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

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Presentation on theme: "Risk Assessment for Cryptosporidiosis: Incorporating human susceptibility factors John Balbus, MD, MPH Anna Makri, MA Lucy Hsu, MPH Lisa Ragain Martha."— Presentation transcript:

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

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

4 Summary of Host Susceptibility

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

6 Model fit to Dupont, et al. data P(Inf) 0.0 0.2 0.4 0.6 0.8 1.0 Oocyst Dose Ingested 01010 2 10 3 10 4 10 5 10 6 Simulated curve of 3 x “r” RR  3 RR  2 RR  1 Comparing attack rates on D-R curve

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

22 Model Summary n Stratified by age, AIDS, DC ward

23 Results

24 Results, cont.


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

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

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

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

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