data on 1,276 cases of human stec infection reported in ontario from january 1996 to december 1998 were obtained from the reportable disease information system ( rdis ) of the ontario ministry of health and long term care . infection with stec is notifiable in ontario , and more than 95% of reported cases are due to e. coli o157:h7 ( 3 ) . cases were excluded if they were identified as part of a communitywide outbreak resulting from a single source , such as contaminated water supply . consolidated census subdivision ( ccs ) identifiers were added to the database via a software package that links ccs to appropriate postal codes ( postal code conversion file ; statistics canada ) . the human population distribution of ontario was obtained from georef 1996 census ( statistics canada ) , which is based on data collected from all households in the province . ccs areas , which are coded in square kilometers , were also extracted from the georef database . livestock distribution and land use data were collected from the 1996 census of agriculture ( statistics canada ) , and area units were converted from hectares to square kilometers . information on soil development and drainage characteristics for ontario were obtained from the canadian soil information system ( cansis ) website ( http://res.agr.ca/cansis/ ) . all data were aggregated to the ccs because this was the most detailed level at which agricultural data were available . ccs data from the northern portion of the province were excluded to avoid a potential bias , since this area was sparsely populated and had little agricultural activity . incidence rates were determined over a 3-year period , from 1996 to 1998 , and were expressed as the number of stec cases per 100,000 population per year in ontario ccs . arcview version 3.1 ( esri , redlands , ca , usa ) was used to create chloropleth maps based on disease rates and ldi measures . for the purposes of mapping , the method of nested means , as adapted by michel and colleagues ( 9 ) , was used to classify incidence rates . quintile breaks in the data were used to catergorize continuous ldi measures ( for example , the lower 20% quintile for the ratio of beef cattle to human population is 0.000 to 0.049 ) . arcview was also used to calculate ccs centroid locations ( latitude and longitude ) to allow calculation of autocorrelation measures . spacestat version 1.9 ( regional research institute , west virginia university , morgantown , wv , usa ) was used to calculate the euclidean distance between ccs centroids , so that an inverse square distance matrix could be produced . this matrix was used in the calculation of moran s i and gi statistics for the 435 townships in the study area . soil landscape coverage version 2.2 and hydrological data version 2.2 were downloaded and imported into arcview . attributes containing information on the dominant soil type and drainage characteristics of the soil were mapped . arcview s geoprocessing extension was used to clip overlay analysis that would remove areas normally covered with bodies of water . the spatial analyst extension of arcview was used to perform a cross - tab query to obtain soil type and drainage characteristic in each ccs . soil typing within each ccs was based on the predominent type of soil ; if two or more soil or drainage types occupied equal areas within a township , then the variable was set to a null value . soil development and drainage characteristics were based on the canadian system of soil classification ( 10 ) . for this study , an ldi was defined as a measure of livestock farming intensity that captures information on the number of animals or the amount of their fecal waste relative to various agricultural and environmental factors within a given geographic area . ldis were created by combining a series of variables that were considered a priori to be potentially spatially associated with human stec infection , based on a comprehensive analysis of possible sources and pathways of infection . we grouped these variables into  dimensions  and  components  ( table 1 ) . dimensions included variables related to number of animals , area of manure application , land uses , and human population within a given ccs . within the dimensions were components that provided further refinement . for example , components within the dimension  animal  included numbers of various animal species within a given ccs , while components within  manure  consisted of specific manure characteristics and methods of application ( table 1 ) . ( table 2 ) to form the ldi . within each frame , all possible combinations of the relevant components were used . for example , frame 3 ( human population / manure ) was used to create four separate ldi consisting of the ratios of total human population in a ccs to the area having manure applied either 1 ) by solid spreader , 2 ) by irrigation , 3 ) as liquid on the soil surface , or 4 ) as liquid by injection into the soil . each generated ldi was examined for biological and logical plausibility , and those considered inappropriate were discarded . data manipulation , merging of data sets , and statistical analyses were conducted by using the statistical analysis system for personal computers , version 6.12 ( sas institute inc . , univariate associations between each indicator and the incidence of human stec infection were examined by using poisson regression analysis . the glimmix macro in sas was used , and census division was entered as a repeated effect to induce a correlation structure in an attempt to control for the spatial effects inherent in the data . initially , ldi were grouped along common components in their numerator ( e.g. , all ldi with dairy cattle in the numerator were combined into a single group ) . ldi within each group were then entered into a separate multivariate model , and non - significant ldi were removed by backward elimination until a minimum of one variable remained ( stage 1 models ) . variables thus identified were then offered to a second series of multivariate models ( stage 2 models ) , each of which was subjected to a backward elimination procedure . variables offered to stage 2 models consisted of all combinations of one variable from each of the models developed in stage 1 . moran s i and gi statistics were calculated for stec incidence rates , which provided a measure of overall and local spatial autocorrelation . for all statistical analyses , data on 1,276 cases of human stec infection reported in ontario from january 1996 to december 1998 were obtained from the reportable disease information system ( rdis ) of the ontario ministry of health and long term care . infection with stec is notifiable in ontario , and more than 95% of reported cases are due to e. coli o157:h7 ( 3 ) . cases were excluded if they were identified as part of a communitywide outbreak resulting from a single source , such as contaminated water supply . consolidated census subdivision ( ccs ) identifiers were added to the database via a software package that links ccs to appropriate postal codes ( postal code conversion file ; statistics canada ) . the human population distribution of ontario was obtained from georef 1996 census ( statistics canada ) , which is based on data collected from all households in the province . ccs areas , which are coded in square kilometers , were also extracted from the georef database . livestock distribution and land use data were collected from the 1996 census of agriculture ( statistics canada ) , and area units were converted from hectares to square kilometers . information on soil development and drainage characteristics for ontario were obtained from the canadian soil information system ( cansis ) website ( http://res.agr.ca/cansis/ ) . all data were aggregated to the ccs because this was the most detailed level at which agricultural data were available . ccs data from the northern portion of the province were excluded to avoid a potential bias , since this area was sparsely populated and had little agricultural activity . incidence rates were determined over a 3-year period , from 1996 to 1998 , and were expressed as the number of stec cases per 100,000 population per year in ontario ccs . arcview version 3.1 ( esri , redlands , ca , usa ) was used to create chloropleth maps based on disease rates and ldi measures . for the purposes of mapping , the method of nested means , as adapted by michel and colleagues ( 9 ) , was used to classify incidence rates . quintile breaks in the data were used to catergorize continuous ldi measures ( for example , the lower 20% quintile for the ratio of beef cattle to human population is 0.000 to 0.049 ) . arcview was also used to calculate ccs centroid locations ( latitude and longitude ) to allow calculation of autocorrelation measures . spacestat version 1.9 ( regional research institute , west virginia university , morgantown , wv , usa ) was used to calculate the euclidean distance between ccs centroids , so that an inverse square distance matrix could be produced . this matrix was used in the calculation of moran s i and gi statistics for the 435 townships in the study area . soil landscape coverage version 2.2 and hydrological data version 2.2 were downloaded and imported into arcview . attributes containing information on the dominant soil type and drainage characteristics of the soil were mapped . arcview s geoprocessing extension was used to clip overlay analysis that would remove areas normally covered with bodies of water . the spatial analyst extension of arcview was used to perform a cross - tab query to obtain soil type and drainage characteristic in each ccs . soil typing within each ccs was based on the predominent type of soil ; if two or more soil or drainage types occupied equal areas within a township , then the variable was set to a null value . soil development and drainage characteristics were based on the canadian system of soil classification ( 10 ) . for this study , an ldi was defined as a measure of livestock farming intensity that captures information on the number of animals or the amount of their fecal waste relative to various agricultural and environmental factors within a given geographic area . ldis were created by combining a series of variables that were considered a priori to be potentially spatially associated with human stec infection , based on a comprehensive analysis of possible sources and pathways of infection . we grouped these variables into  dimensions  and  components  ( table 1 ) . dimensions included variables related to number of animals , area of manure application , land uses , and human population within a given ccs . within the dimensions were components that provided further refinement . for example , components within the dimension  animal  included numbers of various animal species within a given ccs , while components within  manure  consisted of specific manure characteristics and methods of application ( table 1 ) . dimensions were combined mathematically according to equations denoted as  frames  ( table 2 ) to form the ldi . within each frame , all possible combinations of the relevant components were used . for example , frame 3 ( human population / manure ) was used to create four separate ldi consisting of the ratios of total human population in a ccs to the area having manure applied either 1 ) by solid spreader , 2 ) by irrigation , 3 ) as liquid on the soil surface , or 4 ) as liquid by injection into the soil . each generated ldi was examined for biological and logical plausibility , and those considered inappropriate were discarded . data manipulation , merging of data sets , and statistical analyses were conducted by using the statistical analysis system for personal computers , version 6.12 ( sas institute inc . , univariate associations between each indicator and the incidence of human stec infection were examined by using poisson regression analysis . the glimmix macro in sas was used , and census division was entered as a repeated effect to induce a correlation structure in an attempt to control for the spatial effects inherent in the data . initially , ldi were grouped along common components in their numerator ( e.g. , all ldi with dairy cattle in the numerator were combined into a single group ) . ldi within each group were then entered into a separate multivariate model , and non - significant ldi were removed by backward elimination until a minimum of one variable remained ( stage 1 models ) . variables thus identified were then offered to a second series of multivariate models ( stage 2 models ) , each of which was subjected to a backward elimination procedure . variables offered to stage 2 models consisted of all combinations of one variable from each of the models developed in stage 1 . moran s i and gi statistics were calculated for stec incidence rates , which provided a measure of overall and local spatial autocorrelation . for all statistical analyses , a significance level of 5% was used ( p=0.05 ) . geographic distribution of the yearly incidence of human stec infection in ontario between 1996 and 1998 is shown in figure 1 . according to the nested means techniques , stec incidence rates were classified as very low incidence ( 0.00 to 0.95 per 100,000 ) in 204 ccs areas , low ( 0.96 to 4.54 per 100,000 ) in 95 , average ( 4.55 to 5.38 per 100,000 ) in 15 , high ( 5.39 to 15.01 per 100,000 ) in 78 , and very high ( 15.02 to 77.52 per 100,000 ) in 41 . ccs where the incidence of stec infection was classified as high or very high were located primarily in the northwestern portion of southern ontario , with smaller numbers in eastern ontario . yearly incidence of shiga toxin - producing escherichia coli infection ( per 100,000 population ) , southern ontario , 1996 - 1998 . moran s i calculation for the incidence of stec infection indicated a significantly positive autocorrelation ( p=0.012 ) . gi statistics for the ccs areas with the 10 highest and 10 lowest incidence rates were also statistically significant ( p<0.003 ) . a total of 8,316 ldi were generated , of which all but 80 were eliminated on the basis of biological plausibility . of these 80 variables , of these 33 , 9 ( 27.3% ) were based on the number of beef cattle , the total number of cattle per ccs , and measures of manure application ; 4 ( 12.1% ) were based on the number of standardized animal units per ccs ; and 1 ( 3.0% ) was based on both the number of dairy cattle and chickens per ccs . the number of sheep or goats , soil type , or drainage characteristics were not significantly associated with the incidence of human stec infection in the univariate analysis . the 10 ldi having the highest r values in univariate analyses are shown in table 3 . all of these ldi were based on either the number of cattle , beef cattle , or animal units per ccs . multivariate modeling resulted in the creation of 16 unique stage 2 models with r values ranging from 0.0932 to 0.266 . the multivariate model having the highest r value , shown in table 4 , consisted of humbcow ( the ratio of the number of beef cattle to the human population in a ccs ) , pigfarm ( the total number of swine per km of farm land in a ccs ) , smanccs ( the proportion of land in a ccs in which manure is applied by a solid spreader ) , and lsmancrp ( the proportion of cropland in a ccs in which liquid manure is applied to the soil surface . humbcow , smanccs , and lsmancrp were all positively ( and independently ) associated with the incidence of human stec infection , whereas pigfarm was negatively associated . the results of our analyses are consistent with the findings of michel et al ( 3 ) , who demonstrated a higher incidence of human stec infection in rural areas of ontario , as opposed to urban areas , and a spatial association between the incidence of human stec infection and cattle density . these findings are also consistent with other reports in the literature , including outbreaks of stec infection related to consumption of unpasteurized milk ( 2 ) and water from shallow wells , direct contact with cattle ( 5 ) , and an association between endemic stec infection and exposure to agricultural environments ( 2,11 ) . to our knowledge , this is the first time the application of manure to land has been identified as a potential risk factor for endemic human stec infection . runoff from agricultural land that has been treated with manure has the potential to contaminate local surface water and wells that supply water for human consumption ( 12 ) . a relationship between agricultural activities , such as manure spreading , animal density , and elevated fecal bacterial counts in local streams . was demonstrated in 1989 by meals ( 13 ) . an outbreak of stec infection in new york state was associated with contaminated well water used in the preparation of beverages and ice at a county fair ( 6 ) . it was thought that the well in question became contaminated with manure - laden water as a result of recent heavy rains . more recently , contamination of a municipal water supply with e. coli o157:h7 and campylobacter spp . in walkerton , ontario , canada , resulted in the largest documented outbreak of gastroenteritis caused by multiple pathogens . strong evidence suggests that contamination of walkerton s water supply was due to manure runoff from a nearby farm that entered a shallow well supplying the municipal water system ( 14 ) . the density of swine within a ccs was negatively associated with the incidence of human stec infection . this apparent protective effect may simply be the result of a relative absence of cattle in areas where swine are intensively farmed . although swine commonly harbor stec within their intestinal tract , they are not considered to be important reservoirs of e. coli o157:h7 ( 15 ) . past studies have identified sheep and goats as important reservoirs for stec ( 15,16 ) , but these animals were not identified as important predictors of human stec infection in our study . one explanation may be the relatively low numbers of these animals compared with other livestock types . the approach enabled us to examine a large pool of potential covariates from which appropriate indicators could be assessed and used to evaluate the association between livestock intensity and incidence of human stec infection . the chosen indicators were biologically plausible and allowed for identification of a previously unreported risk factor . by using a systematic construction , we identified ldi that were more strongly associated with the incidence of stec infection than has been reported previously ( 3 ) . when modeled at the same geographic scale , the r value for the best model from our investigation ( i.e. , the ratio of beef cattle to human population as a measure of cattle density ) was 0.27 compared with 0.14 for the total cattle density model used in michel s report ( 3 ) . these differences in r values may be the result of our selecting beef cattle for the ldi , rather than total number of cattle . it is worth noting that this difference in association is not necessarily evident from maps ( compare figures 2 and 3 ) , because both indicators suggest roughly similar distributions of cattle density , with the greatest concentration in ccs located in south - central and eastern ontario . ratio of beef cattle to human population ( number of animals per person ) , southern ontario , 1996 ) . caution should be exercised when interpreting our study results , however , because not all potential confounding variables ( e.g. , age or gender of the infected humans ) were included in the analysis . also , systematic errors arising from differential reporting rates may have biased the relationship between the incidence of human stec infection and the risk factors studied . since several ldi were investigated , some associations we observed may have arisen from chance alone . through linkage of existing data sources , spatial analytic techniques provide a means of identifying populations at high risk and potential risk factors for stec infection . the approach outlined in this study provides a rational , practical , and powerful tool for public health . as spatial analysis becomes more widely used in epidemiology , we anticipate that the development of such approaches will take on increasing importance .
the impact of livestock farming on the incidence of human shiga toxin - producing escherichia coli ( stec ) infection was assessed by using several livestock density indicators ( ldi ) that were generated in a systematic approach . 
 a total of 80 ldi were considered suitable proxy measures for livestock density . 
 multivariate poisson regression identified several ldi as having a significant spatial association with the incidence of human stec infection . 
 the strongest associations with human stec infection were the ratio of beef cattle number to human population and the application of manure to the surface of agricultural land by a solid spreader and by a liquid spreader showed . 
 this study demonstrates the value of using a systematic approach in identifying ldi and other spatial predictors of disease .