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

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.

A statewide outbreak of Cryptosporidium and its association with the distribution of public swimming pools.

In order to characterize the association between county-level risk factors and the incidence of Cryptosporidium in the 2007 Iowa outbreak, we used generalized linear mixed models with the number of Cryptosporidium cases per county as the dependent variable. We employed a spatial power covariance structure, which assumed that the correlation between the numbers of cases in any two counties decreases as the distance between them increases. County population size was included in the model to adjust for population differences. Independent variables included the number of pools in specific pool categories (large, small, spa, wading, waterslide) and pool-owner classes (apartment, camp, country club or health club, hotel, municipal, school, other) as well as the proportion of residents aged <5 years. We found that increases in the number of bigger pools, pools with more heterogeneous mixing (municipal pools vs. country club or apartment pools), and pools catering to young children (wading pools) are associated with more cases at the county level.