Dr. David L. Pearl Dept. of Population Medicine, University of Guelph Guelph, Ontario, Canada

1. The role of multi-level modeling in understanding risk factors for reported rates of human cases of Escherichia coli O157:H7 Dr. David L. Pearl Dept. of Population Medicine, University of Guelph Guelph, Ontario, Canada

2. Outline • Review epidemiology of E. coli O157:H7. • Utility of multi-level modeling for zoonotic diseases in human populations. • Multi-level study of E. coli O157:H7 in Alberta, Canada. • Future of multi-level modeling for E. coli O157:H7 and other zoonoses.

3. Epidemiology of E. coli O157:H7

4. Nature of disease • Mild diarrhea to hemorrhagic diarrhea to HUS. • HUS – anemia, thrombocytopenia, renal failure (2-10% of cases). • Highest incidence of illness in children less than five and elderly. • Increased risk of exposure if rural vs. urban. • Greater incidence of disease in summer vs. other. • Asymptomatic and intermittent shedding in cattle (major reservoir).

5. More than a “Hamburger Disease” • Meat • Waterborne • Cross-contamination in food preparation • Contamination of produce with ruminant feces • Person-to-person • Animal-to-person* • Other foods of animal origin

6. Canadian statistics • 3-5 cases per 100 000 population. • 365 hospitalizations per 1000 cases . • 39 deaths per 1000 cases. • 1 reported case for 4-8 symptomatic cases (Ontario).*

8. E. coli O157:H7 & Alberta • Crude rates higher than national average. • > 9 cases/100 000 from 2000-2002. • Southern RHAs report highest rates. • Province only reports community outbreaks to federal government (few and small).

9. Passive surveillance in Alberta • Alberta Health and Wellness collects Notifiable Disease Reports – (17 RHAs) • Provincial Laboratory for Public Health (Microbiology) performs PFGE

10. Spatial clustering • Spatial scan statistics show clustering in the south. • Cluster location varies depending on use of sporadic vs. all cases. • More complex than just issue of cattle density. Pearl et al., 2006

11. Utility of multi-level modeling for zoonotic diseases

12. Multi-level modeling • Hierarchical or random effects models. • Statistical model including fixed and random effects. • Fixed effects represent the mean effects typically seen in ordinary regression models. • Random effects adjust for auto-correlated data while allowing the proper estimation of variables from different hierarchical levels.

13. Multi-level modeling – cont. • Random effects correspond to effects (e.g., clusters) in the model being randomly selected from a population. • With random effects, focus shifts from individual group to variability in the population of groups (σ2group). • Can have random effects for multiple hierarchical levels. • Variance components assist in identifying level where intervention will have the most impact.

14. Multi-level modeling – cont. • Diez-Roux (2000) emphasized multilevel modeling in public health research. • Already common in veterinary epidemiology to account for farm-level clustering. • Similar approaches used to account for spatial clustering among counties to investigate role of agriculture on rates of disease from E. coli O157 (Michel et al. 1999; Valcour et al. 2002).

15. Zoonoses and multi-level modeling • Individual-level factors – age, socio-economic factors, food-handling practices. • Community-level factors – migration patterns, health policy and legislation, socio-economic factors as contextual variables. • Regional factors – type of water-shed, density of animal reservoir.

16. Multi-level study of E. coli O157:H7 in Alberta, Canada(Pearl et al., 2009)

17. Objectives • Estimate the association between socio-economic and agricultural variables related to income, migration, degree of integration with urban core, aboriginal status, and cattle density on rates of E. coli O157:H7 in Albertan communities. • Determine the impact of using all available data or only sporadic data on the associations observed.

18. Study Data - Alberta 2000-2002 • 875 cases recorded by passive surveillance • 826 human cases examined using PFGE • NDR provided spatial, temporal, and epidemiological data • 2001 Canada Census location of CSD centroids and socio-agricultural data • Based on review of NDR 78.9 % of cases were sporadic (14 community outbreaks & 55 household outbreaks)

19. Hierarchical structure of data

20. Variables available through the 2001 Canada Census • Cattle density (CCS) – forced into all models • Metropolitan influenced zones (CSD) • Percent movers (CSD) • High aboriginal population (CSD) • Percent low income households (CSD)* * Non-significant

21. Statistical model building • Models: Negative binomial models & Poisson models with random effects. • Outcome and exposure - case counts and expected counts, respectively. • Methods: Adaptive quadrature for multi-level models (GLLAMM procedure). • Levels: 2 & 3. ln E(Y) = ln η + β0 + β1 X1i j+….+ βk Xkij + u(ccs(j)) u(ccs(j)) ~ N(0, σ2 u)

22. Statistical model building – cont. 5. Data used: All or sporadic. 6. Assessed linearity assumption for continuous variables. 7. Tested interactions among cattle density and socio-economic factors. 8. Likelihood-ratio tests to assess significance of fixed effects. 9. Additional random effect at CSD-level assessed for residual overdispersion. 10. Used AIC and BIC scores to compare different models for quality of fit.

23. Best fitting models • Poisson models built with sporadic cases alone: 2-level model with a random intercept for CCS. 2. Poisson models built with outbreak and sporadic cases: 2-level model with random intercepts for CSD and CCS.

24. Fixed effects – Poisson 2-level (A) * Statistically non-significant in model using sporadic data.

25. Major findings • Economic link to urban centers associated with community risk. • Population stability associated with community risk. • High aboriginal population appears to have a sparing effect (reporting issues). • Statistical significance of cattle density influenced by models (power issues or type of industry). • Quality of surveillance system may impact our understanding.

26. Study limitations • Specificity of agricultural and socio-economic variables limited due to confidentiality issues with Census. • Limited to ecological studies without data concerning individual-level exposures. • Inferences should be limited to contextual effects.

27. Future of multi-level modeling for E. coli O157:H7 and other zoonoses in human populations

28. Requirements for future studies • Access to high resolution census data (Research Data Centres (RDC) Program). • Incorporating contextual variables with case-control data at the individual level. • Further development of statistical methodologies to integrate hierarchical approaches with spatial and social-network effects.

29. Acknowledgements Scott McEwen and Wayne Martin (University of Guelph) Kathryn Dore, Karen Grimsrud, and Pascal Michel (Public Health Agency of Canada) Marie Louie and Linda Chui (Alberta Provincial Laboratory for Public Health) Larry Svenson (Alberta Health and Wellness)