Risk Assessment for Cryptosporidiosis: Incorporating human susceptibility factors

1. Risk Assessment for Cryptosporidiosis: Incorporating human susceptibility factors John Balbus, MD, MPH Anna Makri, MA Lucy Hsu, MPH Lisa Ragain Martha Embrey, MPH

2. Overview • Sources of data for human susceptibility • Translating epidemiologic data into risk assessment parameters • Review of important host factors • Case study of cryptosporidiosis risk for susceptible populations in DC

3. Risk Assessors vs. Epidemiologists Vs. No infection Exposure Recovery Asymptomatic Chronic Symptomatic Dead

4. Summary of Host Susceptibility

5. Human Data Sources for Dose Response • Challenge studies (dose-response data) • very small “n”, healthy adults • strain controlled • Outbreak data (absolute and relative rates) • include children, HIV/AIDS • strain poorly characterized • dose poorly characterized • attack rates influenced by dose

6. Comparing attack rates on D-R curve RR1 P(Inf) RR2 1.0 0.8 0.6 RR3 0.4 0.2 0.0 0 10 102 103 104 105 106 Oocyst Dose Ingested Simulated curve of 3 x “r” Model fit to Dupont, et al. data

7. Variability vs. Susceptibility • Not all differences in rates are due to susceptibility • Between outbreaks • comparison between populations confounded by dose and strain differences • Between individuals • challenge studies show significant variability • unclear whether due to chance or differences in susceptibility

8. HIV/AIDS as Susceptibility Factor • Unclear increase in infection risk (Pozio, et al., 1997) • Poor outcome associated with CD4 count <140-200 • Flanigan (1992): 34/34 HIV+ pts with persistent disease had CD4<200 • Confirmed by Pozio (1997) • HAART is protective; failure and non-compliance negatively affect risk. Carr (1998) Miao (1999)

9. Immunology of Susceptibility • CMI defect or Ig defect? • Complex and conflicting data • Many authors note elevated serum IgG, IgM in persistent AIDS-related crypto • Flanigan (1994): Salivary IgA correlated with clearance of crypto, not for Cozon (1994). • HIV+ less likely to seroconvert IgG post infection. Pozio (1997)

10. Other Immunosuppressive States • Transplantation • Bone Marrow - highest risk 30-100 days post transplant. Martinon (1998) Nachbaur (1997) • Solid organ transplants (renal and liver) • Chemotherapy -often associated with lymphomas and leukemias. Russell (1998) Vargas (1993) • Immunodeficiency states, esp. IgA. Current (1983)

11. Prior Exposure as Protective Factor • Pre-existing antibody appears to convey decreased illness risk and possible resistance to infection • Chappell (1999): ID50 in IgG+ volunteers >20 times higher • Prevalence of prior exposure not taken into account in population-based RA’s

12. Nutrition and Crypto • Causal association unclear; Griffiths (1998) • ?malnutrition>depressed immunity, or chronic infection> malabsorption • Association with malnutrition strongest in children of developing countries. Sallon (1988) Javier Enriquez (1997) • Many associations between vitamin and trace element deficiency and impaired innate immunity • relation to crypto is unclear

13. Pre-existing GI disease • Manthey et al. (1997) reported 12 cases of IBD sickened in Milwaukee outbreak • no denominator to estimate attack rate • illness indistinguishable from flare of IBD • symptoms persisted longer than “controls” (med. 17 vs. 9 d) • all cleared by 60 days

14. Age as Susceptibility Factor • Elderly • High rates of morbidity and mortality from diarrheal disease. Lew (1991) Gangarosa (1992) • Decreased CMI, sensitivity to dehydration • Higher incidence of malnutrition • No clear increased risk of infection • Infants • May be at higher risk of exposure • Higher risk from dehydration

15. Institutional Hospital and residential care Pediatric units Bone marrow transplant units HIV Nursing homes Occupational Zoonoses Vets/students Handlers Researchers Hospital Staff Direct patient care Day Care Providers Working with diaper age children Social Factors and Exposure

16. Attack Rate Comparison for Milwaukee MacKenzie et al., 1994

17. Washington, DC Case Study-Approach • Demographics based • By ward • AIDS population data available • Informed by focus group and survey data • Limited DC-specific water data • adopted parameters from previous studies

18. Concentration of Oocysts • Minimal water monitoring data of Potomac • No data available on DC/Dalecarlia treatment process • Adoption of range of DW concentration from Teunis et al. (median 1.24 EE-8)

19. Drinking Water Consumption • National surveys do not give region specific data • GW drinking water survey not designed for risk assessment • Focus groups give insight into behaviors of susceptible subpopulations • Adoption of Kahn, et al. CSFII data

20. Dose response modeling • “r” adopted from Teunis, et al. (0.0042) • factor of 3 for AIDS patients adopted from Perz et al., “confirmed” in Pozio et al.

21. Clinical outcome modeling • Illness given infection (Teunis, et al.) • non-AIDS= 0.58 (beta dist.) • AIDS = 0.95 (constant) • Chronic Illness (> 7 days; from Perz, et al.) • non-AIDS = 0.15 (constant) • AIDS = 0.95 (constant)

22. Model Summary • Stratified by age, AIDS, DC ward

23. Results

24. Results, cont.

25. Results, cont.

26. Limitations • DC specific data on source water, consumpton • Prevalence of IgG • Prevalence of HAART

27. Conclusions • Consumption drives the results • good data on source waters and specific systems needed • knowledge of drinking behaviors of susceptible subpopulations essential • Distribution of AIDS population makes risk heterogeneous • Lack of specific data makes numerical estimates of little value

28. Lessons Learned • Risk assessment for susceptible subpopulations is data intensive • Data availability (AIDS behaviors) • Data “release”ability (AIDS prevalence by small geographical division) • Data compatibility (age/zip code vs. census) • Data applicability (consumption surveys measuring the right parameters)

29. Lessons learned, cont. • Small numbers increase uncertainty • Long chain of multiplied factors leads to great uncertainty if data quality is poor