Associations between indicators of livestock farming intensity and incidence of human Shiga toxin-producing Escherichia coli infection

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. This study demonstrates the value of using a systematic approach in identifying LDI and other spatial predictors of disease.     

Geographical and temporal distribution of human giardiasis in Ontario,Canada

Background  

Giardia is the most frequently identified intestinal parasite in North America. Although information on geographical distribution of giardiasis is critical in identifying communities at high risk, little has been done in this area. Therefore, the objective of this study was to investigate the geographical and temporal distribution of human giardiasis in Ontario in order to identify possible high risk areas and seasons. Two spatial scales of analyses and two disease measures were used with a view to identifying the best of each in assessing geographical patterns of giardiasis in Ontario. Global Moran's I and Moran Local Indicators of Spatial Associations were used to test for evidence of global and local spatial clustering, respectively.     

Determinants of the geographical distribution of endemic giardiasis in Ontario, Canada: a spatial modelling approach

Giardiasis surveillance data as well as drinking water, socioeconomic and land-use data were used in spatial regression models to investigate determinants of the geographic distribution of endemic giardiasis in southern Ontario. Higher giardiasis rates were observed in areas using surface water [rate ratio (RR) 2.36, 95 % CI 1.38-4.05] and in rural areas (RR 1.79, 95 % CI 1.32-2.37). Lower rates were observed in areas using filtered water (RR 0.55, 95 % CI 0.42-0.94) and in those with high median income (RR 0.62, 95 % CI 0.42-0.92). Chlorination of drinking water, cattle density and intensity of manure application on farmland were not significant determinants. The study shows that waterborne transmission plays an important role in giardiasis distribution in southern Ontario and that well-collected routine surveillance data could be useful for investigation of disease determinants and identification of high-risk communities. This information is useful in guiding decisions on control strategies.     

Microbial pollution in wildlife: Linking agricultural manuring and bacterial antibiotic resistance in red-billed choughs

The spread of pathogens in the environment due to human activities (pathogen pollution) may be involved in the emergence of many diseases in humans, livestock and wildlife. When manure from medicated livestock and urban effluents is spread onto agricultural land, both residues of antibiotics and bacteria carrying antibiotic resistance may be introduced into the environment. The transmission of bacterial resistance from livestock and humans to wildlife remains poorly understood even while wild animals may act as reservoirs of resistance that may be amplified and spread in the environment. We determined bacterial resistance to antibiotics in wildlife using the red-billed chough Pyrrhocorax pyrrhocorax as a potential bioindicator of soil health, and evaluated the role of agricultural manuring with waste of different origins in the acquisition and characteristics of such resistance. Agricultural manure was found to harbor high levels of bacterial resistance to multiple antibiotics. Choughs from areas where manure landspreading is a common agricultural practice harbor a high bacterial resistance to multiple antibiotics, resembling the resistance profile found in the waste (pig slurry and sewage sludge) used in each area. The transfer of bacterial resistance to wildlife should be considered as an important risk for environmental health when agricultural manuring involves fecal material containing multiresistant enteric bacteria including pathogens from livestock operations and urban areas. The assessment of bacterial resistance in wild animals may be valuable for the monitoring of environmental health and for the management of emergent infectious diseases influenced by the impact of different human activities in the environment.     

Lincomycin and spectinomycin concentrations in liquid swine manure and their persistence during simulated manure storage

Antimicrobials administered to livestock can be excreted up to 75% in the feces and urine. Liquid swine manure from confined animal feeding operations is generally retained in lagoon storage until it is applied as a nutrient source to crop and pasture land. Thus, the applied manure becomes a possible source of antimicrobials to aquatic ecosystems. In the prairie region of Canada, lincomycin and spectinomycin are two antimicrobials that are frequently administered to pigs for prevention of post-weaning diarrhea. In order to assess the potential for contamination of prairie wetlands, concentrations of both antimicrobials were monitored in liquid manure from a commercial-scale barn during a 5-week study, and their persistence during simulated manure storage was investigated. LC-MS/MS analysis of manure extracts showed that concentrations of lincomycin and spectinomycin in the accumulating liquid manure at the end of the study were equivalent to 32 and 3%, respectively, of the doses administered to weanling pigs in their feed. In a laboratory study in which lagoon storage was simulated at room temperature using fortified liquid manure, concentrations of both antimicrobials showed a rapid initial decrease during the first 6 days, followed by a much slower dissipation, over a period of 5 months. Such persistence indicates that lincomycin and spectinomycin may be present in lagoon manure when applied as an amendment to agricultural land since many lagoons are emptied every 6 months (early spring and late fall).     

Temporal and spatial distribution of human cryptosporidiosis in the west of Ireland 2004-2007

Background  

Cryptosporidiosis is increasingly recognised as a cause of gastrointestinal infection in Ireland and has been implicated in several outbreaks. This study aimed to investigate the spatial and temporal distribution of human cryptosporidiosis in the west of Ireland in order to identify high risk seasons and areas and to compare Classically Calculated (CC) and Empirical Bayesian (EB) incidence rates. Two spatial scales of analysis were used with a view to identifying the best one in assessing geographical patterns of infection. Global Moran's I and Local Moran's I tests of autocorrelation were used to test for evidence of global and local spatial clustering.     

Assessing Spatial Relationships Between Rates of Crime and Rates of Gonorrhea and Chlamydia in Chicago, 2012

Sexually transmitted infections (STIs) remain serious public health problems particularly in urban environments in the USA. Despite accumulating research into the role of aggregate rates of crime in shaping rates of STIs, few studies account for spatial dependence in the structure of geographical data. Using multiple spatial analysis methodologies, the following study investigated spatial patterns in community area rates of violent, drug, and property crimes and rates of infection of gonorrhea and chlamydia in 77 community areas in Chicago. Moran’s I analyses confirmed global spatial dependence and statistically significant clusters of STI. Spatial lag regression analyses found that greater rates of drug crimes were associated with higher rates of chlamydia and gonorrhea after adjusting for percent in poverty and racial composition. Finally, a weighted geographic regression identified regions in the urban environment in which local regression coefficient values diverged from their global estimates. Spatial heterogeneity of STIs suggest that public health interventions must be targeted to specific areas of the urban environment with particular attention to substance use.     

The association between farming activities,precipitation, and the risk of acute gastrointestinal illness in rural municipalities of Quebec,Canada: a cross-sectional study

Background  

Increasing livestock density and animal manure spreading, along with climate factors such as heavy rainfall, may increase the risk of acute gastrointestinal illness (AGI). In this study we evaluated the association between farming activities, precipitation and AGI.     

Assessing household wealth in health studies in developing countries: a comparison of participatory wealth ranking and survey techniques from rural South Africa

Background

Environmental pollution as a cause of congenital anomalies is sometimes suspected because of clustering of anomalies in areas of higher exposure. This highlights questions around spatial heterogeneity (clustering) in congenital anomaly rates. If spatial variation is endemic, then any one specific cluster is less remarkable, though the presence of uncontrolled geographically clustered risk factors is suggested. If rates are relatively homogeneous across space other than around specific hazards, then evidence for these hazards causing the clusters is strengthened. We sought to estimate the extent of spatial heterogeneity in congenital anomaly rates in the United Kingdom.

Methods

The study population covered about one million births from five registers in Britain from 1991–1999. We estimated heterogeneity across four geographical levels: register area, hospital catchment, electoral ward, and enumeration district, using a negative binomial regression model. We also sought clusters using a circular scan statistic.

Results

Congenital anomaly rates clearly varied across register areas and hospital catchments (p < 0.001), but not below this level (p > 0.2). Adjusting for socioeconomic deprivation and maternal age made little difference to the extent of geographical variation for most congenital anomaly subtypes. The two most significant circular clusters (of four ano-rectal atresias and six congenital heart diseases) contained two or more siblings.

Conclusion

The variation in rates between registers and hospital catchment area may have resulted in part from differences in case ascertainment, and this should be taken into account in geographical epidemiological studies of environmental exposures. The absence of evidence for variation below this level should be interpreted cautiously in view of the low power of general heterogeneity tests. Nevertheless, the data suggest that strong localised clusters in congenital anomalies are uncommon, so clusters around specific putative environmental hazards are remarkable when observed. Negative binomial models applied at successive hierarchical levels provide an approach of intermediate complexity to characterising geographical heterogeneity.     

GIS-supported investigation of human EHEC and cattle VTEC O157 infections in Sweden: geographical distribution, spatial variation and possible risk factors

This article describes the spatial and temporal distribution of verotoxin-producing Escherichia coli among humans (EHEC) and cattle (VTEC) in Sweden, in order to evaluate relationships between the incidence of EHEC in humans, prevalence of VTEC O157 in livestock and agricultural structure by an ecological study. The spatial patterns of the distribution of human infections were described and compared with spatial patterns of occurrence in cattle, using a Geographic Information System (GIS). The findings implicate a concentration of human infection and cattle prevalence in the southwest of Sweden. The use of probability mapping confirmed unusual patterns of infection rates. The comparison of human and cattle infection indicated a spatial and statistical association. The correlation between variables of the agricultural structure and human EHEC incidence was high, indicating a significant statistical association of cattle and farm density with human infection. The explained variation of a multiple linear regression model was 0.56.     

Penalized likelihood and multi-objective spatial scans for the detection and inference of irregular clusters

Background  

Irregularly shaped spatial clusters are difficult to delineate. A cluster found by an algorithm often spreads through large portions of the map, impacting its geographical meaning. Penalized likelihood methods for Kulldorff's spatial scan statistics have been used to control the excessive freedom of the shape of clusters. Penalty functions based on cluster geometry and non-connectivity have been proposed recently. Another approach involves the use of a multi-objective algorithm to maximize two objectives: the spatial scan statistics and the geometric penalty function.     

Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation

Background  

Smoothing methods have been developed to improve the reliability of risk cancer estimates from sparsely populated geographical entities. Filtering local details of the spatial variation of the risk leads however to the detection of larger clusters of low or high cancer risk while most spatial outliers are filtered out. Static maps of risk estimates and the associated prediction variance also fail to depict the uncertainty attached to the spatial distribution of risk values and does not allow its propagation through local cluster analysis. This paper presents a geostatistical methodology to generate multiple realizations of the spatial distribution of risk values. These maps are then fed into spatial operators, such as in local cluster analysis, allowing one to assess how risk spatial uncertainty translates into uncertainty about the location of spatial clusters and outliers. This novel approach is applied to age-adjusted breast and pancreatic cancer mortality rates recorded for white females in 295 US counties of the Northeast (1970–1994). A public-domain executable with example datasets is provided.     

Geographical Disparities in HIV Seroprevalence Among Men Who Have Sex with Men and People Who Inject Drugs in Nigeria: Exploratory Spatial Data Analysis

BackgroundThe assessment of geographical heterogeneity of HIV among men who have sex with men (MSM) and people who inject drugs (PWID) can usefully inform targeted HIV prevention and care strategies.ObjectiveWe aimed to measure HIV seroprevalence and identify hotspots of HIV infection among MSM and PWID in Nigeria.MethodsWe included all MSM and PWID accessing HIV testing services across 7 prioritized states (Lagos, Nasarawa, Akwa Ibom, Cross Rivers, Rivers, Benue, and the Federal Capital Territory) in 3 geographic regions (North Central, South South, and South West) between October 1, 2016, and September 30, 2017. We extracted data from national testing registers, georeferenced all HIV test results aggregated at the local government area level, and calculated HIV seroprevalence. We calculated and compared HIV seroprevalence from our study to the 2014 integrated biological and behavioural surveillance survey and used global spatial autocorrelation and hotspot analysis to highlight patterns of HIV infection and identify areas of significant clustering of HIV cases.ResultsMSM and PWID had HIV seroprevalence rates of 12.14% (3209/26,423) and 11.88% (1126/9474), respectively. Global spatial autocorrelation Moran I statistics revealed a clustered distribution of HIV infection among MSM and PWID with a <5% and <1% likelihood that this clustered pattern could be due to chance, respectively. Significant clusters of HIV infection (Getis-Ord-Gi* statistics) confined to the North Central and South South regions were identified among MSM and PWID. Compared to the 2014 integrated biological and behavioural surveillance survey, our results suggest an increased HIV seroprevalence among PWID and a substantial decrease among MSM.ConclusionsThis study identified geographical areas to prioritize for control of HIV infection among MSM and PWID, thus demonstrating that geographical information system technology is a useful tool to inform public health planning for interventions targeting epidemic control of HIV infection.     

Geographic analysis of pertussis infection in an urban area: a tool for health services planning.

OBJECTIVE: This study examined whether incident cases of pertussis cluster in urban census tracts and identified community characteristics that predict high-incidence areas. METHODS: An ecological study design was used. The study population included all persons diagnosed with pertussis from January 1, 1986, through December 31, 1994. Maps of rates were constructed via a geographic information system and clustering was statistically confirmed. Associations between pertussis rates and community characteristics were tested. RESULTS: Mapping and statistical analysis revealed spatial clustering of pertussis. Higher age-adjusted rates of pertussis infection were associated with higher proportions of residents below poverty level. CONCLUSIONS: In urban areas pertussis infection clusters in areas of poverty.     

Spatial cluster detection for ordinal outcome data

In geographical disease surveillance, spatial scan statistics are used to identify areas having unusually high or low rates of disease outcomes and to determine the statistical significance of detected clusters. The spatial scan statistic for ordinal data such as stage of cancer has been developed to detect clusters representing areas with high rates of more serious stages compared with the surrounding areas. Such areas were expressed using likelihood ratio ordering, which is a rather strict order restriction, and hence, the method might fail to detect spatial clusters with high rates of worse categories (e.g., later stage). In this paper, we relax the order restriction using stochastic ordering and examine differences between the two approaches in detecting spatial clusters. Through simulation studies, we show that the stochastic ordering‐based approach has higher power, sensitivity, and positive predictive value under several scenarios. We illustrate the two methods with the use of a real data example. Copyright © 2012 John Wiley & Sons, Ltd.     

Geographic disparity in premature mortality in Ontario, 1992–1996

Background  

Standardized mortality ratios are used to identify geographic areas with higher or lower mortality than expected. This article examines geographic disparity in premature mortality in Ontario, Canada, at three geographic levels of population and considers factors that may underlie variations in premature mortality across geographic areas. All-cause, sex and disease chapter specific premature mortality were analyzed at the regional, district and public health unit level to determine the extent of geographic variation. Standardized mortality ratios for persons aged 0–74 years were calculated to identify geographic areas with significantly higher or lower premature mortality than expected, using Ontario death rates as the basis for the calculation of expected deaths in the local population. Data are also presented from the household component of the 1996/97 National Population Health Survey and from the 1996 Statistics Canada Census.     

A flexibly shaped space-time scan statistic for disease outbreak detection and monitoring

Background  

Early detection of disease outbreaks enables public health officials to implement disease control and prevention measures at the earliest possible time. A time periodic geographical disease surveillance system based on a cylindrical space-time scan statistic has been used extensively for disease surveillance along with the SaTScan software. In the purely spatial setting, many different methods have been proposed to detect spatial disease clusters. In particular, some spatial scan statistics are aimed at detecting irregularly shaped clusters which may not be detected by the circular spatial scan statistic.     

Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island,New York

Background  

Complete Spatial Randomness (CSR) is the null hypothesis employed by many statistical tests for spatial pattern, such as local cluster or boundary analysis. CSR is however not a relevant null hypothesis for highly complex and organized systems such as those encountered in the environmental and health sciences in which underlying spatial pattern is present. This paper presents a geostatistical approach to filter the noise caused by spatially varying population size and to generate spatially correlated neutral models that account for regional background obtained by geostatistical smoothing of observed mortality rates. These neutral models were used in conjunction with the local Moran statistics to identify spatial clusters and outliers in the geographical distribution of male and female lung cancer in Nassau, Queens, and Suffolk counties, New York, USA.     

Antimeningococcal herd immunity in the Czech Republic--influence of an emerging clone, Neisseria meningitidis ET-15/37

The distribution of 3001 cases of verocytotoxigenic Escherichia coli (VTEC) reported in the Province of Ontario, Canada, were examined to describe the magnitude of this condition geographically and to evaluate the spatial relationship between livestock density and human VTEC incidence using a Geographical Information System. Incidence of VTEC cases had a marked seasonal pattern with peaks in July. Areas with a relatively high incidence of VTEC cases were situated predominantly in areas of mixed agriculture. Spatial models indicated that cattle density had a positive and significant association with VTEC incidence of reported cases (P = 0.000). An elevated risk of VTEC infection in a rural population could be associated with living in areas with high cattle density. Results of this study suggested that the importance of contact with cattle and the consumption of contaminated well water or locally produced food products may have been previously underestimated as risk factors for this condition.     

The geographic distribution of un-immunized children in Ontario,Canada: Hotspot detection using Bayesian spatial analysis

BackgroundIn Ontario, Canada, little is currently known about the extent to which un-immunized children may cluster geographically. Our objectives were to: describe the geographic distribution of fully un-immunized children; identify geographic clusters (hotspots) of un-immunized children; and to characterize the contribution of spatial effects and covariates on hotspots, where found.MethodsOur analytic cohort consisted of Ontario students aged 7–17 years in the 2016–2017 school year. We defined students as un-immunized if they had zero doses of any vaccine and a non-medical exemption recorded in Ontario’s registry. We calculated unadjusted proportions of un-immunized students by Census Subdivision (CSD) and then used a sequential approach to identify hotspots starting first with hotspot identification at the CSD level and then probed identified hotspots further by Dissemination Area (DA) and including covariates. Hotspots were identified using the Besag-York-Mollie Bayesian spatial model and were defined as areas with >95% probability of having two times the proportion of un-immunized students, relative to the province overall.ResultsWe identified 15,208 (0.94%) un-immunized children within our cohort consisting of more than 1.61 million students. Unadjusted proportions of un-immunized students varied greatly by geography, ranging from 0% to 21.5% by CSD. We identified 16 hotspot CSDs which clustered in five distinct areas, all of which were located in southern Ontario. The contribution of covariates and spatial effects on the risk of having un-immunized students varied greatly across hotspot areas.ConclusionsAlthough the provincial proportion (0.94%) of un-immunized students is small, geographical clustering of such students is evident in Ontario and in some areas presents an important risk for future outbreaks. Further qualitative work within these hotspot areas would be a helpful next step to better characterize the factors associated with vaccine refusal in these communities.