Objective: To evaluate the effect that seasonality has on the occurrence of respiratory symptoms in a Brazilian city with a tropical climate. Methods: This was a cross-sectional study, in which data related to subjects who sought outpatient treatment at a primary health care clinic in the city of Goiânia, Brazil, were correlated with daily meteorological data. Over a one-year period, all the patients who met the inclusion criteria were interviewed on 44 distinct, randomly selected days (11 days per season). We used ANOVA in order to compare the means of the dependent variables by season. Correlations were drawn between each dependent variable and each meteorological variable. The effects of the meteorological variables were analyzed with an AutoRegressive Moving Average with eXogenous input (ARMAX) model. Results: Of the 3,354 participants, 494 (14.6%) had respiratory symptoms. Although temperature variation alone had no effect on the number of individuals with respiratory symptoms, the low levels of humidity during winter resulted in a statistically significant difference among the seasons (p < 0.01). The mean minimum relative humidity on the three days prior to the interviews correlated negatively with the number of subjects with respiratory symptoms (p = 0.04). An ARMAX model including the same variable showed a statistically significant coefficient (p < 0.0001). Conclusions: In this sample, the number of subjects with respiratory symptoms increased significantly when the relative humidity dropped, and this increase could be predicted using meteorological data.

Keywords: Seasons; Tropical climate/adverse effects; Signs and symptoms, respiratory; Logistic models.