(Adapted from: National Academy of Sciences - National Research Council framework)

CHARACTERIZATION OF HUMAN HEALTH EFFECTS

ELEMENTS CONSIDERED IN HOST CHARACTERIZATION

(previous lecture)• Age

• Immune status

• Concurrent illness or infirmity

• Genetic background

• Pregnancy

• Nutritional status

• Demographics of the exposed population

(density, etc.)

• Social and behavioral traits

CHARACTERIZATION OF HUMAN HEALTH EFFECTS

ELEMENTS CONSIDERED IN HEALTH EFFECTS

(previous lecture)

• Duration of illness

• Severity of illness

• Infectivity

• Morbidity, mortality, sequelae of illness

• Extent or amount of secondary spread

• Quality of life

• Chronicity or recurrence

Characteristics or Properties of Pathogens

Interactions with Hosts (previous lecture)

• Disease characteristics and spectrum

• Persistence in hosts:

– Chronicity

– Persistence

– Recrudescence

– Sequelae and other post-infection health

effects• cancer, heart disease, arthritis,

neurological effects• Secondary spread

Epidemiology in Microbial Risk Analysis

• Problem Formulation: What’s the problem? Determine what infectious disease is posing a risk, its clinical features, causative agent, routes of exposure/infection and health effects

• Exposure Assessment: How, how much, when, where and why exposure occurs; vehicles, vectors, doses, loads, etc.

• Health Effects Assessment:

– Human clinical trials for dose-response

– field studies of endemic and epidemic disease in populations

• Risk characterization: Epidemiologic measurements and analyses of risk: relative risk, risk ratios, odds ratios; regression models of disease risk; dynamic model of disease risk– other disease burden characterizations: relative contribution tooverall disease burdens; effects of prevention and control measures; economic considerations (monetary cost of the disease and cost effectiveness of prevention and control measures

Approaches to Risk Estimation

• Direct approach: The intervention trial

– Can be used to assess risk from drinking water and

recreational water exposures

– Problems with sensitivity (sample size issue)

– Trials can are expensive (esp. in developed countries)

• Indirect approach: Mathematical models

– Must account for properties of infectious disease

processes

– Pathogen specific models

– Uncertainty and variability may make interpretation

difficult.

Elements That May be Included in Dose-Response Analysis

• Statistical model(s) to analyze of quantify dose-

response relationships

• Human dose-response data

• Animal dose-response data

• Utilization of outbreak or intervention data

• Route of exposure or administration

• Source and preparation of challenge material or

inoculum

• Organism type and strain– including virulence factors or other measures of pathogenicity

• Characteristics of the exposed population– age, immune status, etc.

• Duration and multiplicity of exposure

Dose-Response Data

and Probability of Infection for Human Rotavirus

Dose # Dosed # Infected90,000 3 39,000 7 5900 8 790 7 69 7 10.9 7 00.09 5 0

Dose-Response Models and Extrapolation to Low Dose Range

• Most dose-response data for microbes are for high

doses of microbes and few hosts

– due practicalities and cost limits

• Real-world exposures to microbes from water, food

and air are often to much lower microbial doses

• One must extrapolate the dose-response

relationship to the low dose range where there are

no experimental data points

– a best-fit modelling approach is employed

Models Typically Applied in Microbial Dose-Response Analyses

• Exponential model: Pinfection = 1 – e -rµ

where r = probability of infection and µ = mean concentration/dose

– assumes organisms are distributed randomly (Poisson) and

– probability of infection = r

– approaches a linear model at low doses

• Exponential (linear) model; two populations:

– one-hit kinetics, but

– two classes of human susceptibility to microbe

• Beta-Poisson: a distributed threshold model

– assumes Poisson distribution of microbes and a Beta-

distributed probability of infection • r is not a constant but a probability distribution (Beta-distribution)

– two variables in the model

Probabilities of Exposure and Infection

• Pexp (j Dose) = Probability of having j pathogenic microbes in an ingested dose

• Pinf (j Inf) = Conditional probability of infection from j pathogens ingested

Probability of Exposure

Exponential Dose-Response Model

Beta-Poisson Dose-Response Model

Rotavirus Dose-Response Relationships:

Experimental Data, Exponential Model and Beta-Poisson Model

Daily and Annual Risks of Various Outcomes from Exposure to Water

Containing 4 Rotaviruses per 1000 Liters

Volunteer Dose-Response Data for Norwalk Virus*

Dose (ml) No. Dosed No Ill % Ill

4 16 11 69

1 21 14 67

0.01 4 2 50

0.0001 4 0 0

*"1st passage NV": Dolin et al. 1972; Wyatt et al., 1974.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 0.0001 0.01 1 4

Dose (ml)

P(D

) F

ract

ion

wit

h E

ffe

ct Measured

Linear (exp)

Lin (2pop)

b-Poisson

Norwalk Virus Dose-Response Analysis Using Alternative Models

Dose-Response Relationships for Various

Waterborne Pathogens: Downward Extrapolation

to Low-Dose Range

Approaches for Risk Estimation: Risk Assessment Model Model

Cryptosporidium Giardia Viruses

1. Source water

Concentration (organisms per liter) (Normal Mean (SD)*)

1.06 (2.24)

2.68 (24.20)

0.93 (3.00)

Recovery rate 0.40 0.40 0.48

2. Treatment efficiency (logs removal)

Sedimentation and filtration (Mean (SD)*)

3.84 (0.59)

3.84 (0.59)

1.99 (0.52)

Chlorination (Mean (SD)

0 3.5 (2.93) 4 (2.93)

3. Water Consumption

in liters (mean (SD) ‡)

0.094 (0.42)

0.094 (0.42)

0.094 (0.42)

4. Dose Response § λ: 0.004078 λ:

0.01982 α,β: 0.26, 0.42

5. Morbidity Ratio# 0.39 0.40 0.57

Types of Epidemiological Studies Used in Risk Assessment

Databases for Quantification and Statistical

Assessment of Disease

• National Notifiable Disease Surveillance

System

• National Ambulatory Medical Care Survey

• International Classification of Disease

(ICD) Codes

• Other Databases

– Special surveys

– Sentinel surveillance efforts

Infectious Disease Transmission (SIR) Model:

Host States in Relation to Pathogen Transmission

Susceptible Infected Resistantβ β β

Pathogen

Exposure

β = the rate or probability of movement from one state to another

“Dynamic State” Epidemiological Model of Microbial

Risk - Modeling Infectious Disease Dynamics and

Transmission in Populations

• Members of population move between states

– States describe status with respect to a pathogen

• Movement from state-to-state is modeled with ordinary differential equations;

– define rates of movement between states: rate terms

• Each transmission process is assumed to be independent

• Change in fraction of population in any state from one time period to another can be described and quantified

• Different sources of pathogen exposure can be identified and included in the model

“Dynamic State” Epidemiological Model of

Microbial Risk - State Variables

“SIR” Model of Infectious Disease

State Variables: track no. people in each state at a point in time

• S = susceptible = not infectious; not symptomatic

• I = Infected

– C = carrier = infectious; not symptomatic

– D = disease = infectious; symptomatic

• R = Resistant; same as P = post infection (or) not infectious; not symptomatic; short-term or partial immunity

• In epidemiology these states are called SIR

Infectious Disease Transmission Model at

the Population Level: Dynamic Model

• Risk estimation depends on transmission dynamics

and exposure pathways. Example: Water

Model Development: Household-level Model

of Pathogen Transmission from Water

“Dynamic State” Epidemiological Model

of Microbial Transmission and Disease Risk

Diseased ICarrier I

Susceptible

Post-infection

Predicted Waterborne Cryptosporidiosis in NYC in AIDS PatientsCompared to the General Population

Adults Children Adultswith AIDS

Pediatric AIDS

Total NYC population 6,080,000 1,360,000 30,000 1,200Reported cases (1995)

40 30 390 10

Predicted tapwater-related reported cases (%of total actually reported)

2 (5%) 3 (10%) 33 (8.5%) 1(10%)

Predicted annual riskfrom tapwater unreported(% of those predicted tobe reported)

5,400(0.03%)

940(0.3%)

56 (59%) 1 (100%)

Perz et al., 1998, Am. J. Epid., 147(3):289-301

Percent of Study Subjects Reporting HCGI Symptoms and Mean Number ofEpisodes per Unit of Observation in Both Periods Combined

Group

Filtered Water (n=272) Tap Water (n=262)

Unit ofObservation

% withEpisodesa

Mean Numberof Episodesb

% withEpisodes

Mean Numberof Episodes

Family 62.0 3.82 67.7 4.81

Informant 20.0 1.70 23.1 2.10

Youngestchild

42.3 1.83 46.3 2.37

aDerived by logistic regression with covariables age, sex, geographic sub-region.bMean number of episodes among those subjects who reported at least oneepisode.

Impacts of Household Water Quality on Gastrointestinal

Illness - Payment Study #1 (An Intervention Study)

Additional Analyses of Health Effects:Health Effects Assessments

• Health Outcomes of Microbial Infection

• Identification and diagnosis of disease caused by

the microbe

– disease (symptom complex and signs)

– Acute and chronic disease outcomes

– mortality

– diagnostic tests

• Sensitive populations and effects on them

• Disease Databases and Epidemiological Data

Methods to Diagnose Infectious Disease

• Symptoms (subjective: headache, pain) and

Signs (objective: fever, rash, diarrhea)

• Clinical diagnosis: lab tests

– Detect causative organism in clinical specimens

– Detect other specific factors associated with

infection

• Immune response

– Detect and assay antibodies

– Detect and assay other specific immune

responses

Health Outcomes of Microbial Infection

• Acute Outcomes

– Diarrhea, vomiting, rash, fever, etc.

• Chronic Outcomes

– Paralysis, hemorrhagic uremia, reactive

arthritis, etc.

• Hospitalizations

• Deaths

Morbidity Ratios for Salmonella (Non-typhi)

Study Population/Situation Morb. (%) 1 Children/food handlers 502 Restaurant outbreak 553 College residence outbreak 694 Nursing home employees 75 Hospital dietary personnel 86 " 67 Nosocomial outbreak 278 Summer camp outbreak 809 Nursing home outbreak 23

10 Nosocomial outbreak 4311 Foodborne outbreak 5412 Foodborne outbreak 66

Avg. 41

Acute and Chronic Outcomes Associated with

Microbial Infections

Microbe Acute Outcomes Chronic Outcomes

Campylobacter Diarrhea Guillain-Barre Syndrome

E. coli O157:H7 Diarrhea Hemolytic Uremic Syn.

Helicobacter Gastritis Ulcers & Stomach Cancer

Sal., Shig., Yer. Diarrhea Reactive arthritis

Coxsackie B3 Encephalitis, etc. Myocarditis & diabetes

Giardia Diarrhea Failure to thrive; joint pain

Toxoplasma NewbornSyndrome

Mental retardation,dementia, seizures

Outcomes of Infection Process to be Quantified

Hospitalization

Infection Asymptomatic Infection

Mortality

DiseaseAdvanced

Illness,

Chronic

Infections

and

Sequelae

Acute Symptomatic Illness:

Severity and Debilitation

Exposure

Sensitive Populations

Health Effects Outcomes: E. coli O157:H7

Health Effects Outcomes: Campylobacter

Sensitive Populations

• Infants and young children

• Elderly

• Immunocompromized

– Persons with AIDs

– Cancer patients

– Transplant patients

• Pregnant

• Malnourished

Mortality Ratios for Enteric Pathogens in Nursing

Homes Versus General Population

Mortality Ratio (%) in:Microbe

General Pop. Nursing Home Pop.

Campylobacterjejuni

0.1 1.1

E. coli O157:H7 0.2 11.8

Salmonella 0.01 3.8

Rotavirus 0.01 1.0

Snow Mtn. Agent 0.01 1.3

Impact of Waterborne Outbreaks of Cryptosporidiosis

on AIDS Patients

Outbreak Attack Rate Mortal. Ratio (%)

Comments

Oxford/ Swindon, UK, 1989

36 Not repor- Ted

3 of 28 renal transplants pts. Shedding oocysts asymptomatically

Milwaukee, WI, 1993

45 68 17% biliary disease; CD4 counts <50 associated with high risks

Las Vegas, NV, 1994

Not known; incr. Crypto-+ stools

52.6 CD4 counts <100 at high risk; bottled water case-controls protective

Mortality Ratios Among Specific Immunocompromised

Patient Groups with Adenovirus Infection

Patient Group % Mortality (Case-Fatality Ratio)

Overall Mean Age of Patient Group (Yrs.)

Bone marow transplants

60 15.6

Renal transplant recipients

18 35.6

Cancer patients 53 25

AIDS patients 45 31.1

Databases for Quantification and Statistical

Assessment of Disease

• National Notifiable Disease Surveillance System

• National Ambulatory Medical Care Survey

• International Classification of Disease (ICD) Codes

• Other Databases

– Special surveys

– Sentinel surveillance efforts

Waterborne Outbreak Attack Rates

Waterborne Outbreak Hospitalizations

Elements That May Be Considered in Risk

Characterization

• Evaluate health consequences of exposure scenario– Risk description (event)

– Risk estimation (magnitude, probability)

• Characterize uncertainty/variability/confidence in

estimates

• Conduct sensitivity analysis– evaluate most important variables and information needs

• Address items in problem formulation (reality check)

• Evaluate various control measures and their effects

on risk magnitude and profile

• Conduct decision analysis– evaluate alternative risk management strategies

Approaches for Risk Estimation: Risk Characterization Model

Cryptosporidium Giardia Viruses

1. Source water

Concentration (organisms per liter) (Normal Mean (SD)*)

1.06 (2.24)

2.68 (24.20)

0.93 (3.00)

Recovery rate 0.40 0.40 0.48

2. Treatment efficiency (logs removal)

Sedimentation and filtration (Mean (SD)*)

3.84 (0.59)

3.84 (0.59)

1.99 (0.52)

Chlorination (Mean (SD)

0 3.5 (2.93) 4 (2.93)

3. Water Consumption

in liters (mean (SD) ‡)

0.094 (0.42)

0.094 (0.42)

0.094 (0.42)

4. Dose Response § λ: 0.004078 λ:

0.01982 α,β: 0.26, 0.42

5. Morbidity Ratio# 0.39 0.40 0.57

Comparing Risks of Disease Agents

• Need to compare chemical to microbial risks as

well as among agents of each type

• Effects vary widely in severity, mortality rates

and time scale of exposure and effects

• Need to protect both quality and quantity of life

• Drinking water policy needs to be linked to

overall public health policy

• Decision making process needs to take social

and economic factors into account

Desirable attributes of an

integrated measure of risk• Address probability, nature, magnitude

and duration of adverse health consequences

• Incorporate age and health status of those affected

DALYs as unit measures

for health• Conceptually simple:

– health loss = N x D x S

• N = number of affected persons

• D = duration of adverse health effect

• S = measure for severity of the effect

• Disability Adjusted Life Years

– mortality: years of life lost (YLL)

– morbidity: years lived with disability (YLD)

– DALY = YLL + YLD

Hypothetical example

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80

Age

Dis

ab

ilit

y w

eig

ht

Residual

disability

Premature

deathAcute

(infectious)

disease

Key Question: define health?

� ‘a state of complete physical, mental and social well-being, and not merely the absence of disease or infirmity’(WHO charter, 1946)

� ‘the ability to cope with the demands of daily life’ (the Dunning Committee on Medical Cure and Care, 1991)

� the absence of disease and other physical or psychological complaints (NSCGP, 1999)

Deriving severity weights

• Global Burden of Disease Project

– Define 22 indicator conditions

– Use Person Trade Off method to elicit

severity weights

– Panel of physicians and public health

scientists

– Use scale of indicator conditions to

attribute severity weights to other

conditions

– Methodology also applied in other studies