This article was downloaded by: [Osaka University] On: 30 October 2014, At: 00:49 Publisher: Routledge Informa Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House, 37-41 Mortimer Street, London W1T 3JH, UK

African Journal of AIDS Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/raar20

Acceptability of routine HIV counselling and testing among a sample of South African students: Testing the Health Belief Model a

b

Jani Nöthling & Ashraf Kagee a

Department of Psychiatry, Stellenbosch University, PO Box 19063, Tygerberg 7505, South Africa b

Department of Psychology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa Published online: 19 Dec 2013.

To cite this article: Jani Nöthling & Ashraf Kagee (2013) Acceptability of routine HIV counselling and testing among a sample of South African students: Testing the Health Belief Model, African Journal of AIDS Research, 12:3, 141-150, DOI: 10.2989/16085906.2013.863214 To link to this article: http://dx.doi.org/10.2989/16085906.2013.863214

PLEASE SCROLL DOWN FOR ARTICLE Taylor & Francis makes every effort to ensure the accuracy of all the information (the “Content”) contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content. This article may be used for research, teaching, and private study purposes. Any substantial or systematic reproduction, redistribution, reselling, loan, sub-licensing, systematic supply, or distribution in any form to anyone is expressly forbidden. Terms & Conditions of access and use can be found at http:// www.tandfonline.com/page/terms-and-conditions

Copyright © NISC (Pty) Ltd

African Journal of AIDS Research 2013, 12(3): 141–150 Printed in South Africa — All rights reserved

AJAR

ISSN 1608-5906 EISSN 1727-9445 http://dx.doi.org/10.2989/16085906.2013.863214

Acceptability of routine HIV counselling and testing among a sample of South African students: Testing the Health Belief Model Jani Nöthling1 and Ashraf Kagee2* Department of Psychiatry, Stellenbosch University, PO Box 19063, Tygerberg 7505, South Africa Department of Psychology, Stellenbosch University, Private Bag X1, Matieland 7602, South Africa *Corresponding author, email: [email protected] 1

Downloaded by [Osaka University] at 00:49 30 October 2014

2

Routine HIV counseling and testing (RCT) is a necessary first step in accessing health care for persons who may test HIV-positive. Despite the availability of RCT in many South African settings, uptake has often been low. We sought to determine whether the main components of the Health Belief Model (HBM), namely perceived susceptibility, perceived severity, perceived benefits and perceived barriers could predict acceptance of RCT, and whether cues to action predicted uptake of RCT. A sample of 1 113 students at a large South African university completed a battery of instruments measuring acceptability of RCT, previous uptake of HIV testing, and the various HBM variables. Regression analysis showed that perceived susceptibility to HIV, perceived severity of HIV, perceived benefits of RCT, and perceived barriers to RCT explained 25.1% of the variance in acceptance of RCT. The findings of the study are located in the context of existing literature on RCT. Keywords: Health Belief Model, routine HIV counselling and testing, South Africa

Introduction Globally, approximately 33 million people are infected with HIV (UNAIDS 2010). Sub-Saharan Africa is severely affected by the HIV epidemic with 22.3 million people infected (UNAIDS 2010). Approximately 5.6 million South Africans are infected with HIV, with an overall prevalence rate of 11% (UNAIDS 2010). The prevalence rate in South Africa in the age group of 15 to 29 ranges between 2.5% and 15.7% in men and 6.7% and 32.7% in women, making this a high risk group (Shisana et al. 2009). While the prevalence rate of South Africa’s tertiary student population has been estimated at 3.4% in a recent study (HEAIDS 2010), it is still viewed as a high risk population due to high risk behaviour within this group (Peltzer et al. 2004, HEAIDS 2010). HIV testing is regarded as an important and effective HIV prevention strategy, as knowledge of HIV status is associated with a significant reduction in risk behaviour and consequent transmission of HIV in both the student and general population (Peltzer et al. 2004, Hutchinson and Mahlalela 2006). Voluntary HIV counseling and testing (VCT) has until recently been the standard method used in most clinics and tertiary institutions and has generally been initiated by individuals themselves. In an effort to increase awareness of HIV status and the benefits associated with early entry into care, the World Health Organization has recommended Routine HIV counseling and testing (RCT) in addition to VCT (UNAIDS/WHO 2007). RCT occurs when healthcare providers offer HIV testing to all individuals as an integral component of standard health care, regardless of

risk or symptoms (UNAIDS/WHO 2007). RCT is therefore initiated by the healthcare provider rather than by the person seeking services. The rationale for RCT it that it is likely to yield greater detection rates than VCT (Myers et al. 2009, Kiene et al. 2010, Topp et al. 2011), leading to higher levels of awareness of HIV status (Bassett et al. 2007) and a reduction in risk behaviour (Kiene et al. 2010). RCT can increase diagnoses in the early stage of HIV infection thus allowing those testing positive to access care earlier in the disease trajectory, and RCT can decrease HIV stigma through the normalisation of HIV testing (Weiser et al. 2006, Bassett et al. 2007, Yudin et al. 2007, Young et al. 2009). High acceptance rates of RCT have been reported in various countries and populations, for example, 35% (Cunningham et al. 2009), 59.7% (Brown et al. 2007) and 67% (Myers et al. 2009) in the USA; 43% in Ethiopia (Fetene and Feneke 2010); 74% (Kiene et al. 2010), 95% (Nakanjako et al. 2007) and 98% in Uganda (Wanyenze et al. 2008); 48.6% in South Africa (Bassett et al. 2008); and 83% in Zambia (Topp et al. 2011). Previous studies within student populations have found that perceived benefits of HIV testing, access to HIV care and support and encouragement from others is associated with VCT uptake (Dorr et al. 1999, Peltzer et al. 2004). Perceived barriers to HIV testing are negatively associated with VCT uptake among students (Dorr et al. 1999). This study is, to the best of our knowledge, the first to investigate acceptance and uptake of RCT in a student population based on individual perceptions, experiences and belief.

African Journal of AIDS Research is co-published by NISC (Pty) Ltd and Routledge, Taylor & Francis Group

142

Downloaded by [Osaka University] at 00:49 30 October 2014

The Health Belief Model The Health Belief Model (HBM) was developed in the 1950s as a means to explain varying levels of participation in preventative medical care and screening tests for the early detection of disease (Rosenstock 1966). The HBM is generally used in research to predict preventative health behaviour, adherence to medical regimens and the utilisation of healthcare services (Weissfeld et al. 1987). The model assumes the central role of individual attitudes and beliefs in conscious decision making as they apply to health promoting behaviours (Janz and Becker 1984). According to the HBM, the probability that the individual will engage in health behaviour such as RCT is determined by his or her perceptions and beliefs concerning a health threat and the belief that a recommended action will reduce such a threat effectively (Rosenstock et al. 1988). Perception of a health threat in turn is determined by two factors: perceived susceptibility, which entails an individual’s subjective assessment of their risk of getting a condition; and perceived severity, which is an individual’s subjective assessment of the seriousness of the condition, and its potential consequences (Rosenstock 1966). The perceived effectiveness of the recommended health behaviour is determined by a further two factors: perceived benefits, which refers to an individual’s subjective assessment of the positive consequences of adopting the behaviour; and perceived barriers, which is an individual’s subjective assessment of the influences that may discourage the adoption of the health promoting behaviour, for example, financial cost, noxiousness, convenience etc. (Janz and Becker 1984). The HBM has received considerable empirical support in predicting health promoting behaviour (Janz and Becker 1984) such as condom use (Volk and Koopman 2001, Lin et al. 2005), sexual abstinence (Iriyama et al. 2007), HIV testing and HIV risk behaviour (Dorr et al. 1999, De Paoli et al. 2004, Zak-Place and Stern 2004, de Wit and Adam 2008, Walker 2009), and adherence to antiretroviral therapy (ART) (Malcolm et al. 2003). Existing evidence suggests that individuals who view themselves as susceptible to HIV are more likely to be aware of their HIV status (de Wit and Adam 2008) and are more willing to accept VCT (De Paoli et al. 2004, Cunningham et al. 2009). Yet, the relationship between perceiving HIV infection as severe and acceptance of RCT is mixed. For example, De Paoli et al. (2004) found that perceived severity was positively associated with intentions to participate in VCT, but other studies showed a null relationship between these variables (e.g. Dorr et al. 1999, Zak-Place and Stern 2004). A positive association between the experience of benefits related to HIV testing and willingness to consider VCT has been found in various studies (Dorr et al. 1999, Zak-Place and Stern 2004, de Wit and Adam 2008). Additional factors that increase the likelihood of accepting RCT and participating in VCT have also been identified in the literature including having access to HIV care (Peltzer et al. 2004, Mwamburi et al. 2005, Hutchinson and Mahlalela 2006, Nakanjako et al. 2007, de Wit and Adam 2008); a high subjective personal risk assessment (Gage and Ali 2005, Cunningham et al. 2009); knowledge of how HIV is transmitted (Hutchinson

Nöthling and Kagee

et al. 2004, Mwamburi et al. 2005, Gage and Ali 2005, Bassett et al. 2008, de Wit and Adam 2008); appropriate positive social support and encouragement from others and a decrease in stigma associated with HIV (Fortenberry et al. 2002, Spielberg et al. 2003, Gage and Ali 2005, Weiser et al. 2006, de Wit and Adam 2008); and support and encouragement from romantic partners (Hutchinson et al. 2004, Peltzer et al. 2004, Gage and Ali 2005). Various factors that decrease the likelihood of accepting RCT and participating in VCT have also been identified in the literature. These include the potential negative consequences of an HIV-positive result, such as a fear of dying (Brown et al. 2008, Cunningham et al. 2009); a low perceived risk of HIV infection (Klein et al. 2003, Lapidus et al. 2006); a fear of being stigmatised; and the social consequences of testing positive, such as losing a romantic partner (Bassett et al. 2007, Simbayi et al. 2007). Other factors that have been negatively associated with testing include concerns about the anonymity and confidentiality of testing (Bassett et al. 2007, Brown et al. 2008, Cunningham et al. 2009); a fear of losing financial aid and insurance as a result of testing HIV-positive (Peltzer et al. 2002, Gage and Ali 2005, Brown et al. 2008) and not being psychologically prepared to face the possibility of testing HIV-positive (Nuwaha et al. 2002, Hutchinson et al. 2004). Demographic determinants of RCT Various studies have found that women are significantly more likely to accept HIV testing and to be aware of their HIV status than men are (Fortenberry et al. 2002, Peltzer et al. 2002, Bassett et al. 2008, Peltzer et al. 2009). Studies on age as a predictor of RCT appear to be contradictory. For example, Brown et al. (2007) have found that individuals in higher age groups are less likely to accept HIV testing, than individuals in the age groups of 25 years old or lower (Brown et al. 2007) However, older age has been associated with acceptance of HIV testing and knowledge of HIV status (Mwamburi et al. 2005, Gage and Ali 2005). Higher levels of education have been associated with higher levels of acceptance of HIV testing and knowledge of HIV status (Bassett et al. 2008, Cunningham et al. 2009, Peltzer et al. 2009). Studies on employment also appear to be contradictory. Several authors have reported that employment status is not significantly associated with acceptance of VCT (Kalichman and Simbayi 2003, De Paoli et al. 2004). However, Peltzer et al. (2009) found that employment and consequently income is significantly associated with knowledge of HIV status. Lastly, race has been found to be a significant predictor of knowledge of HIV status with Black South Africans being less likely to know their status compared to White, Coloured and Asian South Africans (Peltzer et al. 2009). Other studies have found that race is not a significant predictor of knowledge of HIV status (Kalichman and Simbayi 2003, De Paoli et al. 2004). Our study aimed to determine the extent to which the main dimensions of the HBM, namely perceived susceptibility, perceived severity, perceived benefits, perceived barriers and cues to action could predict acceptability of RCT among a population of university students in South Africa. A further

African Journal of AIDS Research 2013, 12(3): 141–150

aim of the study was to determine if significant differences existed in terms of these variables between individuals who had been tested for HIV and those who had not. Method

Downloaded by [Osaka University] at 00:49 30 October 2014

Participants Participants were students at a large residential university in South Africa. A total of 24 685 invitations to students were sent out by electronic mail. Procedure An invitation to complete an internet based survey was sent by email to all registered students at the university. An internet based survey was deemed a suitable collection method, given the large population size and the time and cost saving advantages associated with it. The invitation included an informed consent form. Confidentiality and anonymity were assured. Participants consented to the study by selecting the ‘I agree’ button following the terms and conditions of the study. The survey was automatically closed and no information was stored if participants selected the ‘I do not agree’ button. Participants were free to withdraw from the study at any point of data collection by selecting a ‘quit’ button which appeared on each page of the survey. Participants who submitted a completed survey were entered into a lucky draw for a cash prize. In an effort to increase the response rate, reminders to complete the survey were sent regularly to individuals who have not completed the survey. Ethical clearance was obtained from the Human Research Ethics Committee of the concerned university. Instruments Demographic information Demographic information was obtained using a demographic questionnaire which asked about the participants’ gender, age, race, level of education, employment and income status. Acceptability of RCT The subscale acceptability of RCT consisted of seven items to measure favourability towards RCT. Examples of items were: ‘I am in favour of routine HIV counseling and testing’ and ‘Routine HIV counseling and testing makes it easier for me to get tested’. Responses were measured on a five point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’, with higher scores indicating more favourability towards acceptance of RCT. The subscale acceptability of RCT showed internal consistency with a Cronbach’s alpha score of 0.83. Perceived susceptibility The subscale perceived susceptibility consisted of 12 items measuring participants’ perceptions of personal susceptibility to HIV. The items for the subscale were obtained and adjusted from the HBM Scale (Lux and Petosa 1994), the Sexual Risk Scale (De Hart and Birkimer 1997) and the AIDS Health Belief Scale (Zagumny and Brady 1998). Examples of items were: ‘There is a possibility that I have

143

HIV/AIDS’ and ‘I may have had sex with someone who was at risk for HIV/AIDS’. Responses were measured on a five point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’, with higher scores indicating a higher level of perceived susceptibility to HIV. The subscale perceived susceptibility showed internal consistency with a Cronbach’s alpha score of 0.74. Perceived severity The subscale perceived severity consisted of 15 items measuring participants’ perceptions of the severity of HIV. The items for the subscale were obtained and adjusted from the AIDS Health Belief Scale (Zagumny and Brady 1998) and the Champion’s Health Belief Model Scale (Champion 1984). Examples of items were: ‘I would rather have any other terminal illness than AIDS’ and ‘If I had HIV/AIDS my whole life would change’. Responses were measured on a five point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’, with higher scores indicating a higher level of perceived severity of HIV. The subscale perceived severity showed internal consistency with a Cronbach’s alpha score of 0.86. Perceived benefits The subscale perceived benefits comprised 11 newly constructed items measuring participants’ perceptions of the benefits related to RCT. Examples of items were: ‘Knowing my HIV status will give me peace of mind’ and ‘Routine HIV testing can reduce stigma by normalising HIV testing’. Responses were measured on a five point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’, with higher scores indicating a higher level of perceived benefits related to RCT. The subscale perceived benefits showed internal consistency with a Cronbach’s alpha score of 0.86. Perceived barriers The subscale perceived barriers consisted of 20 items measuring participants’ perceptions of the barriers related to RCT. Five items were newly constructed and the remaining items were obtained and adjusted from the Attitudes about HIV Anti-body Testing Scale (Boshamer and Bruce 1999). Examples of items were: ‘People assume that everyone who is tested for HIV is infected with HIV’ and ‘I would not get tested for HIV, because I would be asked information that is too personal’. Responses were measured on a five point Likert scale ranging from ‘strongly disagree’ to ‘strongly agree’, with higher scores indicating a higher level of perceived barriers related to RCT. The subscale perceived barriers showed internal consistency with a Cronbach’s alpha score of 0.89. Cues to action The subscale cues to action consisted of five newly developed items measuring the presence of cues to action, related to HIV testing. Examples of items were: ‘I have recently read a newspaper/magazine article on the importance of getting tested for HIV’ and ‘I have recently seen a poster of an HIV testing campaign’. Responses were measured on a three point Likert scale with the following options: ‘no’, ‘not sure’ and ‘yes’. Higher scores indicated

144

Nöthling and Kagee

the presence of more cues to action. The subscale cues to action showed low internal consistency, after deletion of poor items, with a Cronbach’s alpha score of 0.58.

The subscale, cues to action, revealed modest consistency (α = 0.55), which improved to 0.58 when the first item (I have been tested for HIV before) was removed from the analysis.

Results

Predicting acceptability of RCT

Of the 24 685 students who were invited to participate in the study, 1 113 completed questionnaires were received, representing 4.5% of the available sample pool. A similar response rate has been found, in the same population, using an electronic survey (Bester 2011). The demographic information of the participants is presented in Table 1.

Table 3 represents the correlation coefficients for the criterion variable acceptability of RCT and the predictor variables perceived susceptibility, perceived severity, perceived benefit, perceived barriers and cues to action. Significant positive correlations were found between perceived susceptibility (r = 0.15, p < 0.001), perceived benefits (r = 0.44, p < 0.001) cues to action (r = 0.17, p < 0.001) and acceptability of RCT. A significant negative correlation was found between acceptability and perceived barriers (r = −0.37, p < 0.001). A non-significant negative correlation was found between perceived severity (r = −0.01, p = 0.72) and acceptability of RCT. Table 4 presents the predictor variables as they were entered into the model as well as their standardised regression coefficients and significance levels. Table 5 presents the summary statistics for the regression analysis with block A representing the main dimensions (perceived susceptibility, perceived severity, perceived benefits and perceived barriers) of the HBM and block B representing the main dimensions of the HBM with the addition of the variable cues to action. The linear combination of the main dimensions of the HBM significantly explained 25.1%, F(4, 1 108) = 94.04, p < 0.001 of the variance in acceptability of RCT. The addition of the variable cues to action lead to an increase of 0.2% in explained variance which was non-significant (p = 0.044). In block A, the predictor variable perceived benefits β = 0.32, t(1 113) = 11.12, p < 0.001, made the largest significant contribution to predicting acceptability of RCT followed by perceived barriers β = −0.27, t(1 113) = 9.01, p < 0.001 and perceived susceptibility β = 0.09, t(1 113) = 2.48, p = 0.001. The predictor variable perceived severity β = 0.06, t(1 113) = 2.11, p = 0.035 did not contribute significantly to predicting RCT.

Downloaded by [Osaka University] at 00:49 30 October 2014

Data screening The internal consistency of the entire measuring instrument and the subscales of the instrument were measured using Cronbach’s alpha and these scores are reported in Table 2.

Table 1: Demographic characteristics and descriptive statistic

Age Gender Female Male Race and nationality White South African Coloured South African Black South African Indian South African Asian South African International students Other Level of education Matric Bacholor’s degree Honour’s degree Master’s degree Doctoral degree Employment status Full-time student Part-time employee Full-time employee Other Income per household per month More than R10 000 R5 001–R10 000 R2 001–R5 000 R1 001–R2 000 Less than R1 000

n 1 113 1 113 715 398 1 113 752 154 76 16 5 100 10 1 113 797 156 95 58 7 1 113 883 124 87 19 1 113 596 180 144 98 95

%

M 22.2

64.2 35.8 67.6 13.8 6.8 1.4 0.4 9.0 0.9 71.6 14.0 8.5 5.2 0.6 79.3 11.1 7.8 1.7 53.5 16.2 12.9 8.8 8.5

Table 2: Cronbach’s alpha scores for the entire scale and subscales Scale Entire scale Acceptability of RCT Perceived susceptibility Perceived severity Perceived benefits Perceived barriers Cues to action

Cronbach’s alpha 0.82 0.83 0.74 0.86 0.86 0.89 0.58

HIV testing Most participants (70.17%; n = 781) indicated that they had been tested for HIV by the time of data collection. Table 6 presents the results of the ANOVA tests, with the F-statistic, the significance of the F-statistic, the mean scores and their standard deviations. MANOVA was used to test for a significant difference between participants who had and had not been tested for HIV in terms of the HBM variables. The results indicated that a significant difference between the two groups did exist  = 0.85, F(6, 1 094) = 32.13, p < 0.001. Post hoc ANOVA indicated that significant differences existed between the two groups for: acceptability of RCT F(1, 1 099) = 117.00, p < 0.001; perceived susceptibility F(1, 1 099) = 18.95, p < 0.001; p = 0.001 perceived benefits F(1, 1 099) = 61.12, p < 0.001 and cues to action F(1, 1 099) = 22.86, p < 0.000. Individuals who had been tested for HIV (M = 30.34, SD = 4.59) were significantly more likely to indicate acceptability of RCT, compared to individuals who had not been tested for HIV (M = 27.09,

African Journal of AIDS Research 2013, 12(3): 141–150

145

Table 3: Correlation matrix for the predictor variables and the criterion variable

Acceptability of RCT Perceived susceptibility Perceived severity Perceived benefits Perceived barriers Cues to action

Acceptability of RCT – 0.15** −0.01 0.44** −0.37** 0.17**

Perceived susceptibility

Perceived severity

Perceived benefits

Perceived barriers

– 0.02 0.30** −0.03

– −0.38** 0.21**

– −0.15**

– 0.06 0.16** −0.01 0.16**

Cues to action

–

** Correlation is significant at the 0.01 level (1-tailed)

Table 4: Parameters for the variables predicting acceptability of RCT Unstandardised

Downloaded by [Osaka University] at 00:49 30 October 2014

Block A

B

Acceptability (constant) Perceived susceptibility Perceived severity Perceived benefits Perceived barriers Acceptability (Constant) Perceived susceptibility Perceived severity Perceived benefits Perceived barriers Cues to action

B

SE

18.96 0.06 0.03 0.25 −0.11 18.10 0.06 0.03 0.24 −0.11 0.10

1.32 0.02 0.01 0.02 0.01 1.39 0.02 0.01 0.02 0.01 0.05

95% confidence interval Standardised Beta coefficients 0.09 0.06 0.32 −0.27 0.09 0.06 0.31 −0.26 0.05

t

p

Lower limit

Upper limit

14.33 3.48 2.11 11.12 −9.01 13.05 3.19 2.15 10.70 −8.85 2.02

0.000 0.001 0.035 0.000 0.000 0.000 0.001 0.032 0.000 0.000 0.044

16.36 0.03 0.00 0.20 −0.14 15.38 0.02 0.00 0.20 −0.13 0.00

21.55 0.10 0.05 0.29 −0.09 20.83 0.09 0.05 0.28 −0.09 0.21

Table 5: Model summary predicting acceptability of RCT Block A B

R 0.503 0.506

R² 0.253 0.256

ΔR² 0.251 0.253

SE 4.135 4.130

F 94.040 4.080

df1 4 1

df2 1 108 1 107

p 0.000 0.044

A. Predictors: (constant), perceived barriers, perceived susceptibility, perceived severity, perceived benefits B. Predictors: (constant), perceived barriers, perceived susceptibility, perceived severity, perceived benefits, cues to action

Table 6: Results of the ANOVA and descriptive statistics for the differences between individuals who had and had not been tested for HIV HBM variables Acceptability

Group

F 117.00

p 0.000

Tested Not tested Susceptibility

18.95

10.43

61.12

103.05

22.86 Tested Not tested

35.69 33.64

6.85 7.68

46.54 48.78

10.51 10.27

46.43 43.29

5.81 6.63

35.22 42.60

10.61 11.79

12.13 11.34

2.45 2.52

0.000

Tested Not tested Cues

4.59 4.36

0.000

Tested Not tested Barriers

30.34 27.09

0.000

Tested Not tested Benefits

SD

0.000

Tested Not tested Severity

M

0.000

Downloaded by [Osaka University] at 00:49 30 October 2014

146

Nöthling and Kagee

SD = 4.36). Individuals who had been tested for HIV (M = 35.69, SD = 6.85) viewed themselves as significantly more susceptible to HIV than those who had not (M = 33.64, SD = 7.68). Individuals who had been tested for HIV (M = 46.43, SD = 5.81) reported significantly more perceived benefits related to RCT than those who had not been tested (M = 43.29, SD = 6.63). Individuals who had been tested for HIV (M = 12.13, SD = 2.45) also reported significantly more cues to action than individuals who had not (M = 11.34, SD = 2.52). The ANOVA tests also indicated that a significant relationship existed between participants who had been tested for HIV and participants who had not been tested for HIV, for the variables: perceived severity F(1, 1 099) = 10.43, p = 0.001 and perceived barriers F(1, 1 099) = 103.05, p < 0.001. Individuals who had been tested for HIV (M = 46.54, SD = 10.51) viewed HIV as significantly less severe than with individuals who have not been tested for HIV (M = 48.78, SD = 27.10). Individuals who had been tested for HIV (M = 35.22, SD = 10.61) reported significantly fewer barriers related to RCT than did individuals who had not been tested (M = 42.60, SD = 11.79).

Results of the ANOVA indicated significant gender differences for perceived benefits F(1, 1 111) = 24.78, p < 0.001 and perceived barriers F(1, 1 111) = 16.21, p < 0.001. Female participants reported significantly more benefits related to RCT (M = 46.17, SD = 5.90) than male participants did (M = 44.25; SD = 6.63) and significantly fewer barriers to RCT (M = 36.46, SD = 11.23) than male participants (M = 39.35, SD = 11.94). The ANOVA test and post-hoc procedures for race indicated significant differences between the different racial groups for perceived susceptibility F(6, 1 106) = 5.06, p < 0.001 and cues to action F(6, 1 106) = 8.16, p < 0.001. Black South Africans (A = 37.67, SD = 7.46) viewed themselves as significantly more susceptible to HIV than did White South Africans (A = 34.38, SD = 6.73) and also reported significantly more cues to action (A = 13.11, SD = 2.12) than White South Africans (A = 11.59, SD = 2.49). Furthermore, Indian South Africans (A = 14.00, SD = 1.03) experienced significantly more cues to action than White South Africans.

Demographic variables Table 7 presents the results of the MANOVA and ANOVA tests, and the descriptive statistics of the significant post-hoc procedures for the differences in health beliefs and acceptability of HIV testing, between the groups within the demographic variables. The MANOVA tests indicated that significant differences existed between the groups for the demographic variables: gender Λ = 0.97, F(6, 1 106) = 6.62, p < 0.001 and race Λ = 0.92, F(36, 4 838) = 2.75, p < 0.001. A significant negative correlation was also found between age and perceived severity of HIV. No significant differences were found between the groups for employment, income and level of education.

The main purpose of this study was to determine the extent to which the main dimensions of HBM could predict acceptability of RCT among a sample of South African university students. Our results indicate that the variables of HBM significantly predicted 25.1% of the variance in acceptability of RCT. Perceived benefits of RCT explained the largest proportion of the variance, followed by perceived barriers and perceived susceptibility. The findings of this study are in keeping with those from previous studies. A positive relationship has previously been found between the experience of benefits related to VCT and willingness to consider HIV testing among both a student population and the general population (Dorr et al. 1999, Zak-Place and Stern 2004, de Wit and Adams

Discussion

Table 7: Results of the MANOVA, ANOVA and descriptive statistics for the demographic groups and the variables of the HBM Variable and group Demographic variables HBM variables Gender Benefits

Group

 0.97

MANOVA F 6.62

ANOVA p 0.000

F

p

24.78

0.000

1 Male 1 Female Barriers

16.21

0.92

2.75

5.06

Cues

8.16 1 White 1 Black 2 White 2 Indian 0.54 1.05 1.43

39.35 36.46

11.94 11.23

34.38 37.67

6.73 7.46

11.59 13.11 11.59 14.00

2.49 2.12 2.49 1.03

0.000

1 White 1 Black

0.99 0.98 0.97

6.63 5.90

0.000

Susceptibility

Employment status Income Level of education

44.25 46.17 0.000

1 Male 1 Female Race

Descriptive A SD

0.941 0.396 0.079

0.000

Downloaded by [Osaka University] at 00:49 30 October 2014

African Journal of AIDS Research 2013, 12(3): 141–150

2008). Dorr et al. (1999) found that students who experienced fewer barriers related to HIV testing were more likely to consider VCT. de Wit and Adams (2008) also reported that individual experiences of barriers to VCT negatively influenced the likelihood of getting tested. A significant positive correlation has been found between perceived susceptibility and willingness to accept RCT among general populations and to consider VCT among a student population (De Paoli et al. 2004, Cunningham et al. 2009). Other studies have reported that individuals who view themselves as more susceptible to HIV were more likely to be aware of their HIV status (see, for example, de Wit and Adams 2008). Finally, students and other individuals who viewed HIV as more severe did not have higher intentions to receive VCT (Dorr et al. 1999, Zak-Place and Stern 2004). In contrast to the above mentioned finding, De Paoli et al. (2004) found that perceived severity was indeed associated with higher intentions to consider VCT. The non-significant relationship between perceived severity and acceptability of RCT may be attributed to the fact that participants may not have perceived HIV as a realistic threat. The variable cues to action did not significantly contribute to explaining acceptance of RCT. This finding contradicts previous research showing that students and other individuals who undergo VCT and RCT do so due to advice and encouragement from family, friends, romantic partners and individuals who have been tested (Spielberg et al. 2003, Gage and Ali 2005, Weiser et al. 2006). Individuals who have friends or relatives who are HIV-positive or have died due to AIDS related diseases are also more likely to get tested for HIV. The second aim of the study was to determine if significant differences existed in individual experiences and beliefs concerning HIV testing and acceptability of RCT among individuals who had and had not been tested for HIV. Most participants (70%) had been tested for HIV. Previous research shows that approximately 52% of students in the Western Cape of South Africa had been tested in the past (HEAIDS 2010). The number of tested students in this study was therefore higher than the 52% found previously. However, various efforts were made at the concerned university to increase participation in VCT through testing campaigns, which included incentives for testing and efforts to reduce stigma through promoting general discussion around HIV and knowing one’s HIV status (HEAIDS 2010). These efforts might be reflected in the high percentage of tested students. Furthermore, the results of this study indicate that individuals who had been tested for HIV were significantly more likely to accept RCT; viewed themselves as significantly more susceptible to HIV; viewed HIV as significantly less severe; reported significantly more benefits associated with HIV testing; reported significantly fewer barriers associated with HIV testing; and reported significantly more cues to action than individuals who had not been tested for HIV. These findings correspond with those of previous research. Individuals who had undergone VCT viewed themselves as significantly more susceptible to HIV (Morin et al. 2006, Norman 2006, Grispen et al. 2011); experience significantly more benefits related to HIV testing; and experience significantly more cues to action than do individuals who had not

147

been tested for HIV (Grispen et al. 2011). Kalichman and Simbayi (2003) also found that individuals who had participated in VCT viewed HIV testing as less beneficial, reported more negative attitudes towards HIV testing, and reported more social barriers related to HIV testing. We found significant differences between the different groups within the demographic variables gender, age and race. Previous research indicates that women are more likely to accept RCT and participate in VCT (Fortenberry et al. 2002, Peltzer et al. 2002, Zak-Place and Stern 2004, Norman 2006, Bassett et al. 2008). No significant difference in acceptability of RCT was found between men and women in this study. However, the results of the study indicated that women experienced significantly more benefits related to RCT and significantly fewer barriers related to RCT than men did. No significant correlation between age and acceptability of RCT was found in this study, but a significant negative correlation between age and perceived severity was found. The data on the relationship between age and acceptability of RCT are mixed: some studies show that individuals in higher age groups are less likely to accept RCT and participate in VCT, than are individuals in age groups below 25 years old (Norman 2006, Brown et al. 2007). Others have found that older age is associated with participation in VCT and knowledge of HIV status (Fortenberry et al. 2002, Gage and Ali 2005, Mwamburi et al. 2005). The results of this study indicated that Black students viewed themselves as significantly more susceptible to HIV than White students. A possible explanation for this is that racial groups in South Africa are disproportionately affected by the virus. HIV prevalence is significantly higher among Black South Africans with a prevalence rate of 13.6%, compared to Coloured South Africans with an estimated prevalence rate of 1.7%, and White South Africans with a prevalence rate of 0.3% (Shisana et al. 2009). The disproportionate prevalence rates could possibly have led to a higher level of perceived susceptibility among Black students. Black students also experienced significantly more cues to action tan White students did. An important cue to action is knowledge of a relative or friend infected with HIV (Gage and Ali 2005). Given the disproportionate prevalence among racial groups, it is again possible that Black students were more likely to be familiar with an individual infected with HIV. Implications and directions for future research The results of this study indicated that most of the main dimensions of the HBM could predict acceptability of RCT. The HBM therefore appears to be a suitable model to explain health promoting behaviour among students in higher education institutions. The variable of the HBM model perceived severity, however, did not contribute significantly to explaining acceptability of RCT. Interventions aimed at improving acceptability could focus on increasing perceived benefits related to RCT, decreasing perceived barriers related to RCT and increasing perceived susceptibility to HIV, as these variables contributed to the acceptability of RCT. Our findings suggest that HIV/

148

Nöthling and Kagee

Downloaded by [Osaka University] at 00:49 30 October 2014

AIDS divisions and healthcare providers within higher education institutions need to examine ways to improve knowledge related to the benefits of HIV testing and decreasing barriers related to RCT. Appropriate personal risk evaluation may also be facilitated to increase perceived susceptibility. We also found that individuals who have been tested for HIV were more likely to accept RCT. Individuals who have been tested also perceived themselves as more susceptible to HIV, experienced more benefits related to RCT, experienced fewer barriers related to RCT and experienced more cues to action. The improvement in likelihood to accept RCT indicates that routine testing could be a successful method to increase awareness of HIV status and to prevent HIV transmission. Healthcare providers within higher education institutions should therefore propose HIV testing to students as part of routine health care. Limitations of the study The main limitation of using internet based surveys is that they are highly vulnerable to volunteer bias. Volunteer bias might be reflected in the number of students who had been tested and participated in the study. These participants may have had a higher risk perception which would bias the results in favour of HBM. More females than males participated in the study indicating gender bias as well. A very low response rate of 4.5% was found in this study, despite efforts to increase participation, for example, an incentive and various reminders to participate. The low response rate negatively influences generalisability. However, the demographic information of the sample did represent the demographic characteristics of the population. A further limitation of the study is that a ceiling effect was identified within the results of the study. The results indicated that 14% of the participants obtained a perfect score for the variable acceptability of RCT, 8% obtained a perfect score for the variable perceived benefits and 20% obtained a perfect score for the variable cues to action. It is therefore possible that greater differences existed among the groups. A final limitation of the study concerns the relatively low alpha reliability score for the subscale that measured cues to action. The subscale only contained five items and this could possibly have caused the low reliability score. Despite these limitations, the study provided some support for the use of HBM in explaining acceptability of RCT among students in higher education institutions. It appears that the HBM may therefore be useful in understanding acceptability of RCT. The authors — Jani Nöthling, MA, works University. Her research interests are in HIV, common anxiety disorders. Ashraf Kagee is professor of Psychology University. His research interests include behavioural aspects of HIV and AIDS.

at Stellenbosch PTSD and other at Stellenbosch the social and

References Bassett IV, Giddy J, Nkera J, Wang B, Losina E, Lu Z, Freedberg KA, Walensky RP. 2007. Routine voluntary HIV testing in Durban, South Africa: The experience from an outpatient department. Journal of Acquired Immune Deficiency Syndrome 46: 181–186, doi:10.1097/QAI.06013e31814277c8. Bassett IV, Giddy J, Wang B, Lu Z, Losina E, Freedberg KA, Walensky RP. 2008. Routine, voluntary HIV testing in Durban, South Africa: correlates of HIV infection. HIV Medicine 9: 863–867, doi: 10.1111/j.1468-1293.2008.00635.x. Bester J. 2011. The perception of career barriers among South African university students. Unpublished MA thesis, University of Stellenbosch, South Africa. Boshamer CB, Bruce KE. 1999. A scale to measure attitudes about HIV-antibody testing: development and psychometric validation. AIDS Education and Prevention 11: 400–413. Available at http:// guilfordjournals.com/loi/aeap [accessed 5 march 2011]. Brown J, Shesser R, Simon G, Bahn M, Czarnogorski M, Kuo I, Magnus M, Sikka N. 2007. Routine HIV screening in the emergency department using the new US centres for disease control and prevention guideline. Journal of Acquired Immune Deficiency Syndrome, 46: 395–401, doi: 10.1097/ QAI.06013e3181582d82. Brown J, Kuo I, Bellows J, Barry R, Bui P, Wohlgemuth J, Wills E, Parikh N. 2008. Patient perceptions and acceptance of routine emergency department HIV testing. Public Health Report 123: 21–26. Available at http://www.hcbi.hlm.nih.gov.ez.sun.ac.za/ pmc/articles/PMC2567016/?tool=pubmed [accessed 13 April 2011]. Champion VL. 1984. Instrument development for health belief model constructs. Advances in Nursing Science 6: 73–87. Cunningham CO, Doran MS, DeLuca J, Dyksterhouse R, Asgary R, Sacajiu G. 2009. Routine opt-out HIV testing in an urban community health centre. AIDS Patient Care 23: 619–623, doi: 10.1089/apc.2009.0005. De Hart DD, Birkimer JC. 1997. Trying to practice safer sex: development of the sexual risk scale. The Journal of Sex Research 34: 11–25. Available at http://www.jstor.org/discover/1 0.2307/3813297?uid=3739368&uid=2129&uid=2&uid=70&uid=4 &sid=21101302143923 [accessed 5 March 2011]. De Paoli MM, Manongi R, Klepp KI. 2004. Factors influencing acceptability of voluntary counselling and HIV-testing among pregnant women in Northern Tanzania. AIDS Care 16: 411–425, doi:10.1080/09540120410001683358. de Wit JBF, Adam PCG. 2008. To test or not to test: Psychosocial barriers to HIV testing in high-income countries. HIV Medicine 9: 20–22, doi: 10.1111/j.1468-1293.2008.00586. Dorr N, Krueckenberg S, Strathman A, Wood MD. 1999. Psychosocial correlates of voluntary HIV antibody testing in college students. AIDS Education and Prevention 11: 14–26. Available at http://proquest.umi.com.ez.sun.ac.za/pqdlink?index =7&did=39372320&SrchMode=3&sid=2&Fmt=6&VInst=PROD& VType=PQD&RQT=309&VName=PQD&TS=1307726470&clientI d=57290&aid=2 [accessed 13 April 2011]. Fetene NW, Feleke AD. 2010. Missed opportunities for earlier HIV testing and diagnosis at the health facilities of Dessie Town, North East Ethiopia. BMC Public Health 10: 362–370, doi: 10.1186/1471-2458-10-362. Fortenberry JD, McFarlane M, Bleakley A, Bull S, Fishbein M, Grimley DM, Malotte CK, Stoner BP. 2002. Relationships of stigma and shame to Gonorrhea and HIV screening. American Journal of Public Health 92: 378–381. Available at http://web.ebscohost.com.ez.sun.ac.za/ehost/pdfviewer/ pdfviewer?vid=3&hid=105&sid=50412192-cf3f-4d97-a41a7181792d2e2a%40sessionmgr113 [accessed 26 March 2011].

Downloaded by [Osaka University] at 00:49 30 October 2014

African Journal of AIDS Research 2013, 12(3): 141–150

Gage AJ, Ali D. 2005. Factors associated with self-reported HIV testing among men in Uganda. AIDS Care 17: 153–165, doi: 10.1080/0940120512331325635. Grispen EJ, Ronda G, Dinant G, de Vries NK, van der Weijden T. 2011. To test or not to test: A cross-sectional survey of the psychosocial determinants of self-testing for cholesterol, glucose, and HIV. BMC Public Health 11: 1–10, doi: 10.1186/1471-2458-11-112. HEAIDS (Higher Education HIV/AIDS Programme). 2010. HIV prevalence and related factors — higher education sector, South Africa, 2008–2009. Pretoria: Higher Education South Africa. Hutchinson AB, Corbie-Smith G, Thomas SB, Mohanan S, del Rio C. 2004. Understanding the patient’s perspective on rapid and routine HIV testing in an inner-city urgent care centre. AIDS Education and Prevention 16: 101–114. Available at http://web.ebscohost.com.ez.sun.ac.za/ehost/pdfviewer/ pdfviewer?vid=3&hid=13&sid=ff1de033.bf5c-4621-9a7b9bdd340af8f0%40sessionmgr12 [accessed 5 March 2011]. Hutchinson PL, Mahlalela X. 2006. Utilization of voluntary counselling and testing services in the Eastern Cape, South Africa. AIDS Care 18: 446–455, doi: 10.1080/09540120500213511. Iriyama S, Nakahara S, Jimba M, Ichikawa M, Wakai S. 2007. AIDS health beliefs and intention for sexual abstinence among male adolescent students in Kathmandu, Nepal: A test of perceived severity and susceptibility. Journal of the Royal Institute of Public Health 121: 64–72, doi:10.1016/j.puhe.2006.08.016. Janz NK, Becker MH. 1984. The Health Belief Model: a decade later. Health Education Quarterly 11: 1–47. Kalichman SC, Simbayi LC. 2003. HIV testing attitudes, AIDS stigma, and voluntary HIV counselling and testing in a black township in Cape Town, South Africa. Sexually Transmitted Infections 79: 442–447, doi: 10.1136/sti.79.6.442. Kiene SM, Bateganyam M, Wanyenzem R, Lulem H, Nantaba H, Stein MD. 2010. Initial outcomes of provider-initiated routine HIV testing and counseling during outpatient care at a rural Ugandan hospital: risky sexual behavior, partner HIV testing, disclosure, and HIV care seeking. AIDS Patient Care and STDs 24: 117–126, doi: 10.1089/apc.2009.0269. Klein H, Elifson KW, Sterk CE. 2003. “At risk” women who think that they have no change of getting HIV: Self-assessed perceived risk. Women Health 38: 47–63, doi: 10.1300/ J013v38n02_04. Lapidus JA, Bertolli J, McGowan K, Sullivan P. 2006. HIV-related risk behaviours, perceptions of risk, HIV testing, and exposure to prevention messages and methods among urban American Indians and Alaska natives. AIDS Education and Prevention 18: 546–559, doi: 10.1521/aeap.2006.18.6.546. Lin P, Simoni JM, Zemon V. 2005. The Health Belief Model, sexual behaviours, and HIV risk among Taiwanese immigrants. AIDS Education and Prevention 17: 469–483. Available at http://web.ebscohost.com.ez.sun.ac.za/ehost/pdfviewer/ pdfviewer?vid=3&hid=13&sid=ff1de033-bf5c-4621-9a7b9bdd340af8f0%40sessionmgr12 [accessed 5 March 2011]. Lux KM, Petosa R. 1994. Preventing HIV infection among juvenile delinquents: educational diagnosis using the health belief model. International Quarterly of Community Health Education 15: 145–163. Malcolm SE, Ng JJ, Rosen RK, Stone VE. 2003. An examination of HIV/AIDS patients who have excellent adherence to HAART. AIDS Care 15: 251–261, doi: 10.1080/0954012031000068399. Morin SF, Khumalo-Sakutukwa G, Charlebois ED, Routh J, Fritz K, Lane T, Vaki T, Fiamma A, Coates T. 2006. Removing barriers to knowing HIV status, same-day mobile HIV testing in Zimbabwe. Journal of Acquired Immune Deficiency Syndrome 41: 218–224, doi: 10.1097/01.qai.0000179455.01068.ab.

149

Mwamburi DM, DlaDla N, Qwana E, Lurie MN. 2005. Factors associated with wanting to know HIV results in South Africa. AIDS Patient Care and STDs 19: 518–525, doi: 10.1089/ apc.2005.19.518. Myers, J.J., Modica, C., Dufour, M.K., Bernstein, C., & McNamara, K. (2009). Routine rapid HIV screening in six community health centers serving populations at risk. Journal of General Internal Medicine, 24(12), 1269–1274. doi: 10.1007/s11606-009-1070-1 Nakanjako D, Kamya M, Daniel K, Mayanja-Kizza H, Freers J, Whalen C, Katabira E. 2007. Acceptance of routine testing for HIV among adult patients at the medical emergency unit at a national referral hospital in Kampala, Uganda. AIDS and Behaviour 11: 753–758, doi: 10.1007/s10461-006-9180-9. Norman LR. 2006. HIV testing practices in Jamaica. HIV Medicine, 7(4), 231–242, doi:10.1111/j.1468-1293.2006.00368.x. Nuwaha F, Kabatesi D, Muganwa M, Whalen CC. 2002. Factors influencing acceptability of voluntary counselling and testing for HIV in Bushenyi district of Uganda. East African Medical Journal 79: 626–632, doi: 10.4314/eamj.v79i12.8669. Peltzer K, Mpofu E, Baguma P, Lawal B. 2002. Attitudes towards HIV-antibody testing among university students in four African countries. International Journal for the Advancement of Counselling 24: 193–203, doi: 10.1023/A:1022991522264. Peltzer K, Nzewi E, Mohan K. 2004. Attitudes toward HIV-antibody testing and people with AIDS among university students in India, South Africa and United States. Indian Journal of Medical Sciences 58: 95–108. Available at http://web.ebscohost.com. ez.sun.ac.za/ehost/pdfviewer/pdfviewer?vid=3&hid=12&sid= b97b5f35-feb3-47e9-b957-6c8348c511b2%40sessionmgr10 [accessed 6 March 2011]. Peltzer K, Matseke G, Mzolo T, Majaja M. 2009. Determinants of knowledge of HIV status in South Africa: results from a population-based HIV survey. BMC Public Health 9: 174–184, doi:10.1186/1471-2458-9-174. Rosenstock IM. 1966. Why people use health services. The Milbank Memorial Fund Quarterly 44: 94–124. Available at http:// www.jstor.org/pss/3348967 [accessed 5 March 2011]. Rosenstock I, Stretcher V, Becker M. 1988. Social Learning Theory and the Health Belief Model. Health Education Quarterly 15: 175–183. Available at http://deepblue.lib.umich.edu/bitstr eam/2027.42/67783/2/10.1177_109019818801500203.pdf [accessed 5 March 2011]. Shisana O, Rehle T, Simbayi LC, Zuma K, Jooste S, Pillay-vanWyk V, Mbelle N, Van Zyl J, Parker W, Zungu NP, Pezi S, the SABSSM III Implementation Team. 2009. South African national HIV prevalence, incidence, behaviour and communication survey 2008: A turning tide among teenagers? Available at http://www. mrc.ac.za/pressreleases/2009/sanat.pdf [accessed 29 March 2011]. Simbayi LC, Kalichman S, Strebel A, Cloete A, Henda N, Mqeketo A. 2007. Internalized stigma, discrimination, and depression among men and women living with HIV/AIDS in Cape Town, South Africa. Social Sciences & Medicine 64: 1823–1831, doi:10.1016/j.socscimed.2007.01.006. Spielberg F, Branson BM, Goldbaum GM, Lockhart D, Kurth A, Celum CL, Rossini A, Critchlow CW, Wood RW. 2003. Overcoming barriers to HIV testing: Preferences for new strategies among clients of a needle exchange, a sexually transmitted diseases clinic, and sex venues for men who have sex with men. Journal of Acquired Immune Deficiency Syndrome 32: 318–327. Available at http://journals.lww.com/ jaids/fulltext/2003/03010/overcoming_barriers_to_hiv_testing__ preferences.12.aspx [accessed 13 April 2011].

Downloaded by [Osaka University] at 00:49 30 October 2014

150

Topp SM, Chipukuma JM, Chiko MM, Wamulume CS, Moore CB, Reid SE. 2011. Opt-out provider-initiated HIV testing and counseling in primary care outpatient clinics in Zambia. Bulletin of the World Health Organization 89: 328–335, doi: 10.2471/ BLT.10.084442. UNAIDS (Joint United Nations Programme on HIV/AIDS). (2010). Report on the global AIDS epidemic. Available at http://www. unaids.org/documents/20101123_GlobalReport_em.pdf [accessed 7 March 2011]. UNAIDS/WHO (Joint United Nations Programme on HIV/AIDS/ World Health Organization). 2007. Guidance on providerinitiated HIV testing and counseling in health facilities. Available at http://www.who.int/hiv/pub/guidelines/9789241595568_en.pdf [accessed 7 March 2011]. Volk JE, Koopman C. 2001. Factors associated with condom use in Kenya: a test of the Health Belief Model. AIDS Education and Prevention 13: 495–508, doi: 10.1521/aeap.13.6.495.21438. Walker LJ. 2009. Components of the Health Belief Model and HIV testing decisions. MA thesis, University of North Carolina, Wilmington, USA. Available at http://libres.uncg.edu/ir/uncw/ listing.aspx?id=1371 [accessed 13 March 2011]. Wanyenze RK, Nawavvu C, Namale AS, Mayanja B, Bunnell R, Abang B, Amanyire G, Sewankambo NK, Kamya MR. 2008. Acceptability of routine HIV counselling and testing, and HIV seroprevalence in a Ugandan hospitals. Bulletin of the World Health Organization 86: 302–309, doi:10.2471/BLT.07.042580. Weiser SD, Heisler M, Leiter K, Percy-de Korte F, Tlou S, DeMonner S, Phaladze N, Bangsberg DR, Lacopino V. 2006. Routine HIV testing in Botswana: a population-based study on attitudes, practices and human rights concerns. PLoS Medicine 3: 261, doi: 10.1371/journal.pmed.0030261.

Nöthling and Kagee

Weissfeld JL, Brock BM, Kirscht JP, Hawthorne VM. 1987. Reliability of health belief indexes: Confirmatory factor analysis in sex, race, and age subgroups. Health Services Research 21: 777–793. Available at http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC1068989/pdf/hsresearch00506-0066.pdf [accessed 5 March 2011]. Young SD, Monin B, Owens D. 2009. Opt-out testing for stigmatized diseases: A social psychological approach to understanding the potential effect of recommendations for routine HIV testing. Health Psychology 28: 675–681, doi:10.1037/a0016395. Yudin MH, Moravac C, Shah RR. 2007. Influence of an ‘Opt-Out’ test strategy and patient factors on human immunodeficiency virus screening in pregnancy. Journal of Obstetrics & Gynaecology 110: 81–86, doi: 10.1097/01. AOG.0000267497.39041.06. Zagumny MJ, Brady DB. 1998. Development of the AIDS health belief scale (AHBS). AIDS Education and Prevention 10: 173–179, doi: apa.org/?uid=1998-02003-005. Zak-Place J, Stern M. 2004. Health belief factors and dispositional optimism as predictors of STD and HIV preventive behaviour. Journal of American College Health 52: 229–236, doi: 10.3200/ JACH.52.5.229-236.