Surveys conducted in 20 cities in Brazil met the study criteria providing 13 prevalence estimates for the age group 6 to 7 year and 19 estimates for the age group 13 to 14 years [16
]. When the survey results were published more than once, information from the first published report was included, as in all cases the rates were similar. We excluded reports from 5 surveys because of the following reasons: different age group (2 to 10 years) [27
]; sampling procedure was not described [28
]; presented only combined results for more than one study site [29
]; study population was not a city but a selected area of the city and area-specific socio-economic data comparable to the other study sites were not available [30
]; change of the question on wheezing in the questionnaire [31
], as it has been argued that results might not be comparable with those from other surveys [32
At visual inspection (Figure ), variables that seemed to have a clear pattern in relation to asthma prevalence were those with statistically significant coefficients in the linear regression or that remained in the selected models. Other variables studied had an erratic pattern at visual inspection (data not shown).
In the bivariable analysis, mortality rate for external causes in the children aged 6–7 years and GINI index in those aged 13–14 years were statistically associated with wheezing (Table ), such as that the higher the estimate the higher the prevalence of asthma found. The coefficient for mortality rate for external causes (β = 0.14; 95% C.I.: 0.01; 0.26) would mean that an increase in 1 per 10,000 mortality rate would increase asthma prevalence by 0.14 percent points in the children aged 6–7 years (or, a rise by 35.7/10,000 in mortality would increase 5 percent points asthma prevalence). The coefficient for the GINI index (scale from 1 to 100) (β = 0.46; 95% C.I.: 0.15; 0.90) would mean that a rise of 1 point in the GINI index would increase the asthma prevalence by 0.46 percent points in children aged 13–14 years (or, a rise by 10.87 points in GINI would result in an increase of 5 percent points in asthma prevalence). The relationships with asthma for 4 of the other indicators were consistent for both age groups: higher proportion of houses with bad water supply was associated with lower prevalence of asthma; while poor sanitation, infant mortality at the year of the survey, and hospital beds per 10,000 increased the prevalence of asthma. The effect of illiteracy rate, proportion of poor, average income, the human development index and infant mortality at birth of the studied children had opposite directions in the young and old age groups (Table ).
Results of the linear regression between asthma prevalence (%) and health and socio-economic indicators; outcome: percentage (%) of children with wheezing in the last 12 months in the study population
For children aged 6–7 years, the best multivariable model (adjusted R = 0.69) included the variables mortality for external causes rate, percent of houses with poor sanitation, and infant mortality at the year of birth. Only mortality for external causes showed a significant result (P = 0.007), the coefficient (β = 0.15; 95% C.I.: 0.05; 0.24) being interpreted as a rise in asthma prevalence of 0.15 percent points for each 1/10,000 increase in mortality for external causes; that is, an increase of 33.3/10,000 in mortality for external causes would increase asthma prevalence by 5 percent points.
For children aged 13–14 years the best multivariable model (adjusted R = 0.68) included two variables: illiteracy rate (β = -1.06; 95% C.I.: -1.50; -0.62) and infant mortality rate at the survey year (β = 0.47; 95% C.I.: 0.27; 0.68). An increase in the illiteracy rate of 1 percent point would decrease the prevalence of asthma by 1.06 percent points; that is, an increase in illiterate rate of 4.72 percent points would result in an decrease in asthma prevalence by 5 percent points. Conversely, An increase by 1 per 1,000 in infant mortality in the survey year would increase the prevalence of asthma by 0.47 percent points, and an increase by 10.64 in infant mortality would result in an increase by 5% percent points in asthma prevalence. No evidence of colinearity was found as standard errors of the estimators only changed slightly in different models.