@EpiGrad has a really nice answer here. I will try to throw in a few points that are hopefully useful and complementary.
(By the way, @Mimi, you should click the check next to an answer. You have asked for help, and people have spent their time to help you. It's polite to acknowledge that your question has been answered. My apologies for sounding preachy. I can delete this paragraph later.)
It depends on what your goals are. For example, you may want to predict the value of some variable given some information; alternatively, you may just want to understand the forces at work within this dynamic. The latter goal is necessarily tied up with issues of causality, whereas prediction can ignore causality. It would be perfectly reasonable for people in public health (say, in a government agency or a social worker, etc.) to want to be able to predict something like this. It is possible to predict an effect from a cause, or a cause from an effect, or one effect from another effect, etc. Should this be your goal, you want to gather the covariates that such users of your prediction model will have access to. Not much else really matters.
On the other hand, if you want to understand this dynamic, you must come to understand the underlying causal pattern. You should know that this is not remotely an easy task. You should do the things that EpiGrad recommends; however, you need to know that they do not guarantee that your estimate of the relationship between AIDS awareness and unprotected sex is an unbiased estimate of the true causal relationship. Determining other possible variables and partialling them out will only yield an unbiased estimate if the list of variables you come up with constitutes all the relevant variables. It is very possible that there are relevant variables that no one has ever thought of yet. Because regression methods assume predictor variables are measured without error (all error variability is considered part of the response variable) these other, unthought-of variables will be collapsed into the response variable's error term. This causes the problem of endogeneity. Any good econometrics book will discuss these issues extensively. Common approaches to dealing with endogeneity include quasi-experimental studies, instrumental variables regression (covered thoroughly in any econometrics text), and propensity score matching.