Introduction

Urinary tract infection (UTI) is considered to be the most common bacterial infection, with Escherichia coli (E. coli) involved in 70–80% of cases [1], [2]. It affects mainly women. By the age of 24, one third of women have experienced at least one UTI [3].

Seasonal variation in the incidence of E. coli bloodstream infection has recently been reported in different countries [4]. UTI is the primary source of these infections [5]. Despite this fact, little is known about the seasonality of UTIs in the community. If seasonality was evidenced, it could help to implement infection control interventions and to orient research along pathophysiological pathways [6], [7].

There is a lack of historical systems of UTI surveillance in the population. Indeed, to our knowledge, there is no available UTI incidence time series to test the seasonality of UTI in the community. Epidemiological studies based on microbiological databases are exposed to biases as many uncomplicated UTI are diagnosed and treated without microbiological confirmation. In such a context, non-clinical databases such as medication sales and web searches can be used for studying seasonal trends. They have already proved to be useful proxies for other diseases [8], [9], [10], [11], [12].

Here, we analysed multiple time series of medication sales and Google search data 1) to test if a seasonality of UTI exists or not, and 2) if seasonality exists, to determine its magnitude.

Materials and Methods

Results

All the time series could be analysed with the exception of South Africa, where the volume of search data was not reported before 2011. We thus discarded this series.

Annual peaks were in the summer for UTI drug sales in France and Google searches in France, Germany, Italy, the USA, and China. They were in the winter for Google searches in Brazil and Australia, i.e. during the austral summer for this southern hemisphere country. The mean (sd) of drug sales in France during the summer was 74.4 (13.0) boxes per 100,000 inhabitants per week. It was 62.2 (10.9) boxes per 100,000 inhabitants per week during the winter, that is, a 20% increase (p<0.001). UTI-related searches on Google were more frequent in the summer compared with the winter, with increases ranging from 8% to 19% (Table 1).

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Table 1. Seasonal variations in the eight time series between summer and winter.

https://doi.org/10.1371/journal.pone.0076020.t001

The fundamental period found by HPS was one year for the eight series: 52.17 weeks for sales of the therapeutic class G04A1 in France, and 52.22 weeks for the Google search series in France, Italy, Germany, the USA, China, Brazil, and Australia. Figure 1 plots the one-year pattern fitted to all time series: it clearly captures the major seasonal variations.

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Figure 1. Time series of antibiotic sales and Google search queries for urinary tract infections.

Using “fosfomycin and sulfamethazole” for antibiotic sales, 2000–2012, in France and “cystitis or urinary tract infection” for Google search queries, 2004–2012 in France, the USA, Brazil, Italy, Germany, China and Australia indicated in grey. Black lines show the model's predictions. The horizontal axis represents the time of year, the vertical axis for antibiotic sales represents drug sales (in number of boxes per 100,000 inhabitants), and the vertical axis for Google search queries represents search fraction. Search fractions are rescaled between 0 and 100 by Google Trends internal processes at download.

https://doi.org/10.1371/journal.pone.0076020.g001

Discussion

This study provides evidence of the seasonality of UTI, based on specific non-clinical indicators. Peaks were in the summer in France, Germany, Italy, the USA, and China, and during the austral summer in Brazil and Australia. The summer increase ranged from 8% to 20%, compared to winter periods. The fundamental frequency analysis conducted here was not hypothesis-driven, yet consistently returned a 52-week fundamental period for all series.

The seasonality of UTIs was first suspected in Canada [23]. The authors conducted their study in a specific population, and only culture-positive cases of UTI were included. Also, the method used did permit an estimate of the magnitude of the seasonal variations. Time series analyses were later performed for bloodstream infections in different countries [5], [24], [25], [26]. All these studies suggested seasonality of E. coli bloodstream infections, with summer increases ranging from 12% to 35% compared to the winter or to the rest of the year. No explanation was provided to explain the summer increases. In some previous studies, it was shown that higher temperature was associated with increased frequency of E. coli bloodstream infections [5], [24], [26]. Other hypotheses than the effect of temperature have been proposed, such as reduced micturition due to relative dehydration or more frequent sexual activity due to behavioural changes in the summer [27]. Divergent results have been found, for community-acquired infections caused by extraintestinal pathogenic E. coli, including UTI in children, with peak periods during the winter months [28], [29]. Such discrepancy could be explained by the fact that these studies did not include UTI only, but also any other kind of E. coli infections, or within specific populations (i.e. children). This could result in different pathophysiology and thus in different epidemiology.

During 2011, Google modified their geographical assignment, with retroactive application from 01/01/2011 [30]. Search trend data being given as fraction of the total search volume, this change in geographical assignment would have an impact only if the new defined areas were very discrepant from the old ones in terms of search patterns. This did not appear to be the case, as no irregularity like a sudden increase or drop in mean search fraction was noticed around the date of 01/01/2011 on the time series we downloaded. Also, during 2011 Google updated its categorization taxonomy [31]. However, this did not affect our data since we did not use categories when specifying our queries of interest, but used only keywords.

It is noteworthy here that consistent results were obtained from two different data sources: medication sales and Google search trends, and from seven different countries. When no historical systems of disease surveillance in the population are available, such proxies have already been validated for studying seasonal trends of other diseases, such as gastroenteritis, influenza, dengue, and depression [8], [9], [10], [11].

This study has several limitations. First, we supposed that there was a stationary link between the incidences and the proxies we used. However, regarding the Google searches, any publicised event relating to the studied disease can prompt searches, as has been seen previously, for example, in the case influenza during the increase in avian flu cases in 2005 or during the 2009 pandemic [8], [32], [33]. In our study, the Google UTI searches in Brazil peaked in January 2009, in conjunction with the death of a Brazilian top model from a UTI, which was highly covered by the press [34]. The interpretation of Google searches must therefore always be cautious, and based on several years of observation to avoid such biases, as was done in the present study. Second, there may be a bias in the number of Google searches related, say, to the more frequent use of search engines in the summer. However, Google uses a method to rescale the fraction of searches. If people use search engines more in the summer, this is taken into account in their model. So, the time series reflected well the seasonal variation in specific queries. Moreover, we verified that the Google searches fit well with antibiotic sales in France, which provided reassurance on the pertinence of this proxy for studying seasonality (the lag was at two weeks, data not shown). Third, drug sales estimates were based on a therapeutic class that did not include all antibiotics given for UTI treatment. However, our aim was not to estimate UTI incidence, but only to test seasonality. Thus, completeness was not necessary here, as it was more important to have a specific indicator of UTI, as was the case with the therapeutic class used in this study. Fourth, we observed temporal trends in multiple UTI time series, which increased regularly between 2000 and 2012, either in drug sales or in Google searches. We have no explanations for these trends (the method we used does not permit us to conclude that they reflected an increase in the incidence of UTI). However, our analyses were adjusted based on these trends, which consequently did not alter seasonality estimation.

Conclusions

To conclude, it was shown here that seasonality of UTI searches exists in various countries, resulting in increases in the summer. The magnitude of these increases has been estimated for the first time, to our knowledge. As UTI is a very frequent disease, any intervention that can help to decrease its incidence will have a useful impact. For example, a next step following this work would be to test control measures such as the reinforcement of UTI prevention measures during the summer.

Acknowledgments

We thank IMS–Health France for providing their databases.