Download A literature review of analytical research studies assessing

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. 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/.
World health organization. Climate change and health. Fact sheet n 266. October 2012.
http://www.who.int/mediacentre/factsheets/fs266/en/index.html.
Semenza JC, Menne B: Climate change and infectious diseases in Europe. The Lancet
infectious diseases 2009, 9(6):365-375.
Cann KF, Thomas DR, Salmon RL, Wyn-Jones AP, Kay D: Extreme water-related weather
events and waterborne disease. Epidemiology and infection 2013, 141(4):671-686.
http://www.who.int/globalchange/publications/atlas/report/en/index.html
AoHaCJpWHOaMWOIAa.
01/12/2012 ECfDCCcCciEhweeeehccPiaA.
Britton E, Hales S, Venugopal K, Baker MG: The impact of climate variability and change on
cryptosporidiosis and giardiasis rates in New Zealand. Journal of water and health 2010,
8(3):561-571.
Codeco CT, Lele S, Pascual M, Bouma M, Ko AI: A stochastic model for ecological systems
with strong nonlinear response to environmental drivers: application to two water-borne
diseases. Journal of the Royal Society, Interface / the Royal Society 2008, 5(19):247-252.
Constantin de Magny G, Murtugudde R, Sapiano MR, Nizam A, Brown CW, Busalacchi AJ,
Yunus M, Nair GB, Gil AI, Lanata CF et al: Environmental signatures associated with cholera
epidemics. Proceedings of the National Academy of Sciences of the United States of America
2008, 105(46):17676-17681.
Curriero FC, Patz JA, Rose JB, Lele S: The association between extreme precipitation and
waterborne disease outbreaks in the United States, 1948-1994. American journal of public
health 2001, 91(8):1194-1199.
Drayna P, McLellan SL, Simpson P, Li SH, Gorelick MH: Association between rainfall and
pediatric emergency department visits for acute gastrointestinal illness. Environmental
health perspectives 2010, 118(10):1439-1443.
Emch M, Feldacker C, Yunus M, Streatfield PK, DinhThiem V, Canh do G, Ali M: Local
environmental predictors of cholera in Bangladesh and Vietnam. The American journal of
tropical medicine and hygiene 2008, 78(5):823-832.
Emch M, Yunus M, Escamilla V, Feldacker C, Ali M: Local population and regional
environmental drivers of cholera in Bangladesh. Environmental health : a global access
science source 2010, 9:2.
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(1):93-108.
Hashizume M, Armstrong B, Hajat S, Wagatsuma Y, Faruque AS, Hayashi T, Sack DA:
Association between climate variability and hospital visits for non-cholera diarrhoea in
Bangladesh: effects and vulnerable groups. International journal of epidemiology 2007,
36(5):1030-1037.
Hu W, Tong S, Mengersen K, Connell D: Weather variability and the incidence of
cryptosporidiosis: comparison of time series poisson regression and SARIMA models. Annals
of epidemiology 2007, 17(9):679-688.
14
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
Kelly-Hope LA, Alonso WJ, Thiem VD, Anh DD, Canh do G, Lee H, Smith DL, Miller MA:
Geographical distribution and risk factors associated with enteric diseases in Vietnam. The
American journal of tropical medicine and hygiene 2007, 76(4):706-712.
Nichols G, Lane C, Asgari N, Verlander NQ, Charlett A: Rainfall and outbreaks of drinking
water related disease and in England and Wales. Journal of water and health 2009, 7(1):1-8.
Rind E, Pearce J: The spatial distribution of campylobacteriosis in New Zealand, 1997-2005.
Epidemiology and infection 2010, 138(10):1359-1371.
Sasaki S, Suzuki H, Fujino Y, Kimura Y, Cheelo M: Impact of drainage networks on cholera
outbreaks in Lusaka, Zambia. American journal of public health 2009, 99(11):1982-1987.
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. Environmental health
perspectives 2001, 109(2):155-159.
Teschke K, Bellack N, Shen H, Atwater J, Chu R, Koehoorn M, MacNab YC, Schreier H, IsaacRenton JL: 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.
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.
International journal of environmental health research 2006, 16(3):167-180.
Wang L LX, Wang D, Cao W, Kan B. Association between the incidence of typhoid and
paratyphoid fever and meteorological variables in Guizhou, China. Chinese Medical Journal
2012; 125 (3); 455-460.
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):200-208.
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 2012, 6(2):e1483.
Chen M LC, Wu, Y, Wu P, Lung S, Su H. Effects of Extreme Precipitation to the Distribution of
Infectious Diseases in Taiwan, 1994–2008. PLoS ONE 7(6): e34651.
doi:10.1371/journal.pone.0034651.
Wheeler JG, Sethi D, Cowden JM, Wall PG, Rodrigues LC, Tompkins DS, Hudson MJ, Roderick
PJ: 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(7190):1046-1050.
Dominici F, McDermott A, Zeger SL, Samet JM: On the use of generalized additive models in
time-series studies of air pollution and health. American journal of epidemiology 2002,
156(3):193-203.
Hajat S, Haines A: Associations of cold temperatures with GP consultations for respiratory
and cardiovascular disease amongst the elderly in London. 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