Analysis of simultaneous space-time clusters of Campylobacter spp. in humans and in broiler flocks using a multiple dataset approach

A One Health real-time surveillance system for nowcasting Campylobacter gastrointestinal illness outbreaks, Norway, week 30 2010 to week 11 2022

BackgroundCampylobacter is a leading cause of food and waterborne illness. Monitoring and modelling Campylobacter at chicken broiler farms, combined with weather pattern surveillance, can aid nowcasting of human gastrointestinal (GI) illness outbreaks. Near real-time sharing of data and model results with health authorities can help increase potential outbreak responsiveness.AimsTo leverage data on weather and Campylobacter on broiler farms to build a risk model for possible human Campylobacter outbreaks and to communicate risk assessments with health authorities.MethodsWe developed a spatio-temporal random effects model for weekly GI illness consultations in Norwegian municipalities with Campylobacter monitoring and weather data from week 30 2010 to 11 2022 to give 1-week nowcasts of GI illness outbreaks. The approach combined a municipality random effects baseline model for seasonally-adjusted GI illness with a second model for peak deviations from that baseline. Model results are communicated to national and local stakeholders through an interactive website: Sykdomspulsen One Health.ResultsLagged temperature and precipitation covariates, as well as 2-week-lagged positive Campylobacter sampling in broilers, were associated with higher levels of GI consultations. Significant inter-municipality variability in outbreak nowcasts were observed.ConclusionsCampylobacter surveillance in broilers can be useful in GI illness outbreak nowcasting. Surveillance of Campylobacter along potential pathways from the environment to illness such as via water system monitoring may improve nowcasting. A One Health system that communicates near real-time surveillance data and nowcast changes in risk to health professionals facilitates the prevention of Campylobacter outbreaks and reduces impact on human health.

Analyzing the Spatiotemporal Distribution and Characteristics of Liver Cirrhosis in Hospitalized Patients in Wuwei, Gansu Province During 1995-2016: A Long-Term Retrospective Study

Objectives: This was a long-term retrospective study, aiming to understand the temporal and spatial trend of cirrhosis in Wuwei from 1995 to 2016, explore its spatio-temporal aggregation, and find out the high incidence areas. To provide theoretical basis for the formulation of comprehensive prevention and treatment strategy of cirrhosis in Wuwei. Methods: Herein, we extracted data of cirrhosis patients who were treated in 12 sentinel hospitals in Wuwei from their medical records. We used SAS and Joinpoint Regression Program for data analysis, SaTScan 9.4 software for clustering area detection, and ArcGIS 10.2 software for geographical distribution mapping. Results: Among 3308 patients with liver cirrhosis (average age, 55.34 years) included in this study, 15.9% were aged 50-54 years. The majority were men (2716, 65.8%), with a sex ratio of 1.92:1 and peasants by occupation (1369, 60.3%). The basic social medical insurance system covered the healthcare costs of 1271 patients (63%). A Joinpoint regression analysis done for 1995-2016 revealed an increase in the standardized cirrhosis rate [average annual percent change (AAPC) = 16.7% (95% CI, 10.2-23.5%)] with three joinpoints in 2010, 2013, and 2016. The annual percent change (APC) from 1995 to 2010 was 11.13% (95% CI: 6.5-16.0), and APC from 2010 to 2013 was 66.48% (95% CI:16.0-138.9); conversely, from 2013 to 2016, APC was 4.4% (95% CI, -7.5-17.8%). Hongshagang Town showed the highest average incidence. Each township showed a gradual increase in the incidence after 2010. The results revealed that in each township, liver cirrhosis incidence had some spatial aggregation and was nonrandom. Four liver cirrhosis clusters were noted in 75 townships in Wuwei. Data were gathered from 2011 to 2016. Conclusions: From 1995 to 2016, the incidence of cirrhosis in Wuwei still showed an increasing trend, but the growth rate slowed down since 2013. In Wuwei, the rate of standardization of cirrhosis in female patients increased steadily and faster than in male patients. It is necessary to strengthen the diagnosis, treatment, prevention, and control measures of cirrhosis-related diseases. The results of spatial scanning, basic spatial distribution, aggregation time, and time trend analysis were consistent.

Analysis of inpatients' characteristics and spatio-temporal distribution of liver cancer incidence in Wuwei, Gansu Province, from 1995 to 2016: A long-term span retrospective study

This long-term span retrospective study aimed to determine the incidence and spatial-temporal trends of liver cancer in Wuwei city from 1995 to 2016 to provide scientific knowledge on the prevention and treatment of liver cancer.Data from the medical records of liver cancer patients treated in 12 sentinel hospitals in Wuwei city were extracted. SAS and Joinpoint software were used for data analysis, ArcGIS 10.2 software was used to make geographical distribution map, and SaTScan 9.4 software was used for clustering area detection.Of 2271 patients with liver cancer (average age, 58.73 years), 17.7% were 60 to 64 years of age. Majority were males (1680, 74%), with a sex ratio of 2.84. Joinpoint regression analysis from 1995 to 2016 revealed that the standardized liver cancer rate increased [annual average rate of change (AAPC) = 12.80% (95% CI: 9.5%-16.7%)], with a joinpoint in 2009. From 1995 to 2009, the change in overall APC was statistically significant [APC = 16.7 (95% CI: 12.3%-21.3%)]. The average incidence was the highest in Hongshagang Town. After 2005, the incidence gradually increased in each township. Five clusters of liver cancer were noted in Wuwei, including 37 townships.Males had a higher standardized liver cancer rate. After 2009, increase in the incidence of liver cancer was less rapid. The incidence of liver cancer in townships of Wuwei city was non-random, with certain spatial aggregations, covering 37 townships.

Insights into Livestock-Related Microbial Concentrations in Air at Residential Level in a Livestock Dense Area

Microbial air pollution from livestock farms has raised concerns regarding public health. Little is known about airborne livestock-related microbial levels in residential areas. We aimed to increase insights into this issue. Air measurements were performed in 2014 and 2015 at 61 residential sites in The Netherlands. Quantitative-PCR was used to assess DNA concentrations of selected bacteria (commensals: Escherichia coli and Staphylococcus spp.; a zoonotic pathogen: Campylobacter jejuni) and antimicrobial resistance (AMR) genes ( tetW, mecA) in airborne dust. Mixed models were used to explore spatial associations (temporal adjusted) with livestock-related characteristics of the surroundings. DNA from commensals and AMR genes was detectable even at sites furthest away from farms (1200 m), albeit at lower levels. Concentrations, distinctly different between sites, were strongly associated with the density of farms in the surroundings especially with poultry and pigs. C. jejuni DNA was less prevalent (42% of samples positive). Presence of C. jejuni was solely associated with poultry (OR: 4.7 (95% CI: 1.7-14), high versus low poultry density). Residential exposure to livestock-related bacteria and AMR genes was demonstrated. Identified associations suggest contribution of livestock farms to microbial air pollution in general and attribution differences between farm types. This supports the plausibility of recent studies showing health effects in relation to residential proximity to farms.

Space-time clusters and co-occurrence of chikungunya and dengue fever in Colombia from 2015 to 2016

Vector-borne diseases (VBDs) infect over one billion people and are responsible for over one million deaths each year, globally. Chikungunya (CHIK) and Dengue Fever (DENF) are emerging VBDs due to overpopulation, increases in urbanization, climate change, and other factors. Colombia has recently experienced severe outbreaks of CHIK AND DENF. Both viruses are transmitted by the Aedes mosquitoes and are preventable with a variety of surveillance and vector control measures (e.g. insecticides, reduction of open containers, etc.). Spatiotemporal statistics can facilitate the surveillance of VBD outbreaks by informing public health officials where to allocate resources to mitigate future outbreaks. We utilize the univariate Kulldorff space-time scan statistic (STSS) to identify and compare statistically significant space-time clusters of CHIK and DENF in Colombia during the outbreaks of 2015 and 2016. We also utilize the multivariate STSS to examine co-occurrences (simultaneous excess incidences) of DENF and CHIK, which is critical to identify regions that may have experienced the greatest burden of VBDs. The relative risk of CHIK and DENF for each Colombian municipality belonging to a univariate and multivariate cluster is reported to facilitate targeted interventions. Finally, we visualize the results in a three-dimensional environment to examine the size and duration of the clusters. Our approach is the first of its kind to examine multiple VBDs in Colombia simultaneously, while the 3D visualizations are a novel way of illustrating the dynamics of space-time clusters of disease.

Residential proximity to high-density poultry operations associated with campylobacteriosis and infectious diarrhea

Poultry carry zoonotic bacteria that can cause gastroenteritis in humans. Environmental transmission of pathogens from poultry operations may increase gastrointestinal infection risk in surrounding communities. To evaluate associations between residential proximity to high-density poultry operations and individual-level diarrheal illnesses, we conducted a nested case-control study among 514,488 patients in Pennsylvania (2006-2015). Using electronic health records, we identified cases of five gastrointestinal outcomes: three pathogen-specific infections, including Escherichia coli (n = 1425), Campylobacter (n = 567), and Salmonella (n = 781); infectious diarrhea (n = 781); and non-specific diarrhea (2012-2015; n = 28,201). We estimated an inverse-distance squared activity metric for poultry operations based on farm and patient addresses. Patients in the second and fourth (versus first) quartiles of the poultry operation activity metric had increased odds of Campylobacter (AOR [CI], Q2: 1.36 [1.01, 1.82]; Q3: 1.38 [0.98, 1.96]; Q4: 1.75 [1.31, 2.33]). Patients in the second, third, and fourth quartiles had increased odds of infectious diarrhea (Q2: 1.76 [1.29, 2.39]; Q3: 1.76 [1.09, 2.85]; Q4: 1.60 [1.12, 2.30]). Stratification revealed stronger relations of fourth quartile and both Campylobacter and infectious diarrhea in townships, the most rural community type in the study geography. Increasing extreme rainfall in the week prior to diagnosis strengthened fourth quartile Campylobacter associations. The poultry operation activity metric was largely unassociated with E. coli, Salmonella, and non-specific diarrhea. Findings suggest high-density poultry operations may be associated with campylobacteriosis and infectious diarrhea in nearby communities, highlighting additional public health concerns of industrial agriculture.

Whole-Genome Sequencing in Epidemiology of Campylobacter jejuni Infections

This review describes the current state of knowledge regarding the application of whole-genome sequencing (WGS) in the epidemiology of Campylobacter jejuni, the leading cause of bacterial gastroenteritis worldwide. We describe how WGS has increased our understanding of the evolutionary and epidemiological dynamics of this pathogen and how WGS has the potential to improve surveillance and outbreak detection. We have identified hurdles to the full implementation of WGS in public health settings. Despite these challenges, we think that ample evidence is available to support the benefits of integrating WGS into the routine monitoring of C. jejuni infections and outbreak investigations.

Methodological challenges to multivariate syndromic surveillance: a case study using Swiss animal health data

BACKGROUND

In an era of ubiquitous electronic collection of animal health data, multivariate surveillance systems (which concurrently monitor several data streams) should have a greater probability of detecting disease events than univariate systems. However, despite their limitations, univariate aberration detection algorithms are used in most active syndromic surveillance (SyS) systems because of their ease of application and interpretation. On the other hand, a stochastic modelling-based approach to multivariate surveillance offers more flexibility, allowing for the retention of historical outbreaks, for overdispersion and for non-stationarity. While such methods are not new, they are yet to be applied to animal health surveillance data. We applied an example of such stochastic model, Held and colleagues' two-component model, to two multivariate animal health datasets from Switzerland.

RESULTS

In our first application, multivariate time series of the number of laboratories test requests were derived from Swiss animal diagnostic laboratories. We compare the performance of the two-component model to parallel monitoring using an improved Farrington algorithm and found both methods yield a satisfactorily low false alarm rate. However, the calibration test of the two-component model on the one-step ahead predictions proved satisfactory, making such an approach suitable for outbreak prediction. In our second application, the two-component model was applied to the multivariate time series of the number of cattle abortions and the number of test requests for bovine viral diarrhea (a disease that often results in abortions). We found that there is a two days lagged effect from the number of abortions to the number of test requests. We further compared the joint modelling and univariate modelling of the number of laboratory test requests time series. The joint modelling approach showed evidence of superiority in terms of forecasting abilities.

CONCLUSIONS

Stochastic modelling approaches offer the potential to address more realistic surveillance scenarios through, for example, the inclusion of times series specific parameters, or of covariates known to have an impact on syndrome counts. Nevertheless, many methodological challenges to multivariate surveillance of animal SyS data still remain. Deciding on the amount of corroboration among data streams that is required to escalate into an alert is not a trivial task given the sparse data on the events under consideration (e.g. disease outbreaks).

An evaluation of SaTScan for the prospective detection of space-time Campylobacter clusters in the North East of England

Campylobacteriosis is the commonest cause of bacterial enteritis in England yet the epidemiology of apparently sporadic cases is not well understood. Here we evaluated the feasibility of applying a space-time cluster detection method to routine laboratory surveillance data in the North East of England by simulating prospective weekly space-time cluster detection using SaTScan as if it had been performed for 2008-2011. From the 209 simulated weekly runs using a circular window, 20 distinct clusters were found which contained a median of 30 cases (interquartile range 15-66) from a median population of ~134,000 persons. This corresponds to detection of a new cluster every 10 weeks. We found significant differences in age, sex and deprivation score distributions between areas within clusters compared to those without. The results of this study suggest that space-time detection of Campylobacter clusters could be used to find groups of cases amenable to epidemiological investigation.

Socioeconomic determinants of geographic disparities in campylobacteriosis risk: a comparison of global and local modeling approaches

Background

Socioeconomic factors play a complex role in determining the risk of campylobacteriosis. Understanding the spatial interplay between these factors and disease risk can guide disease control programs. Historically, Poisson and negative binomial models have been used to investigate determinants of geographic disparities in risk. Spatial regression models, which allow modeling of spatial effects, have been used to improve these modeling efforts. Geographically weighted regression (GWR) takes this a step further by estimating local regression coefficients, thereby allowing estimations of associations that vary in space. These recent approaches increase our understanding of how geography influences the associations between determinants and disease. Therefore the objectives of this study were to: (i) identify socioeconomic determinants of the geographic disparities of campylobacteriosis risk (ii) investigate if regression coefficients for the associations between socioeconomic factors and campylobacteriosis risk demonstrate spatial variability and (iii) compare the performance of four modeling approaches: negative binomial, spatial lag, global and local Poisson GWR.

Methods

Negative binomial, spatial lag, global and local Poisson GWR modeling techniques were used to investigate associations between socioeconomic factors and geographic disparities in campylobacteriosis risk. The best fitting models were identified and compared.

Results

Two competing four variable models (Models 1 & 2) were identified. Significant variables included race, unemployment rate, education attainment, urbanicity, and divorce rate. Local Poisson GWR had the best fit and showed evidence of spatially varying regression coefficients.

Conclusions

The international significance of this work is that it highlights the inadequacy of global regression strategies that estimate one parameter per independent variable, and therefore mask the true relationships between dependent and independent variables. Since local GWR estimate a regression coefficient for each location, it reveals the geographic differences in the associations. This implies that a factor may be an important determinant in some locations and not others. Incorporating this into health planning ensures that a needs-based, rather than a “one-size-fits-all”, approach is used. Thus, adding local GWR to the epidemiologists’ toolbox would allow them to assess how the impacts of different determinants vary by geography. This knowledge is critical for resource allocation in disease control programs.

Identifying the seasonal origins of human campylobacteriosis

Human campylobacteriosis exhibits a distinctive seasonality in temperate regions. This paper aims to identify the origins of this seasonality. Clinical isolates [typed by multi-locus sequence typing (MLST)] and epidemiological data were collected from Scotland. Young rural children were found to have an increased burden of disease in the late spring due to strains of non-chicken origin (e.g. ruminant and wild bird strains from environmental sources). In contrast the adult population had an extended summer peak associated with chicken strains. Travel abroad and UK mainland travel were associated with up to 17% and 18% of cases, respectively. International strains were associated with chicken, had a higher diversity than indigenous strains and a different spectrum of MLST types representative of these countries. Integrating empirical epidemiology and molecular subtyping can successfully elucidate the seasonal components of human campylobacteriosis. The findings will enable public health officials to focus strategies to reduce the disease burden.

Thermal inactivation of Campylobacter jejuni in broth

New Zealand has a high rate of reported campylobacteriosis compared with other developed countries. One possible reason is that local strains have greater heat tolerance and thus are better able to survive undercooking; this hypothesis is supported by the remarkably high D-values reported for Campylobacter jejuni in The Netherlands. The objective of this study was to investigate the thermal inactivation of isolates from New Zealand in broth, using strains that are commonly found in human cases and food samples in New Zealand. Typed Campylobacter strains were heated to a predetermined temperature using a submerged-coil heating apparatus. The first-order kinetic model has been used extensively in the calculation of the thermal inactivation parameters, D and z; however, nonlinear survival curves have been reported, and a number of models have been proposed to describe the patterns observed. Therefore, this study compared the conventional first-order model with eight nonlinear models for survival curves. Kinetic parameters were estimated using both one- and two-step regression techniques. In general, nonlinear models fit the individual inactivation data sets better than the log-linear model. However, the log-linear and the (nonlinear) Weibull models were the only models that could be successfully fitted to all data sets. For seven relevant New Zealand C. jejuni strains, at temperatures from 51.5 to 60°C, D- and z-values were obtained, ranging from 1.5 to 228 s and 4 to 5.2°C, respectively. These values are in broad agreement with published international data and do not indicate that the studied New Zealand C. jejuni strains are more heat resistant than other strains, in contrast with some reports from The Netherlands.