Risk Assessment for Cryptosporidiosis:

Incorporating human susceptibility factors

John Balbus, MD, MPHJohn Balbus, MD, MPH

Anna Makri, MAAnna Makri, MA

Lucy Hsu, MPHLucy Hsu, MPH

Lisa RagainLisa Ragain

Martha Embrey, MPHMartha Embrey, MPH

Overview

Sources of data for human susceptibilitySources of data for human susceptibility Translating epidemiologic data into risk Translating epidemiologic data into risk

assessment parametersassessment parameters Review of important host factorsReview of important host factors Case study of cryptosporidiosis risk for Case study of cryptosporidiosis risk for

susceptible populations in DCsusceptible populations in DC

Risk Assessors vs. Epidemiologists

),(inf),()(inf, illchronilldingestcrypto PPPVCR

Exposure

No infection

Asymptomatic

Symptomatic

Recovery

Dead

Chronic

Summary of Host Susceptibility

Pinfection|dose Pillness|infection Exposure

Immune status Immune status Occupation

Nutrition GI disease Consumption

Non-specificimmunity

Age SexualpracticeInstitutionResidence

Human Data Sources for Dose Response Challenge studies (dose-response data)Challenge studies (dose-response data)

very small “n”, healthy adultsvery small “n”, healthy adults strain controlledstrain controlled

Outbreak data (absolute and relative rates)Outbreak data (absolute and relative rates) include children, HIV/AIDSinclude children, HIV/AIDS strain poorly characterized strain poorly characterized dose poorly characterizeddose poorly characterized attack rates influenced by doseattack rates influenced by dose

Model fit to Dupont, et al. data

P(Inf)

0.0

0.2

0.4

0.6

0.8

1.0

Oocyst Dose Ingested

0 10 102 103 104 105 106

Simulated curve of 3 x “r”

RR3

RR2

RR1

Comparing attack rates on D-R curve

Variability vs. Susceptibility

Not all differences in rates are due to Not all differences in rates are due to susceptibilitysusceptibility

Between outbreaksBetween outbreaks comparison between populations confounded by comparison between populations confounded by

dose and strain differencesdose and strain differences Between individualsBetween individuals

challenge studies show significant variabilitychallenge studies show significant variability unclear whether due to chance or differences in unclear whether due to chance or differences in

susceptibilitysusceptibility

HIV/AIDS as Susceptibility Factor Unclear increase in infection risk (Pozio, et al., Unclear increase in infection risk (Pozio, et al.,

1997)1997) Poor outcome associated with CD4 count <140-Poor outcome associated with CD4 count <140-

200200 Flanigan (1992): 34/34 HIV+ pts with persistent Flanigan (1992): 34/34 HIV+ pts with persistent

disease had CD4<200disease had CD4<200 Confirmed by Pozio (1997)Confirmed by Pozio (1997) HAART is protective; failure and non-compliance HAART is protective; failure and non-compliance

negatively affect risk. Carr (1998) Miao (1999)negatively affect risk. Carr (1998) Miao (1999)

Immunology of Susceptibility

CMI defect or Ig defect?CMI defect or Ig defect? Complex and conflicting dataComplex and conflicting data Many authors note elevated serum IgG, IgM in Many authors note elevated serum IgG, IgM in

persistent AIDS-related cryptopersistent AIDS-related crypto Flanigan (1994): Salivary IgA correlated with Flanigan (1994): Salivary IgA correlated with

clearance of crypto, not for Cozon (1994). clearance of crypto, not for Cozon (1994). HIV+ less likely to seroconvert IgG post HIV+ less likely to seroconvert IgG post

infection. Pozio (1997)infection. Pozio (1997)

Other Immunosuppressive States

TransplantationTransplantation Bone Marrow - highest risk 30-100 days post Bone Marrow - highest risk 30-100 days post

transplant. transplant. Martinon (1998) Nachbaur (1997)Martinon (1998) Nachbaur (1997)

Solid organ transplants (renal and liver)Solid organ transplants (renal and liver) Chemotherapy -Chemotherapy -often associated with lymphomas often associated with lymphomas

and leukemias. and leukemias. Russell (1998) Vargas (1993)Russell (1998) Vargas (1993)

Immunodeficiency states, esp. IgA. Immunodeficiency states, esp. IgA. Current Current (1983)(1983)

Prior Exposure as Protective Factor

Pre-existing antibody appears to convey Pre-existing antibody appears to convey decreased illness risk and possible decreased illness risk and possible resistance to infectionresistance to infection Chappell (1999): ID50 in IgG+ volunteers >20 Chappell (1999): ID50 in IgG+ volunteers >20

times highertimes higher Prevalence of prior exposure not taken into Prevalence of prior exposure not taken into

account in population-based RA’saccount in population-based RA’s

Nutrition and Crypto Causal association unclear; Causal association unclear; Griffiths (1998)Griffiths (1998)

?malnutrition>depressed immunity, or chronic ?malnutrition>depressed immunity, or chronic infection> malabsorptioninfection> malabsorption

Association with malnutrition strongest in children Association with malnutrition strongest in children

of developing countriesof developing countries. . Sallon (1988) Javier Enriquez Sallon (1988) Javier Enriquez (1997)(1997)

Many associations between vitamin and trace Many associations between vitamin and trace element deficiency and impaired innate immunityelement deficiency and impaired innate immunity relation to crypto is unclearrelation to crypto is unclear

Pre-existing GI disease

Manthey et al. (1997) reported 12 cases of Manthey et al. (1997) reported 12 cases of IBD sickened in Milwaukee outbreakIBD sickened in Milwaukee outbreak no denominator to estimate attack rateno denominator to estimate attack rate illness indistinguishable from flare of IBDillness indistinguishable from flare of IBD symptoms persisted longer than “controls” symptoms persisted longer than “controls”

(med. 17 vs. 9 d)(med. 17 vs. 9 d) all cleared by 60 daysall cleared by 60 days

Age as Susceptibility Factor ElderlyElderly

High rates of morbidity and mortality from High rates of morbidity and mortality from diarrheal disease. Lew (1991) Gangarosa (1992)diarrheal disease. Lew (1991) Gangarosa (1992)

Decreased CMI, sensitivity to dehydrationDecreased CMI, sensitivity to dehydration Higher incidence of malnutritionHigher incidence of malnutrition No clear increased risk of infectionNo clear increased risk of infection

InfantsInfants May be at higher risk of exposureMay be at higher risk of exposure Higher risk from dehydrationHigher risk from dehydration

Social Factors and ExposureInstitutionalInstitutional Hospital and Hospital and

residential careresidential care Pediatric unitsPediatric units Bone marrow Bone marrow

transplant unitstransplant units HIV HIV

Nursing homesNursing homes

OccupationalOccupational ZoonosesZoonoses

Vets/studentsVets/students HandlersHandlers ResearchersResearchers

Hospital Staff Hospital Staff Direct patient careDirect patient care

Day Care ProvidersDay Care Providers Working with diaper Working with diaper

age childrenage children

Attack Rate Comparison for Milwaukee

MacKenzie et al., 1994

Washington, DC Case Study-Approach Demographics basedDemographics based

By wardBy ward AIDS population data availableAIDS population data available

Informed by focus group and survey dataInformed by focus group and survey data Limited DC-specific water dataLimited DC-specific water data

adopted parameters from previous studiesadopted parameters from previous studies

Concentration of Oocysts

Minimal water monitoring data of PotomacMinimal water monitoring data of Potomac No data available on DC/Dalecarlia No data available on DC/Dalecarlia

treatment processtreatment process Adoption of range of DW concentration Adoption of range of DW concentration

from Teunis et al. (median 1.24 EE-8)from Teunis et al. (median 1.24 EE-8)

Drinking Water Consumption

National surveys do not give region specific National surveys do not give region specific datadata

GW drinking water survey not designed for GW drinking water survey not designed for risk assessmentrisk assessment

Focus groups give insight into behaviors of Focus groups give insight into behaviors of susceptible subpopulationssusceptible subpopulations

Adoption of Kahn, et al. CSFII dataAdoption of Kahn, et al. CSFII data

Dose response modeling

““r” adopted from Teunis, et al. (0.0042)r” adopted from Teunis, et al. (0.0042) factor of 3 for AIDS patients adopted from factor of 3 for AIDS patients adopted from

Perz et al., “confirmed” in Pozio et al.Perz et al., “confirmed” in Pozio et al.

drDInf eP 1),(

Clinical outcome modeling

Illness given infection (Teunis, et al.)Illness given infection (Teunis, et al.) non-AIDS= 0.58 (beta dist.)non-AIDS= 0.58 (beta dist.) AIDS = 0.95 (constant)AIDS = 0.95 (constant)

Chronic Illness (> 7 days; from Perz, et al.)Chronic Illness (> 7 days; from Perz, et al.) non-AIDS = 0.15 (constant)non-AIDS = 0.15 (constant) AIDS = 0.95 (constant)AIDS = 0.95 (constant)

Model Summary

Stratified by age, AIDS, DC wardStratified by age, AIDS, DC ward

),(inf),()(inf, illchronilldingestcrypto PPPVCR

Results

Age groups Mean 5th %ile median 95th %ile Mean 5th %ile median 95th %ile1 to 14Immunocompetent 1.20E-08 3.65E-13 3.41E-11 2.32E-09 7.28E-09 1.83E-13 1.93E-11 1.38E-09Immunocompromised 3.26E-08 1.05E-12 1.01E-10 6.86E-09 3.52E-08 1.03E-12 9.60E-11 6.71E-0915 to 24Immunocompetent 2.03E-08 5.71E-13 5.64E-11 4.33E-09 1.41E-08 3.48E-13 3.43E-11 2.61E-09Immunocompromised 6.24E-08 1.76E-12 1.76E-10 1.31E-08 6.00E-08 1.53E-12 1.67E-10 1.23E-0825 to 54Immunocompetent 2.46E-08 6.23E-13 6.49E-11 4.92E-09 1.41E-08 3.76E-13 3.85E-11 2.96E-09Immunocompromised 6.95E-08 1.91E-12 2.00E-10 1.45E-08 7.38E-08 1.97E-12 1.94E-10 1.42E-0855+Immunocompetent 2.44E-08 8.03E-13 7.09E-11 4.59E-09 1.39E-08 4.05E-13 3.84E-11 2.75E-09Immunocompromised 6.87E-08 2.40E-12 2.07E-10 1.37E-08 6.63E-08 2.21E-12 1.98E-10 1.34E-08

Daily Risk of Infection Daily Risk of Illness

Results, cont.

Age groups Mean 5th %ile median 95th %ile1 to 14Immunocompetent 1.03E-09 2.92E-14 2.86E-12 2.13E-10Immunocompromised 3.06E-08 9.82E-13 8.97E-11 6.07E-0915 to 24Immunocompetent 1.88E-09 4.87E-14 4.96E-12 3.94E-10Immunocompromised 6.79E-08 1.47E-12 1.59E-10 1.18E-0825 to 54Immunocompetent 2.05E-09 5.62E-14 5.86E-12 4.44E-10Immunocompromised 6.77E-08 1.82E-12 1.77E-10 1.34E-0855+Immunocompetent 2.03E-09 6.62E-14 5.90E-12 4.19E-10Immunocompromised 6.33E-08 2.07E-12 1.86E-10 1.23E-08

Daily Risk of Severe Illness

Results, cont.

Age groups Mean 5th %ile median 95th %ile1 to 14Immunocompetent 3.68E-07 1.11E-11 1.06E-09 7.62E-08Immunocompromised 1.17E-05 3.39E-10 3.34E-08 2.33E-0615 to 24Immunocompetent 6.83E-07 1.84E-11 1.88E-09 1.45E-07Immunocompromised 2.19E-05 5.69E-10 5.86E-08 4.29E-0625 to 54Immunocompetent 7.87E-07 2.21E-11 2.22E-09 1.64E-07Immunocompromised 2.57E-05 7.18E-10 6.75E-08 4.91E-0655+Immunocompetent 7.57E-07 2.29E-11 2.14E-09 1.51E-07Immunocompromised 2.25E-05 7.67E-10 6.57E-08 4.52E-06

Yearly Risk of Severe Illness

Limitations

DC specific data on source water, DC specific data on source water, consumptonconsumpton

Prevalence of IgGPrevalence of IgG Prevalence of HAARTPrevalence of HAART

Conclusions

Consumption drives the resultsConsumption drives the results good data on source waters and specific systems good data on source waters and specific systems

neededneeded knowledge of drinking behaviors of susceptible knowledge of drinking behaviors of susceptible

subpopulations essentialsubpopulations essential Distribution of AIDS population makes risk Distribution of AIDS population makes risk

heterogeneousheterogeneous Lack of specific data makes numerical estimates Lack of specific data makes numerical estimates

of little valueof little value

Lessons Learned

Risk assessment for susceptible Risk assessment for susceptible subpopulations is data intensivesubpopulations is data intensive Data availability (AIDS behaviors)Data availability (AIDS behaviors) Data “release”ability (AIDS prevalence by Data “release”ability (AIDS prevalence by

small geographical division)small geographical division) Data compatibility (age/zip code vs. census)Data compatibility (age/zip code vs. census) Data applicability (consumption surveys Data applicability (consumption surveys

measuring the right parameters)measuring the right parameters)

Lessons learned, cont.

Small numbers increase uncertaintySmall numbers increase uncertainty Long chain of multiplied factors leads to Long chain of multiplied factors leads to

great uncertainty if data quality is poorgreat uncertainty if data quality is poor