BMC Public Health 2009, 9:310 http://www.biomedcentral.com/1471-2458/9/310 cal pathways to HIV infection, the underlying variables must operate through proximate variables, for example, age at sexual debut, being sexually active, and number of lifetime sexual partners. Distinguishing underlying varia- bles from proximate variables is important for the concep- tualization of causal pathways leading to HIV infection. Variables The neighbourhood variables were constructed based on the aggregation method in SPSS 15. This is a procedure that allows summarizing of a dataset or a variable by col- lapsing it into summary statistics (for example, mean, median, sum and standard deviation) on the basis of a break variable [27]. A break variable is a variable for which summary statistics are to be computed, for instance, clusters (SEAs) in this study. Each of the neigh- bourhood-level measures were grouped into three levels, the lowest level denoting low mean educational level, low wealth level or low employment level (i.e. high unem- ployment), and high denoting high mean educational level, high wealth level or high employment level. Values in-between were considered "middle". To measure the variable educational attainment, respond- ents were asked how many years they had spent in school, and they gave the answer in actual number of years. Dif- ferent cut-off points had to be used for the urban com- pared with the rural setting due to the marked difference in the distribution of educational attainment. Zambia's educational system is divided into four levels, that is, pri- mary school (grade 1 – 7), junior secondary school (grade 8 – 9), senior secondary school (grade 10 – 12), and terti- ary education (above grade 12). However, we set the fol- lowing cut-off points for individual-level educational attainment in the Chelston neighbourhoods to avoid making one group very small: 0 – 7 years (low educational level), 8 – 11 years (middle educational level) and 12 years and above (high educational level). Since the reported number of years in school was skewed towards primary education in Kapiri Mposhi, the following group- ings were employed: 0 – 4 years (low educational level), 5–7 years (middle educational level) and 8 years and above (high educational level). The neighbourhood-level educational attainment was estimated by calculating the mean number of years in school for all respondents in the neighbourhood (aged 15 – 59 years). The neighbour- hoods were then grouped into three socio-economic classes: low, middle and high. This was done separately for the urban and rural neighbourhoods. Using the pro- portion with secondary school or higher level of educa- tion gave the same categorization of clusters as when employing mean years-in-school. The variable employment was based on a question on cur- rent employment status, and the response options were 'unemployed', 'unpaid family worker', 'self-employed', 'employee', and 'employer'. After assessing the data, we reduced the scale from five items to two items. 'Unem- ployed' and 'unpaid family worker' were combined and the new category was called 'unemployed', and 'self- employed', 'employee' and 'employer' were merged to form a category called 'employed'. To be considered employed, respondents were supposed to have a job that paid them money or to be engaged in some form of busi- ness that earned them an income. Otherwise, they were considered unemployed. Thus housewives were consid- ered to be unemployed. Students were not included in the two categories. For each cluster the proportion of unem- ployed respondents aged 15–59 was calculated, and the clusters were grouped into three categories; low, medium and high employment rate (i.e. the inverse of unemploy- ment in order to obtain a "high"-category that corre- sponded to higher socioeconomic status). The categorization was done separately for the urban and rural areas, and cut-off points were selected that resulted in groups of similar sizes. The individual level wealth index was derived using prin- ciple component analysis based on six household items, i.e. electricity, refrigerator, radio, bicycle, plough and don- key. We created two separate wealth indices for the rural and the urban areas to account for the fact that different items were of different importance for urban and rural res- idents. Three individual wealth index categories were made based on the values of Component 1 obtained in the principal component analyses. In addition, the values of Component 1 for all respondents aged 15–59 were used to calculate the mean wealth status of each cluster using the aggregation procedure, and three categories rep- resenting the relative wealth status of the neighbourhoods were then created (this was done separately for the urban and rural clusters). The variable travel was measured by a question on trips made in the past year that involved absence from home for several days. The response options were 'never', 'some- times', 'often', and 'very often', and the variable was dichotomised into 'never/seldom' and 'often' (often and very often). Statistical analysis All analyses were restricted to young women aged between 15–24 years with known HIV status. Young women were selected based on the premise that they are highly vulnerable to HIV infection as indicated by the very high HIV prevalence relative to young men [23]. Further- more, the bulk of infections in this group are relatively recent and mortality is low. The analyses controlled for the effect of the potential confounder age, which was adjusted for as a linear effect. Page 3 of 11 (page number not for citation purposes)