Epidemiology in Water Sanitation and Health ENVR 890-Sec. 003/ENVR 296-Sec. 003 Mark D.

Transcript Epidemiology in Water Sanitation and Health ENVR 890-Sec. 003/ENVR 296-Sec. 003 Mark D.

Epidemiology in Water Sanitation and Health

ENVR 890-Sec. 003/ENVR 296-Sec. 003 Mark D. Sobsey With material from Prof. Jack Colford, UC-Berkeley Dr. Steve Luby, ICDDR,B Joseph Eisenberg, U. of Michigan

Epidemiology: A Critical Science for WSH

• • •

What it is: Study of disease and other health related phenomena in populations, time and space What it Does: Direct observations and analyses to estimate health status, disease burdens and disease surveillance, etiological agents and emerging disease threats, magnitudes and sources of health risk, impact of prevention and control measures, other population-based factors and measures.

How it does it: A powerful and mature science strongly grounded in careful observation, quantitative measurements and comparisons using robust analytical methods, many of which are statistically-based.

Epidemiology - Definition

• The logic of observation and the methods to quantify these observations in populations (groups) of individuals. • The study of the distribution of health-related states or events in specified populations and the application of this study to the control of health problems.

• Epidemiology includes: – 1) methods for measuring the health of groups and for determining the attributes and exposures that influence health; – 2) study of the occurrence of disease in its natural habitat rather than the controlled environment of the laboratory (exception: clinical trails); and – 3) methods for the quantitative study of the distribution, variation, an determinants of health-related outcomes in specific groups (populations) of individuals, and the application of this study to the diagnosis, treatment, and prevention of these states or events.

Infectious Disease Epidemiology: Classical Epidemiology

• the study of epidemics • the study of the dynamic factors involved in the transmission of infectious agents in populations • the natural history of disease – how a disease spreads through groups or a population – how a case of that disease develops in an individual

Basic Epidemiological Concepts and Terms

• Incidence: # of new cases of disease/total # at risk.

• Incidence rate: Incidence/unit of time.

• Prevalence: # cases (or # with defined condition) existing at one time.

• Prevalence rate: # of such cases/total # at risk.

• Epidemic: – # cases in excess of expected # for population – the uncontrolledspread of a disease (or condition) in a community.

• Herd immunity: cumulative # of immune persons in population or % of population immune.

Definitions of Relative Risk (RR) and Odds Ratio (OR)

• In controlled epidemiological studies one compares outcomes (e.g.,

attack rates or other disease measures) between:

• an experimental group exposed to the hazard and • an unexposed control group, selected to be otherwise as identical to

the experimental group as possible.

• Such studies apply statistical analysis to disprove the null hypothesis: – there is no significant difference in the outcome between the two

groups.

– Results are usually presented in the form of: – relative risk (risk of outcome in the exposed group/risk of outcome

in the control group, or

– odds ratio (odds of outcome in the exposed group/odds of

outcome in the control group),

– a statement of the level of statistical significance (the probability

that the stated result could have occurred by chance) is given

Definitions of Relative Risk (RR) and Odds Ratio (OR)

•

By way of definition, if the baseline rate of illness unrelated to exposure is r (the fraction of the control group who become ill) and exposure to the hazard studied increases it by a factor b, the rate observed in the exposed group is obviously br, and the relative risk is br/b = r. The odds ratio is defined as [br/(1 - br)]/[r/(1 - r)], or the ratio of ill to well exposed subjects divided by the ratio of ill to well control subjects. The odds ratio is larger than the relative risk, but the differences are small when the direct risks are 1% or less. Odds ratios are readily calculated in the analytical procedure known as logistic regression analysis, which is commonly used to analyse the effects of different factors on illness in large, multivariate epidemiological studies. Relative risk has no real meaning in retrospective case control studies of outbreaks, where the number of well, but exposed, subjects is an unknown fraction of the total population who were exposed to the hazard, and the odds ratio is therefore given instead

Outbreaks or Epidemics

A disease or condition at involves many or an excessive number of people at the same time and the same place The occurrence of a disease or condition at a frequency that is unusual or unexpected increase above background or endemic level Requirements for an outbreak or epidemic: • (i) presence of an infected host or other source of infection.

• (ii) adequate number of susceptibles • (iii) an effective method of contact for transmission to occur.

Types of Epidemiological Studies

• •

Descriptive studies Intended to describe the distribution of cases of disease in time, place and person Descriptive studies used in WSH:

– –

Ecological study Time series study

• • •

Analytical studies Case control cohort type In both, individuals/groups are compared on the basis of something, often a risk or risk factor

•

Intervention studies experimental studies that observe the impact of certain intervention (introduced change in or on people and populations) on the risk of illness

–

Example WSH intervention: POU-HH water treatment

Types of Epidemiological Studies: Ecological

Description:

Determines relationship between

disease

and

risk factors

Compares

incidence of disease

in different communities

with varying exposure to risk factors Advantages/Disadvantages: Relatively inexpensive

to do

if data are available

and

risk factors

disease rates Data available only for groups

, so

not known

individuals with disease

are

exposed to risk factor

Good for

hypotheses generation

;

can not be used as evidence of epidemiological proof

Types of Epidemiological Studies:

Time Series

• •

Description: Determines relationship between

incidence in population factor over time

and

disease variation in a risk

A kind of ecological study Advantages/Disadvantages: As a

kind of ecological study

, with the

same advantages and disadvantages

Types of Epidemiological Studies: Case-Control

Description: • Determines the relationship between disease and risk factors • Compares disease incidence in exposed individuals to matched controls Advantages/Disadvantages: • Relatively inexpensive to carry out • Generates data on individuals exposed to the risk

factors in comparison with healthy individuals

– Easy to compare diseased and healthy individuals in relation to possible risk factors

Retrospective Case-Control Studies

• Used to determine if a particular personal characteristic or

environmental factor is related to disease occurrence.

• Cases: persons who have a specific illness or disease. • Controls, those who do not have the illness or disease • Select both. – Selection may seek to "match" for variables such as age,

race, sex, etc.

• Cases and controls are queried to determine if their exposure to

environmental hazards have been similar or different

Retrospective Case-Control Studies

• Useful in disease outbreaks where it is possible to determine if certain

activities or exposures were related to the disease or illness under investigation.

– Example, cases of cholera and their matched controls are asked about

their past activity with respect to food consumption, drinking water and swimming events

– Results of questioning may show that consuming a certain drinking water

source is more likely to have occurred with cholera cases than with controls,

– This indicates a potential association between drinking water and the

disease.

– John Snow’s investigation of cholera in London was partly a case-control

study. (It was an intervention study, too - he took off the pump handle and cholera cases stopped)

• Snow on Cholera - (If you have not read this, you really must do that soon!) • The linkage between disease and exposure can be determined, but it is seldom

possible to determine the magnitude of the exposure.

Types of Epidemiological Studies: Cohort

Description:

• Compares disease rate

two, or more, populations

with

different levels of exposure

over a

specific time period

randomly selected individuals • •

Advantages/Disadvantages: Relatively expensive Generates

risk factor data

in populations by

comparing groups of randomly selected individuals

Types of Epidemiological Studies: Prospective Cohort Application to Recreational Waters

• • •

individuals are recruited immediately before or, more commonly, after participation in some form of recreational activity in which there is water exposure. A control group is similarly recruited and both cohorts are followed up for a period of time. T he exposure status of the bath. During the follow-up period, data are acquired on the symptoms experienced by the two cohorts using questionnaire interviews, either in person or by means of telephone inquiry. The quality of the recreational water environment is defined through environmental sampling on the day of exposure. The exposure data are often combined to produce a "daily mean" value for the full group of bathers using a particular water on any one day. Many days of exposure are required to define adequately the relationship between "exposure day" water quality and disease. Thus, data on "exposure" are available which can be related to "illness" outcome through an exposure-response curve predicting illness from indicator bacterial concentration. However, this approach will not provide a unique exposure measure (i.e. microbial indicator concentration) for each exposed individual and may lead to systematic misclassification bias. In addition, indicator organism counts are an indirect, and very often very

Types of Epidemiological Studies: Intervention Description:

• Compares disease rates

two or more groups (cohorts)

randomly chosen individuals after intervening to change the exposure level • •

Advantages/disadvantages: Gold standard for epidemiological proof Can be time consuming and costly

– Less costly high

and a in

developing countries

where

disease burdens are single type of WSH intervention can be studied for small cohorts

Health Outcome Evaluation for Point of Use Water Treatment

Steve Luby, MD Centers for Disease Control & Prevention ICDDRB, Centre for Health and Population Research

Outline

•

Why we evaluate health outcomes

•

Key issues for evaluating health outcomes

•

Example of a health outcome evaluation from Kenya

Why evaluate health outcome?

•

The primary rationale for improving water quality is improving health

Why evaluate health outcome?

• –

For decision makers – improving health is a compelling rationale

For public health decision makers

– • •

Within governments Within health organizations, e.g. WHO, and NGOs For individual users – appeal to health, a belief that it will help their family.

Why evaluate health outcome?

• • –

The health outcome of a water treatment intervention cannot be automatically inferred from its microbiological performance.

Laboratory conditions are different from field conditions

–

Trained laboratory workers are different from low income household users.

– –

A health outcome evaluation combines a practical evaluation of field performance an evaluation of its effect on health.

flocculant-disinfectant

Key issues for evaluating health outcomes

•

Contemporaneous control group

–

Why can’t we just look at the rates of diarrhea, before and after the intervention?

12 10 8 6 4 2 0

Diarrhea incidence by week and intervention

“Before” Observation Period “After” Observation Period Baseline diarrhea rates are different for “before” and “after” periods of study!

3 7 11 15 19

2002 Karachi Soap Health Study, Karachi 2002-3 2003

Key issues for evaluating health outcomes

•

Contemporaneous control group

–

Why can’t we just look at the rates of diarrhea, before and after the intervention?

–

Why can’t we compare to historical rates of diarrhea?

Control group diarrhea incidence by month & year Manzoor, Mujahid, & Bilal Colonies, Karachi, Pakistan 12 10 8 6 4 2 0 May Jun July Aug Sep Oct 2000

Control group diarrhea incidence by month & year Manzoor, Mujahid, & Bilal Colonies, Karachi, Pakistan 12 10 2 0 8 6 4 May Jun July Aug Sep Oct 2000 2001

Control group diarrhea incidence by month & year Manzoor, Mujahid, & Bilal Colonies, Karachi, Pakistan 12 10 8 6 4 2 0 May Jun July Aug Sep Oct 2000 2001 2002

Control group diarrhea incidence by month & year Manzoor, Mujahid, & Bilal Colonies, Karachi, Pakistan 12 Diarrhea Incidence changes yearly!

10 8 6 4 2 0 May Jun July Aug Sep Oct 2000 2001 2002 2003

Key issues for evaluating health outcomes

•

Contemporaneous control group

–

Don’t use a before and after comparison

–

Don’t use historical rates for comparison

Key issues for evaluating health outcomes

• •

Contemporaneous control group Intervention assignment

–

Consider level of assignment

Intervention Assignment n = 200

x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

Intervention Assignment n=2

Intervention Assignment n=200

Intervention Assignment n=26

Cluster Random Sampling in two locations (villages); not a bad appproach

Key issues for evaluating health outcomes

• •

Contemporaneous control group Intervention assignment

–

Consider level of assignment

–

Control population should be very similar to the intervention population

•

any difference in diarrhea rates is a result of the intervention

•

Randomized assignment works best

Kenya Intervention Assignment

600 family compounds 300 pond water users Enroll Randomize 300 river water users Traditional Sodium hypochlorite Flocculant disinfectant Traditional Sodium hypochlorite Flocculant disinfectant

Family compound characteristics

Number of compounds Mean persons per compound Female Household head literacy Primary water source Pond River Spring Borehole Water storage vessel Clay pot Plastic container Metal Flocculant disinfectant n (%) 201 10.6

1,160 (55) 127 (64) 101 (51) 96 (48) 3 (2) 0 (0) 122 (61) 76 (38) 0 (0) Sodium hypochlorite n (%) 203 11.1

1,227 (55) 127 (62) 101 (49) 99 (48) 4 (2) 1 (0) 131 (61) 74 (36) 0 (0) Control n (%) 201 11.3

1,227 (54) 125 (63) 99 (50) 98 (49) 3 (2) 1 (0) 123 (62) 77 (39) 1 (1)

Key issues for evaluating health outcomes

• • •

Contemporaneous control group Random assignment Careful, explicit sample size calculation

Sample Size Calculation

– – – – • •

How much diarrhea do you expect?

Guided by the literature or previous experience But remember there is variability

•

How much of an effect do you expect Exponential relationship between the expected difference in outcome between groups and the required sample size.

Account for clustering Account for repeated measures

Observations n = 200

Observations 4 children, each observed 50 times

Sample Size Assumptions Kenya

• • • •

Diarrhea prevalence in the bleach group would be 20% among children <2 years of age



20% difference in diarrhea prevalence between the flocculant-disinfectant versus bleach arms. 20 weeks of follow-up.

–

90% of the children would be evaluated each week.

–

Two fold loss of statistical power due to repeated measures of the same compounds.

Analysis would be conducted at the level of the compound

Key issues for evaluating health outcomes

• • • •

Contemporaneous control group Random assignment Careful, explicit sample size calculation A robust data management plan, which includes:

•

Questionnaires/Survey Instruments

–

Typically, structured and objective

–

Translated, back-translated, translated

Vetted with focus groups and others

• •

Supporting analytical/measurement data Status variables: Demographics, SES, Geoposition

–

Exposure variables

» » »

Water quality Sanitation Hygiene

–

Outcome variables

Diarrhea and other health effects

A robust data management plan

• •

More than a pile of paper forms and a spreadsheet are required Issues include:

– – – –

Valid identification of all data collected Robust error checking Linking several weeks of follow-up data with basic identification data

•

Requires relational database structure Assuring that the data base is structured in a way that permits analysis

Key issues for evaluating health outcomes

• • • • •

Contemporaneous control group Random assignment Careful, explicit sample size calculation A robust data management plan Skilled field team

Field team skills

• • •

Have a good relationship with the intervention community Can teach and motivate study activities Meticulous with data collection

Diarrhea prevalence by group children <2 year, Kenya

6 4 2 12 10 8 0 Control *Accounting for clustering n = 9,999 child-weeks of observation 19% reduction* p=0.114

Sodium hypochlorite 21% reduction* p=0.089

Flocculant disinfectant

Diarrhea prevalence by group children <2 year, Kenya

20 18 16 14 12 10 8 6 4 2 0 Control *Accounting for clustering n = 9,999 child-weeks of observation

Expected diarrhea prevalence in hypochlorite group

25% reduction* p=0.114

Sodium hypochlorite 21% reduction* p=0.089

Flocculant disinfectant

Diarrhea prevalence by group children <1 year, Kenya

4 2 8 6 14 12 10 0 Control *Accounting for clustering n = 3,300 infant-weeks of observation 25% reduction* p=0.220

Sodium hypochlorite 42% reduction* p=0.019

Flocculant disinfectant

Conclusions

•

Health outcome trials are the key scientific evidence in support of point of use water treatment

•

Point of use water treatment lends itself to sound randomized controlled trial evaluation

•

Collaborative work between environmental microbiologist, engineers and field epidemiologist can produce sound results

Acknowledgements

•

CDC/KEMRI, Kisumu

– – – –

Turbid water study team John M. Vulule Laurence Slutsker Daniel H. Rosen

•

CDC, Atlanta

– – – – –

John A. Crump Stephen P. Luby Eric D. Mintz Robert E. Quick R. Michael Hoesktra

•

Procter & Gamble Co

–

Bruce H. Keswick

•

Asembo and Gem

–

Study participants

•

SWAK

–

Alie Eleveld

Epidemiology in Water Sanitation and Health ENVR 890-Sec. 003/ENVR 296-Sec. 003 Mark D.

Transcript Epidemiology in Water Sanitation and Health ENVR 890-Sec. 003/ENVR 296-Sec. 003 Mark D.