BMC Public Health 2009, 9:465 http://www.biomedcentral.com/1471-2458/9/465 countries qualified for the study: Burkina Faso (1992/93; 2003), Ghana (1993; 2003), Kenya (1993; 2003), Malawi (1992; 2004); Niger (1992; 2006), Tanzania (1992; 2004) and Senegal (1992; 2005). The response rates in the DHS surveys are normally high. The lowest among the coun- tries studied was 93% in Burkina Faso in the 1992/3 sur- vey. In this paper the earlier dataset for each country is referred to as "survey 1" and the second as "survey 2". In the DHS, mothers of children born during the five-year period before the survey are weighed and their height measured using standard procedures. The height and weight measurements are standard across all countries where DHS are conducted. Field workers are specifically trained to take and record measurements accurately. Measurements are taken by the interviewers at the house- hold using height boards for height and electronic weigh- ing scales for weight. Pregnant women and those who are less than three months postpartum are excluded from the anthropometric measurements. The sample size per coun- try is shown in "Additional file 1". Variables The variables used in this study were obtained from two types of questionnaires: the individual women's and household questionnaire. The individual women's ques- tionnaire provided information on the characteristics of the woman while the household questionnaire provided information on household possessions and amenities such as source of drinking water, toilet facilities and dwell- ing characteristics which were used to create a socio-eco- nomic status index, "wealth index". A scoring method was used to calculate the wealth index based on the presence of amenities. The dependent variable was the women's body mass index (BMI) defined as the weight (in kilo- grams) divided by the square of the height (in meters), also expressed as (kg/m2). For the purpose of this analysis, the variable was re-coded as tricotomous: Not overweight/ obese; overweight; obese. Using the conventional cut-off points, overweight was defined as BMI between 25 and 30; and obesity as BMI greater or equal to 30 [32]. The key covariate of interest was the time (in years) between sur- vey 1 and survey 2 (referred to as "time lapse") which allowed the estimation of change over time in overweight and obesity. The two other central covariates were woman's education and a household wealth index. In the absence of data on income or expenditures, the wealth index has been shown to be a robust measure of house- hold wealth in developing countries [33]. Finally, the con- trol variables included working status, age, marital status, parity, and country. Statistical method Multivariate ordered logistic regressions were fitted in the context of the partial proportional odds model. This model was chosen since the dependent variable was a three-category ordinal outcome. Using multinomial regression would mean that the information conveyed by the ordered nature of the outcome variable is discarded. In addition, not treating the variable as ordered, would potentially lead to loss of efficiency [34]. The partial pro- portional odds model is a special case of the generalized ordered logit model that is less restrictive than the propor- tional odds model. Though proportional odds models are suited for the analysis of ordinal response variables, a crit- ical assumption is that of parallel slopes. This assumption was assessed in preliminary analyses, using Brant test. The test showed that overall, there was a violation of the par- allel regression assumption (X2 = 212.99, p-value < 0.001) on a number of covariates. Details on the statistical theory behind the partial proportional odds model can be found in several sources [35]. The STATA command gologit2 [36] was used to fit the partial proportional odds model. Results Sample Description Figure 1 shows the prevalence of overweight/obesity in the selected countries individually and the pooled data, based on the latest surveys (survey 2). To a large extent, overweight/obesity was concentrated in urban areas. In the pooled dataset, urban women were almost three times more likely to be overweight or obese compared to their rural counterparts. The urban to rural ratio in overweight/ obesity varies from about 2 (in Malawi, Senegal, Kenya and Ghana), and 3 (in Tanzania) to as high as 5 in Niger and 8 in Burkina Faso. The prevalence in overweight and obesity in urban areas ranges from about 23% in Malawi to 35% in Niger and Ghana and 38% in Kenya. From here onwards, the analyses were based on the pooled (survey 1 and survey 2) urban sample which comprises 19,992 women as described in Table 1. eFUsirtgb)uarne-ru1ral differences in overweight/obesity; survey 2 (lat- Urban-rural differences in overweight/obesity; survey 2 (latest). Page 3 of 9 (page number not for citation purposes)