BMC Public Health 2009, 9:465 http://www.biomedcentral.com/1471-2458/9/465 Table 5: Odds ratios of ordered logistic regression on the interactions between "Time lap" and country Variable Time lap (Continuous) Country - Time lap Interaction Ghana - Time Kenya - Time Malawi - Time Niger - Time Senegal - Time Tanzania - Time Odds Ratio 1.071 ** 0.999 1.005 0.964 ** 0.969 ** 0.961 ** 0.985 95% Confidence Interval [1.05; 1.09] [0.97; 1.03] [0.97; 1.04] [0.94; 0.99] [0.95; 0.99] [0.94; 0.98] [0.96; 1.01] †p < .10; *p < .05; **p < .01. Note: Estimates for (Overweight or obese) vs Not overweight/obese are the same as for Obese vs (overweight or not overweight/obese). ulations consuming more refined and energy-dense foods and having fewer opportunities for physical activities. A striking finding from this study was the increase in obes- ity among women of lower socio-economic status. The speed of increase in overweight/obesity emerged in the descriptive analysis to be higher among the poorest group (+50%) than the richest group (+7%), although the differ- ence was not apparent in the multivariate results (interac- tive model). The household wealth measure used in this paper may not be appropriate for detecting changes in household wealth over time as household assets and char- acteristics may not change in the short term secondary to changes in household income. Further research using other measures of wealth (e.g. income or expenditures) might improve our understanding of changes in over- weight and obesity over time by economic differentials. If educational attainment was to be used as a proxy for socioeconomic status of individuals, both our descriptive and multivariate analyses revealed that the changes in the proportion of overweight/obese individuals in the two surveys were highest amongst the poorest segment of the population studied. The descriptive results showed that while the prevalence of overweight/obesity went up by nearly 46% among the non-educated women, it dropped by about 10% among women educated at the level of sec- ondary level or higher. This pattern was confirmed by the multivariate results, with estimated annual rate of increase of 5.4% among the non-educated, compared to 2.3% among those with secondary or higher education. This finding suggests that over time, poor urban residents will become as affected as their wealthier counterparts or even worse off, as is now the case in the developed world. In the context of rapid urbanization in Africa, primarily driven by rural-urban migration [37], the new migrants have been shown to adapt to new urban life styles which ulti- mately predispose them to becoming obese even though their socioeconomic status might be lower than that for long term residents [38,39]. Further, the health conse- quences of big body size may not be known to the poor; and indeed some studies have shown that in some sec- tions of African populations, obesity may be positively regarded as a symbol of high social status [40,41]. This study also found that young and unmarried women were less likely to be obese, a result that differs from that reported in another study which showed that younger women were more likely to be obese than older women [42]. Our result might be explained by the fact that single women unlike their married counterparts are less likely to be multiparous which is associated with higher risk of obesity [43]. Limitations of the study The study has three main limitations. First, the data used is cross sectional, whereas the ideal situation would have been to track individuals over time and ascertain changes in body mass index and associated risk factors. Second, the female population used in this study may not be rep- resentative of the entire adult female population, given that the anthropometric measurements in the DHS are restricted to women who had given birth in the five years preceding the survey. Lastly, the lack of a uniform defini- tion of what constitutes an urban and rural area poses a challenge in making comparisons and generalizations about observations across countries. The DHS uses the official country specific designations. More often than not the classifications vary across countries taking into account, to varying degrees, the following attributes; pop- ulation density, administrative function, availability of social amenities and physical infrastructure such as hospi- tals, post office, schools, and markets. These differences pose a challenge in the interpretation of results from multi-country studies that attempt to make rural-urban distinctions and comparisons as the classifications may not be accurate enough to allow for this [44,45]. Conclusion Results from this study can be considered as an early warning that overweight and obesity, as a disease in its Page 7 of 9 (page number not for citation purposes)