Tobacco smoking and obesity greatly contribute to premature death and disease in developed countries. In order to measure the extent to which these risk factors affect a population, as well as to describe the progression of these epidemics over time, routine surveillance of the prevalence of obesity and smoking is carried out by international organizations, national departments of health, and statistical offices. To this end, summary measures—like age-standardized rates, and tabular and graphical representations, such as maps—are used. In this study, we argue that population pyramids, a widely used demographic tool, may be easily adapted to provide relevant visual information for public health purposes. By means of two juxtaposed histograms, one for each gender, population pyramids show either the proportion or the actual number of subjects in each age and gender subgroup. We suggest that stratifying each bar of the two histograms according to ordinal categories of the health condition or risk factor examined may provide useful details on the relationship between this condition or factor and key demographic variables like age and gender. In addition, the actual number of exposed subjects can be immediately read from the graph. We therefore built a statistical routine with Stata to create modified population-pyramid plots separately for overweight/obese and current/former smoker. Data were derived from five National Health Interview Surveys carried out in Italy between 1983 and 2005. For each survey, data on age, gender, smoking status, height, and weight were extracted for subjects aged 20–99. Age and gender-specific prevalence rates of overweight/obese, and of current/former/never smoker were computed and applied to population estimates performed by the Italian national statistical institute (ISTAT). The resulting estimated numbers of underweight/normal weight/overweight/obese individuals and of former/current/never smokers were used to create the modified population pyramids. In conclusion, modified population pyramids may contribute to assessing the impact of risk factors on a population in absolute terms, to evaluating how these risk factors are distributed by age and gender, and to assessing how the age and gender distribution of these risk factors changes over time.
