Warmer weather as a risk factor for hospitalisations due to urinary tract infections

Abstract

The incidence of urinary tract infections (UTIs) is seasonal, and this seasonality may be explained by changes in weather, specifically, temperature. Using data from the Nationwide Inpatient Sample, we identified the geographic location for 581 813 hospital admissions with the primary diagnosis of a UTI and 56 630 773 non-UTI hospitalisations in the United States. Next, we used data from the National Climatic Data Center to estimate the monthly average temperature for each location. Using a case-control design, we modelled the odds of a hospital admission having a primary diagnosis of UTI as a function of demographics, payer, location, patient severity, admission month, year and the average temperature for the admission month. We found, after controlling for patient factors and month of admission, the odds of a UTI diagnosis increased with higher temperatures in a dose-dependent manner. For example, relative to months with average temperatures of 5-7.5 °C, an admission in a month with an average temperature of 27.5-30 °C has 20% higher odds of a primary diagnosis of UTI. However, in months with extremely high average temperatures (above 30 °C), the odds of a UTI admissions decrease, perhaps due to changes in behaviour. Thus, at a population level, UTI-related hospitalisations are associated with warmer weather.

Keywords: Urinary tract infections (UTIs).

Fig. 1.

Locations of 2604 hospitals used in our analysis. Additional states contribute to the NIS project; however, some omit key variables (e.g. Florida does not report month of hospitalisation) while others do not report the hospital's AHA identifier.

Fig. 2.

Adjusted odds ratios (ORs) for diagnosis of UTI as a function of temperature (graphical display of data in Table 3). The dot denotes the point estimate of the OR and the error bars reflect the 95% CI about that estimate. ORs are adjusted for year, month-of-year, climate and demographic factors.

Fig. 3.

Monthly odds ratios for UTI in models with and without temperature included. The triangles reflect the estimated monthly effects for a model including year, month-of-year, climate and demographics, but not including the mean monthly temperature. The circles are the estimated monthly effects in a model with temperature data included. The error bars are 95% CIs.