Geographic and Temporal Epidemiology of Campylobacteriosis

Campylobacteriosis is a leading cause of gastroenteritis in the United States. The focus of this research was to (i) analyze and predict spatial and temporal patterns and associations for campylobacteriosis risk and (ii) compare the utility of advanced modeling methods. Laboratory-confirmed Campylobacter case data, obtained from the Foodborne Diseases Active Surveillance Network were used in all investigations. We compared the accuracy of forecasting techniques for campylobacteriosis risk in Minnesota, Oregon and Georgia and found that time series regression, decomposition, and Box-Jenkins Autoregressive Integrated Moving Averages reliably predict monthly risk of infection for campylobacteriosis. Decomposition provided the fastest, most accurate, user-friendly method. Secondly, forecasting models were used to predict monthly climatic effects on the risk of campylobacteriosis in Georgia. The objectives were to (i) assess temporal patterns of campylobacteriosis risk (ii) compare univariate forecasting models with those that incorporate precipitation and temperature and (iii) investigate alternatives to random walk series and non random occurrences that could be outliers. We found significant regional associations between campylobacteriosis risk and climatic factors and control charting identified high risk time periods. Our spatial study in Tennessee compared standardized risk estimates and investigated high risk spatial clustering of campylobacteriosis at three geographic scales. Spatial scan methods identified overlapping clusters (p Objectives of the second study were to (i) identify socioeconomic determinants of the geographic disparities of campylobacteriosis risk (ii) investigate if regression coefficients demonstrate spatial variability and (iii) compare the performance of four modeling approaches: negative binomial, spatial lag, global and local Poisson geographically weighted regression. Local models had the best fit and identified associations between socioeconomic factors and geographic disparities in campylobacteriosis risk. Significant variables included race, unemployment rate, education attainment, urbanicity, and divorce rate. Recent technological advancements have opened a virtually limitless ‘toolbox’ of analytical methods and offer novel means of identifying temporal spikes, spatial clusters and geographic disparities in campylobacteriosis risk that expand and hone our ability to create cost efficient, needs-based prevention and control measures

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

Comparison of three time-series models for predicting campylobacteriosis risk

Three time-series models (regression, decomposition, and Box–Jenkins autoregressive integrated moving averages) were applied to national surveillance data for Campylobacteriosis with the goal of disease forecasting in three US states. Datasets spanned 1998–2007 for Minnesota and Oregon, and 1999–2007 for Georgia. Year 2008 was used to validate model results. Mean absolute percent error, mean square error and coefficient of determination (R2) were the main evaluation fit statistics. Results showed that decomposition best captured the temporal patterns in disease risk. Training dataset R2 values were 72.2%, 76.3% and 89.9% and validation year R2 values were 66.2%, 52.6% and 79.9% respectively for Georgia, Oregon and Minnesota. All three techniques could be utilized to predict monthly risk of infection for Campylobacter sp. However, the decomposition model provided the fastest, most accurate, user-friendly method. Use of this model can assist public health personnel in predicting epidemics and developing disease intervention strategies

Detection of high risk campylobacteriosis clusters at three geographic levels

Abstract. Campylobacteriosis is a leading cause of bacterial gastroenteritis in the United States and many other developed countries. Understanding the spatial distribution of this disease and identifying high-risk areas is vital to focus resources for prevention and control measures. In addition, determining the appropriate scale for geographical analysis of surveillance data is an area of concern to epidemiologists and public health officials. The purpose of this study was to (i) compare standardized risk estimates for campylobacteriosis in Tennessee over three distinct geographical scales (census tract, zip code and county subdivision), and (ii) identify and investigate high-risk spatial clustering of campylobacteriosis at the three geographical scales to determine if clustering is scale dependent. Significant high risk clusters (P <0.05) were detected at all three spatial scales. There were overlaps in regions of high-risk and clusters at all three geographic levels. At the census tract level, spatial analysis identified smaller clusters of finer resolution and detected more clusters than the other two levels. However, data aggregation at zip code or county subdivision yielded similar findings. The importance of this line of research is to create a framework whereby economically efficient disease control strategies become more attainable through improved geographical precision and risk detection. Accurate identification of disease clusters for campylobacteriosis can enable public health personnel to focus scarce resources towards prevention and control programmes on the most at-risk populations. Consistent results at multiple spatial levels highlight the robustness of the geospatial techniques utilized in this study. Furthermore, analyses at the zip code and county subdivision levels can be useful when address level information (finer resolution data) are no