Bayesian Analysis to determine
Individual Characteristics for
HIV/AIDS Testing
in Sub-Saharan Africa
MULUGETA GEBREGZIABHER, PHD
PRESTON CHURCH, MD
ABEBA TEKLEHAIMANOT, MS
PATRICK MAULDIN, PHD
MEDICAL UNIVERSITY
OF SOUTH CAROLINA
CHARLESTON, SC 29425
Introduction
 Knowledge of one's HIV/AIDS infection status is of
upmost importance.
 For HIV negative individuals, knowledge of status can

help to devise and implement specific risk-reduction practices to
remain disease free
 For HIV positive individuals, knowledge of status can



promote changes in lifestyle practices to better enhance the safety of
other individuals in their daily lives
foster more timely access to treatment and care
initiate a more appropriate planning of their future
HIV Testing Rates
 Despite 2007 WHO recommendation- routine HIV/AIDS
testing for timely diagnosis,

HIV testing rates in Sub-Saharan African countries are low (DHS
2005)
30
25
20
Male
15
Female
10
5
0
Ethiopia
Kenya
Rwanda
 Individual characteristics related to HIV testing
decisions remain not fully understood
Measure DHS, ICF Macro STATCompiler
Why are testing rates low?
 Some known reasons include
 Demographic and Socioeconomic
 Access (Rural vs. Urban),
 Low Awareness
 HIV-related stigma (Ostermann 2011)
 Descriptive data from the Demographic Health
Survey (DHS) are given below
Disparity in HIV-Testing Rates
(Residence, gender-DHS 2005)
50
45
40
35
30
Urban_Male
Urban_Female
25
Rural_Male
20
Rural_Female
15
10
5
0
Ethiopia
Kenya
Rwanda
Note: Most of the population in Sub-Saharan Africa lives in rural areas
Measure DHS, ICF Macro STATCompiler
Disparity in HIV-Testing Rates
(Education, gender-DHS 2005)
50
45
40
35
NoEduc_Male
30
NoEduc_Female
PrmEduc_Male
25
PrmEduc_Female
20
SecEduc_Male
SecEduc_Female
15
10
5
0
Ethiopia
Kenya
Rwanda
Note: Most of the population in Sub-Saharan Africa is with no education
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Knowledge about AIDS (five Qs)
(by Residence and Gender, DHS 2005)
60
50
40
Male
30
Female
20
10
0
Ethiopia
Kenya
Rwanda
70
60
50
Urban Male
40
Urban Female
30
Rural Male
20
Rural Female
10
0
Ethiopia
Kenya
Rwanda
Measure DHS, ICF Macro STATCompiler
Stigma: Accepting attitudes
(by Residence and Gender, DHS 2005)
70
60
50
40
Male
30
Female
20
10
0
Ethiopia
Kenya
Rwanda
70
60
50
Urban_Male
40
Urban_Female
30
Rural_Male
20
Rural_Female
10
0
Ethiopia
Kenya
Rwanda
Measure DHS, ICF Macro STATCompiler
Observations
 The higher testing rates in Kenya and Rwanda are
accompanied by higher HIV/AIDS knowledge and
acceptance (lower stigma) rates and the reverse
applies to Ethiopia
 The differences seem to vary Urban/Rural and
Female/Male
 Question:

Would these hold true in a multivariate analysis of the
predictors of testing?
Objectives
 To study individual predictors of HIV testing using
Multivariate Bayesian methods
Data and Methods
 Secondary Data are abstracted from the 2005
standard Demographic Health Survey (DHS)
 Sample: adults 15-49 years of age
 Sample size: 14,070 from Ethiopia, 8,195 from
Kenya, and 11,321 from Rwanda.
 questions that measure an individual’s:

sexual behaviors/attitudes, HIV knowledge, STI burden,
female empowerment and media-consumption, stigma,
demographic and socioeconomic status
 We will present multivariate analysis results from
Ethiopia DHS 2005
Variables
 HIV testing- ever been tested for AIDS?
 Empowerment-can refuse sex & ask partner to use condom
 HIV Knowledge- Quartiles from a composite of correct





answers to 24 questions (eg. can reduce HIV infection by
avoiding kissing, mosquito bite, by using condoms, etc)
Media use-frequency of watching TV or radio
STI symptoms- having genital ulcer, discharge or any STD
Demographic-(age, sex, residence, marital status, etc)
Wealth- Quintile of wealth index
nStigma- un-accepting attitudes: composite of 5 questions (eg.
know someone with AIDS denied of health service, social event,
shamed, blamed)
 pStigma- accepting attitudes: composite of 3 questions (eg. Willing to
care for family with HIV, wiling to buy fresh vegetables form vendor with
HIV, not secretive about family member’s HIV status)
Analytic Model
HIV knowledge
HIV stigmata
Media use
Female empowerment
STI
Symptoms
o demographic
• Age
• Gender
• marital status
• residence
• education
o- wealth
HIV
Testing
Results: Sample Characteristics (n=12845)
Variable
Residence
Gender
Education
Rural
Urban
Male
Female
None
Primary
Sec or Higher
Total
n
Percent
Percent
testing
9138
2707
6033
6812
6512
3435
2898
12845
79
21
47
53
50
27
23
100
2.0
18.0
5.1
4.0
1.0
4.3
18.1
4.5
Results: Bayesian Logistic Regression
Variables
Residence
Categories
Rural
Primary
Education
Secondary
Higher
Marital Status Married
Formerly
Gender
Male
Wealth
Wealth index
Own Phone
yes
STI symptoms 1
2
OR= posterior summary odds ratio
UCL= 95% upper Credible Interval
OR
LCL
UCL
0.2
1.6
3.5
4.6
1.4
1.1
1.3
5.5
2.5
4.4
16.1
0.1
0.5
2.7
3.0
1.1
0.8
0.9
2.5
1.6
1.4
2.6
0.4
1.8
4.4
6.8
2.0
1.3
1.8
14.9
3.3
13.8
98.6
LCL= 95% lower Credible Interval,
Results: Bayesian Logistic Regression
Variables
can refuse sex
Empowerment can ask condom
both
median
HIV knowledge
75th percentile
pStigma
yes
nStigma
yes
Media Use
yes
STI symptoms
1
OR= posterior summary odds ratio,
UCL= 95% upper Credible Interval
Adjusted
OR
1.6
1.8
5.2
2.0
5.3
1.6
0.7
2.0
5.0
LCL
1.0
1.0
1.1
2.2
1.4
1.2
0.5
1.2
1.8
UCL
2.8
3.7
35.0
3.3
37.2
2.2
0.9
3.3
14.9
LCL= 95% lower Credible Interval
Summary
 After controlling for socio-demographic factors,





Those who can refuse sex or can ask partner to use condom
are about 2 to 5 times more likely to get tested
Those who have higher knowledge about HIV AIDS are
about 2 to 5 times more likely to get tested
Those who frequently tune to radio/TV are 3 times more
likely to test
Rural residents are 80% less likely to test relative to urban
Those with high negative stigma are 30% less likely and
those with positive stigma 60% more likely to test
Conclusions
 Noting limitations: unaccounted latent factors,
measurement bias (due to self report) & volunteer
bias
 Our results imply, the need to
improve testing in rural residents
 Empower women with tools that can help them to
make decisions about sexual practices which could
translate into decisions making to test
 Exploit the Media (to reduce impact of stigma,
empower women, educate to increase knowledge)

Future Plan
 Expand this to other countries using DHS 2010
Acknowledgement
 Thanks to my colleagues
 Preston Church
 Abeba Teklehaimanot
 Patrick Mauldin
 Funding:
- MUSC Office of the President
 Thank you for your attention.
Why are testing rates low?
 Some known reasons include



Demographic and Socioeconomic
Access (Rural vs. Urban), low awareness
HIV-related stigma (Ostermann 2011)
 Paradoxically, most studies on predictors of HIV
testing are mainly from studies of high-risk groups
in developed countries (Weinhardt 1999).
 Problem: Data assessing individual-level
characteristics associated with HIV testing from the
general population in developing countries is not widely
available (Vankatsh et al 2011).
 Good News: There has been progress in data
availability from DHS and other surveys
Methods
 To apply Bayesian methods to obtain
 measures of association based on posterior summaries that
incorporate prior knowledge about the association between
predictors and HIV testing from existing data and literature
 Robust treatment of missing data
 inference in probabilistic terms using credible intervals
Bayesian Logistic Regression
 Let Y represents the observed data on HIV testing
(1=Yes, 0=No) and  represent the parameter
measuring the association between individual
characteristics X (eg. Sex, residence) and Y
logit[Pr(y=1|X, )]= X
 Let p()- prior distribution that allows to incorporate
knowledge about the likely range of values of true 
 The information in the data is summarized by
likelihood p(y|)
 We can update our beliefs about  by combining
information from p() and the data through posterior
distribution
p(|y)  p() x p(y|)
Prior Distribution
 Informative Prior: Review literature & choose a
prior distribution of  centered on previous
estimates of .

In the absence of previous estimates, choose a subjective
value synthesizing knowledge of the literature
 Non-Informative Prior: a prior that has high
variance which in some sense is used to express
ignorance about 

Often yields similar results to maximum likelihood
Estimation
p(  | y) 

N p (  ,  ) p ( y | x,  )
N
p
(  ,  ) p ( y | x,  ) d 
Where N(,) is Normal prior and p(y|x,) is the likelihood
 Markov Chain Monte Carlo (MCMC)
 Gibbs Sampling with NBI=2000, NMC=5000
 We get 95% Credible intervals that have
probabilistic interpretation compared to
confidence intervals
Some References
 Macro International, 2008. HIV Prevalence Estimates
from the Demographic and Health Surveys. Macro
International, Calverton, Maryland.
 ICF Macro, 2010. HIV Prevalence Estimates from the
Demographic and Health Surveys. ICF Macro,
Calverton, Maryland.
 Central Statistical Agency [Ethiopia] and ORC Macro.
2006. Ethiopia Demographic and Health Survey
2005.Addis Ababa, Ethiopia and Calverton, Maryland,
USA: Central Statistical Agency and ORC Macro.
Calverton, USA.
Some References
 Bunnell R et al (2008). HIV transmission risk behavior
among HIV-infected adults in Uganda: results of a
nationally representative survey. AIDS. 2008;22:617–
624.
 Ostermann J et al. (2011) Who Tests, Who Doesn’t, and
Why? Uptake of Mobile HIV Counseling and Testing in the
Kilimanjaro Region of Tanzania. PLoS ONE 6(1): e16488.
 Venkatesh KK et al (2011). Who Gets Tested for HIV in a
South African Urban Township? Implications for Test and
Treat and Gender-Based Prevention Interventions. J
Acquir Immune Defic Syndr 2011;56:151–165
 Weinhardt LS et al (1999). Effects of HIV counseling and
testing on sexual risk behavior: a meta-analytic review of
published research, 1985-1997. Am J Public Health:
89:1397–1405.
Empowerment: ability to negotiate safer sex
(by Residence and Gender, DHS 2005)
100
95
90
Male
85
Female
80
75
70
Ethiopia
Kenya
Rwanda
100
95
90
Urban_Male
85
Urban_Female
Rural_Male
80
Rural_Female
75
70
Ethiopia
Kenya
Rwanda
Measure DHS, ICF Macro STATCompiler