Risk Assessment for Cryptosporidiosis: Incorporating human susceptibility factors

Transcription

1 Risk Assessment for Cryptosporidiosis: Incorporating human susceptibility factors John Balbus, MD, MPH Anna Makri,, MA Lucy Hsu, MPH Lisa Ragain Martha Embrey,, MPH I'd like to first acknowledge my colleagues in this work, Anita Makri, Lucy Hsu, Lisa Ragain and Martha Embrey. I d also like to acknowledge support for this work from a cooperative agreement held by the George Washington University Center for Risk Science and Public Health with the US EPA Office of Water.

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 This morning I d like to talk about the host factors for susceptibility to Cryptosporidium infection, specifically in the context of risk assessment. Rather than focusing on the list of what susceptibility factors are and to what degree they are large or small, I'm going to put this in the context of the sources of data available for this susceptibility, and look at how we translate epidemiologic risk into risk assessment parameters. I'll review the important host factors, and then, for the last part of the talk, I'd like to share with you the initial results of a case study that we're doing, incorporating susceptibility factors into a cryptosporidiosis risk assessment for Washington, D.C.

3 Risk Assessors vs. Epidemiologists R = C crypto V ingest P (inf, d ) P ( ill,inf) P ( chron, ill) No infection Exposure Asymptomatic Symptomatic Recovery Chronic Dead I've put up two simplified models: the way risk assessors work and the way epidemiologists work. I think the dream for the risk assessor is to be able to sit in an armchair and contemplate the risk that starts with the raindrop falling out of the sky and landing on a piece of cow manure and ends with the payment of a hospital bill. Risk assessors hope to quantify the process at every step of the way and then express it as a linear chain of factors that get multiplied together. The epidemiologist, on the other hand, starts at the opposite end. If we're talking about field epidemiology, they're usually starting out in the field, with the body sitting there. Their big consideration is not so much the quantitation or distributions, but whether or not somebody's sick; if they're sick, whether or not they were exposed, and then what they were exposed to. This leads to a different kind of thinking, and to different kinds of results that don't always easily lend themselves to being incorporated into risk assessment. That said, I'm going to focus and try to present this information in a way useful for risk assessors and try to look at the evidence for susceptibility and break it up into the risk assessor's language: either those factors that increase the risk of infection; those that increase the risk of a bad outcome, given infection; and those that may change the actual exposure.

4 Summary of Host Susceptibility Pinfection dose Pillness infection Exposure Immune status Immune status Occupation Nutrition GI disease Consumption Non-specific immunity Age Sexual practice Institution Residence Immune status is going to drive the changes in susceptibility to cryptosporidiosis. To some degree, nutrition and nonspecific or innate immunity may affect the probability of infection given dose. We know that underlying GI disease and, to some extent, the extremes of age, influence the outcomes when somebody is infected, and there are many factors that actually influence the likelihood that somebody is going to be exposed and become ill.

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 For a risk assessment, the human data sources fall into two categories. The first one is the laboratory-based challenge studies. These provide the most precise doseresponse data, because these studies are the ones where we know what the exact dose. They're characterized by having a very small number of subjects. The studies that have been done have involved healthy adults and have not included those sensitive subpopulations that we think of--children, elderly, AIDS patients, for ethical reasons. In laboratory based studies we know that everyone who is exposed gets exposed to the same strain, so the virulence and some of the host factors are controlled. The second data source is the large number of outbreak investigations. Here, the parameters we use to infer dose-response are the absolute and relative rates; either attack rates or incidence rates. Here, we have data on sensitive subpopulations but, we have problems because strain and dose are generally very poorly characterized, so the utility for dose-response data is more limited.

6 Comparing attack rates on D-R curve RR 1 P(Inf) 1.0 RR RR Oocyst Dose Ingested Model fit to Dupont, et al. data Simulated curve of 3 x r In particular, I want to discuss how attack rates, which is the parameter we're really looking at to distinguish different populations as epidemiologists, are influenced by dose. This is the dose-response curve from the DuPont, et al. research, the very first feeding study. This is a hypothetical curve with three times the infectivity parameter superimposed on that. The point I want to make is what you see when you look at your outbreak investigation depends very much on the doses you're looking at. If we're down in the endemic range, the kind of background range, you can see that no matter what the susceptibility is, what's really driving whether somebody gets sick or not is consumption. Interestingly, most of the waterborne outbreaks we've seen have attack rates somewhere in the 20 to 50 percent range, so we can infer if this doseresponse is right, the dose is somewhere in the first or second order of magnitude exposure in the waterborne outbreak setting. There, we do see a difference in attack rates from a difference in intrinsic susceptibility. Then, as we get higher up in the curve up to large numbers ingested, which tends to be what we see more frequently in a food-borne setting, the differences between susceptibility starts to get lost as we basically reach a high infective dose for susceptibile and nonsusceptible people alike.

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 Not all the differences in rates are due to susceptibility. Some of this depends on other factors. If we're comparing different outbreaks, or comparing attack rates between populations, like an outbreak in a nursing home and an outbreak in a military base, we could say that differences in those rates were due to the intrinsic susceptibility of elderly people versus young, healthy military recruits. In an outbreak setting, we're not able to characterize strain and dose. What looks like a difference in susceptibility may actually be a difference in dose or virulence of the infecting Cryptosporidium. Even if we look between individuals in these dosing studies, it's not always clear what we can infer. We know that any time you have a dose response and you don't have a step shape to the dose-response but you have some kind of slope to the dose-response, there is some kind of variability in infectivity. What's unclear is whether that is due to chance--to some kind of stochastic factor or whether it's due to some difference in innate susceptibility, cell surface receptors, the whole genetic background, or acquired differences in the immune response.

8 HIV/AIDS as Susceptibility Factor Unclear increase in infection risk (Pozio( Pozio,, et al., 1997) Poor outcome associated with CD4 count < 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) With that as background, let's take a look at some of the data for the different factors that play into immunity. It's clear that immune status is the largest susceptibility factor among humans. What's less clear is whether or not that susceptibility is due to a difference in the risk of acquiring an infection or whether it's all due to the risk of becoming very ill once an infection has set in. The study by Pozio, et al., which looked at an outbreak in Italy where there were co-exposed HIV-positive and non-hiv-positive populations, gives unclear data. We know that among the AIDS population, a very poor outcome is clearly associated with CD4 count, starting in the range of 140 to 200 cells per cubic millimeter. Below that, you start having problems clearing the infection, and below 100 is when you see the inability to clear infection and high mortality rates. The original data for this came from a study by Flanigan, in which 34 out of 34 HIV-positive patients who had persistent cryptosporidiosis, had CD4 counts less than 200. This was confirmed by the Pozio study. Interestingly, AIDS, much like chemotherapy, has become a state of reversible immune deficiency in the last three years, as highly active antiretroviral therapy has come into play. There is evidence that if you raise the CD4 count with HAART, people start to clear their infections. Conversely, failure and noncompliance with the HAART can return the risk to the baseline.

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) One of the questions with the HIV population, and immune status in general, is whether the susceptibility lies within a cell-mediated immunity, T-cell, defect or an immunoglobulin, B-cell, mediated defect. At this point, the data are complex and conflicting. We know clearly that cell-mediated immunity is involved. That's the major defect in the populations susceptible to cryptosporidiosis, whether it's chemotherapy or AIDS patients. But there's a lot of evidence for immunoglobulin disregulation, and a large question is whether or not there's a defect in the ability to generate the right specific immunoglobulin, usually secretory IgA, in the right place at the right time and maybe even to the right antigens. We'll be hearing about that later today.

10 Other Immunosuppressive States Transplantation Bone Marrow - highest risk days post transplant. Martinon (1998) Nachbaur (1997) Solid organ transplants (renal and liver) Chemotherapy -often often associated with lymphomas and leukemias. Russell (1998) Vargas Chemotherapy (1993) Immunodeficiency states, esp. IgA (1983) IgA. Current In addition to HIV status, other immunosuppressive states have been shown to increase the risk of bad outcomes with cryptosporidiosis. It is unclear whether this is increased risk of infection or just inability to clear it. People who undergo bone marrow transplantation are at their highest risk 30 to 100 days post transplant. That correlates with their greatest loss of both immunoglobulins and cell-mediated immunity, and the same kind of phenomenon is seen with people who undergo solid organ transplants. Chemotherapy, especially that associated with hematopoietic cancers, where the target cells of the chemotherapy are actually the immune cells themselves, is associated with increased risk of severe cryptosporidiosis. The infection resolves when the drop in the white count resolves. Lastly, inborn or genetic immunodeficiency states, especially IgA deficiency, has been associated with severe cryptosporidiosis.

11 Prior Exposure as Protective Factor Pre-existing 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 Immunosuppression and immunodeficiency are clearly very major susceptibility factors. One protective factor, if you will, or change in susceptibility, that has not been well worked into risk assessment to this point, is prior exposure. It appears that preexisting antibodies convey at least a decreased illness risk. One study by Dr. Chappel did show a 20-fold increase in the ID 50 among healthy volunteers who were IgG positive, so there's some evidence for a change in the actual probability of infection in people who have preexisting exposure. In most of the major risk assessments for cryptosporidiosis that have been done, prevalence of prior exposure in the population has not been taken into account. As we get more sophisticated, I think we will want to include this factor.

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 What about nutrition and malnutrition as a risk factor or a susceptibility factor for cryptosporidiosis? There's a lot of evidence, particularly among children in developing countries, for a very strong association between malnutrition, depressed immunity and chronic cryptosporidiosis. What's unclear is if this is a chicken and egg phenomenon. And it's difficult to sort this out. We know severe malnutrition will, after a time, result in depressed cell-mediated immunity, and so it's unclear whether the malnutrition is the actual risk factor that leads to chronic cryptosporidiosis. We could also infer that multiple different kinds of intestinal infections may alter the intestinal mucosa and alter the immune function of the intestine, leading to increased susceptibility. Or, conversely, the malabsorption and malnutrition seen is a result of the infection and that's what is actually preceding the malnutrition. This is more of a factor in the developing countries where severe levels of malnutrition are observed. It is less clear if trace element deficiency and the kind of the subtle differences in nutrition that we see in the developed world, particularly in the elderly, lead to any kind of impaired innate immunity. There's some speculation on that and not a lot of hard data.

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 What about preexisting GI disease? This is something that, as a physician, intuitively makes sense. There is not much data out there about this. One report that came out of the Milwaukee outbreak noted, in a single GI practice, 12 people who had previously well-controlled inflammatory bowel disease presented with an illness that was more or less indistinguishable from their inflammatory bowel disease: they had cramping, diarrhea, and some bleeding, which is not a usual factor in normal subjects, but in people with inflammatory bowel disease it was associated with cryptosporidial infection. The people who developed this had more chronic, or longer lasting symptoms than the median from the Milwaukee study in general, but none of them had severe outcomes or inability to clear the infection, as was found with the HIV population.

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 It's very clear, both in the elderly and in infants, that there is a high rate of mortality and morbidity from diarrheal disease in general. I think the evidence points mostly to age-related changes in physiology and susceptibility to diarrhea and dehydration, which can lead to morbidity and death. Especially in the elderly poor, there may be a higher incidence of malnutrition. But in the elderly, there's really no clear increased risk of infection. It's more of a question of developing a bad outcome, given the infection. Infants in particular are less likely, of course, to be previously exposed, and so that may be a minor susceptibility factor during the time between when they are protected by maternal antibodies and when they would have to generate their own immunoglobulins. They're clearly at higher risk for poor outcomes, as they get dehydrated much more quickly and can die much more quickly.

15 Social Factors and Exposure 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 This is a list of different kinds of factors that relate to increased exposure. The ones that I've listed here relate to where people live or setting where they may be exposed. People who are institutionalized in hospitals, particularly if they are immunosuppressed already, have been reported to be at risk. Nursing homes may be a kind of captive population that are at risk from outbreaks. And then, where people work or go during the day is also a risk factor for getting them in contact with Cryptosporidium.

16 Attack Rate Comparison for Milwaukee MacKenzie et al., 1994 I want to show, as a last part of this discussion, how we use attack rates to make inferences. This is from Mac Kenzie et al., a study on the Milwaukee outbreak from the New England Journal of Medicine. You can see there was not a huge difference in attack rates. Let me repeat that in using this kind of data to infer susceptibility, we're assuming that everybody has more or less the same exposure. I think this shows, if we look at age, a kind of hump in the middle where young adults had the highest attack rate, so there is no evidence from this for lower infectious doses at the extremes of age. But as I've already said, this may be driven by consumption and dose much more than any intrinsic susceptibility. The importance of dose is shown here by the proximity to the waterworks.

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 I am going to move very quickly through my case study. The idea of this case study was to try to operationalize some of the concepts of susceptibility on a regional basis, which may be of use to regional water utilities and public health officials. We had done some limited focus group and survey work in the Washington D.C. area that tried to capture attitudes and beliefs about drinking-water consumption among the general population, and among people with diseases, such as HIV, to get their attitudes and practices with drinking water. We wanted to try to incorporate this into a risk assessment process. Unfortunately, due to lack of availability, we started off with very limited D.C.-specific water data. We adopted parameters from other risk assessments that had been done. I want to emphasize again these are preliminary results.

18 Concentration of Oocysts Minimal water monitoring data of Potomac No data available on DC/Dalecarlia Dalecarlia treatment process Adoption of range of DW concentration from Teunis et al. (median 1.24 EE-8) For the concentration of oocysts, we didn't have any region-specific data. We adopted the range of drinking water concentration from Teunis et al., which was based on the Netherlands water supply, probably an underestimate for Washington, D.C.

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 For drinking water consumption, we were hopeful that we would be able to use our water survey to inform the national surveys. The national surveys didn't give region-specific data. We adopted the most recent CSFII (Continuing Survey of Food Intakes by Individuals) data by Kahn, et al.

20 Dose response modeling P Inf = 1 e (, D) r d r adopted from Teunis,, et al. (0.0042) factor of 3 for AIDS patients adopted from Perz et al., confirmed in Pozio et al. For our dose-response model, we adopted the exponential model. The R value was estimated by Teunis from the DuPont study, and we used the factor of three for AIDS patients that was developed by Perz, et al. There is not a lot of strong evidence for increased risk of infection in the AIDS population. We did a backwards inference. Noting a 10-fold increase in risk of bad outcomes and other data that suggested maybe a 1.5-times higher risk of clinical illness, we inferred that there may be some element of increased risk of infection given exposure among the AIDS population, and used the factor of three that Perz had used.

21 Clinical outcome modeling Illness given infection (Teunis( 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) For clinical outcome modeling, we used the parameters from Teunis, and for chronic illness, we used the parameters from Perz. We defined chronic illness as being greater than seven days.

22 Model Summary R = Ccrypto Vingest P(inf, d) P ( ill,inf) P ( chron, ill) Stratified by age, AIDS, DC ward This was our final model, and we tried to stratify by age groups, AIDS population, and look at the data ward by ward. We would like to match this to a map of water distribution of the city and try to infer risk from different kinds of distribution accidents or events.

23 Results Daily Risk of Infection Daily Risk of Illness Age groups Mean 5th %ile median 95th %ile Mean 5th %ile median 95th %ile 1 to 14 Immunocompetent 1.20E E E E E E E E-09 Immunocompromised 3.26E E E E E E E E to 24 Immunocompetent 2.03E E E E E E E E-09 Immunocompromised 6.24E E E E E E E E to 54 Immunocompetent 2.46E E E E E E E E-09 Immunocompromised 6.95E E E E E E E E Immunocompetent 2.44E E E E E E E E-09 Immunocompromised 6.87E E E E E E E E-08 These are our results. We modeled daily risk of infection, daily risk of illness, and on the next slide, you'll see daily risk of chronic illness for these age groups. Our numbers are very small, which is related to the very small concentration from the Netherlands. Here's the daily risk of illness. We calculated yearly risk by multiplying the daily risk by 365.

24 Results, cont. Daily Risk of Severe Illness Age groups Mean 5th %ile median 95th %ile 1 to 14 Immunocompetent 1.03E E E E-10 Immunocompromised 3.06E E E E to 24 Immunocompetent 1.88E E E E-10 Immunocompromised 6.79E E E E to 54 Immunocompetent 2.05E E E E-10 Immunocompromised 6.77E E E E Immunocompetent 2.03E E E E-10 Immunocompromised 6.33E E E E-08

25 Results, cont. Yearly Risk of Severe Illness Age groups Mean 5th %ile median 95th %ile 1 to 14 Immunocompetent 3.68E E E E-08 Immunocompromised 1.17E E E E to 24 Immunocompetent 6.83E E E E-07 Immunocompromised 2.19E E E E to 54 Immunocompetent 7.87E E E E-07 Immunocompromised 2.57E E E E Immunocompetent 7.57E E E E-07 Immunocompromised 2.25E E E E-06

26 Limitations DC specific data on source water, consumpton Prevalence of IgG Prevalence of HAART Among the limitations of the study was a great lack of D.C.-specific data. We were not able to incorporate prevalence of prior exposure, or the prevalence of people on HAART. I'd say that the prevalence in Washington D.C. is probably lower than the populations in some other areas, but we don't have hard data for that assumption.

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 Our conclusions were: consumption drives the results; for this level, the distribution of the AIDS populations makes the risk heterogeneous more than any other factor; and certainly, the lack of specific data makes the actual numerical estimates of little value at this point.

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) The major lesson learned from this exercise is that risk assessment is incredibly data-intensive. If you try to do risk assessment for susceptible populations, your data needs increase exponentially. There's a problem with data availability for susceptible subpopulations. They're hard to survey, hard to access.

29 Lessons learned, cont. Small numbers increase uncertainty Long chain of multiplied factors leads to great uncertainty if data quality is poor When you get into small numbers at a regional level, you get problems of whether or not the data will be released to you. If you're trying to do a geographic analysis, your cell numbers may be too small for the public health agencies to release information due to confidentiality restrictions. There are problems with data compatibility. When you use more and more different kinds of sources of data, you're more likely to have a data sets stratified in a different way. Lastly, the data applicability; whether or not the consumption surveys are measuring the right kind of parameters for you is an important consideration. AUDIENCE PARTICIPANT: What's the treatment for cryptosporidiosis once it's diagnosed? DR. BALBUS: The question was what's the treatment for cryptosporidiosis once it's diagnosed. I don't think that there is a standard treatment. In general, in the general population, there's no need for treatment, it's self-clearing. There have been a number of agents that have been tried in immunosuppressed populations, including azithromycin, something called paromomycin and a number of other experimental drugs. I'm not an infectious disease expert, so I can't tell you where we are right now at September 28th. There is some thought that the use of azithromycin as a prophylactic medication for AIDS patients for Microbacterium avium has actually led to an observed decrease in cryptosporidiosis over the last couple of years in conjunction with the use of antiretroviral therapy. (transcript continued next page)

30 Lessons learned, cont. Small numbers increase uncertainty Long chain of multiplied factors leads to great uncertainty if data quality is poor Transcript continued: I think that the future is actually looking as much towards immune--either vaccination or immune modulation and immune therapies as to traditional kind of antibiotics. AUDIENCE PARTICIPANT: [Off mic.] DR. BALBUS: One of the things that I wanted to say is that one of the differences between risk assessors and epidemiologists is that risk assessors never have to deal with sick people. You know, risk assessment exists in the sterile environment of the silica chip, and so these are putative cases. This is not based on survey data, this is an estimated risk based on the parameters that were given there. The actual number of cases is actually much higher, which, I think, is due to the fact that the exposure levels are probably much higher in D.C. than they are in the Netherlands. Thank you.