors including USAID, CDC, Global Fund, Bill & Melinda Gates
Foundation, Hilton Conrad Foundation, Chevron, and Coca-Cola.
LK is a Technical Director of the USAID Kizazi Kipya Project at Pact, Tanzania.
The program serves over 1 million orphans, vulnerable children, youth and
their caregivers at risk of, or are affected/infected by the HIV/AIDS pandemic.
She has worked on children, women and other vulnerable groups for over
20 years. She is a social and public health expert and she hold a Bachelor of
Laws from the University of Dar es salaam and a master’s degree in Business
Administration from ESAMI/Maastricht school of Management.
EJ has a Master of Public Health degree and 20 years of experience
working on various health and HIV projects in East and Southern Africa.
She currently works for Pact Tanzania as the Chief of Party on the
USAID Kizazi Kipya project, a PEPFAR funded project focused on
expanding community level services for orphans and vulnerable children
(OVC).
Funding
No direct funding was received for the purpose of producing this
manuscript. However, data used are from the USAID Kizazi Kipya, a five-year
project (July 2016 to June 2021) in Tanzania with funding from the U.S. President’s Emergency Plan for AIDS Relief (PEPFAR) through the United States
Agency for International Development (USAID). The contents of this paper;
the study design, data collection, analysis, and interpretation; and the
manuscript’s writing remain the sole responsibility of the authors and do not
necessarily reflect the views of USAID or the United States Government.
Availability of data and materials
This study is based on data from the USAID Kizazi Kipya project (2016–2021)
in Tanzania. Pact Tanzania is the prime organization implementing the
project, hence owns the data. The datasets analyzed during the current
study are not publicly available due to confidentiality restrictions pertaining
to records of the project beneficiaries, but are available from Pact Tanzania
on reasonable request.
Ethics approval and consent to participate
Ethics approval was received from the Medical Research Coordinating
Committee (MRCC) of the National Institute for Medical Research (NIMR) in
Tanzania with certificate number NIMR/HQ/R.8a/Vol.IX/3024. Screening and
enrollment of beneficiaries into the USAID Kizazi Kipya Project was entirely
voluntary. The FCAA tool was completed only after each participant signed a
statement of an informed consent. CCWs are always available during their
regular household visits to answer questions or concerns which the project
beneficiaries may have. Access to the datasets analyzed for this study can be
provided by Pact Tanzania, and there may be no need to apply for another
ethics approval.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
Pact, P.O. Box 6348, Dar es Salaam, Tanzania. 2Pact, Inc., 1828 L St NW Suite
300, Washington, DC 20036, USA. 3National Institute for Medical Research
(NIMR), P.O Box 9653, Dar es Salaam, Tanzania.
1
Received: 21 February 2020 Accepted: 9 August 2020
References
1. UNAIDS. Global HIV & AIDS statistics — 2019 fact sheet. 2019. Available
from: https://www.unaids.org/en/resources/fact-sheet. Cited 2019 Jul 25.
2. UNAIDS. Fact sheet - Global AIDS update 2019. 2019. Available from: https://
www.unaids.org/sites/default/files/media_asset/UNAIDS_FactSheet_en.pdf.
Cited 2019 Jul 29.
3. UNAIDS. Country factsheets. United Republic of Tanzania; 2018. Available
from: https://www.unaids.org/en/regionscountries/countries/
unitedrepublicoftanzania. Cited 2019 Jul 29.
4. UNAIDS. 90-90-90: an ambitious treatment target to help end the AIDS
epidemic. Geneva: UNAIDS Joint United Nations Programme on HIV/AIDS;
2014. Available from: https://www.unaids.org/sites/default/files/media_asset/
90-90-90_en_0.pdf. Cited 2019 Jun 20.
5. World Health Organization. Consolidated guidelines on the use of
antiretroviral drugs for treating and preventing HIV infection:
recommendations for a public health approach. 2016. Available from: http://
apps.who.int/iris/bitstream/10665/208825/1/9789241549684_eng.pdf. Cited
2019 Jul 29.
6. Vandormael A, Newell M-L, Bärnighausen T, Tanser F. Use of antiretroviral
therapy in households and risk of HIV acquisition in rural KwaZulu-Natal,
South Africa, 2004–12: a prospective cohort study. Lancet Glob Health. 2014;
2(4):e209–15.
7. Ahmed S, Autrey J, Katz IT, Fox MP, Rosen S, Onoya D, et al. Why do people
living with HIV not initiate treatment? A systematic review of qualitative
evidence from low- and middle-income countries. Soc Sci Med. 2018;213:
72–84.
8. Tanzania Commission for AIDS (TACAIDS). National HIV and AIDS response
report for 2017 - Tanzania Mainland. 2018. Available from: http://library.
tacaids.go.tz/bitstream/handle/123456789/134/National%20HIV%20and%2
0AIDS%20Response%20Report%20for%202017%20-%20Tanzania%2
0Mainland.pdf?sequence=1&isAllowed=y. Cited 2020 Mar 27.
9. National AIDS Control Programme. Tanzania Health Sector HIV and AIDS
Strategic Plan IV, 2017–2022 (HSHSP IV) monitoring and evaluation plan. Dar es
Salaam: Ministry of Health, Community Development, Gender, Elderly and
Exavery et al. BMC Public Health
10.
11.
12.
13.
14.
15.
16.
17.
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
(2020) 20:1251
Children; 2018. Available from: https://www.measureevaluation.org/resources/
publications/tr-18-302/at_download/document. Cited 2020 Mar 27.
Tanzania Commission for AIDS (TACAIDS), Zanzibar AIDS Commission (ZAC).
Tanzania HIV Impact Survey (THIS) 2016–2017: final report. Dar es Salaam;
2018. Available from: https://www.nbs.go.tz/nbs/takwimu/this2016-17/
THIS_2016-2017_Final_Report.pdf. Cited 2018 Sep 18.
Frank TD, Carter A, Jahagirdar D, Biehl MH, Douwes-Schultz D, Larson SL,
et al. Global, regional, and national incidence, prevalence, and mortality of
HIV, 1980–2017, and forecasts to 2030, for 195 countries and territories: a
systematic analysis for the global burden of diseases, injuries, and risk
factors study 2017. Lancet HIV. 2019;6(12):e831–59.
Posse M, Meheus F, Asten HV, Ven AVD, Baltussen R. Barriers to access to
antiretroviral treatment in developing countries: a review. Tropical Med Int
Health. 2008;13(7):904–13.
Govindasamy D, Ford N, Kranzer K. Risk factors, barriers and facilitators for
linkage to antiretroviral therapy care: a systematic review. AIDS. 2012;26(16):
2059–67.
Bajunirwe F, Tumwebaze F, Akakimpa D, Kityo C, Mugyenyi P, Abongomera
G. Towards 90-90-90 target: factors influencing availability, access, and
utilization of HIV services—a qualitative study in 19 Ugandan Districts.
BioMed Res Int. 2018; Available from: https://www.hindawi.com/journals/
bmri/2018/9619684/. Cited 2019 Jul 29.
Nakigozi G, Atuyambe L, Kamya M, Makumbi FE, Chang LW, Nakyanjo N,
et al. A qualitative study of barriers to enrollment into free HIV care:
perspectives of never-in-care HIV-positive patients and providers in Rakai,
Uganda. BioMed Res Int. 2013;2013:470245.
Huerga H, Van Cutsem G, Ben Farhat J, Puren A, Bouhenia M, Wiesner L,
et al. Progress towards the UNAIDS 90–90-90 goals by age and gender in a
rural area of KwaZulu-Natal, South Africa: a household-based community
cross-sectional survey. BMC Public Health. 2018;18(1):303.
Nakigozi G, Makumbi F, Reynolds S, Galiwango R, Kagaayi J, Nalugoda F,
et al. Non-enrollment for free community HIV care: findings from a
population-based study in Rakai. Uganda AIDS Care. 2011;23(6):764–70.
Tomori C, Kennedy CE, Brahmbhatt H, Wagman JA, Mbwambo JK,
Likindikoki S, et al. Barriers and facilitators of retention in HIV care and
treatment services in Iringa, Tanzania: the importance of socioeconomic
and sociocultural factors. AIDS Care. 2014;26(7):907–13.
Huy BV, Teeraananchai S, Oanh LN, Tucker J, Kurniati N, Hansudewechakul R,
et al. Impact of orphan status on HIV treatment outcomes and retention in
care of children and adolescents in Asia. J Virus Erad. 2016;2(4):227–31.
Urassa DP, Matemu S, Sunguya BF. Antiretroviral therapy clinic attendance
among children aged 0-14 years in Kahama district, Tanzania: a crosssectional study. Tanzan J Health Res. 2018;20(1). https://www.ajol.info/index.
php/thrb/article/view/162229/157223.
Williams M(M), Van Rooyen DRM, Ricks EJ. Accessing antiretroviral therapy
for children: Caregivers’ voices. Health SA Gesondheid. 2016;21:331–8.
Gichane MW, Sullivan KA, Shayo AM, Mmbaga BT, O’Donnell K,
Cunningham CK, et al. Caregiver role in HIV medication adherence among
HIV-infected orphans in Tanzania. AIDS Care. 2018;30(6):701–5.
Hendrickson C, Evans D, Brennan AT, Patz S, Untiedt S, Bassett J, et al.
Treatment outcomes among HIV-positive orphaned and non-orphaned
children on antiretroviral therapy in Johannesburg, South Africa. S Afr Med
J. 2019;109(9):679–85.
Vreeman R, Wiehe S, Ayaya S, Musick B, Nyandiko W. Association of
antiretroviral and clinic adherence with orphan status among HIVinfected children in western Kenya. J Acquir Immune Defic Syndr 1999.
2008;49:163–70.
Nyandiko W, Ayaya S, Nabakwe E, Tenge C, Sidle J, Yiannoutsos C, et al.
Outcomes of HIV-infected orphaned and non-orphaned children on
antiretroviral therapy in western Kenya. J Acquir Immune Defic Syndr 1999.
2007;43:418–25.
Kikuchi K, Poudel KC, Muganda J, Majyambere A, Otsuka K, Sato T, et al.
High risk of ART non-adherence and delay of ART initiation among HIV
positive double orphans in Kigali, Rwanda. PLoS One. 2012;7(7):e41998.
Humphrey JM, Genberg BL, Keter A, Musick B, Apondi E, Gardner A, et al.
Viral suppression among children and their caregivers living with HIV in
western Kenya. J Int AIDS Soc. 2019;22(4):e25272.
Kuo C, Operario D. Caring for AIDS-orphaned children: an exploratory study
of challenges faced by carers in KwaZulu-Natal, South Africa. Vulnerable
Child Youth Stud. 2010;5(4):344–52.
Page 12 of 13
29. Heymann J, Earle A, Rajaraman D, Miller C, Bogen K. Extended family caring
for children orphaned by AIDS: balancing essential work and caregiving in a
high HIV prevalence nations. AIDS Care. 2007;19(3):337–45.
30. Miller CM, Gruskin S, Subramanian SV, Rajaraman D, Heymann SJ. Orphan
care in Botswana’s working households: growing responsibilities in the
absence of adequate support. Am J Public Health. 2006;96(8):1429–35.
31. Rajaraman D, Earle A, Heymann SJ. Working HIV care-givers in Botswana:
spill-over effects on work and family well-being. Community Work Fam.
2008;11(1):1–17.
32. Thielman N, Ostermann J, Whetten K, Whetten R, O’Donnell K, Positive
Outcomes for Orphans Research Team. Correlates of poor health among
orphans and abandoned children in less wealthy countries: the importance
of caregiver health. PLoS One. 2012;7(6):e38109.
33. Maundeni T, Malinga-Musamba T. The role of informal caregivers in the
well-being of orphans in Botswana: a literature review. Child Fam Soc Work.
2013;18(2):107–16.
34. Ministry of Health, Community Development, Gender, Elderly and Children
(MoHCDEC) (Tanzania). National integrated case management training
manual for community case workers. 2017. Available from: https://
bantwana.org/wp-content/uploads/2018/07/National-Integrated-CaseManagement-System-Framework_June-2018.pdf.
35. PEPFAR. Monitoring, evaluation, and reporting indicator reference guide.
MER 2.0 (Version 2.4); 2019. p. 251. Available from: https://www.state.gov/
wp-content/uploads/2019/10/PEPFAR-MER-Indicator-Reference-GuideVersion-2.4-FY20.pdf. Cited 2020 Apr 1.
36. Ministry of Health, Community Development, Gender, Elderly and Children
(MoHCDEC) (Tanzania). National integrated case management system
framework. 2017. Available from: https://bantwana.org/wp-content/
uploads/2018/07/National-Integrated-Case-Management-SystemFramework_June-2018.pdf. Cited 2020 Apr 1.
37. USAID Kizazi Kipya Project Case Management and Child Protection Advisor.
Standard operating procedure: case management within the USAID Kizazi
Kipya project (version 1). Dar es Salaam: Pact; 2017.
38. Pact. Kizazi Kipya: new generation: Pact; 2019. Available from: http://www.
pactworld.org/country/tanzania/project. Cited 2018 Aug 20.
39. Vyas S, Kumaranayake L. Constructing socio-economic status indices: how
to use principal components analysis. Health Policy Plan. 2006;21(6):459–68.
40. Snijders TAB, Bosker RJ. Multilevel analysis: an introduction to basic and
advanced multilevel modeling. London: SAGE; 2011. p. 370.
41. University of Bristol. What are multilevel models and why should I use
them? 2018. Available from: http://www.bristol.ac.uk/cmm/learning/
multilevel-models/what-why.html. Cited 2018 Jan 31.
42. Koo TK, Li MY. A guideline of selecting and reporting intraclass correlation
coefficients for reliability research. J Chiropr Med. 2016;15(2):155–63.
43. Rodrıguez G, Elo I. Intra-class correlation in random-effects models for
binary data. Stata J. 2003;3(1):32–46.
44. Pourhoseingholi MA, Baghestani AR, Vahedi M. How to control
confounding effects by statistical analysis. Gastroenterol Hepatol Bed
Bench. 2012;5(2):79–83.
45. Kahlert J, Gribsholt SB, Gammelager H, Dekkers OM, Luta G. Control of
confounding in the analysis phase – an overview for clinicians. Clin
Epidemiol. 2017;9:195–204.
46. McNamee R. Regression modelling and other methods to control
confounding. Occup Environ Med. 2005;62(7):500–6.
47. Murray KW, Duggan A. Understanding confounding in research. Pediatr Rev.
2010;31(3):124–6.
48. Scully EP. Sex differences in HIV infection. Curr HIV/AIDS Rep. 2018;
15(2):136–46.
49. Hilber A, Malungo J, Musheke M, Merten S. Sex differentials in the uptake of
antiretroviral treatment in Zambia. AIDS Care. 2014;26(10):1258–62.
50. Purnamawati K, Ong JA-H, Deshpande S, Tan WK-Y, Masurkar N, Low JK,
et al. The importance of sex stratification in autoimmune disease biomarker
research: a systematic review. Front Immunol. 2018;9:1208.
51. Pacheco PRG, Zara ALSA, Silva e Souza LC, Turchi MD. Late onset of
antiretroviral therapy in adults living with HIV in an urban area in Brazil:
prevalence and risk factors. J Trop Med. 2019; Available from: https://www.
hindawi.com/journals/jtm/2019/5165313/. Cited 2019 Jul 30.
52. Wilson LE, Korthuis T, Fleishman JA, Conviser R, Lawrence PB, Moore RD,
et al. HIV-related medical service use by rural/urban residents: a multistate
perspective. AIDS Care. 2011;23(8):971–9.
Exavery et al. BMC Public Health
(2020) 20:1251
53. Tromp N, Michels C, Mikkelsen E, Hontelez J, Baltussen R. Equity in
utilization of antiretroviral therapy for HIV-infected people in South Africa: a
systematic review. Int J Equity Health. 2014;13(1):60.
54. MacKenzie LJ, Hull MW, Samji H, Lima VD, Yip B, Zhang W, et al. Is there a rural/
urban gap in the quality of HIV care for treatment-naïve HIV-positive individuals
initiating antiretroviral therapy in British Columbia? AIDS Care. 2017;29(10):1218–26.
55. Posse M, Baltussen R. Barriers to access to antiretroviral treatment in
Mozambique, as perceived by patients and health workers in urban and
rural settings. AIDS Patient Care STDs. 2009;23(10):867–75.
56. Maqutu D, Zewotir T, North D, Naidoo K, Grobler A. Determinants of
optimal adherence over time to antiretroviral therapy amongst HIV positive
adults in South Africa: a longitudinal study. AIDS Behav. 2011;15(7):1465–74.
57. Venables E, Casteels I, Manziasi Sumbi E, Goemaere E. “Even if she’s really
sick at home, she will pretend that everything is fine.”: Delays in seeking
care and treatment for advanced HIV disease in Kinshasa, Democratic
Republic of Congo. Madiba S, editor. PLoS One. 2019;14(2):e0211619.
58. Fox MP, Mazimba A, Seidenberg P, Crooks D, Sikateyo B, Rosen S. Barriers to
initiation of antiretroviral treatment in rural and urban areas of Zambia: a
cross-sectional study of cost, stigma, and perceptions about ART. J Int AIDS
Soc. 2010;13:8.
59. Patenaude BN, Chimbindi N, Pillay D, Bärnighausen T. The impact of ART
initiation on household food security over time. Soc Sci Med. 2018;198:175–84.
60. Mshana GH, Wamoyi J, Busza J, Zaba B, Changalucha J, Kaluvya S, et al.
Barriers to accessing antiretroviral therapy in Kisesa, Tanzania: a qualitative
study of early rural referrals to the national program. AIDS Patient Care
STDs. 2006;20(9):649–57.
61. Chomi EN, Mujinja PG, Enemark U, Hansen K, Kiwara AD. Health care
seeking behaviour and utilisation in a multiple health insurance system:
does insurance affiliation matter? Int J Equity Health. 2014;13(1):25.
62. Robyn PJ, Hill A, Liu Y, Souares A, Savadogo G, Sié A, et al. Econometric analysis
to evaluate the effect of community-based health insurance on reducing
informal self-care in Burkina Faso. Health Policy Plan. 2012;27(2):156–65.
63. Osei Asibey B, Agyemang S. Analysing the influence of health insurance status
on peoples’ health seeking behaviour in rural Ghana. J Trop Med. 2017;2017.
http://downloads.hindawi.com/journals/jtm/2017/8486451.pdf.
64. Nash D, Tymejczyk O, Gadisa T, Kulkarni SG, Hoffman S, Yigzaw M, et al. Factors
associated with initiation of antiretroviral therapy in the advanced stages of HIV
infection in six Ethiopian HIV clinics, 2012 to 2013. J Int AIDS Soc. 2016;19(1):20637.
65. Parcesepe AM, Bernard C, Agler R, Ross J, Yotebieng M, Bass J, et al. Mental
health and HIV: research priorities related to the implementation and scale
up of ‘treat all’ in sub-Saharan Africa. J Virus Erad. 2018;4(Suppl 2):16.
66. Brandt R. The mental health of people living with HIV/AIDS in Africa: a
systematic review. Afr J AIDS Res. 2009;8(2):123–33.
67. Corrigan PW. The stigma of disease and disability: understanding causes and
overcoming injustices. Washington D.C.: American Psychological Association; 2014.
68. Chanvilay T, Yoshida Y, Reyer JA, Hamajima N. Factors associated with
access to antiretroviral therapy among people living with HIV in Vientiane
capital, Lao PDR. Nagoya J Med Sci. 2015;77(1–2):29.
69. Talisuna-Alamo S, Colebunders R, Ouma J, Sunday P, Ekoru K, Laga M, et al.
Socioeconomic support reduces nonretention in a comprehensive,
community-based antiretroviral therapy program in Uganda. J Acquir
Immune Defic Syndr 1999. 2012;59(4):e52–9.
70. Layer EH, Kennedy CE, Beckham SW, Mbwambo JK, Likindikoki S, Davis WW,
et al. Multi-level factors affecting entry into and engagement in the HIV
continuum of care in Iringa, Tanzania. PLoS One. 2014;9(8):e104961.
71. WHO. Progress report 2016 - prevent HIV, test and treat all: WHO support
for country impact: WHO. Available from: https://apps.who.int/iris/bitstream/
handle/10665/251713/WHO-HIV-2016.24-eng.pdf. Cited 2020 Apr 5.
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