BMC Public Health 2007, 7:352 http://www.biomedcentral.com/1471-2458/7/352 programs. For instance, someone who intends to use con- doms to avoid HIV infection may actively seek out sources of information about condom use and where to obtain them. Standard single stage regression techniques always assume that all predictor variables are exogenous to the model. When program exposure, however, is endogenous and the error terms of program exposure and condom use variables are correlated, the estimate for the effect of pro- gram exposure on condom use may be biased. Researchers can avoid this pitfall by using two-stage regression models in those instances when program exposure shows sub- stantial endogeneity [35-37]. The proposed two-stage model first requires estimating program exposure using a set of exogenous variables. As the program exposure indicators used here are count vari- ables, we used Poisson regression for this first step. In the second step, the estimated values for program exposure are used in the model for reproductive health behaviors rather than the observed program exposure. As our con- dom use measures are dichotomous, logistic regression is used for this second step. To test for endogeneity Bollen et al. [35] suggested a simple test consisting of including the residual for program exposure of the stage one equation together with the observed program exposure variable in the stage two equation. The t-test for the effect of the resid- ual is used to test for endogeneity. Where no significant endogeneity in program exposure was observed, the standard one-stage model is used. To estimate a two-step model, one needs to assure that the model is identified. In a strict sense this condition is ful- filled because the sets of exogenous variables in the two structural equations are not identical. That is, the exoge- nous predictors of program exposure and of condom use are not all the same. However, ideally one would also like that the variable excluded from the structural model for condom use are unrelated to these outcome variables. To test the identification of the model we used a test pro- posed by Bollen et al. [35], based on the comparison of the goodness-of-fit of the unrestricted reduced models and the structural models for condom use in which the predicted program exposure was substituted for observed exposure. This test indicated that there were no identifica- tion problems for the either the male or female sample. To estimate program exposure, the following variables were included in the model: age, residence, education, number of partners, perceived AIDS risk, and the media exposure indicators. In the model for condom use, the fol- lowing variables were included: program exposure, age, religion, residence, education, number of partners, per- ceived AIDS risk, and the proximate determinants of con- dom use. Because contraceptive use could potentially confound the relationship between program exposure and condom use, we also tested a model that included contraceptive use. Because the effect of contraceptive use was not significant, and because data on contraceptive use were collected for the female sample only, this variable was not included in the final model. The results are presented in the form of odds ratios. In the case of parametric variables, such as the number of repro- ductive health programs respondents were exposed to, we also discuss the interdecile odds ratio (ORD). The inter- decile odds ratio rank-orders respondents in terms of level of exposure, and then estimates likelihood that the out- come was experienced by those 10% of respondents who had the highest level of exposure, relative to the likeli- hood that it was experienced by the 10% of the respond- ents with the lowest levels of exposure. Sample description Table 1 shows the sample characteristics. The female and male samples are significantly different on most socio- demographic characteristics, although many of the differ- ences are small. Over two thirds of respondents lived in rural areas, while 18% lived in a town, 6% in a small city and 9% in the capital Lusaka. The male sample was on the average 2.5 years older than the female one due to the dif- ferent eligibility criteria for the two samples. Women are more likely than men to have had no formal education (14% vs. 5%) and less likely than men to have had sec- ondary or higher education (26% vs. 42%). Ownership of radio and TV was also significantly higher among men than among women. While 50% of men reported owning a radio and 22% a TV, among women only 43% and 18% reported owning one, respectively. Men report higher levels of risky sexual behavior than females. Where only 2% of the women reported more than one sex partner in the past year, 20% of men did. Similarly, 11% of men but only 1% of women report that their last sexual partner was a casual partner. Nevertheless, men were more likely than women to consider themselves at no or at low risk of HIV/AIDS. Forty-six percent of females and 57% of males considered themselves at no or low risk of getting AIDS. A significantly higher percentage of men than women desired a child within the next two years (30% vs. 18%). Women reported significantly less condom use than men (Figure 1). Only 23% of the sexually active women reported ever having used a condom, compared to 51% of men. Similarly, 11% of women and 16% of men report condom use during last intercourse. Page 4 of 12 (page number not for citation purposes)