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A literature review of analytical research studies assessing associations between weather events and waterborne outbreaks or illness Bernardo Guzman Herrador1,*, Birgitte Freiesleben de Blasio1,2, Emily MacDonald1,3, Gordon Nichols4, Bertrand Sudre4, Line Vold1, Jan C. Semenza4, Karin Nygård1 1. Department of Infectious Disease Epidemiology, Norwegian Institute of Public Health, Oslo, Norway 2. Department of Biostatistics, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway 3. European Programme for Intervention Epidemiology Training (EPIET), European Centre for Disease Prevention and Control, Stockholm, Sweden 4. European Centre for Disease Prevention and Control, Stockholm, Sweden *Corresponding author: Authors emails: Bernardo R. Guzman-Herrador: [email protected] Birgitte Freiesleben de Blasio: [email protected] Emily MacDonald: [email protected] Gordon Nichols: [email protected] Bertrand Sudre: [email protected] Line Vold: [email protected] Jan C. Semenza: [email protected] Karin Nygård: [email protected] Abstract Several waterborne outbreaks have been described where extreme weather events have preceded the outbreak. However, despite the abundance of environmental and epidemiologic surveillance data available, these data are often not linked, preventing the public health community from gaining more comprehensive understanding of the impact of climate events on waterborne illness. To document the current knowledge on this topic, we performed a literature review of analytical research studies that have combined epidemiological and environmental surveillance data in order to analyze associations between weather events and waterborne outbreaks or illness. A search of Ovid MEDLINE and EMBASE was conducted on October 2012, using search terms related to “water source”, “waterborne infections” and “weather/climate conditions”. Results were limited to those research studies published in English between January 2001 and January 2013. Twenty-one relevant articles were identified, predominantly from Asia and North-America, with only one from Europe. Four articles used waterborne outbreaks as study units, while the remaining 17 used number of cases of specific types of wateborne infections, most commonly cholera (six studies), followed by cryptosporidiosis (two studies). Rainfall and air temperature were the weather events most frequently studied. Count model regression analysis was the most frequently used method when the study units were cases of infection (used in seven studies), while case-crossover analysis was most frequently used when the study units were outbreaks (two studies). Our review showed that there are few studies performed on the influence of climate on waterborne infections, especially in Europe. This highlights the need to generate more scientific evidence of the linkages between health and climate factors. Key words Review, extreme weather event, waterborne, outbreak, precipitation Background Climate change is occurring globally and in Europe. Water scarcity is expected to become an important challenge in Mediterranean countries, especially during summers. Increased frequency of heavy rainfall events is predicted for central and northern Europe, and higher overall levels of precipitation are anticipated in northern Europe, particularly during winters [1]. The degree of future impacts depends on the magnitude of climate change and on socio-economic and environmental co-factors. Future impacts can be substantially reduced by mitigation policies and adaptation strategies at national and transnational level [2]. Climate change affects several social determinants of health, such as clean air, safe drinking water, sufficient food and secure shelter [3]. In addition, many infectious agents and their vector and reservoir cycles are sensitive to climatic conditions [4]. In areas where precipitation or extreme flooding is expected to increase, the occurrence of waterborne infections could subsequently increase [2]. Aging water treatment and distribution systems and sewage systems are particularly susceptible to extreme weather events, which increases the vulnerability of the drinking water supply. A recently published review examined waterborne outbreaks following extreme water-related weather events. The review suggests that waterborne disease outbreaks are associated with extreme climatic events. The review also concludes that improving the understanding of the impact extreme water events have on waterborne diseases will be an important step forwards in finding ways to mitigate the risk and reduce harmful effects on the health of the population and waterborne diseases burden [5]. 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 [6]. Despite the abundance of environmental and epidemiologic data, these are often not linked, thereby preventing the scientific community from gaining more comprehensive understanding of the multi-causal pathways in question [7]. To document the currently available knowledge on this topic, we performed a literature review of relevant analytical research studies that have combined available epidemiological and environmental surveillance data to determine associations between weather events and waterborne outbreaks or illness. Review Methods Search strategy, keywords and inclusion/exclusion criteria The keywords used for searching relevant articles included both general and specific terms related to water, waterborne infections and weather/climate conditions (Table 1). These three groups of keywords were combined and the search strategy was run on October 2012 and repeated in January 2013 in the medical databases Ovid MEDLINE and EMBASE. Titles and abstracts of publications were searched for keywords. The search was limited to studies involving humans, published in English between January 2001 and January 2013. In addition, a snowballing technique was used to review the reference lists of selected studies to identify additional articles. 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 criteria specified in Table 2. The full text of relevant studies were retrieved and assessed for eligibility using the inclusion and exclusion criteria defined in Table 2. 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. The following data were extracted from the articles: first author, publication year, main objective, location of study, study period, waterborne pathogen studied, infection unit counted (i.e. number of cases of disease, number of outbreaks), data sources, types of weather events studied, methods used (explanatory and outcome variables and statistical methods used) and main result or conclusion. Results Once duplicates were removed, a total of 1,418 titles were obtained using the initial search terms. Following screening of titles, results were limited to 435 articles. After screening abstracts for relevance, 72 full-text articles were read full text, of which 53 were excluded. Two articles were included after checking the reference lists of the already selected articles. In total, 21 analytical research articles [8-28], in which the association between weather events and waterborne outbreaks or trends in waterborne infections had been analyzed, were included in the literature review (Figure 1). All but two of the research articles included were published between 2006 and 2012, while the remainder were published earlier, both in 2001. The study periods analyzed ranged from 3 to 90 years with a median of 18 years. There were seven studies from Asia, which included number of countries: China (n=1), Taiwan (n=1), India (n=1), Vietnam (n=2) and Bangladesh (n=4). Six articles were from North America, including Canada (n=3) and USA (n=3). Four studies took place in Oceania. One study took place in Africa (Zambia), and one in Europe (England and Wales). Two articles presented studies that included data from more than one continent. The common goal of all the studies was to test associations between weather events, mainly extreme events (as defined by the study authors), and waterborne infections or outbreaks. Table presents an overview of the types of explanatory and outcome variables, the statistical analysis used in the studies and their main results or conclusions. Almost all studies found a significant association between the explanatory and outcome variables. The strength of this association varied depending on the way the weather variables had been categorized or the time lag between weather event and occurrence of a given waterborne disease chosen. In two studies no association between weather events and waterborne illness were found [20, 23]. In a third study finding or not an association depended on the statistical method used [26]. Waterborne infections (outcome variables) and data sources Four studies used waterborne outbreaks as outcome variables [11, 19, 24, 27], while the remaining 17 used number of cases of specific type of infections. Cholera was the most frequently studied infection (6 studies), followed by cryptosporidiosis (2 studies). Other infections, such as shigellosis, leptospirosis, campylobacteriosis, giardiasis, non-cholera diarrhea and typhoid and paratyphoid fever, were included in individual studies. Different data sources were used to gather the data on waterborne infections. The most frequent approach was to use surveillance data already available through national, regional or local surveillance systems. Health registries or records from relevant hospitals or clinics were also frequently used. Other less commonly used sources were databases designed for research purposes or compilations of published outbreaks. Weather events /environmental factors (explanatory variables) and data source Rainfall was the weather event most frequently studied (n=20), followed by air temperature (n=12), and oceanographic or river properties (n=4) such as sea surface temperature, ocean chlorophyll concentration or sea surface height. Other environmental factors taken into account in the studies were stream flow, humidity, UV index forecast and snow depth. Weather and environmental data were mainly gathered from national or regional meteorological or water departments, or by weather stations located in the relevant areas. The definition of extreme event varied between the studies. There were also different ways of categorizing weather-related variables, according to the amount of rainfall (i.e. four groups: 010mm, >10 to≤ 20mm, >20 to ≤40mm and >40 mm; accumulated; smoothed using a five-day moving average; dichotomical, above and below a threshold; total in a given period; exceeded the upper limit of the 95% reference range; regular: <130 mm, heavy: 130-200mm, torrential: 201-350mm, and extreme torrential: >351mm); or range of air temperature (maximum air smoothed temperature using a five-day moving average; degree days above 0 C). Aggregation of rainfall and temperature by week, month or year was used on several occasions due to data availability constraints or absence of linear correlation between the weather variables and the waterborne outbreak/infection variables Statistical analysis Count model regression analysis was most frequently used method when the study units were cases of infection, while case-crossover analysis was most frequently used when the study units were outbreaks: -Count model regression analysis: This was the most common analysis performed and was used in eight studies, one with outbreaks [27] and seven with incidence of infections [8, 10, 15-17, 22, 26, 28],. The Poisson distribution is the natural starting point for modeling counts of rare events occurring during an interval of time. The distribution has only a single parameter µ equal to the mean and the variance. In some cases the Poisson regression model was adjusted to account for: Overdispersion: Empirical count data typically exhibit large variability implying that the variance exceeds the mean. Overdispersion was adjusted for by estimating an additional dispersion parameter using quasi-Poisson regression models [10] or more formally with use of negative binomial (NB) regression models where the variance is given by µ + a µ 2 and a≥0 is the overdispersion parameter. For a=0, the NB distribution reduces to the Poisson distribution [8]. Excess zero counts: Zero-inflated Poisson regression models were used in dealing with excess of zero counts in the observations [15, 27]. In this case the excess zero counts is estimated independently from the count process using logistic regression Seasonality: Adequate control for the presence of a seasonal component is necessary before studying an outbreak-weather association. Seasonal trend decomposition was conducted by adding a sinusoid component [22] or trend and seasonal components, into the Poisson regression [17] , or by using Fourier terms [16, 26] or local nonparametric regression (LOESS) [15]. Temporal correlations: Autocorrelation in time was handled by using generalized additive models (GAM) with time and sometimes other variables related to weather were added as smoother variables [28]. In the GAM analysis, time and other predictor variables related to weather are separated into sections (separated by ‘knots’), and then polynomial functions are fitted to each section separately. The smoothing procedure works in a similar way as locally weighted regression. One study explored the difference in the predictive ability between Poisson regression and autoregressive integrated moving average models with seasonality (SARIMA) [17]. -Case-crossover analysis: This approach is a common way to investigate the effects of acute, rare and transient outcomes with recurrent exposures. The method was used in three studies, two with outbreaks [19, 24] and one with cases of campylobacteriosis [26]. In the analysis, the weather exposure at the location of an outbreak is compared with the exposures at the same location and same time of the year during control periods in years without an outbreak using 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. Discussion Only twenty-one relevant research studies combining weather data with infectious disease or outbreak data to assess associations were identified in the literature search. Of these, only one study presented European data [19], resulting in limited evidence-based information on the influence of climate on waterborne infections in this region. The main findings and conclusions from the studies indicate a significant association between extreme weather events and an increase in waterborne infections or outbreaks. Data handling prior to the analysis The use of surveillance data presents a challenge in statistical analyses. 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 [29]. However, in terms of estimating the association between weather events and illness or outbreaks, underreporting will only be the cause of bias if reporting is correlated with weather and hydrological variables [2]. We want to emphasize four points that were identified when reviewing the articles: -The case definition of extreme weather event varied from study to study. An association might be found more easily depending on the threshold level that was used to classify extreme rainfall 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. -Small data sets in terms of number of observations limit the ability to achieve statistical significance. 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 -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 [15]. 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 [8]. -The optimal choice of time lag between weather event and occurrence of a given waterborne disease event should be identified, as these events generally do not occur simultaneously [10]. Moreover, using the same time lag for all cases linked to weather events is not possible given the variation in incubation periods for specific diseases and delays in notification. Understanding all these issues is necessary in order to select the time lag most relevant for a given disease. The analysis Count model regression was the technique most frequently used in the studies included in the review, mainly in those studies in which the study unit was cases of infection. This type of analysis has been increasingly used in epidemiological research, not only for infectious diseases but also when studying other health risks such as air pollution and health, or cardiovascular diseases [30, 31]. It allows for nonparametric adjustments for nonlinear confounding effects of seasonality, trends or weather variables [30, 31]. SARIMA models employed in time series analysis have been traditionally used in economics and have become well established in the commercial and industrial fields. Hu W. et al explored the difference in the predictive ability between Poisson regression of times series data and SARIMA models in one study included in the review. Although both methods showed a clear association between weather and cryptosporidiosis, SARIMA models allowed for more complex description of seasonality and autocorrelation structure of the series and seemed to be more acceptable in regards to goodness of fit, conformity with assumptions and predictive accuracy. However, a SARIMA model needs a large amount of data and is based on the assumption of normality, thus being inappropriate for rare outcomes with small counts or periods where no cases occur [17]. The time stratified case-crossover analysis, the most frequently used when the study units were outbreaks, is intended for the study of a transient effect of an irregular exposure on the occurrence of a rare acute outcome. This design uses real weather data for all control time periods, instead of simulating such events using Monte Carlo simulation [11]. The controls are matched to the onset day of the outbreaks, controlling time-variant confounders [24]. Misclassification of cases and controls is possible if control periods may have coincided with unreported outbreaks. Together with time series analysis, case-crossover analysis has the advantage of being able to control for confounding by seasonal oscillating environmental factors [26]. In addition, when having limited number of observations or units in the study (i.e. outbreaks), case-crossover designs have the advantage of being able to increase the power of the sample, by choosing up to four controls [24]. This highlights that the decision to use outbreaks or individual cases as the study unit will have an implication on the choice of analysis strategy. Conclusions The literature on the association between weather events and waterborne outbreak is sparse. The results of the present review show there is potential to generate more scientific evidence on the influence of climate on waterborne outbreaks in Europe using already available surveillance data sources and reports. A variety of methods can be used to assess the potential impact of climate on waterborne infections, providing different insights into understanding this association. Despite the range of options of data sources and statistical analysis, all methodologies present limitations and challenges that have to be addressed or controlled in order to interpret the results correctly. List of abbreviations WHO World Health Organization ECDC European Centre for Disease Prevention and Control GAM Generalized additive model ARIMA Autoregressive integrated moving average models SARIMA Autoregressive integrated moving average models with seasonality Competing interests None Author contributions BGH, BFB, EM, KN and LV conceived the study question and the search strategy. JS, BS and GN provided input to the methods proposal and search strategy. EM and BGH ran the search strategy and reviewed the titles, abstracts and full texts. BFB reviewed the full texts. All authors participated in manuscript writing and revision. All authors read and approved the final manuscript. Acknowledgements This systematic 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 the Swedish Institute for Communicable (Smittskyddsinstitutet, SMI) for her input on the manuscript. Diseases Control References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. European Centre for Disease Prevention and Control. Assessing the potential impacts of climate change on food- and waterborne diseases in Europe. Stockholm: ECDC; 2012. Available at http://ecdc.europa.eu/en/publications/Publications/1203-TER-Potentialimpacts-climate-change-food-water-borne-diseases.pdf. Environmental European Agency. 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International journal of epidemiology 2002, 31(4):825-830. 15 16 Tables Table 1 Keywords used for searching in the literature Water source Waterborne infections General terms Water; Water supply Waterborne; Gastroenteritis; Outbreak; Diseases outbreaks Specific terms Groundwater; Surface water; Water disinfection; Water purification; Sewage Campylobacteriosis; Escherichia coli; Giardiasis; Cholera; Cryptosporidiosis; Hepatitis A; Salmonellosis; Shigellosis; Tularemia; Typhoid fever; Norovirus Weather/Climate conditions Climate; Climate change; Weather Precipitation; rain*; Temperature; Humidity; Seasonality; Flood*; Drought*; Snow 17 Table 2 Inclusion and exclusion criteria Inclusion Criteria Exclusion Criteria Analytical research studies in which the main objective was To estimate the association between weather events and waterborne outbreaks or trends in waterborne infections. Study type: -Outbreak reports reporting a single outbreak event. -Pure discussion papers or reviews without specific studies or analytical 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 -Estimation of the association between weather events or environmental conditions and concentration of microorganisms in water, but without data on human illness or outbreaks presented in the paper. -Main route of transmission other than drinking water. -Study of seasonality not related to weather or climate data. Search strategy limited to: Population: -Humans Publication year: January 2001-January 2013 Language: -English 18 Additional file 1: Table 3 Region, study period, waterborne infections and weather events studied in the included articles by type of study unit (1-4= outbreaks; 5-21=cases of disease). Literature Review (n=21) Additional file 2: Table 4 Objective, results/conclusions and methods used in the included articles by type of study unit (Id 1-4= outbreaks; 5-21=cases of disease). Literature Review (n=21) Figures Figure 1 Flow chart of the search strategy 19 Figure 1 Titles screened fo relevance (n=1,418) Titles excluded (n=983) Abstracts screened (n=435) Abstracts excluded (n=363) Full-text articles assesed for eligibility (n=72) Full text articles included when checking the reference lists (n=2) Research studies included in the review (n=21) Full-text articles excluded (n=53) 29 microbiological investigation, no human cases 13 outbreaks reports presenting one single outbreak 7 reviews 1 no statiscal methods (survey) 1 association with weather not evaluated 2 main transmission route not drinking water Additional files provided with this submission: Additional file 1: additional file_1.pdf, 337K http://www.ehjournal.net/imedia/7602993121303385/supp1.pdf Additional file 2: Additional file_2.pdf, 292K http://www.ehjournal.net/imedia/1063601482130338/supp2.pdf