This study analysed the extent and the determinants of both condom use and condom refusal. The overall rates of condom use we found in this study among the Free State population - i.e., 61.3% used condom during last sexual intercourse - compare well with the findings (64.8%) of the 2008 National HIV Prevalence Survey. The geographic and socio-economic features of people who were more likely than others to use condoms were similar to those reported in other studies [30
The two different analytical techniques, logistic regressions and classification trees, used in this paper, make different assumptions about data and have different strengths. The logistic regression models are useful in determining factors that are associated with the response variable, in the whole population. On the other hand, the classification trees divide the sample in two according to a cut-off value and then further analyses these two sub-populations. Splitting a sample in two results in two specific subsamples and in each of the segments or sub-samples, determinants can play a different role than in the general, initial population. The results of the two logistic regressions and CT are therefore not always the same.
Results of both methods indicate that different underlying constructs may partially influence condom use and condom refusal. As shown, only the variables knowledge about correct condoms use and sexual risk behaviour were associated with the respective outcome variable (condom use versus condom refusal) in the multivariate model, whereas the CT suggested that perceived need for a condom and knowledge of correct condom use were most influential for condom use, compared to condom stigma and sexual risk behaviour for condom refusal. This indicates that contextual factors such as societal norms should be considered more important in explaining condom refusal, whereas individual factors account for actual condom use. This has implications for developing prevention messages.
The regression results indicate that this research like much research in Africa (and elsewhere) finds that condom use and refusal responds to many determinants at multiple levels. The non-parametric classification tree allows further exploring the complexity related to condom use and refusal. To our knowledge this is the first time that such a model is adopted for studying condom use.
The multivariate models reveals factors that predict the behavioural outcome in the whole population, while the CT help detecting segments in the population that have specific prevention needs. Segmenting populations supports decision makers in targeting their efforts to specific subgroups.
While we do not claim that one technique is superior over the other, the strength of this study lies in reporting results using two methodologies, thereby increasing the study’s rigour and achieving a more comprehensive assessment of condom use and condom refusal determinants.
The CT results for example show that in the specific subgroup that knows about condom use and lives as a couple, condoms are not available, condom use is 73% compared to a 90% use when condoms are available. Furthermore, when individuals do not know how to correctly use condoms this leads to lower use (39%) in especially the group older than 33.5 years of age and less in the younger group (88%).
The CT model also provides the relative importance of the variables. The following five variables had the highest discriminatory power in relation to condom use: perceived need for condom, knowledge about correct use of condom, availability, age and marital status. All these variables were also significant in the multivariate logistic regression, with the exception of “perceived need” for condoms, which was significant only in the univariate analysis.
The CT, at the first split, revealed indeed a strong difference in condom use between those with and without a perceived need for condoms. Of the respondents with a perceived need, 39% used condoms, while of the respondents without a perceived need, 90% used condoms. The univariate logistic regression models also indicated that a strong predictor of condom use was its perceived need. However, when controlling for other variables in the multivariate analysis this variable did not remain significant. This may indicate one of the characteristics of the CT, i.e., if a variable on its own explains a high degree of the variability, it will be used as a first split and appear as a strong univariate predictor. The multivariate analysis allows for assessing the effect, while controlling for other variables.
Using the CT results to assess the relative importance of a predictor, indicated that knowledge of correct condom use was shown to be the second strongest predictor. This knowledge may result in additional condom user confidence.
Our findings show that especially older people, and those who are married or living together do not use condoms, which concur with earlier research finding that couples with stable relations are less likely to use condoms [31
]. HIV transmission risk in such relations is dependent on knowing the disease status of both partners and strictly adhering to the ‘be faithful’ prevention strategy, which may be subject to false assumptions. HIV- and condom knowledge and belief in the ability of condoms to prevent HIV were non-significant in predicting condom use in the multivariate models and non-important in the CT. This corroborates the contention that knowledge, belief and attitude as such may not be sufficient to achieve behaviour change, calling for multi-level models integrating more comprehensive perspectives. Such a multilevel approach has been used in Kenya and Zambia for instance [32
]. In Zambia, evidence showed that in addition to individual factors community-level factors can be important and that condom-promotion efforts should pay attention to community-level social norms, population trends, informal social relationships and interpersonal communication. Findings of the study in Kenya also support the relation seen in this study between condom use and age and marital status.
The CT also allows segmenting the population in terms of the condom refusal. It indicates that refusal is especially high when stigma is present; half of this group refusing the use of a condom, while one in three refused the use when stigma was not present. When this stigma is not present, refusal is higher in those with sexual risk behaviour and especially when knowledge of condoms is absent (42% with adequate knowledge versus 34% with low knowledge refused condoms). Such segmentations through interactions are a natural outcome of CT and complement the aforementioned multivariate models. It suggests that knowledge about condoms and sexual risk behaviour are important, but mainly in the group that does not report shame associated to condoms.
In the CT analysis the following top five variables were related to condom refusal: shame associated with condoms, sexual risk behaviour, knowledge about own HIV status, knowledge about condoms, and older age. Shame associated with condoms, sexual risk behaviour and knowledge about condoms as influencing factors were corroborated by the multivariate parametric logistic regression. Affordability of condoms did not turn out to be significantly related to condom refusal in the univariate model and the CT model. However, this variable is significant with an odds ratio greater than one in the multivariate analysis. This finding can be explained by a strong relation between affordability and availability. Controlling for variables such as availability in particular but also stigma and knowledge of correct use makes affordability significant. The significant effect of affordability may indicate that where there is considerable ambivalence about condoms (affordability but also availability) there will be more opportunities for refusal when condoms are affordable than when they are not affordable.
The importance of condom stigma for condom refusal may be explained by its association with HIV stigma. A body of literature shows that HIV-related stigma acts as a strong barrier to actual condom use [34
]. This clearly demonstrates the influence of cultural values and social norms in adopting safer sex behaviours [35
Understanding the reasons behind the refusal to use condoms is particularly important in South Africa because further improvement from its current level of use require innovative and targeted interventions.
The strongest predictor of condom refusal observed in this study, i.e. shame associated with condoms in interaction with other variables stresses the need for changing socio-cultural norms. The strong association of condom refusal with sexual risk behaviour, especially in the group where shame was not expressed, reporting multiple partners, and frequent partner change may require effective counselling.
The social norms and cultural values expressed as shame associated with condom use that may link using condoms to taboo behaviours such as promiscuous sex may lead to condom refusal even in the presence of other factors facilitating condom use (e.g., knowledge of HIV and condom, its availability and affordability and belief that condom can prevent HIV). Additionally, the in-depth exploration of ‘condom refusal’ identified sexual relationships where condom use may be perceived as less important because partners know their HIV status and live in stable relationships. Since heterosexual HIV transmission for both men and women often takes place within marriage or cohabitation, carefully tailored messages would also be needed here [36
The tree sensitivity and specificity for condom refusal are lower than the tree sensitivity and specificity for condom use, indicating that the variables used assist better in detecting condom users than condom refusers.
This study is subject to some limitations: Data on sexual behaviour were self-reported, thus a social desirability bias may apply, as is generally the case in studies using self-reported data to assess sexual risk behaviour. Another limitation is the issue of causality, as the study uses life time outcome measures with predictors measured at the time of study. Moreover, we did not ask how frequently respondents changed partners and this could have provided further useful insights.