Background

Mechanisms through which extreme precipitation, both increased and decreased, can contribute to the occurrence of waterborne infections are well documented. Heavy precipitation events increase the likelihood of water supply contamination due to the risk of sewer overflows [1]. Aging water treatment and distribution systems are particularly susceptible to heavy precipitation events, increasing the vulnerability of the drinking water supply. On the other hand, low precipitation may contribute to waterborne infections by increasing the percentage of sewage effluent in rivers when rainfall decreases or by increasing risk of groundwater contamination when the water table drops. In addition, many infectious agents and their vector and reservoir cycles are sensitive to temperature conditions [2].

A considerable amount of research is being conducted to map and assess risks, vulnerabilities and the impact of climate change in waterborne disease [3-5]. A recently published review [6] identified waterborne outbreaks potentially linked to an extreme water-related weather event and assessed how the different types of extreme weather events impact the occurrence of waterborne disease. Authors concluded that improving the understanding of the effects that different extreme water-related weather events have on waterborne disease is an important step towards finding ways to mitigate the risks.

Both the World Health Organization (WHO) and the European Centre for Disease Prevention and Control (ECDC) have emphasized the need for strengthening partnerships between health and climate experts, to improve scientific evidence of the linkages between health and climate drivers [7,8]. Despite the abundance of meteorological and epidemiological registries and databases, these are often not linked, preventing a more comprehensive understanding of potential associations [8]. Other publications have also highlighted additional obstacles to data access for research related to climate and water [9], and claim a reprioritization of public health research to ensure that funding is dedicated to explicitly studying the effects of changes in climate variables on food- and waterborne diseases [10].

To document the available knowledge, we performed a literature review of analytical research studies that have combined epidemiological and meteorological data to assess associations between extreme precipitation or air temperature and waterborne infections. This will help to identify specific areas where more specific research on this topic is needed.

Methods

Search strategy

The keywords used for searching relevant articles included both general and specific terms related to water, waterborne infections and precipitation or temperature related conditions (Table 1). These three groups of keywords were combined. The search strategy was run in the medical databases Ovid MEDLINE and EMBASE and in the multidisciplinary databases SCOPUS and Web of Science. Titles and abstracts of publications were searched for keywords. In order to focus on the most relevant and recent research, the search was limited to studies involving humans published in English between January 2001 and December 2013. In addition, a snowballing technique was used to review the reference lists of selected studies to identify additional articles.

Table 1 Keywords used for searching in the literature
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Data extraction strategy

Two independent reviewers screened titles for relevance obtained after running the search strategy. In a second step, selected abstracts were screened using the inclusion and exclusion criteria specified in Table 2. The full text of relevant studies were retrieved and assessed for eligibility. A sample of ten articles was reviewed by two independent reviewers in order to determine what data should be extracted. Dummy tables were designed for this purpose.

Table 2 Inclusion and exclusion criteria
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The following data were extracted from the articles and included in Tables 3 and 4: first author, publication year, location of study (continent, country or region), study period (in years), waterborne infection studied and data source, study objective, exposure variable studied (precipitation or/and temperature) and data source, analytical methods used, additional information (whether the study took into account in the analysis seasonality, water source, water treatment, or water supply involved), and main associations and conclusions found in the study. Articles were classified according to the study units used (outbreaks or cases of infection).

Table 3 Region, study period, waterborne infections and data sources in the included articles by type of study unit
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Table 4 Region, objective, exposure variables and data sources, analytical method, results and conclusions in the included articles by type of study unit
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Results

Once duplicates were removed, a total of 1907 titles were obtained using the initial search terms. Following screening of titles, results were limited to 457 articles. After screening abstracts for relevance, 79 full-text articles were read full text, of which 57 were excluded. Two articles were included after checking the reference lists of the already selected articles. In total, 24 analytical research articles, in which the association between extreme precipitation or air temperature and waterborne infections had been assessed, were included in the literature review (Figure 1).

Figure 1
figure 1

Article selection strategy.

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Studies of drinking water-related waterborne infections, geographical location and data sources

Articles using outbreaks as study units (n = 4)

Four studies used drinking water related waterborne outbreaks as study units [11-14]. Two articles presented studies that were performed using data from North America (Canada and United States) [11,14] while one used data from Europe (England and Wales) [13]. One study included data from several continents [12]. There were different data sources used to obtain outbreak data, including surveillance data, publicly available databases, previous published compilations and unpublished reports. The four studies assessed the association between outbreaks and precipitation. Two of them also studied the relationship with temperature. Meteorological data under study were obtained from records available at international organizations or from readings from the relevant weather stations.

Articles using cases of infection as study units (n = 20)

The remaining 20 articles used cases of infection as study units [15-34]. Most of the articles (n = 7) were performed in Asia (Bangladesh, Indonesia, Vietnam, India, Taiwan and China) [23,24,27,29-31,33]. Four were performed in North America (United States and Canada) [16,21,25,26], four in Oceania (Australia, New Zealand and Pacific Islands) [17,20,28,34], two in Europe (Sweden; and England and Wales) [18,22], one in central America (Haiti) [15], and one in Africa (Lusaka) [19]. One article used data from more than one continent, Asia and Central America [32].

The most common approach was to use cases of gastrointestinal infections without specifying the type of microorganism (n = 6). Among those studies focusing on specific microorganisms, cholera was most frequently studied (n = 6), followed by campylobacteriosis (n = 3) and typhoid fever (n = 3). Other infections, such as shigellosis, cryptosporidiosis, giardiasis, hepatitis A and paratyphoid fever, were also studied.

Cases of infection were obtained from several sources, including surveillance data, clinical records and registries, governmental reports and nurse advice telephone lines. All studies assessed the association between cases of infection and precipitation, while eleven of them also examined the relationship with temperature. The meteorological data under study were obtained from records available at international organizations, satellite sensors, gauge estimates, interviews or from local weather stations.

Definition extreme precipitation or temperature, covariates and statistical analysis

The definition of extreme weather events varied across the studies. There were different ways of categorizing meteorological variables, according to the amount or range of precipitation (i.e. groups including different categories; accumulated; smoothed using a certain number of days moving average; dichotomous, above and below a threshold; total in a given period; exceeded the upper limit of a given reference range). Only seven articles presented analyses stratified by water source or type of water supply, aiming to disentangle differences in the association with the occurrence of waterborne infections.

Analysis using Poisson regression or other types of count model regression was the most commonly adopted method to investigate whether variation in disease occurrence could be partly explained by changes in variables related to extreme weather events. Count model regression was used in eleven studies, one with outbreaks [12] and ten with cases of infections [15-17,20,22,25,27-30]. In some cases, the Poisson regression model was adjusted to account for: a) overdispersion, either by estimating an additional dispersion parameter using quasi-Poisson regression models [15,30] or more formally by using negative binomial regression models [28], b) excess zero counts in the observations, by using Zero-inflated Poisson regression models [12,16]. Time series data are prone to be influenced by seasonal and long-term variations, which may mask the short-term association between disease and extreme weather events. Seasonal trend decomposition was conducted in different ways, such as by adding trend and seasonal components into the Poisson regression [17], or by using Fourier terms [20,25,27]. In some studies, temporal correlations were handled by using generalized additive models (GAM) with time and sometimes other variables related to weather were added as smoother variables [16,29]. Delayed effects and a time varying relationship between the exposure and outcome variables were considered using generalized additive mixed models (GAMM) [29] or nonlinear distributed lag functions [15,22]. Case-crossover analysis was most frequently used when the study units were outbreaks [11,13]. It was also used in two studies using cases of infections [15,25]. In this analysis, the weather exposure at the location of an outbreak was compared with the exposures at the same location and same time of the year during control periods without an outbreak through use of conditional logistic regression. The method controls for time-invariant seasonal and geographic differences by design, although it assumes that neither exposure nor confounders change in a systematic way over the course of the study.

Findings of the studies

All four publications studying outbreaks found an association between precipitation and waterborne disease. Three found a positive association with extremes of precipitation [11,13,14], and one found an inverse association between waterborne outbreaks and average precipitation [12]. Among the two studies that assessed the association with temperature, one found a significant positive association [11]. Of the twenty articles using cases of waterborne infection as study units, amount of precipitation was found to have a positive association with infection in nine of them [15,16,19,22,24,26,28,29,32]. Two studies found a positive association in both extremes of precipitation (low and high) [20,27] and six did not find an association [17,18,21,25,31,34]. In three studies, statistically significant results were heterogeneous depending on the diseases or geographical regions they were assessing [23,30,33]. Regarding temperature, seven studies found a direct association between infections and temperature [17,18,20,24,25,27,32] and four did not find an statistical association [16,31,33,34]. In one study, statistically results depended on the disease that was being studied [28].

Discussion

This review has identified twenty four analytical research studies in which epidemiological and meteorological data have been linked in order to assess associations between extreme precipitation or air temperature and waterborne outbreaks or cases of infection. The findings presented in the different articles are heterogeneous, highlighting the complex relationship between precipitation or temperature driven transmission and waterborne infections. Although most of the studies identified a positive association between increased precipitation or temperature and infection, there were several in which this association was not evidenced. A number of articles also identified an association between decreased precipitation and infections. Very few articles presented stratified analyses that took into account the type of water treatment, water source or water supply involved.

Although research on this topic has been performed in different continents, most of the studies were conducted in Asian countries. Only few articles have presented data from Europe or Africa and none presented results from South America, resulting in limited evidence-based information on the influence of extreme weather on waterborne infections in these regions. Most of the publications used cases of infection as study units and only four used outbreaks as units. Of those using cases of infection, cholera or cases of gastroenteritis without a specific etiology were the infections most frequently studied. A variety of study designs and statistical methods, mainly count model regressions and case-crossover analysis, were used.

Several limitations and challenges of the studies were stated by the authors of the reviewed studies. Underreporting is an inherent problem in surveillance systems, and with respect to waterborne outbreaks or infections, the notified cases likely represent just the tip of the iceberg of the true disease burden [35]. However, in terms of estimating the association between weather events and infections or outbreaks, underreporting would only be the cause of bias if reporting is correlated with weather variables [36]. There is lack of consensus about the definition of extreme precipitation or temperature. An association might be found more easily depending on the threshold level that was used to classify extreme precipitation or temperature events. The classification of an extreme weather event is a key issue and needs to be defined according to the regional meteorological pattern. In certain occasions, small data sets in terms of number of observations limit statistical power. One possible solution for sparse data is to aggregate explanatory and outcome variables by week, month or year. However, this may reduce the variation in the data and smooth the relationships with previous weather events. Extreme weather events generally occur on a local scale. This implies that the results obtained from analyzing national, regional or local level will be different and may have noticeable consequences for the interpretations. As an example, presenting results by census area unit instead of national level could allow for variation in exposure across a region or country, although this is not always possible due to limited availability of data. The optimal choice of time lag between weather event and occurrence of a given waterborne disease event is challenging, as these events generally do not occur simultaneously. Using the same time lag for all cases linked to specific weather events is not possible given the variation in incubation periods among and within different infections. Understanding all these issues is necessary in order to select the time lag most relevant for a given disease.

Our review has covered a period of 13 years and has used four different databases, two medical and two multidisciplinary, to identify potential relevant peer reviewed publications in a systematic way. Although relevant literature could have been missed for a number of reasons (not peer reviewed, published before 2001 or in other languages than English, not identified by our search terms, unpublished results), our results show that there is potential to generate more scientific evidence to better understand the association between extreme precipitation or air temperature and waterborne outbreaks or cases of infection.

Conclusion

The heterogeneity of results presented in the articles identified in this review reflect the complexity of the relationship between extreme precipitation or air temperature and waterborne disease .There are several factors that could play a role on it, such as the specific type of microorganism, the geographical region, season, type of water supply, water source or water treatment. We encourage researchers to conduct studies examining these potential effect modifiers, in order to assess how they modulate the relationship between heavy rain events or temperature and disease. Addressing the gaps will be central for public health experts in order to identify the priority areas where action is needed to minimize negative impact on the health in future climate.