Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Analytical studies assessing the association between extreme precipitation or temperature and drinking water-related waterborne infections: a review

  • Bernardo R Guzman Herrador1Email author,
  • Birgitte Freiesleben de Blasio1, 2,
  • Emily MacDonald1, 3,
  • Gordon Nichols4, 5, 6, 7,
  • Bertrand Sudre4,
  • Line Vold1,
  • Jan C Semenza4 and
  • Karin Nygård1
Environmental Health201514:29

https://doi.org/10.1186/s12940-015-0014-y

Received: 20 May 2014

Accepted: 4 March 2015

Published: 27 March 2015

Abstract

Determining the role of weather in waterborne infections is a priority public health research issue as climate change is predicted to increase the frequency of extreme precipitation and temperature events. To document the current knowledge on this topic, we performed a literature review of analytical research studies that have combined epidemiological and meteorological data in order to analyze associations between extreme precipitation or temperature and waterborne disease.

A search of the databases Ovid MEDLINE, EMBASE, SCOPUS and Web of Science was conducted, using search terms related to waterborne infections and precipitation or temperature. Results were limited to studies published in English between January 2001 and December 2013.

Twenty-four articles were included in this review, predominantly from Asia and North-America. Four articles used waterborne outbreaks as study units, while the remaining articles used number of cases of waterborne infections. Results presented in the different articles were heterogeneous. 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. This highlights the complex relationship between precipitation or temperature driven transmission and waterborne disease. We encourage researchers to conduct studies examining potential effect modifiers, such as the specific type of microorganism, geographical region, season, type of water supply, water source or water treatment, in order to assess how they modulate the relationship between heavy rain events or temperature and waterborne disease. Addressing these gaps is of primary importance in order to identify the areas where action is needed to minimize negative impact of climate change on health in the future.

Keywords

ReviewPrecipitationRainfallTemperatureWaterborne infection

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

Thematic areas

Specific terms*

Water source

Water, water supply, groundwater, surface water, water purification, water disinfection, sewage

Waterborne infection

Waterborne, gastroenteritis, outbreak, campylobacteriosis, Escherichia coli, cholera, cryptosporiosis, hepatitis A, giardiasis, salmonellosis, shigellosis, norovirus, typhoid fever

Weather conditions

Climate, weather, precipitation, rain, rainfall, temperature, humidity, season, flood, drought, snow

*Terms in the same box were combined with “or” in the search. Terms in the different rows were combined with “and” in the search.

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

Inclusion criteria

Analytical research studies in which the main objective was

To estimate the association between extreme precipitation or temperature and drinking water-related waterborne outbreaks or infections

Exclusion criteria

Study type:

-Outbreak reports reporting a single outbreak event.

-Pure discussion papers or reviews without specific statistical analysis and results presented.

-Studies without statistical analysis of associations (i.e. surveys).

Events presented:

-Outbreaks or trends of food-borne and vector-borne outbreaks or infections

-Study of environmental conditions other than precipitation or air temperature

-Main route of transmission other than drinking water.

-Estimation of the association between extreme precipitation or temperature and concentration of microorganisms in water, but without data on human illness presented in the paper.

-Study of seasonality not related to weather or climate data.

Search strategy limited to:

Population: Humans

Publication year: January 2001-December 2013

Language: English

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

Study units

First author publication year

Continent

Country/Region

Study period

Waterbone disease under study

Waterborne disease Data source

Outbreaks

Yang [12]; 2012

Global

-

1991-2008 (18 years)

Drinking water related waterborne disease outbreaks (+ other water-associated diseases)

Database developed by the Global Infectious Disease Epidemiology Network (GIDEON)

Curriero [14]; 2001

North America

United States

1948-1994 (47 years)

Drinking water related waterborne disease outbreaks with contamination at the water source

Surveillance data at national level

Thomas [11]; 2006

North America

Canada

1975-2001 (27 years)

Drinking water related waterborne disease outbreaks

Published compilation at national level

Nichols [13]; 2009

Europe

England and Wales

1910-1999 (90 years)

Drinking water related waterborne disease outbreaks

Medline search, published papers and unpublished reports

Cases of infection

Tornevi [22]; 2013

Europe

Gothenburg, Sweden

2007-2011 (5 years)

Telephone calls to acute gastrointestinal illnesses

Nurse advice line

Louis [18]; 2005

Europe

England and Wales

1990-1999 (10 years)

Campylobacteriosis cases

Surveillance data at national level

Eisenberg [15]; 2013

Central America

Haiti

2010-2011

Cholera cases

Registry at a hospital

Internally displaced person camp data

Reports at the ministry

White [25]; 2009

North America

Philadelphia, United States

1994-2007 (14 years)

Campylobacteriosis cases

Surveillance data at national level

Drayna [26]; 2010

North America

Wisconsin, United States

2002-2007 (6 years)

Physician visits of gastrointestinal infections/diarrhea

Administrative records

Teschke [21]; 2010

North America

Vancouver, Canada

1995-2003 (9 years)

Physician visits and hospitalization records of various gastrointestinal diseases with potential to be waterborne

Administrative records

Harper [16]; 2011

North America

Nunatsiavut, Canada

2005-2008 (4 years)

Gastrointestinal illness related visits

Administrative records

Hashizume [27]; 2007

Asia

Dhaka, Bangladesh

1996-2002 (7 years)

Weekly number of patients visiting a hospital due to non-cholera diarrhea

Administrative records

Vollaard [23]; 2004

Asia

Jakarta, Indonesia

2001-2003 (3 years)

Typhoid or paratyphoid fever cases

Consultations at hospitals and outpatient health centers

Kelly-Hope [33]; 2007

Asia

Vietnam

1991-2001 (11 years)

Shigellosis, cholera and typhoid fever cases

Surveillance data at national level and published papers and unpublished reports

Emch [31]; 2008

Asia

-Hue and Nha Tranng, Vietnam

−1985-2003 (23 years)

Cholera cases

Records from a research centre/surveillance data at national level

-Matlab,Bangladesh

−1983-2003 (21 years)

Constantin de Magny [30]; 2008

Asia

-Kolkata, India

1997-2006(10 years)

Cholera cases

Administrative records

-Matlab, Bangladesh

Records from a research center

Wang [24]; 2012

Asia

Guizhou, China

1984-2007 (24 years)

Typhoid and paratyphoid fever cases

Surveillance data at national level

Chen [29]; 2012

Asia

Taiwan

1994-2008 (15 years)

Hepatitis A, enteroviruses, shigellosis cases

Surveillance data at national level

Jutla,[32]; 2013

Asia and Central America

-Northern India and Pakistan

−1875-1900 (26 years)

Cholera cases

Reports from the Government and previous published data

-Haiti

-2010

Singh [20]; 2001

Oceania and Australia

Pacific Islands

1978-1998, with two missing years(19 years)

Diarrhea cases

Surveillance data at national level

Hu [17]; 2007

Oceania and Australia

Brisbane, Australia

1996-2004 (9 years)

Cryptosporidiosis cases

Surveillance data from the regional level

Rind [34]; 2010

Oceania and Australia

New Zealand

1997-2005 (9 years)

Campylobacteriosis cases

Surveillance data at national level

Britton [28]; 2010

Oceania and Australia

New Zealand

1997-2006 (10 years)

Cryptosporidiosis and Giardiasis cases

Surveillance data at national level

Sasaki [19]; 2009

Africa

Lusaka, Zambia

2003-2004; 2005-2006

Cholera cases

Records at a treatment centre

Literature Review (n = 24).

Table 4

Region, objective, exposure variables and data sources, analytical method, results and conclusions in the included articles by type of study unit

Study units

First author publication year

Objective

Exposure variable under study (Precipitation/Air temperature)

Exposure variable data source

Analytical method

Additional information

Association found

Outbreaks

Yang [12]; 2012

Risk factors associated with spatio-temporal distributions of water-associated outbreaks

Average precipitation per year

Records from international organizations

Zero-inflated Poisson regression

-

Waterborne diseases are inversely related to average annual precipitation.

Global average accumulated temperature (degree-days)

No association between temperature and waterborne disease.

Curriero [14]; 2001

Association between extreme precipitation and waterborne disease outbreaks.

Extreme precipitation above certain threshold by watershed

Readings of relevant weather stations

Monte Carlo version of the Fisher exact test

Analysis stratified by water source and control for seasonality

Positive association between extreme precipitation and outbreak occurrence

Both for surface water (strongest association during the month of the outbreak) and groundwater contamination (2-month prior to the outbreaks)

Thomas [11]; 2006

Test the association between high impact weather event and waterborne disease outbreaks

Accumulated precipitation, smoothed using a five-day moving average, maximum percentile of the accumulated precipitation amount, number of days between the maximum percentile and the case or control onset day temperature

Readings of relevant weather stations

Time-stratified matched case-crossover analysis

Control for seasonality

Positive association between accumulated precipitation percentile and outbreak occurrence

Positive association between degree-days above 0 C and outbreak occurrence

Degree-days above 0 C, the maximum temperature smoothed using a five-day moving average, and the number of days between max temp and the case and the control onset day

Nichols [13]; 2009

Association between precipitation and outbreaks of drinking water related disease.

Cumulative precipitation in four time periods prior to each outbreak

Readings of relevant weather stations

Time-stratified matched case-crossover analysis

Water source, season, water supply considered as effect modifiers

Positive association with excess precipitation over the previous week and low precipitation in the three weeks before the week of the outbreak.

Excessive precipitation: total number of days in which the precipitation exceeded a certain upper limit

Greater risk in groundwater, spring and private water supplies. These interactions were non-significant when including them together in a model, suggesting confounding.

Cases of infection

Tornevi [22]; 2013

Determine if variation in the incidence of acute gastrointestinal illnesses is associated with upstream precipitation

Daily precipitation

Readings of relevant weather stations

Poisson regression (with nonlinear distributed lag function)

Control for seasonality

Heavy precipitation was associated with increased calls.

Louis [18]; 2005

Investigate the relationship between environmental conditions and Campylobacter infections

Precipitation divided into three categories up and down a certain threshold

Readings of relevant weather stations

Time series analysis

Seasonality and water supply also included in the study

Campylobacter rates were correlated with temperature

Linear regression

No association with precipitation

No association with surface water.

Daily max and minimum temperature

Eisenberg [15]; 2013

Examine the relationship between cholera and precipitation in Haiti including statistical and dynamic models

Cumulative daily totals for precipitation

Rain gauges and satellite measurements

Statistical modeling

Control for seasonality

All analysis support a strong positive association between precipitation and cholera incidence in Haiti

Quasi-Poisson regression (with nonlinear distributed lag function)

Granger Causality Wald Test

Case-crossover analysis

Dynamic modeling

White [25]; 2009

Association between environmental factors and campylobacter infection

Precipitation

Readings of relevant weather stations

Poisson regression

Control for seasonality

Weekly incidence was associated with increasing mean temperature.

Temperature

Time-stratified matched case-crossover analysis

No association with precipitation

Drayna [26]; 2010

Association between precipitation and acute gastrointestinal illness in pediatric population

Total daily precipitation, extreme considered above a certain percentile

Readings of relevant weather stations

Autoregressive moving average (ARMA) model

Control for seasonality

Positive association between precipitation and daily visits

Teschke [21]; 2010

Association between the incidence of intestinal infections and environmental factors

Precipitation categories according accumulated millimeters of rain over certain periods

Readings of relevant weather stations

Logistic regression

Season, water supply, water source, disinfection and well depth included as variables

The association between incidence of disease and precipitation did not remain when controlling for other variables

Water chlorination was associated with reduced physician visits

Two water systems with the highest proportion of surface water had increased incidence

Private well water and well depth were not associated with increased risk

Harper; [16]; 2011

Association between weather variables and gastrointestinal-related clinic visits

Total daily precipitation

Readings of relevant weather stations

Zero-inflated Poisson regression

Control for seasonality

Positive associations were observed between high levels of water volume input (precipitation + snowmelt) and IGI clinic visits.

Daily average temperature

No association with temperature

Hashizume [27]; 2007

Impact of precipitation and temperature on the number of non-cholera diarrhea cases

Daily Precipitation, weekly means Above/below certain threshold

Records from national level

Poisson regression

Control for seasonality

Non-cholera diarrhea cases increased both above and below a threshold level with high and low precipitation in the preceding weeks. Cases also increased with higher temperature.

Daily minimum/maximum temperature, weekly means

Vollaard [23]; 2004

Determine risk factors for typhoid and paratyphoid fever in an endemic area

Precipitation

Interviews with the participants

Logistic regression

-

Flooding was associated with the occurrence of paratyphoid fever. Flooding was not associated with typhoid fever.

Flooding: defined as inundation of the house of a participant in the 12 months preceding the investigation

Kelly-Hope [33]; 2007

Environmental risk factors of cholera, shigellosis and typhoid fever infections

Precipitation

Worldwide maps generated by the interpolation of information from ground-based weather stations

Linear regression

Type of water supply

Shigellosis and cholera were positively associated with precipitation

Temperature

Typhoid fever was not associated with precipitation

No association with temperature

Emch [31]; 2008

Association between cholera and the local environment

Monthly precipitation

Readings of relevant weather stations

Ordered probit model to analyze ordinal outcome (Bangladesh). Probit model for dichotomous outcome. (Vietnam).

-

Temperature and precipitation not associated with cholera

Monthly temperature

Constantin de Magny [30]; 2008

Association of environmental signatures with cholera epidemics

Monthly precipitation

Merged satellite/gauge estimates

Quasi Poisson regression

Control for seasonality

Positive association between cholera and increased precipitation in Kolkata.

No association cholera and increased precipitation in Matlab

Wang [24]; 2012

Impact of meteorological variations on para/typhoid fever (PTF)

Monthly cumulative precipitation

Records from national level

-Spearman’s rank correlation analysis to analyze the association between the infection incidence and the weather variables

-

Temperature and precipitation were positively associated with the monthly incidence of PTF

Wavelet analysis and wavelet coherence to detect the variation of periodicity over time

Monthly average temperature

Chen [29]; 2012

Association between precipitation and distribution patterns of various infectious diseases, including water-borne

Precipitation coded as: regular, torrential and extreme torrential

Readings of relevant weather stations

Poisson regression (with GAM and GAMM)

Control for seasonality using monthly indicator

Daily extreme precipitation levels correlated with the infections

Jutla, [32]; 2013

Seek an understanding between hydro-climatological processes and cholera in epidemic regions

Precipitation and temperature above/below average during the previous months

Reports from the government

Spearman’s rank correlation analysis

-

India. -Odds of cholera occurring were significantly higher when the temperature was above climatological average over the previous two months. Odds of cholera outbreak was higher when above average precipitation occurs.

satellite sensors

Daily precipitation and temperature

Haiti: Strong correlation between precipitation and cholera cases.

Singh [20]; 2001

Association between climate variability and incidence of diarrhea

Precipitation : dichotomous variable above/below certain threshold

Gridded data from international institute

Linear regression Poisson

Control for seasonality

Positive association between annual average temperature and rates of diarrhea

Extremes of precipitation were independently associated with increased reports of diarrhea

Annual average temperature

regression

Hu [17]; 2007

Impact of weather variability on the transmission of cryptosporidiosis.

Monthly total precipitation

Records from national level

Poisson regression

Control for seasonality

Association between cryptosporidiosis and monthly maximum. temperature

Seasonal auto-regression integrated moving average (SARIMA)

Explore the difference in the predictive ability between Poisson regression and SARIMA models

Monthly mean minimum/maximum temperature

Rind [34]; 2010

Association between climate factors and local differences in campylobacteriosis rates

Monthly mean maximum total precipitation

Records from research center

Linear regression

Water supply, seasonality

No association found between temperature and precipitation and campylobacteriosis rates

Monthly mean maximum daily temperatures

Britton [28]; 2010

Association between precipitation and ambient temperature and notifications of cryptosporidiosis and giardiasis

Average annual precipitation to evaporation ratio

Mathematical surfaces fitted to long run average climate station data

Negative binomial regression

Water supply

Giardiasis: positive association between precipitation and temperature.

Cryptosporidiosis: positive association with precipitation and negative association with temperature. The effect of precipitation was modified by the quality of the domestic water supply

Average annual temperature

 

Sasaki [19]; 2009

Association between precipitation patterns and cholera outbreaks.

Daily precipitation data

Records from national level and readings of relevant weather stations

Spearman rank correlation analysis

 

Increased precipitation was associated with the occurrence of cholera outbreaks

Literature Review (n = 24).

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

Article selection strategy.

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.

Abbreviations

WHO: 

World Health Organization

ECDC: 

European Centre for Disease Prevention and Control

Declarations

Acknowledgements

This review has been performed as part of the ECDC commissioned project “Waterborne outbreaks and climate change” (OJ/06/02/2012-PROC/2012/011).

We would like to thank Vidar Lund, Preben Ottesen and Wenche Jacobsen from the Norwegian Institute of Public Health for their input on the search strategy; and Margareta Löfdahl from Public Health Agency of Sweden for her input on the manuscript.

Authors’ Affiliations

(1)
Department of Infectious Disease Epidemiology, Norwegian Institute of Public Health
(2)
Oslo Centre for Statistics and Epidemiology, Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo
(3)
European Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Prevention and Control
(4)
European Centre for Disease Prevention and Control
(5)
Gastrointestinal, Emerging and Zoonotic Diseases Department, Public Health England
(6)
Norwich Medical School, University of East Anglia
(7)
Department of Hygiene & Epidemiology, University of Thessaly

References

  1. Moors E, Singh T, Siderius C, Balakrishnan S, Mishra A. Climate change and waterborne diarrhoea in northern India: impacts and adaptation strategies. Sci Total Environ. 2013;468–469(Suppl):S139–51.View ArticleGoogle Scholar
  2. Semenza JC, Menne B. Climate change and infectious diseases in Europe. Lancet Infect Dis. 2009;9:365–75.View ArticleGoogle Scholar
  3. Schijven J, Bouwknegt M, Husman AM, Rutjes S, Sudre B, Suk JE, et al. A decision support tool to compare waterborne and foodborne infection and/or illness risks associated with climate change. Risk Analysis. 2013;33:2154–67.View ArticleGoogle Scholar
  4. Semenza JC, Herbst S, Rechenburg A, Suk JE, Hoser C, Schreiber C, et al. Climate change impact assessment of food- and waterborne diseases. Crit Rev Environ Sci Technol. 2012;42:857–90.View ArticleGoogle Scholar
  5. Semenza JC, Suk JE, Estevez V, Ebi KL, Lindgren E. Mapping climate change vulnerabilities to infectious diseases in Europe. Environ Health Perspect. 2012;120(3):385–92.View ArticleGoogle Scholar
  6. Cann KF, Thomas DR, Salmon RL, Wyn-Jones AP, Kay D. Extreme water-related weather events and waterborne disease. Epidemiol Infection. 2013;141:671–86.View ArticleGoogle Scholar
  7. Atlas of Health and Climate. Joint publication World Health Organization and Meteorogical World Organization. 2012. Available at http://www.who.int/globalchange/publications/atlas/en/.
  8. European Centre for Disease Control. Climate change. Climate change in Europe. Available at http://www.ecdc.europa.eu/en/healthtopics/climate_change/Pages/index.aspx.
  9. Beniston M, Stoffel M, Harding R, Kernan M, Ludwig R, Moors E, et al. Obstacles to data access for research related to climate and water: implications for science and EU policy-making. Environ Sci Pol. 2012;17:41–8.View ArticleGoogle Scholar
  10. Semenza JC, Houser C, Herbst S, Rechenburg A, Suk JE, Frechen T, et al. Knowledge mapping for climate change and food- and waterborne diseases. Crit Rev Environ Sci Technol. 2012;42:378–411.View ArticleGoogle Scholar
  11. Thomas KM, Charron DF, Waltner-Toews D, Schuster C, Maarouf AR, Holt JD. A role of high impact weather events in waterborne disease outbreaks in Canada, 1975–2001. Int J Environ Health Res. 2006;16:167–80.View ArticleGoogle Scholar
  12. Yang K, LeJeune J, Alsdorf D, Lu B, Shum CK, Liang S. Global distribution of outbreaks of water-associated infectious diseases. PLoS Neglected Tropical Diseases [electronic resource]. 2012;6:e1483.View ArticleGoogle Scholar
  13. Nichols G, Lane C, Asgari N, Verlander NQ, Charlett A. Rainfall and outbreaks of drinking water related disease and in England and Wales. J Water Health. 2009;7:1–8.View ArticleGoogle Scholar
  14. Curriero FC, Patz JA, Rose JB, Lele S. The association between extreme precipitation and waterborne disease outbreaks in the United States, 1948–1994. Am J Public Health. 2001;91:1194–9.View ArticleGoogle Scholar
  15. Eisenberg MC, Kujbida G, Tuite AR, Fisman DN, Tien JH. Examining rainfall and cholera dynamics in Haiti using statistical and dynamic modeling approaches. Epidemics. 2013;5:197–207.View ArticleGoogle Scholar
  16. Harper SL, Edge VL, Schuster-Wallace CJ, Berke O, McEwen SA. Weather, water quality and infectious gastrointestinal illness in two Inuit communities in Nunatsiavut, Canada: potential implications for climate change. EcoHealth. 2011;8:93–108.View ArticleGoogle Scholar
  17. Hu W, Tong S, Mengersen K, Connell D. Weather variability and the incidence of cryptosporidiosis: comparison of time series poisson regression and SARIMA models. Ann Epidemiol. 2007;17:679–88.View ArticleGoogle Scholar
  18. Louis VR, Gillespie IA, O’Brien SJ, Russek-Cohen E, Pearson AD, Colwell RR. Temperature-driven campylobacter seasonality in England and Wales. Appl Environ Microbiol. 2005;71:85–92.View ArticleGoogle Scholar
  19. Sasaki S, Suzuki H, Fujino Y, Kimura Y, Cheelo M. Impact of drainage networks on cholera outbreaks in Lusaka, Zambia. Am J Public Health. 2009;99:1982–7.View ArticleGoogle Scholar
  20. Singh RB, Hales S, de Wet N, Raj R, Hearnden M, Weinstein P. The influence of climate variation and change on diarrheal disease in the Pacific Islands. Environ Health Perspect. 2001;109:155–9.View ArticleGoogle Scholar
  21. Teschke K, Bellack N, Shen H, Atwater J, Chu R, Koehoorn M, et al. Water and sewage systems, socio-demographics, and duration of residence associated with endemic intestinal infectious diseases: a cohort study. BMC Public Health. 2010;10:767.View ArticleGoogle Scholar
  22. Tornevi A, Axelsson G, Forsberg B. Association between precipitation upstream of a drinking water utility and nurse advice calls relating to acute gastrointestinal illnesses. PLoS ONE [Electronic Resource]. 2013;8:e69918.View ArticleGoogle Scholar
  23. Vollaard AM, Ali S, Van Asten HAGH, Widjaja S, Visser LG, Surjadi C, et al. Risk factors for typhoid and paratyphoid fever in Jakarta, Indonesia. J Am Med Assoc. 2004;291:2607–15.View ArticleGoogle Scholar
  24. Wang LX, Li XJ, Fang LQ, Wang DC, Cao WC, Kan B. Association between the incidence of typhoid and paratyphoid fever and meteorological variables in Guizhou, China. Chin Med J. 2012;125:455–60.Google Scholar
  25. White AN, Kinlin LM, Johnson C, Spain CV, Ng V, Fisman DN. Environmental determinants of campylobacteriosis risk in Philadelphia from 1994 to 2007. EcoHealth. 2009;6(2):200–8.View ArticleGoogle Scholar
  26. Drayna P, McLellan SL, Simpson P, Li SH, Gorelick MH. Association between rainfall and pediatric emergency department visits for acute gastrointestinal illness. Environ Health Perspect. 2010;118:1439–43.View ArticleGoogle Scholar
  27. Hashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque AS, Hayashi T, et al. Association between climate variability and hospital visits for non-cholera diarrhoea in Bangladesh: effects and vulnerable groups. Int J Epidemiol. 2007;36:1030–7.View ArticleGoogle Scholar
  28. Britton E, Hales S, Venugopal K, Baker MG. The impact of climate variability and change on cryptosporidiosis and giardiasis rates in New Zealand. J Water Health. 2010;8:561–71.View ArticleGoogle Scholar
  29. Chen MJ, Lin CY, Wu YT, Wu PC, Lung SC, Su HJ. Effects of extreme precipitation to the distribution of infectious diseases in Taiwan, 1994–2008. PLoS ONE [Electronic Resource]. 2012;7:e34651.View ArticleGoogle Scholar
  30. Constantin de Magny G, Murtugudde R, Sapiano MR, Nizam A, Brown CW, Busalacchi AJ, et al. Environmental signatures associated with cholera epidemics. Proc Natl Acad Sci U S A. 2008;105:17676–81.View ArticleGoogle Scholar
  31. Emch M, Feldacker C, Yunus M, Streatfield PK, DinhThiem V, Canh do G, et al. Local environmental predictors of cholera in Bangladesh and Vietnam. Am J Trop Med Hygiene. 2008;78:823–32.Google Scholar
  32. Jutla A, Whitcombe E, Hasan N, Haley B, Akanda A, Huq A, et al. Environmental factors influencing epidemic cholera. Am J Trop Med Hygiene. 2013;89:597–607.View ArticleGoogle Scholar
  33. Kelly-Hope LA, Alonso WJ, Vu DT, Dang DA, Do GC, Lee H, et al. Geographical distribution and risk factors associated with enteric diseases in Vietnam. Am J Trop Med Hyg. 2007;76:706–12.Google Scholar
  34. Rind E, Pearce J. The spatial distribution of campylobacteriosis in New Zealand, 1997–2005. Epidemiol Infection. 2010;138:1359–71.View ArticleGoogle Scholar
  35. Wheeler JG, Sethi D, Cowden JM, Wall PG, Rodrigues LC, Tompkins DS, et al. Study of infectious intestinal disease in England: rates in the community, presenting to general practice, and reported to national surveillance. The Infectious Intestinal Disease Study Executive. BMJ. 1999;318:1046–50.View ArticleGoogle Scholar
  36. Environmental European Agency. Climate change, impacts and vulnerability in Europe 2012. AN indicator-based report. EEA report. No 12/2012. ISSN 1725–9177. http://www.eea.europa.eu/publications/climate-impacts-and-vulnerability-2012/.

Copyright

© Guzman Herrador et al.; licensee BioMed Central. 2015

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement