Determinants of condom uptake among HIV voluntary counselling and testing clients: experiences from a hospital-based study in south India.

Abstract

HIV voluntary counselling and testing was a key HIV prevention strategy brought to scale by India's National AIDS Control Organization. Condom uptake is an essential metric of intervention impact given the expansion of the epidemic into an increasingly diverse population. With only 20% of first-time counselling and testing clients at the largest HIV treatment hospital in south India reporting previous condom use, the question of intervention impact on condom use deserves investigation. In this study, we track intervention impact across various demographic groups and identify the added value of more thorough counselling.
Data were collected from 8,865 individuals who attended counselling multiple times at the Tamil Nadu Government Hospital of Thoracic Medicine over the years 2004-2009. Counsellors recorded client demographic characteristics, HIV risk behaviours reported, and counselling services provided after each counselling session. Matching and regression methods were used to determine the probability of condom uptake by serostatus, gender, and receipt of personalized risk reduction counselling while controlling for other characteristics.
HIV counselling and testing was associated with condom uptake among 29.2% of HIV positive women (CI 24.5-34.4%), 31.7% of HIV positive men (CI 27.8-35.4%), 15.5% of HIV negative women (CI 11.2-20.8%), and only 3.6% of HIV negative men (CI 1.9-5.9%) who had previously never used condoms. Personalized risk reduction counselling increased impact in some groups; for example an additional 18% of HIV negative women (CI 11.3-24.4%) and 17% of HIV positive men (CI 10.9-23.4%) started using condoms. The number of sexual partners was not associated with the impact of counselling completeness.
Because the components of testing and counselling impact the condom use habits of men and women differently, understanding the dynamics of condom use negotiation between partners is essential to optimizing impact on Indian couples. Clients' predicted condom uptake ranged between 4% and 47% depending on factors like gender, serostatus, and services provided. Personalized risk reduction counselling is associated with increased chance of condom use, with larger gains in HIV negative women and HIV positive men. HIV negative men are least likely to start using condoms and least impacted by additional counselling.

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RESEARCH ARTICLE Open Access
Determinants of condom uptake among HIV
voluntary counselling and testing clients: experiences
from a hospital-based study in south India
Rebecca Myerson1*, Susanna M Makela1, C Chandrasekhar2, Siju Mathew3and Sourabh Chakraborty3
Abstract
Background: HIV voluntary counselling and testing was a key HIV prevention strategy brought to scale by India’s
National AIDS Control Organization. Condom uptake is an essential metric of intervention impact given the
expansion of the epidemic into an increasingly diverse population. With only 20% of first-time counselling and
testing clients at the largest HIV treatment hospital in south India reporting previous condom use, the question of
intervention impact on condom use deserves investigation. In this study, we track intervention impact across
various demographic groups and identify the added value of more thorough counselling.
Methods: Data were collected from 8,865 individuals who attended counselling multiple times at the Tamil Nadu
Government Hospital of Thoracic Medicine over the years 2004-2009. Counsellors recorded client demographic
characteristics, HIV risk behaviours reported, and counselling services provided after each counselling session.
Matching and regression methods were used to determine the probability of condom uptake by serostatus,
gender, and receipt of personalized risk reduction counselling while controlling for other characteristics.
Results: HIV counselling and testing was associated with condom uptake among 29.2% of HIV positive women (CI
24.5-34.4%), 31.7% of HIV positive men (CI 27.8-35.4%), 15.5% of HIV negative women (CI 11.2-20.8%), and only 3.6%
of HIV negative men (CI 1.9-5.9%) who had previously never used condoms. Personalized risk reduction counselling
increased impact in some groups; for example an additional 18% of HIV negative women (CI 11.3-24.4%) and 17%
of HIV positive men (CI 10.9-23.4%) started using condoms. The number of sexual partners was not associated with
the impact of counselling completeness.
Conclusions: Because the components of testing and counselling impact the condom use habits of men and
women differently, understanding the dynamics of condom use negotiation between partners is essential to
optimizing impact on Indian couples. Clients’ predicted condom uptake ranged between 4% and 47% depending
on factors like gender, serostatus, and services provided. Personalized risk reduction counselling is associated with
increased chance of condom use, with larger gains in HIV negative women and HIV positive men. HIV negative
men are least likely to start using condoms and least impacted by additional counselling.
Background
With approximately 2.4 million HIV cases in India and
an adult prevalence of about 0.3%, prevention of new
infections was of utmost importance in working toward
the National AIDS Control Program Phase III (NACP-
III) goal of halting and reversing the HIV/AIDS epi-
demic in India between 2006 and 2011 [1,2]. Monitoring
and evaluation of prevention strategies and programs is
essential.
Voluntary HIV counselling and testing (VCT) is one
of the key NACP strategies for halting the HIV epidemic
in India. NACP-III calls for 22 million clients to be
counselled and tested in public Integrated Counselling
and Testing Centres (ICTCs) each year [2]. VCT clients
are given counselling relevant to their own personal risk
profiles and informed of the purpose, benefits, and risks
of an HIV test. After receiving their test results, clients
also receive post-test counselling on how to prevent
* Correspondence: myerson@uw.edu
1Institute for Health Metrics and Evaluation, Seattle, Washington, USA
Full list of author information is available at the end of the article
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© 2012 Myerson et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.

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future infection or referrals to medical, social and psy-
chological services depending on whether their test
results are negative or positive.
Ideally, counselling services at VCT centres would
facilitate primary prevention by altering clients’ risky
sexual behaviours; however, research findings are mixed
regarding the effectiveness of testing and risk reduction
counselling in reducing high risk behaviour. Some stu-
dies have linked HIV counselling and testing to signifi-
cant increases in condom use [3,4], including increases
of up to 50 percentage points [5], but these dramatic
results are offset by other studies that show a much
smaller effect [6], [7], [8]. This body of research indi-
cates that the impact of counselling and testing on sub-
sequent condom use may vary by individuals’ and
partners’ serostatus, gender and risk group, with more
successful results shown among serodiscordant couples
[5], female sex workers [9,10] and truck drivers [11];
less successful results among HIV negative clients [12];
and mixed results among HIV positive men who have
sex with men [13,14]. There is also evidence that gender
moderates the relationship between HIV positivity and
increased condom use [15].
Increases in condom use, as with any behaviour
change, must be defined relative to a particular time
frame. This study looks at changes in condom use over
a period of one month or longer, which may seem very
short for observing a true change in behaviour. While
many studies have shown increases in condom use over
a follow-up period of over 6 months [3], [4], [5], [10],
[16], [17], [18], [19], several studies have also shown
increases over shorter periods of time. Researchers have
found increases in condom use among truck drivers
after one to two months [11], men who have sex with
men after 12 weeks [14], and Indian men and Ameri-
cans visiting STD clinics [3,10] as well as intravenous
drug users (IDUs) [20], after 6 months.
Previous studies in India have assessed the effective-
ness of VCT on risk behaviour and HIV incidence for
clients in key risk groups, such as men visiting STD
clinics in western India [10] or sex workers in Bombay
[21]. Less is known about the effectiveness of counsel-
ling on encouraging condom use outside those risk
groups in India. Recent research indicates a rising HIV
prevalence in married women, who are often placed at
risk for HIV infection by the sexual behaviour of their
husbands [22], [23], [24]. The international literature
gives cause for optimism about the effectiveness of VCT
services at increasing condom use among couples
[4,12,19], but such results may not be replicated in an
Indian population where gender roles, norms related to
sexual practices, health beliefs and cultural beliefs all
create significant barriers to condom uptake among
married Indian men and women [25,26]. A recent study
of condom use among women participating in a micro-
bicide clinical trial in Pune, India, and their male part-
ners showed increases in condom use by both groups at
2, 4, and 6 months after baseline [27]. This study is of
particular importance as it provides evidence for the
possibility of increased condom use among Indian cou-
ples who are not members of traditional high-risk
groups. However, the study provided only condom
counselling and HIV risk reduction counselling, not
HIV testing and counselling as it would be delivered in
a VCTC setting. Therefore, more research into comple-
teness and effectiveness of counselling services delivered
to clients outside previously studied risk groups is
merited.
25 years after the first case of HIV was found in its
capital of Chennai in 1986, the state of Tamil Nadu has
become a leader in HIV prevention and treatment in
India [28,29]. With its large client load and high profile,
the testing and counselling service of the Government
Hospital of Thoracic Medicine (GHTM) in Chennai
represents a major government endeavour for primary
and secondary prevention of HIV in Tamil Nadu. One
of ten Centres of Excellence in HIV care in India and
one of the largest HIV and AIDS care centres in South
Asia, GHTM has enrolled over 8,600 patients in ART
since 2004, and the testing centre currently serves an
average of 90 (SD = 33) clients daily [30].
The distinction between effectiveness and efficacy is
essential when assessing the impact of voluntary testing
and counselling on clients’ risk behaviours. Dropout or
refusal of services may place clients out of reach of VCT
counsellors, and cultural and sociological influences may
prevent even the best counsellor from changing the
behaviour of a client in two sessions. As we seek to
measure the effectiveness of services in their typical
delivery, our study uses observational data rather than
data from a trial environment, and we track the comple-
teness of services as well as predictors of condom
uptake.
The purpose of this study is to assess the impact of
testing and counselling services at the GHTM on con-
dom uptake by gender, history of multiple sex partners
and serostatus, focusing on clients who reported not
using condoms at baseline and returned to the ICTC
multiple times over a series of months. In contrast to
previous studies, we focus on individuals who do not
belong to traditional higher risk groups such as men
who have sex with men, commercial sex workers, or
truck drivers, as we wish to examine the effects of risk-
reduction counselling clients belonging to the larger
VCT population. Additionally, we recognize that not all
clients received the full intervention, and we analyse the
intervention as delivered in more complete and less
complete versions. This research, paired with future
Myerson et al. BMC Health Services Research 2012, 12:13
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studies probing the societal, psychological and logistical
factors involved, can help staff at HIV counselling and
testing centres in India identify best practices for strate-
gic use of funds and ensure maximum impact on the
HIV and AIDS epidemic.
Methods
Study population and procedures
Data were collected from the Integrated Counselling and
Testing Centre (ICTC) at the Government Hospital of
Thoracic Medicine in Chennai, India from 2004-2009.
At the centre, clients were given counselling relevant to
their own personal risk profiles and informed of the
purpose, benefits, and risks of an HIV test. After receiv-
ing their test results, clients also received post-test
counselling explaining the implications of their test
results and how to prevent future infection or referrals
to medical, social, and psychological services depending
on whether their test results were negative or positive.
Because sex and sexuality are taboo subjects in India,
counsellors worked to develop a rapport with clients to
gather accurate information, and did not take notes dur-
ing interviews.
Although all clients should have received personalized
risk reduction counselling according to national guide-
lines, only 67.7% of clients received such services in
practice. Because almost all HIV in this region is trans-
mitted heterosexually [22], we assumed that risk reduc-
tion counselling would include discussion of condoms.
Data
Primary data was used in the study, in the form of ques-
tionnaires completed by the counsellors after each coun-
selling session. Standard hospital protocol required that
after each visit with a client, counsellors circled items
on a multiple choice questionnaire to indicate which
counselling services they had provided and what they
had learned about the client’s background and risk
behaviours. This took place as soon as possible after
each visit, so that data were collected prospectively.
Patients’ ICTC records were not linked with their medi-
cal records with the exception of HIV status, which was
analysed by the GHTM laboratory and merged using
patients’ unique ID number. HIV status was established
using ELISA and was recorded even for patients who
never returned to retrieve their results. The dataset used
for analysis was stripped of personal identifiers. The
GHTM internal review board gave ethical approval for
the study and granted access to the data.
Among new clients attending the testing centre
between 2004 and 2009, 8,865 subsequently returned at
least one month after their first visit, enabling calcula-
tion of changes in reported condom use. Clients who
returned were more likely to be HIV positive than those
who did not, but the two groups were within a 5-7%
range on other demographic factors.1See Table 1 for a
summary of demographic factors.
See Figure 1 for a flow chart depicting sample selec-
tion and processing of the data. To investigate the deter-
minants of condom uptake with or without risk
reduction counselling, we analysed data from a subset of
6,939 clients who had never used condoms before their
first visit, were sexually active, and had information on
all covariates of interest. We considered clients to have
received more complete counselling (including persona-
lized risk reduction counselling) if counsellors circled
items on the first visit post-counselling questionnaire
indicating that they discussed and recommended con-
dom use to the client or discussed a personalized risk
reduction plan. In addition, we considered clients to
have started using condoms if the counsellor recorded
condom use on any subsequent visit at least one month
after the client’s original visit.
Table 1 Demographics by receipt of a second visit
One visitTwo or more visits
CharacteristicN% HIV
prev.a
N% HIV
prev.a
Total
Sex
94020 34.1886558.6
Female
Male
35083 37.3%
58937 62.7%
38.6
31.5
3486 39.3%
5379 60.7%
68.3
52.3
Age
15-19
20-29
30-39
40-49
50+
1784
22433 23.9%
28396 30.2%
18901 20.1%
22506 23.9%
1.9%16.3
46.7
48.8
29.4
8.5
72
2336 26.4%
3245 36.6%
1727 19.5%
1485 16.8%
0.8% 44.4
74.1
71.8
48.9
17.2
Education
None/primary
Secondary/
graduate
Current marital status
Married
Unmarried
Occupation
Housewife
Other/
unemployed
Professional/
manager
Skilled
Student
Unskilled
Lifetime sexual partners
0 or 1
2 or more
61528 65.4%
32492 34.6%
30.4
41.2
5105 57.6%
3760 42.4%
54.5
64.1
74800 79.6%
19220 20.4%
33.9
34.9
7502 84.6%
1363 15.4%
57.7
63.6
15974 17.0%
9006
39.2
12.9
1763 19.9%
579
64.9
21.69.6%6.5%
26422.8% 48.5260 2.9% 69.2
35733 38.0%
723
29942 31.8%
40.2
5.4
30
3625 40.9%
26
2612 29.5%
65.8
23.1
51.8
0.8%0.3%
57283 60.9%
36737 39.1%
23.5
50.8
4898 55.3%
3967 44.7%
46.4
73.6
aHIV prevalence per 100 clients.
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Statistical analysis
As these data are not from a randomized trial, we
anticipated that delivery of personalized risk reduction
counselling might depend on clients’ backgrounds. For
this reason, we used matching and regression methods
to ensure an identical covariate distribution for the two
counselling completeness groups and to control for
additional confounding.
First, we used a non-parametric matching procedure to
achieve identical covariate distributions in the two treat-
ment groups. Table 2 summarizes this procedure. Clients
who received risk reduction counselling or discussed
condom use with a counsellor were matched with clients
who did not receive these services but reported identical
age group (15-34, 35+), education (less than secondary,
secondary or more), HIV serostatus, total number of pre-
vious and current sex partners reported at first visit (0 or
1, 2 or more), current marital status (married,
unmarried), and gender. Matches were found for 6,929
observations (99.9%). In the input data, 67.7% of clients
had received more complete counselling and 32.3% had
not, but weights were used to assign equal importance to
clients who had received more complete vs. less complete
counselling in the subsequent analysis [31].
After matching treated and untreated clients by age
group, education, HIV serostatus, history of multiple part-
ners, current marital status and gender, we used a logistic
regression to facilitate predictions of clients’ probability of
condom uptake while controlling for demographic and
behavioural factors. Trends in the raw data indicated
three-way interactions between receipt of risk reduction
counselling, sex, and HIV status, as well as between coun-
selling, sex, and history of multiple partners in predicting
condom uptake. For this reason, we included these three-
way interaction terms in the regression, whereas two-way
interactions were included for all other variables. Stata
Note: Groups (A) and (B) are compared in Table 1
Matched on
covariates
Excluded from larger analytical sample:
16,461 previously tested positive for HIV
3,579 below the age of 15
5,830 in traditional high risk groups (commercial
sex workers, men who have sex with men,
intravenous drug users and truck drivers)
3,130 others were missing demographic information
(ie gender, education)
Excluded from effectiveness calculations:
94,020 did not return at least a month after their
first visit (A)
102,885 adults not in traditional high risk groups who
had never previously tested positive for HIV and for
whom we had demographic information
933 had not
received risk
reduction
counseling
6,235 had
received risk
reduction
counseling
8,865 returned at least a month after
their first visit (B)
6,939 included in estimation of
treatment effectiveness
Excluded from effectiveness calculations:
1,697 had previously used condoms during sex
229 others were not sexually active or missing
covariates
131,885 clients served from 2004-2009
Figure 1 Flow chart of data selection and processing.
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dropped some higher-order terms due to collinearity. Due
to the large number of interaction terms, coefficients are
presented rather than odds ratios to encourage the reader
to consider the whole model. See Table 3. After running
the logistic regression, we used simulation modelling
(1,000 simulations per scenario) to create predicted results
for hypothetical sexually active clients by serostatus, his-
tory of multiple sex partners and gender. Predicted con-
dom uptake was calculated for each hypothetical client
under scenarios of more complete and less complete
counselling. Hypothetical clients were assigned the mean
frequency for their gender for the characteristics of inter-
est: positive HIV serostatus (33.2% and 41.3% for men and
women, respectively), secondary education or higher
(37.6% and 31.3%), age 35 or older (67.8% and 43.6%), and
multiple partners (55.9% and 12.3%) [32]. Results are
shown in Table 4.
The relevance of including the interaction terms in the
logistic regression, as shown in Table 3, can be seen in
the simulated results for different client types in Table
4. For example, the model-predicted results capture the
moderating effects of gender on the relationship
between HIV status and condom uptake: men with HIV
were much more likely than men without HIV to start
using condoms, whereas this gap was smaller for
women. These moderating effects extended to estima-
tion of the additional effect of more thorough counsel-
ling. See Table 4 and Figure 2.
Analysis was conducted using Stata 10 and R 2.11.1.
The MatchIt package in R was used for the matching
procedure.
Results
Table 4 shows predicted probability of condom uptake for
hypothetical sexually active clients by gender, serostatus,
education and history of multiple partners. The model-
corrected estimates, some of which are also depicted in
Figure 2, show more modest and consistent treatment
effects than the raw data, with all client groups showing
higher predicted condom uptake with risk reduction coun-
selling than without risk reduction counselling.
We estimated that 15.5% of HIV negative women (CI
11.2-20.8%) and only 3.6% of HIV negative men (CI 1.9-
5.9%) who had never used condoms before attending
the ICTC reported starting to use condoms after receiv-
ing less complete VCT, that is, without risk reduction
counselling. In contrast, 33.5% (CI 29.1-38.3%) of HIV
negative women or 8.9% (CI 7-11.1%) of HIV negative
men reported starting to use condoms after receiving
the fuller quality intervention. HIV positive clients were
more likely than negative clients to report starting to
use condoms even without complete counselling, and
uptake was similar for positive men (31.7%, CI 27.8-
35.4%) and positive women (29.2%, CI 24.5-34.4%).
Receipt of more complete counselling was associated
with an additional increase of 17.2% (CI 10.9-23.4%) for
HIV positive men and an additional increase of 6.1% (CI
1.5-10.7%) for HIV positive women. We did not detect a
significant difference in treatment effect by number of
previous sexual partners.
Table 2 Review of matching procedure used
Less complete vs. more complete VCT
Purpose of matching
Prepare data to calculate the difference in
condom uptake expected after less complete
vs. more complete VCT
Exact matching
Matching strategy
Variables matched in
each pair:
Age group
Education
15-34, 35+
Less than secondary education, secondary or
more
HIV positive, HIV negative
Male, female
Current marital status, history of condom use
(all had not used condoms before)
Completeness of VCT received
HIV status
Gender
Other factors
Variable that differed
in each pair:
Outcome of interest
Condom uptake
Table 3 Logistic regression coefficients
MeanLower
CI
Upper
CI
Main effects:
Currently married
Male
Age 35+
Secondary education
HIV positive
Multiple sexual partners
Received risk reduction counseling
("treated”)
Constant
Interaction terms:
Currently married, treated
Age 35+, treated
Secondary education, treated
HIV negative male, treated
HIV positive female, treated
HIV positive male, untreated
0 or 1 sexual partners, female,
untreated
0 or 1 sexual partners, male, untreated
Multiple sexual partners, male, treated
Multiple sexual partners, female,
untreated
1.11
-1.06
-0.5
0.48
0.94
-0.19
1.33
(0.75
(-1.47
(-0.73
(0.27
(0.52
(-0.73
(0.78
-
-
-
-
-
-
-
1.46)
-0.68)
-0.26)
0.69)
1.34)
0.34)
1.9)
-2.73(-3.25--2.21)
0.17
-0.08
-0.22
-1.26
-0.75
1.51
-0.44
(-0.24
(-0.34
(-0.47
(-1.76
(-1.22
(0.73
(-0.95
-
-
-
-
-
-
-
0.62)
0.2)
0.04)
-0.78)
-0.28)
2.28)
0.1)
-0.89
0.87
-0.01
(-1.51
(0.31
(-0.64
-
-
-
-0.23)
1.4)
0.68)
Note: N = 6,939. STATA automatically dropped some third order interaction
terms due to collinearity
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