prelim guide (autosaved)

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2. DISEASE AND POPULATION

CASE DEFINITION

Does it specify characteristic shared by all members of the class

being defined?

Does it specify what distinguished them from others outside the

class?

Is the data readily available?

What are the options for creating case definition?

When little is known the disease, can be developed inductively

basedon the shared, readily available observable clinical

characteristics

In acute outbreak, shared causes, place and time of occurrence

are themselves sometimes included

When is consistent definition useful?

For valid comparison

When is 2+ case definition useful?

facilitate comparison when case definition has changed over time

when extent of evidence may vary among cases e.g definitive,

probable, possible cases during outbreak

assess degree to which results depend on case definition

What is drawback of broad case definition?

Low specificity, leading to bias and reduced statistical popwer in

case control studies

What is the drawback of narrow case definition?

If later suspected to be too narrow, may be difficult and time

consuming to go back and find the missed cases

Epidemiologic case definition vs Clinical Diagnosis

Clinical diagnosis epidemiologic study

guide treatment choice,

inform about prognosis

Evidence may include costly

and/or invasive tests

quantify population burden,

assess disparities, identify

shared causes

Evidence needed must

usually be feasibly obtainable

on a population scale

DISEASE MODELS

Is it non recurrent disease?

Alzeimers, Osteoarthirtis, Suicide, Homicide, SIDS

Is it recurrent disease?

Depression, UTI, Low back pain

Are all people at risk?

Who are not at risk?

Diseased people are not at risk

Immunized people are not at risk

Does it have susceptible state?

Does it have immune state? Is immunity throughout the life?

Does it have fluctuating at risk periods? Eg: occupational injury

Is duration of disease event negligible?

Use line diagram

POPULATION

Defined vs Undefined population

Why to define population?

to know its size

generalizability

Population definition

Does it specify characteristic shared by all members of the

population being defined?Personal attributes such as age, gender, momebership;

Geographic scope, time or time period

Does it specify what distinguished them from others outside the

population?

Link between Cases and the population at risk

If each of the cases had not developed the disease, would he or

she still have been included in the population?

If each of the non-cases in the population had developed the

disease, would he or she have been included as a case?

Defined population observed over time

Is it a closed population?

all members initially at riskNo gains or losses in embership during period of observation

(except due to disease itself)

Able to identify and count all new disease cases over a fixed time

period

Is it a open population?

population at risk can gain or lose members during time period of

interest. Eg births and deaths, migration, occurrence of new cases

and recovery of old ones


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3. DISEASE FREQUENCY: BASICWhat is dual interpretation of disease frequency?

Population disease burden

Individual disease risk

PREVALENCE

Count of prevalent cases

no. of people in the diseased state at that time

Prevalence

No. of prevalent cases/Size of population

What is possible fixed time?Is it a Calendar time ?

Is it Age?

Is it Time relative to some salient event?

INCIDENCE

Cumulative Incidence

Number of people who develop the disease/ No of people initially

at risk for it

Is it a closed population?

Is timing of disease occurrence within the time period of interest?

Is each person counted as case only once?

What is it statistically?

ProportionWhat are other names?

Incidence proportion

Attack rate

Can time period for each individual be different?

Yes

Incidence Rate

No of incident cases/Amount of person-time at risk What is it statistically?

Rate

How to deal with recurrent event?

If has to be counted in numerator, also add person-time in

denominator

If not to count in numerator, dont add person-time indenominator

Estimating incidence rate when detail data is not available

Two ways

Average number of person * total duration of follow up time

Total person follow up * Average duration of follow up time

What are approaches to estimating average population at risk?

Population size at mid-period

Average of population size at start and at end of observation

period

Average several population size estimates made periodically

during the observation period

Is prevalence case included in population at risk? Is it small enough to be negligible?

Denominators other than person-time

Vehicles miles travelled

Comparison of Cumulative Incidence and Incidence Rate

Variants of Incidence

Mortality

Mortality Rate = No of death/Person time at risk

Mortality Rate= Death rate

Cumulative Mortality = No of death/Population at risk

Cumulative Moratlity = Mortality Density

Fatality

Case fatality = Number of fatal cases/Total number of cases

Proxy Measure of Incidence

Proportional mortality = Deaths from disease/Death from all causes

Be aware of pitfall !

When can proportional morality be valid for comparison?

If the number of denominator is equal and person time of follow-

up is equal between two population

What can increase in proportional mortality mean?

Increase in disease

Decrease in other disease

Increase in relative size of that segment of population

Change in criteria for case definition

Proportional incidence = No of cases of certain disease/no of cases in a

larger category that contains it

Fetal Death ratio= No of fetal deaths/Number of live births

OTHER MEASURES OF DISEASE FREQUENCY

Period prevalence

Hybrid of prevalence and cumulative incidence

Period prevalence = P + (1-P). CI

What is the main limitation?Point prevalence and cumulative incidence convey very different

kinds of information about disease frequency. Those distinction are

lost when they are combined, which limits usefulness as summary

measure.

Years of potential life lost


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4. DISEASE FREQUENCY: ADVANCEDPREVALENCE

Length biased sampling?

A sent of prevalent cases tends to be skewed toward cases with

more chronic forms of the disease, why?

Other things being equal, a persons probability of being

captured as a prevalent case is proportional to the duration of his or

her disease.

What is issue in evaluating screening program?

Screening is like prevalence survey, and cases detected byscreening tend to be skewed toward more slowly progressive forms

of pre-symptomatic disease

Estimate confidence interval of prevalence

Stata command .cii n r

CUMULATIVE INCIDENCE

Estimating Cumulative incidence in presence of Censoring

Kaplan Meier method

.stset y deltadelta indication for censoring

.stsumsummarize total time at risk

.sts, lost gives KM graph

.sts list gives detailed information

.stcigives median survival time with 95% CI

RELATIONSHIPS AMONG DISEASE FREQUENCY MEASURES

Rates in Total population and its sub population

Example

Incidence Rate and Cumulative Incidence

Prevalence, Incidence and Duration of disease

Incidence of Two disease

Incidence rate in two stage process

Mortality, Incidence and Case Fatality

Is all data from same year?

If no, think that they may not have come to equilibrium yet.


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6. SOURCES OF DATA ON DISEASE OCCURRENCE

NUMERATOR DATA

Death Records

Birth Records

Foetal Death Records

Disease report

Cancer, Birth defects, Trauma

Disease Registries

Health Care Records

Hospital Discharge Data

Clinical data (out patients)

Laboratory records

Pharmacy records

SURVEYS

National Health Interview Survey (NHIS)

National Health and Nutrition Examination Survey


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NAMCS and NHAMCS

Behavioral Risk Factor Surveillance System (BRFSS)

DENOMINATOR DATA

Common sources of denominator data

U.S. Census

Administrative records

HMO enrollment records

Employment or labor union records

Alumni rosters

Etc.

Birth certificates for perinatal epidemiology

U.S. census data

USES OF MULTIPLE DATA SOURCES

Excluding ineligible cases

Validating using two data sources

Estimating completeness of data sources

Capture-recapture sampling: data layout

Can estimate x if can assume that capture by each data

source is independent of capture by the other

Independence assumption implies that 50/250 = 10/x

Hence x = 10 250/50 = 50

Estimating total (known + unknown) cases

Estimated total cases (known + unknown) = 360about

15% higher than simple tally of 310 known cases

What is the pitfall of capture recapture method?

Assumption of independent capture not easily tested and

may not always be plausible

ReducingMisclassification

Verifying using two data sources


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7. PERSON, PLACE AND TIME

USES OF DESCRIPTIVE EPIDEMIOLOGY

Quantifying disease burden

Assess health disparity

Generate hypothesis

Help target efforts at early detection

Detect emerging threats to public health

PERSON DISTRIBUTION

AgeImmunity

Human development

Slowly progressive disease

Age related variation in lifestyle

Gender

Biological (anatomic and hormonal)

Non biological (social)

Race and Ethnicity

What is the major problem with race data?

Misclassification

Definition of race of infant is different, so caution in interpretation

Socio-economic status

Marital Status

PLACE DISTRIBUTION

What is the limitation of spot map?

Only number of cases is spoted, do not account for distribution of

population at risk

What might be the underlying causes?

Geographical variation is due to variation in physical environment

Socio cultural variation

Differences in medical practice

TIME DISTRIBUTION

Secular trend

Cyclic variation

Age period and Birth Cohort

Calendar year age = year of birth

COHORT EFFECTS

Lung cancer mortality in U.S. women

Age effect within a calendar year

Calendar effect within age group

In general, women along a diagonal belong to the same birth

cohort

Why consider birth cohort?

Shared experiences at an earlier age can affect future

disease risk

Can provide a simple explanation for otherwise puzzling

pattern of variation in rates by age

Lung cancer death rates

Connected by calendar year of death

Connected by birth cohort


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8. INFERING CAUSAL RELATION BETWEEN EXPOSURE AND

DISEASE

Why is causal relationship important?

Individual decision making

Clinician to take decision on behalf of their patients

Public health practitioners

Cause

If the factor was not present, would some of the disease not

happen? Not Necessary factor

Is it a contributing component of a more complex mechanism

involving other factors?Not sufficient factor

Causal inference guideline

1.Randomized trial evidence exists

2.Temporal sequence is correct

3.Association is strong

4.Association is biologically plausible

5.Association is strongest when predicted to be so

6.No alternative explanations exist (No confounding)

7.Observed evidence is consistent

It is systematic way of thinking causalityIt is no proof, it is judgment

COMMON MISPERCEPTIONS OF THE NATURE OF CAUSES OF ILLNESS

AND INJURY

There are direct and indirect cause of disease, and the direct

causes and more important

In order to be considered a cause of disease, an exposure must be

present in every case

In order to be considered a cause of disease, an exposure must be

capable of producing that disease on its own

If there are heterogeneous conclusion among studies, what are

possible explanations?

Some interacting agents may be missing in oneResults may be different in different settings


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9. MEASURE OF EXCESS RISK

WHEN INCIDENCE RATE CAN BE ASCERTAINED

Measu

re

Formula Helps answer the question

RR Ie/Io Does exposure (E) cause (D)?

AR Ie-Io Among persons exposed to (E) what amount of

incidence of D is E responsible for?

AR% (RR-1)/RR*

100%

(Ie-Io)/Ie *

100%

What proportion of occurrence of disease in

exposed individual was due to (E)? VACCINE

EFFICACY IS JUST THE

AR%: (Iunvac Ivac)/Iunvac x 100%

PAR It-Io Should resources be allocated to controlling E or,

instead, to exposures causing greater health

problems in population?

PAR % (It-Io)/It

*100%

What portion of D in the population caused by E?

Should resources allocated to combating D be

directed toward etiologic research or control of

known etiologies?

1/AR Number needed to treat

Relative effect for positive associations

(Relative excess risk)

= RR 1

Relative effect for negative associations

= 1 RR

WHEN INCIDENCE RATE CAN NOT BE ASCERTAINED

RR is equal, AR is higher in population A comparted to B

For Relative risk of given size, RD or AR associated with a given

exposure will be larger for common illness than for rare illness

What does PAR% depend on?

RR

Proportion of population exposed

Measure Formula

OR ad/bc Use the OR in case-control studies toapproximate the RR only when the

outcome is rare in the source

population from which cases and

controls were drawn

AR It /[Pe+1/(OR-1)]

Io (OR-1)

Pe = proportion exposed in

population as a whole: b/(b+d)

AR% [(OR 1)/OR]

x 100%

PAR AR x Pe

PAR % AR% * Pc Pc = proportion of cases who are

exposed: a/(a+c)


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10. MESUREMENT ERROR

SOURCES OF MEASUREMENT ERROR

Measurement in Exposure status

Interview/Questionnaire:

Has subject been misinformed about his/her exposure status?

Has subject intentionally misrepresented it?

Direct measurement

Does the variable vary over time? Eg Blood pressure, seru

cholesterol

Records:Is record complete and faithful?

Measurement of Health outcome

Asymptomatic cases

Is current diagnostic technology applied to everyone who might

meet criteria for being a case

Diagnostic test

Is it appropriate diagnostic test, able to distinguish between who

had disease and who has not?

Information

Is all information available through the source of information in

use ?

Are both exposed and unexposed group followed for same period

of time?

ASSESSING MEASUREMENT ERROR

Why are quantitative indices of measurement error useful?

choosig among different measures of same characteristic in

design phase

Detecting data quality problems during staff training and data

collection

gauging how large role measurement error may have played in

determining study results during data analysis

RELIABILITY

Concordance

What is limitation of Concordance?

It fails to account for agreement that chance alone could produce

KappaIn STATA

.set obs 4

.input clinc self count

1. 0 0 1117

2. 1 0 1323. 0 1 128

4. 1 1 170

. kap clin self [freq=count], tab

Interpretation Guidelines

What is limitation of Kappa?

Kappa declines as prevalence approached 0 or 1. This property

should be kept in mind when comparing Kappas among populations

in which the prevalence of the characteristic under study differs

substantially

What is impact of low Kappa?

As Kappa approaches 0, attenuation of the OR becomes severe,

and a true exposure-disease association may go undetected due tomeasurement error

Intraclass Correlation Coefficient

VALIDITY

Sensitivity and Specificity

What is properties of sensitivity and specificity?

Not dependent on frequency or prevalence of the trait among

persons tested

Relative stable characteristic of a test, but may vary slightlyaccording to: Who performs the test, test setting, Disease severity

ROC curve

Plot sensitivity against 1-Specificity

The more accurate a test is, the farther toward the upper left its

curve falls in an ROC plot. This rule applies even if the two tests yield

results in entirely different units on totally different scales

AUC (Area under curve)

Summary measure of test accuracy based on ROC curve

0.5 is useless test, 1 is perfect test

CONSEQUENCE OF MEASUREMENT ERROR

Differential misclassification

Is ascertainment of exposure status influenced by the presence or

absence of disease?

Is ascertainment of disease status influenced by the presence of

exposure?

Are exposed and unexposed group followed for same period of

time while ascertaining disease?

What is the impact of differential misclassification?

Can falsely exaggerate or falsely minimize an association

Non differential misclassification

What is the impact of non-differential misclassification?

Bias towards the null

What is strategies for minimizing misclassification?

Sharpen the tool: Use tools with highest reliability and validity


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11. CONFOUNDING AND ITS CONTROL

What are the necessary conditions of being confounding?

Factor is associated with disease of interest

cause or having disease recognized (eg: papsmear and cervical

cancer

It can be cause or correlate of cause (eg: race biological and

socioeconomic; age is correlate of cause)

Factor is associated with exposure of interest

Factor is not in causal pathway between exposure and disease

(mechanism by which exposure causes disease) How to assess confounding?

Step 1: Is smoking associated with MI risk? Look in unexposed

Step 2: Is smoking associated with coffee drinking? Look in non-

diseased

Step 3: Is factor in the causal pathway?

When do Confouder-disease and confounder-exposure result in

confounding?

Strong association between exposure & factor and between

disease & factor

Crude and stratum specific effect measures differ

--?Crude and adjusted effect measures differ

When is a factor not to be considered as a potential confounder?

Its not associated with exposure in source population for cases

(look at source population or control or non-diseased populationnot in cases)

Its a surrogate measure of exposure (Obesity: abdominal

breadth, BMI)

Its a consequence of exposure

Its not predictive of disease occurrence apart from its association

with exposure (only look at unexposed people)

Its a consequence of outcome

When can consequence of exposure be adjusted?

When we are interested to look in the association except from

that pathway

CONFOUNDING CONTROL IN DESIGN PHASE

1. Randomization

What is attraction of randomization?

Removes association between exposure and potential

confounders (usually)

Controls confounding by unknown or immeasurable confounder

What is limitation of randomization?

Confounding may still occur due to accident of randomization

What is remedy of accident of randomization?

Increase size of study, use stratified randomization methods,

handle as for observational study & do multivariate analysis

2. Restriction

Requires all members of study population to have same status on

potential confounder(s)

When is restriction most useful?

Most useful when most potential subjects have same status on

potential confounder (eg: look at only singleton in prenatal studies)

What is limitation of Restriction?

Enhances ability to make statistical inference because we loose

some precision when controlling for confounding

It reduces generalizability, can conclude for those who are

restricted

Eliminates ability to assess effect modification

May be difficult (or expensive) to find sufficient subjects.

3. Matching

Cohort study: Each exposed subject matched to one or more non

exposed subjects on potential confounder

Case control study: Each case matched to one or more control onpotential confounder

What is attraction of Matching?

Allows control for few well known strong risk factors (eg: age)

Increases efficiency of case control study

Precludes examining matching factor(s) as risk factors

What is limitation of Matching

Differential loss to follow up may result in imbalance in matching

factors

Matching can create bias in case control study (if matched on the

factor which is only associated with exposure)

What is main purpose of matching in case control studies?

Increase efficiency of the study

CONFOUNDING CONTROL IN ANALYSIS PHASE

Standardized (adjusted) rates

Summary rate that enables comparison of two or more groups

that differ in their distribution of an important factor

Subgroup differences hidden

When is standardized rate not influenced by choice of standard

population?

When difference in rates of two communities is contant across

age categories, the size of difference between the adjusted rates

will not be influenced by choice of the standard population

Ratio will be same no matter what age distribution is chosen to

assign the weight

Example 1 : Categorical variable

Step 1: Choose standard or reference population with a known

confounder distribution

One of the two groups to be compared

Combination of two groups to be compared

2000 US standard population (age)

IARC world standard population (age)

Step-2: Apply the confounder category specific rates for each

population to the number in the standard population in that category

Step 3: Add up the total number of hypothetical deaths in each

population, divide by the total in the standard population to determine

each populations adjusted rate


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What happens when you fail to adjust for confounding by severity

score?

Mixing of effect of hospital (B vx A, exposure) with the effect of

severity score distribution (potential confounding)

Observed motality rate of Hospital B may be due to in part to

lower severity of illness of hospital B patients (compared to that of

Hospital A)

Observed mortality rate in hospital B (vs A) will be lower than the

adjusted or unconfounded rate because patients at hospital B tend

to also have a factor that is known to decrease mortality (low

severity)

Example 2 : Continuous variable

Standardized Incidence Ratio (SIR) and Standardized Mortality Ratio

(SMR)

SIR and SMR are the standardized rate ratio calculated usig the

exposed group as the standard population

Ratio of the total number of deaths in the exposed group divided

by the number of expected in the exposed group if the rates among

the unexposed prevailed within each age

categories

SMR: Miners and tuberculosis mortality

Is working as miner a risk factor for tuberculosis mortality?

TB mortality rate among miners= 384/294,013= 130.6/100,000TB mortality rate among all 35-64 year old men= 54.1/100,000 person

years

RR = 130.6/54.1 = 2.4

Age adjusted by standardization:

Step 1 Choose a standard population- the miners (exposed group). The

general population is the unexposed group

Step 2 Apply the age group specific TB death rates of the unexposed

population (general popn) to the standard popn (here the # of miners)

in that category to get the hypothetical number of TB deaths in

unexposed popn. If it had the age distribution (and #) of the miners

population

Step 3 Add up the total number of hypothetical deaths in the

unexposed (general) population

50.55+58.32+31.96 = 140.83 (among a hypothetical population of

294,013)

Step 4. Compare with the # of TB deaths actually observed in the

miners (among a real population of 294,013): 384

O= Observed # of deaths in general population

E= Expected # of deaths in general populationO/E = 384/140.83 = 2.73

SMR = 2.73

Crude Vs Stratum-specific rates vs standardized rates

Crude rates Represent reality

Useful for health services needs assessment

Stratum-

specific rates

Represent reality

Detailed information useful

Appropriate when stratum-specific effects

differ

Standardized

rates

Weights for each stratum defined by analyst

Facilitate comparison with other data,

studies particularly when known population

is knownAppropriate when stratum-specific effects

are similar

POOLING USING MANTEL-HAENSZEL ADJUSTED ODDS RATIO

CASE CONTROL STUDIES : ODDS RATIO


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COHORT STUDIES, PERSON-TIME DATA: RELATIVE RISK

COHORT STUDIES, CUMULATIVE INCIDENCE: RELATIVE RISK

COHORT STUDIES, PERSON-TIME DATA: RATE DIFFERENCE

DIRECTION OF CONFOUNDING

1. Positive-positive or Negative-negative

2. Positve-Negative

RESIDUAL CONFOUNDING

Is confounder measured?

Is there incomplete control of confounding?

Is measurement improperly defines categories?

Does measurement correctly capture attributes?

Is measurement imperfect surrogate for confounder?

What is effect of residual confounding?

Adjusted effect measure closer to crude effect measure, if the

measurement is non differential

More precisely we measure confounding, more its effect is

reduced.

CONFOUNDING BY INDICATION (OR SEVERITY)

Non randomized pharmaco-epidemiology studies

Comparison of specific drug takers vs non takers

Drug treatment is marker for characteristic or condition that triggers

use that treatment (and increase risk outcome)

May attenuate beneficial effect new drug

Determine risk factors for disease complication/progression

Adjust for prognostic differences

Stratifying on basis of severity of illness


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12. ECOLOGICAL STUDIESINTRODUCTION

What are reasons to undertake ecological studies?

Only aggregate information on exposure in each group may be

available to the investigator, even though exposure status does

actually vary among individuals within each group.

The exposure of interest may actually vary only at the population

level, not among individual within the study populations. (no

ecological fallacy, no extrapolation)

What is the size of the population for ecological studies?

In principle, the aggregate population in ecological studies can beof any size, including households, classrooms, workplaces,

communities, geographic regions, or entire nations.

Most often they are geopolitically defined populations for which

the necessary data are routinely collected, in as much as most

ecological studies use existing data.

What are the possible study designs?

Group randomized trial

Ecological cohort studies

Cross-sectional ecological studies

Longitudinal ecological studies

LEVELS OF MEASUREMENT

What are the different levels of measurement?

Individual level: such as persons age, gender, gun ownershipstatus, and so on. Individual level measurements, however, are

often unavailable in an ecological study.

Population-level measures can be divided into two kinds:

a.An aggregate measure simply summarizes the distribution of an

individual-level factor that may vary within a population. It is

statistic derived from individual level data. Eg: mean age, median

age, proportion of persons aged 65 years or older.

b.An intrinsic population level measure (termed as integral

measure) characterizes an enteir population as a unit. For eg: a

citys size, its population density, law

How can aggregate measure be contextual variables?

the aggregate measure of an individual characteristic can take onnew meaning at the population level by describing a feature of the

environment people live in.E.g. persons risk of becoming ahomicide victim may be influenced not only by whether he or she

owns gun, but also by the general availability of firearms in the

community, as reflected by the population prevalence of gun

ownership.

.

STUDYING EFFECTS OF INDIVIDUAL LEVEL EXPOSURES

How can individual level exposures be asses sed?

association between exposure prevalence and disease frequency

at the population level serve as a proxy for the individual level

association

What are the advantage?

Examine individual level associations in that pre-existing

population level data may be readily available. If so, it is quick and

low cost.

When exposure frequency varies substantially between

populations but not very much within population

If the exposure is subject to a high degree of measurement error

or short term biological variation at the individual level

Estimating Attributable Risk and Relative Risk

1.Apply regression analysis to the group-level data, modeling disease

rate as a function of exposure prevalence. Several forms of

regression can be used for this purpose such as y=mx+c

2.Use the fitted regression model to predict the disease rate for a

population in which everyone is exposed i.e, when x=0. Call thatrate R1. Similarly, predict the rate for a population in which nobody

is exposed, and call that rate R0

3.Estimate Relative Risk as R1/R0 and attributable risk R1-R0

A theoretically preferable analysis would give greater weight to

data from larger countries thus are subject to less sampling error.

What can be major problems?

(R1) or (R0) can be negative, which is, of course, impossible for a

rate

Exposure prevalence of 0 or 1 often fall way beyond the range of

observed exposure prevaences among the population studied,

leading to large extrapolation errors

Because the number of population data points in an ecological

study is often small, there are very limited power to determine

whether one model form fits significantly better than another

Results are highly model dependent, sample size may be too

small to determine which model fits the data set

What are the pitfalls?

The associations at the population level need not necessarily

reflect association of similar magnitude or even similar direction, at

the individual level. (ecological fallacy)

Cross-level bias occurs when an association at one level of

aggregation is assumed to represent the association at another

level, when in fact the associations at the two levels are unequal.

What can lead to cross level bias?

Group-level association between exposure prevalence and

baseline disease rate (rate in non exposed person) such as country

itself is a group-level confounder: it is associated with both outcome

and exposure due to following reasons:

a. The groups may differ on the distribution of one or more

extraneous individual-level risk factors, such as age and gender

b. An intrinsically group level factor may be a confounder. For eg: lax

(negligent) law

c. The exposure itself may have effects at the group level above and

beyond its effects at the individual level. eg: homicide risk to a gun

non-owner may be greater in a country where owning a gun is

common than where it is rare.

In infectious disease epidemiology is herd immunity.

Unequal distribution of effect modifier in the group:

Model misspecification: For many graded exposures, the

relationship between exposure and risk at the individual level is

nonlinear. Only available data may be the mean exposure level for

each group, which can not capture information about thedistribution of individuals among different exposure levels. The

same mean exposure level could result from most individuals falling

near the mean, or from two subgroups at opposite ends for the

expxoure range. These two patterns could correspond to quite

different epected overall disease rates.

Number of groups available for study may be small. As a result, a

simple linear or log linear model between disease rate and mean

exposure level may appear to fit the ecological data adequately,

even though it is actually a poor reflection of the individual-level

relationship of real interest.

What is the effect of non-differential measurement error?

Non differential misclassification can cause estimates of excessrisk to be biased away from null.

What can be done about non differential measurement error?

If sensitivity and specificity data for the measure of exposure are

available, the seize of this non-conservative bias can be estimated

and correction made.

How can confounding operate?

Confounders can operate at either individual or the group level.

What can be done about confounding?

The possibility of nonlinear associations motivates using more

finely detailed information about distribution of the confounder in

each group, if this information is available. For eg: rather than

including just mean age in a group-level regression analysis in an

attempt to remove confounding by age, better control may be

gained by including several age related variables, each of whichreflects the proportion of group members falling into a particular

age group.

Rate standardization can also be used to control confounding in

ecological studies, while doing so also

standardize the prevalence of exposure and of other covariates to

the same reference population.

When is ecological studies less biased?

when within-group variation in exposure is small but between

group variations in exposure prevalence is large

What is the drawback of this ?

Confounding at group level

STUDYING EFFECTS OF GROUP LEVEL EXPOSURES

to evaluate programs and policies that apply to entire

populations - an intrinsically group-level characteristics

Cross-level bias is also of less concern , because the target level

of inference s at the group level, the level at which such an exposure

would be potentially modifiable.

What is drawback?

individual or group level confounding factors to bias the observed

group level association.

How can potential biased be addressed?

cross-classifying the study population by age, gender, race, state

and calendar time

STUDYING EXPOSRES AT TWO OR MORE LEVELS AT ONCE

Individual level studies may be carried out in only a single setting,


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14

or they may deliberately match or stratify study subjects on area of

residence, thus controlling for neighborhood level influences

. Having information at more than one level can permit a richer

and more complete conceptualization of how disease occurs,

leading in turn to a wider range of opportunities for prevention.

In such a goup level association is present, what might it represent?

treat of methodological artifact, such as measurement error or

residual confounding, always lurks in the background.

Shared environmental exposures

Selection effects: Eg: people with asthma may move to cleaner

placeContagion: prevalence of illness itself can affect the level of risk to

susceptible by influencing their chance of exposure.


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13. RANDOMIZED TRIALCHARACTERISTICS

Comparative study of two or more intervention strategies

Exposure determined by formal chance mechanism

Each participant has a known probability of being assigned to

either arm

Outcome of assignment process is uncertain

What are the special strengths?

Protect against confounding, even by factors that may be

unknown or difficult to measure

Provide sound basis for statistical inferenceFacilitate blinding

What favors RCT?

The exposure must be potentially modifiable

The exposure must be potentially modificable by the investigator

There is genuine uncertainty about which intervention strategy is

superior

The primary outcomes are relatively common and occur relatively

soon

Explanatory vs Pragmatic Aims

Explanatory Pragmatic

Overall aim Test scientific theory Guide practical

decision-making

Experimentalarm

Guided by theory to betested, even if not

necessarily suitable for

wide use

Feasible for routineapplication

Control arm Placebo or other

theoretically relevant

alternative

Best practical

alternative

Eligibility Often narrow, may

include pre-screening

for compliance

Often broad, to

represent potential

target population

Outcomes Those of interest for

testing theory

Those most salient to

key decision makers:

E.g patients, clinicians,

public health policy

makers

What are the treatment arms?

Experimental

Control: Nothing, Placebo, Active alternative, Usual care

SELECTION OF STUDY SUBJECTS

What are the selection criteria

Eligibility

Internal validity

Generalizability

Risk and benefits to subjects

What affects internal validity?

Subject retention

Data qualityCompliance

Drop out

Statistical power: probability of experiencing a key outcome

Number of study subjects

For parallel-groups trail with two equal-sized group and abinary outcome:

Choosing values for

What are pitfalls of taking treatment effect based on pilot study?

underestimating intervention effect, can cause worthwhile

intervention to be abandoned prematurely

Overestimating intervention effect, causeing main study to be

underpowered

INFORMED CONSENTWhat are necessary elements of informed consent?

Awareness of participation in research

Procedures to be followed

Risks and discomfort

Potential benefits to self and others

Alternative treatments or procedures available

Confidentiality, data-retention provisions

Compensation should injury occur (if more than minimal risk)

Whom to contact if questions

Voluntary nature of participation and right to withdraw without

penalty or loss of benefits

RANDOMIZATION

Why to randomize?Protection against known and unknown confounding

Not costly, time consuming or difficult to do properly

Assignment list can usually be made up an dchecked in advance,

before any participants are enrolled, provided it is kept adequately

concealed

What are the three issues in randomization?

1. Sequence generation: Simple, Blocked, Stratified

Suggestion on choice of randomization approach

Method Good choice when

Simple Expected total n>200 and no interim

analyses planned

Block

Single Total sample size known in advance

Manysmall

Wish to keep group sizes balancedthroughout trial to facilitate interim

blocks analyses and possible early termination

Stratified Small trial (n


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What is intent-to-treat principle?

Primary analysis should compare outcomes between the groups

formed by randomization

What are situation tempting to depart from intent-to-treat?

Non compliance

Cross over

Late exclusion after participant dropped from analysis after

randomization

What to do when these things happen?

Should nearly always define the primary comparisonIn pragmatic trials, discrepancies between intended an received

treatments may reflect real life

In explanatory trials, price of intent-to-treat can be higher

Interferes with estimating efficacy

Still, direction of bias is known and conservative

o Design features in explanatory trials often aim at minimizing

discrepancies: e.g tight eligibility criteria, run-in phase

Randomization late as possible

ESTIMATING EFFICACY INDIRECTLY

Example

Counterfactual view of situation

Suppose controls had been allocated to experimental treatment

By virtue of randomization:

o Would expect a similar proportion not to have received active

treatment

Would expect incidence among them to be similar to that actually

observed among non-recipients in experimental group

Can then estimate, by subtraction, experience of controls who

would have received experimental treatment, had they been

assigned to it

Subgroup Analysis

What is proper subgroup analysis based on?

Inherent participant characteristics that treatment group could

not affect

Other characteristics measured before randomization

Limit number os subgroup hypotheses

Use test of interaction to reduce multiple comparison problem

Interpret post-hoc subgroup differences with great caution

What is improper subgroup analysis?

Characteristics measured after randomization that could be

affected by treatment group. Examples: Compliance, response to

treatment

What is pitfall of subgroup analysis?

the more ways one looks for subgroup differences the more likely

it is that some statistically significant ones will be found, even if

they reflect only the play of chance

Because each subgroup is smaller than the full study population,

statistical test for a treatment effect within subgroup have less

power

What can be done if subgroup analysis is important to do?

Increase trial size accordingly during planning phase

DESIGN VARIATIONS

Factorial design

What are the attraction for Factorial design?

Can tease apart two or more interventions

If interventions are synergistic or antagonistic when used in

combination, can find out

For overall effects, get two (or more) studies for not much morethan price of one

Sequential Trails

What is the attraction of sequential trial?

Allows termination of trial if one arm emerges as clearly superior

What is the draw back? And remedy?

Multiple comparison can inflate alpha (probability of type I error)

, remedy is to use biostatistica l method to deal with this

Randomization within individual of Body parts

Attraction?

Minimize confounding

Cross over trial

randomize order of exposureAttraction?

Each study subjects serves as his/her own control

Completely prevents confounding from individual level factors such as

age, gender, comorbidity

Increased statistical power or smaller smaller sample size requirement

N-of-1 trials

randomization of Interval of time

Group randomized trial

What is the attraction?

When by its nature, intervention applies to entire group. E.g.laws/policies, mass media campaign, environmental modifications

Intervention has spillover effects to others through social

interaction

Intervention effects are thought to be transmissible from person

to person

What can be the drawback?

Unacceptable risk of contamination within group if smaller units

randomized

Complexity, cost

When no of groups randomized often small, greater risk of

imbalance in groups formed by randomization; Statistical power

usually much lower than an individually randomized study of same

Requires more complex statistical analysis to obtain valid

confidence interval limits and p-values


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14. COHORT STUDIESCHARACTERISTICS

Measures occurrence of illness in person of differing exposure

Retrospective or prospective

No random assignment of study subjects

Exposure-disease induction/latent period is not too long

COHORT IDENTIFICATION

Geographic

Special exposure group

Special resources for cohort identification: records from lifeinsurance, health insurance, Union records, Alumni records,

population based disease registries, other

What is limitation of using individual level measurement?

SOURCES OF DATA ON EXPOSURE

Records: Occupational, Medial, pharmacy, prepaid health care

plans, census

Interview or questionnaire

Direct measurement: individual and environment

What is the limitation of having direct measurement?

If characteristic being measured exhibits short term variability

(e.g. serum cholesterol or systolic blood pressure) the value

obtained will not necessarily reflect the study participants long

term mean level. The impact of this misclassification will be to dullthe studys ability to assess the degree of association.

ESTIMATING THE EXPECTED OCCURRENCE OF DISEASE AMONG

EXPOSED COHORT MEMBERS

What are the options for comparison group?

Disease status can be contrasted among heterogeneous exposure

status

Members of the cohort who are exposed to other exposure

known not to influence the disease (eg Asbestos worker vs cotton

textile worker)

If no difference is found, what can be the drawback in drawing

conclusion?

There can be two conclusions, either there was no association or,

other exposure is associated with an altered risk to a similar degree

Health outcome present in the geographic population which

cohort members reside

When is this (general population) approach commonly used?

When death is outcome

When will this approach (general population) provide bias?

When size of non-exposed group is relatively small, and the

outcome of interest is relatively uncommon

While interpretation, what should be thought?

Have the outcome events under study been ascertained

comparably between the exposed cohort and the general

population?

To what extent has the rate of illness in the exposed cohort

influenced the size of the rate of the population as a whole?

On an average, are cohort members different from the general

population in ways that bear on disease incidence or mortality,

beyond difference in those demographic characteristics that are

measured in both groups and for which statistical adjustment can be

performed? (e.g. soldiers vs general population) (healthy worker

bias; Sick retiree bias)

What should be considered when comparing rates of illness or

death between patients who have received a specific medical

intervention and the population as a whole?

Could the condition that necessitated the treatment itself have an

impact on the incidence of the disease under study? (confounding

by indication)

At the time treatment was being considered, were members ofthe treated group evaluated for the presence of a condition, with

only those not having the condition allowed to receive the

treatment? (Healthy screenee bias)

What can be done for this?

Omit from the analysis the part of the follow up experience of

exposed individuals that is most susceptible to these biases. i.e. that

which accrues (accumulates or adds) relatively soon after exposure

status is defined. (e.g. breast cancer counted after 3 years among

those with breast implants)

OUTCOME DEFINITION AND ASSESSENT

Is there standard criteria to define outcome?

Is it assessed similarly among cohort and comparison group?

Is outcome measured at same period of time?

FOLLOW UP OF COHORT MEMBERS

When is validity of result threatened?

If the under ascertainment of disease, especially among just

exposed group

What are eligibility criteria?

Reachable throughout study period

Stable to maximize the likelihood of successful follow-up

Restriction of unstable subgroup

NATURE OF THE ILLNESS OUTCOME: INCIDENCE VS PREVALANCE

If the prevalence is measured

It is called cross sectional study

ISSUES IN ANALYSIS AND INTERPRETATION

For purpose of analysis, how soon after exposure should outcome

events that occur in cohort members begin to be counted?

Generally, immediately

Exceptions

Healthy worker bias

Healthy screenee bias

the diagnosis was made early in the follow up period had that

disease present in hidden form before exposure commenced

(presence of disease in those persons could not have been affected

by the exposure, it could possibly have influenced the likelihood of

receipt of exposure, or the presence or level of a characteristic

under study)

Always think if reverse casualty could be possible?

In pharmacological research, what bias can be encountered?

Immortal time bias

How are changes in exposure status of cohort members handled?

Data taken to reduce exposure misclassification

Person years contribute to denominator of exposed group, after

change of status

When duration is important, cohort members cannot be

permitted to contribute events to the numerator nor person-years

in denominator until they meet the criteria for a particular category

of duration

When is counting stopped?

Get full range of consequence as far as possible


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15. CASE CONTROL STUDIES

Are cases and controls enrolled from the same underlying

population at risk?

Is there possibility of reverse casualty?

ASCERTAINMENT OF EXPOSURE

Is exposure measured validly?

Is information on exposure present during an etiologically

relevant period?

Eg: Alcohol Accident : minutes to hours

Alcohol Cirrhosis : years Is the exposure ascertained using same method in both cases and

controls?

Is exposure associated with mortality? Leading to less number of

people present to have developed the disease? (eg. Pg 397)

1. Interviews/questionnaires

Are questions similar and similarly asked ?

Are exposures included after illness began?

Are cases and control recalling exposure differently?

Socially sensitive issue, cases more honest

Other cases, cases recall better as they are interested in the

health event

Is non-response similar among cases and controls?

What are strengths?Information obtainable about non recorded exposures

Information obtainable about etiologically relevant exposure

What can be done to maximize accuracy?

Define reference date for both cases and controls

Use: visual aids like picture, medicine bottles

Use controls with other disease

Withhold information about study hypothesis

Standardized validated instrument

Show cards

Mental prompts: timing in relation to important events

Verification of exposure information

2. Records

What are different sources of records? Vital, Registry, Employment, Medical, Pharmacy

What are the strengths?

objective exposure information routinely collected

Often more detailed exposure information that subject self-report

Vital statistics data routinely available

Hospital discharge data often available

What are limitations of using record?

Usually kept for different purpose, so may not provide

information precisely

Eg: Death certificatemay be occupation but not exposure

Pharmacy list of prescribed drugs, but not information whether

they were taken

Etiologically relevant exposure time period may not be captured

Often more complete for cases than controlData quality and completeness is often issue

Are information restricted to point till when case is diagnosed?

Are information restricted to same point for control?

3. Physical and Laboratory measurement

What are limitations of laboratory measurement?

Post diagnosis exposure levels may not reflect pre-disease levels

Pre-diagnosis exposure may not reflect etiologically relevant time

period

Are levels measured following identification of cases and control?

Can rely: lead in dentine, BCG scar

Cant rely: hormone status Which records to be excluded from analysis?

those obtained with the period prior to diagnosis that might

correspond to the duration of preclinical stage of disease

Exception: genetically determined characteristics

CASE DEFINITION

Are there all (or representative sample) of members of defined

population who developed a given health outcome?

Can some person with disease go undiagnosed? If yes, Is there

reason to believe that exposed persons are relatively less likely to

go undiagnosed?

Is there chance that exposure status may influence cases

likelihood of diagnosis and therefore selection for study?

if yes, define objectivelyfocus on more seriously ill cases

What are the criteria to identify and s elect cases for study?

Objective

Sensitive and specific

Specificity is of particular concern because, inadvertent inclusion

of persons without disease in the case group will generally obscure

any true association with the exposure

Eg: in study of Reyes syndrome and Aspirin, they include only severe

cases, so that non-cases and misclassification could be avoided,

especially when there was general notion among physicians

regarding association with exposure.

What are sources of getting cases?

Geographically, members of health plan, occupational group,

registry What is challenge of ascertainment of case from population based

case selection?

Complete ascertainment

Use capture/recapture method/ out of area health events

Are they drawn in an unselected manner with regard to exposure

status? Eg: including all eligible cases

Are they incident or prevalent cases?

Goal of etiology is to have incident cases

ISSUES IN CASE SELECTION

Inclusion of Prevalent cases

Under what circumstances it may be necessary to enroll prevalent

cases?

For some conditions, date of occurrence is unknown: eg: HIV

infection; For uncommon disease of long duration, incident series

may yield too few cases

What is disadvantage of adding prevalent cases?

Problems of accurate exposure ascertainment

If date of occurrence is known, should be obtained for more

distant points in the past, on average, that would be necessary for

incident series

If date of occurrence is known, there will be uncertainty about

best point in time before which one should elicit (produce)

exposure information. By studying persons remaining alive with a given condition, one is

studying at the same time not only etiologic factors, but factors that

influence the duration of the condition, including those associated

with survival

Length biased sampling: cases with long lasting disease more

likely to be sample associations may be with disease duration not

etiology

Inclusion of Diagnosed cases without disease

What is the impact of including cases without disease?

diagnosis may depend on presence of exposure

Over diagnosis may threaten interval validity more than under

diagnosis

Inclusion of cases only from the portion of the population

What is the impact?

Missing cases are not missed systematically

Missing due to death- those with longest survival are

preferentially included

Characteristics of tertiary care sites cases (clinic based studies)

Asymptomatic and symptomatic undiagnosed cases (population

based studies)

Influence of exposure on likelihood of diagnosis among truly

diagnosed persons

Are controls who would have been diagnosed had they become ill

have similar access to diagnostic

Willing to undergo diagnostic procedure

CONTROL DEFINITION

When was the control not considered?

Occasionally, the proportion of ill person who have had a specific

exposure so high, unequivocally more than that would be expected

in the population they were derived from ,that the presence of an

association (though not its magnitude) can be surmised from a case

series alone. Eg: Pneumonia due to ingestion of adulterated

rapeseed oil in Spain in 1981

Ideal control group

Are controls at risk for developing disease?

Are the controls selected from a population whose distribution of

exposure is that of the population the case arose from?

If not, Selection bias

Are they identical to the cases with respect to their distribution of

all characteristics?


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That influence the likelihood and/or degree of exposure, and

That, independent of their relationship to exposure, are also

related to the occurrence of the illness under study or to its

recognition

If not, Confounding

Can presence of exposure be measured accurately and in a manner

that is identical to that used for cases?

If not, information bias

Minimizing selection bias

Population based controls Are controls selected from same population as cases?

Geographically defined population: Random digit dialing of

telephone numbers, area sampling, neighborhood sampling, voters

list, population registers, motor vehicle licenses, birth certificates

etc

Prepaid health care plan: who were members of the same health

plan when the illness or injury occurred?

Employed population: same group of employees

What are the drawbacks of random digit dialing?

Household identification: change of telephone number, have only

cell phones

Enumeration: answering machine screening of calls, inaccurate

response about eligibility

What are drawbacks of population based controls?Not known to be free from disease

Response rate may be low and may not be unbiased sample of

population

Hard to identify if no list exist

Characteristics of non-responding population based controls are

shown to have more smokers, less educated, younger

What is effect of inclusion of diseased subjects in control group?

Benefit of population based controls over-weights

misclassification of some. (see lecture notes)

Examine for disease (after selection and if feasible)

estimate amount of undiagnosed disease

estimate resulting bias

Clinic based controls

Are cases selected from few hospitals or clinics?

If yes,

Are controls chosen from persons who, had they developed the

illness under study, would have received care at these hospitals or

clinics?

No selection bias

Are person who do and do not receive care from these sources

differ with regard to their frequency or level of exposure?

Yes selection bias

What is drawback of having other ill people as control?

Hospitalized or clinic based controls may not be typical of those in

population from which cases arose in terms of exposure of interest

dont represent population from where cases are coming)

Ill or recently diseased persons tend to have been smokers of

cigarettes more often than other people. Because smoking history of ill

persons overstate the cigarette consumption of the population from

which the cases arouse, the odds ratio associated with smoking based

on the use of ill persons as controls will be spuriously low.

How to remove selection bias when taking ill controls?

Omit potential controls with conditions known to be related

(positively or negatively) to exposure. Eg : in study of bladder cancer

and prior use of sweeteners, excluded control who were hospitalized

for obesity related disease

this is successful, if can be judged correctly which conditions truly

are exposure related, and how accurately the presence of thosecondition can be determine.

But for cigarette smoking and alcohol drinking, it has been shown

that admitting diagnoses or statements of cause of death are incapable

of identifying the persons with illnesses related to these exposures.

What is advantage of selecting controls chosen from individual

who are tested for the presence of disease and are found not to

have?

inexpensive to find

comparability with regard to the choice of health care provider

this will increase studys validity if disease being investigated is

generally asymptomatic and so would not be detected in the

absence of testing

Situ cancer example: oral contraceptive and situ cancer of cervix.

Women who use oral contraceptive were more likely to get

screening, situ cancer can be in asymptomatic form and shall be

discovered only through screening. If controls were chosen from

general population, who may or may not have received cervical

screening, an apparent excess of oral contraceptive users would be

present among cases of in situ cancer even if no true association was

present

What is drawback of having controls that are test negative?

Those with a diagnostic evaluation but confirmed not to have

disease may not be typical of those in the population from which cases

arouse (if they are in hospital, they have some problems so they

Will detract studys validity if large majority of persons who develop

the disease soon would get diagnosed whether or not the test was

administered

Eg: Endometrial cancer and postmenopausal estrogen. Controls were

chosen from those who underwent biopsy and found negative, because

of hidden cases in the population. However, a group of scientist

believed that there were no such hidden cases. And also, estrogen use

predisposes bleeding leading to biopsy. So, they claimed that risk

estimate was spuriously high.

How is selection bias introduced if exposure information is not

received from all participants of study?

If the frequency of missing data and the degree to which exposure

frequencies or level differ between study subjects for whom exposure

status is and is not known.

Minimizing information bias

Are the questions asked in identically to both cases and controls?

Are the past exposures or events more s alient to persons with an

illness? recall bias

Are these socially undesirable questions?

Eg: in prenatal study of malformation, control taken with other types

of malformation; anal intercourse and anal cancer, control were

with colon cancer

Are the questions very subjective? Eg; stress or shock producing

events and down syndrome, Controls general mother with OR=17;

control other mentally retarded childrens mother with OR=4.3

If questions for fatal diseases asked with surrogates of cases, thenwho to ask in control?

Though controls themselves might give more accurate answer, it

is better to ask from their surrogates for purpose of comparability

What is drawback of getting information from surrogates?

Misclassification of the exposure, especially by surrogates of

control, For e.g. study on radiation exposure and cancer

What is way to minimize information bias?

Blinding to those who are collecting information

Example of information bias in records

Records of endometriosis are higher among women with

infertility. However, the women with infertily undergo laproscopy as

a diagnostic tool to investigate the possible presence of conditions

such as endometriosis.

CONTROL OF CONFOUNDING IN CASE CONTROL STUDIES

Is the proportion of cases and control vary across level or

categories of the potential confounding factor?

Means of controlling confounding?

Restriction: Restrict cases and controls to a single category or level

of potentially confounding, e.g. study of physical activity and cardiac

arrest, restrict people who have clinically recognized heart disease

that could both predispose to cardiac arrest and physical activity

What is drawback of restriction?

Shrink pool of available subjects, especially because we are doing

case-control study for rare disease

limits generalization of results

Cant see effect modification

Adjustment: of potentially confounding factor in analysis phase

Matching

Individual matching

Frequency matching

Is matching alone sufficient to control for confounding?

No, should be considered in analysis as well

Appropriate to match Yes Is variable, one of the exposures of interest?

Is variable strongly associated with disease?

Is it inexpensive to do matching?

Is cost of ascertainment of exposure expensive?

What is drawback of matching?Missing of possibly large fraction of cases


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May be overmatching, Is it surrogate for exposure

measurement?

Matching can induce confounding

Is the factor associated with only exposure?

CASE CONTROL STUDIES THAT DIRECTLY COMPARE DISEASE OR

EXPOSURE SUBGROUP

Compare between different types of disease

Compare among different level of exposure

What are the possible interpretations?e.g. OR > 1 with alcohol and HPV +ve Oropharyngeal

alcohol risk factor HPV +ve cancer? OR

Alcohol protective factor for HPVve cancer?

Case control study superior over other study design

Is the disease too rare for prospective studies?

Is the induction period too short? Eg: alcohol-injury

Is the exposure to disease period is very long?

Does it allow studying multiple exposure?

Allows to obtain information when exposure records do not exist

ESTIMATING SAMPLE SIZE REQUIREMENT

How many controls is needed?

4 controls per case is enough for maximizing power

Depends on cost

ANALYSIS OF CATEGORICAL EXPOSURE

Odds ratio

OR is an estimate of RR in case-controls studies

Under assumption that the disease is rare in both exposed and

unexposed persons (


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16. INDUCTIONPERIODANDLATENTPERIOD

Is the time relevant period for etiology?

What happens if the period is not relevant? Induction period Interval between presence of exposure and initial presence ofdisease

Latent periodInterval between initial presence of disease and its recognition

How to measure induction/latent time?

The distribution of the length of time required for an exposure to

give rise to disease can be estimated by examining the relative riskassociated with that exposure over successive periods of time after

it was sustained. E.g. Leukemia in Hiroshima and Nagasaki

Enumerating times when cases occurred following the exposure

when nearly all exposed cases are due to exposure. Eg DES

Examination of variations in disease occurrence across

populations, or within a population over time

INFLUENCE OF THE SUSPECTED INDUCTION/LATENT

PERIOD ON STUDY DESIGNShort induction/Latent period

What is the best study design for short induction/latent period ?

Difficult to perform cohort or case control studies, e.g. alcohol

consumption and myocardial infarction

Case-cross over study can be an option What is problem with RCT and cohort studies?

the incidence in short time is small

What is problem with Case-control studies with short

induction/latent period?

Validity of studies investigating possible short term effects rests

on the comparibility of exposure ascertainment between cases and

controls

May require to recruit control group that is not representation of

the general population

If the exposure of interest is rare during the short period, then

will have little power to assess even moderate or large RR

What can be the possible limitations of case-cross over studies?

How much misclassification of exposure status may have occurred

from incorrectly judging the length of the relevant window of

exposure prior to disease onset?

To what extent was confounding influence of other exposures

taken into account? In case-cross over, confounding by factors that

do not vary to any appreciable extent over short period of time is

eliminated but those that can vary over short period of time can not

be eliminated

Relevancy of case-cross over studies

Can exposure status be assessed during both the hazard interval

and control interval? Can it be assessed comparably in each

interval?

May have recall bias in incident than control period

Has the duration of presumed induction period been judged

correctly?

May have misclassification related to time period

Could exposures other than the one under investigation vary over

time in the same way?

Confounders that vary over time, eg alcohol, smoking

Long induction/latent period

What are the problems?

Current exposures with future follow up will take a long time to

complete

Exposure status may change, necessitating future exposure

measurement

Often hindered by absent or imprecise measures of exposurestatus

What can be done?

Get exposure from records if possible

Memory can be used for specific exposure

Have patience

Invariant exposures can be done with case-control studies

Use surrogate outcomes, surrogate exposure where possible

What can be good research design?

Nested case control study


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17. IMPROVING SENSITIVITY OF EPIDEMIOLOGICAL STUDIES

SENSITIVE STUDY

If an exposure truly has the capacity to cause a disease, at least in

a portion of exposed inividuals, a sensitive epidemiologic study is

one that will observe an association between that exposure and the

disease.

What are the strategies to enchance sensitivity,

irrespective of the size of available study populationDisaggregation of categories of exposure of concern that are

heterogeneious with respect to their impact on disease occurrence

Disaggregation of disease entities that are heterogeneous with

respect to their association with the exposure of concern

Disaggregation of study subjects who, bcause of the presence of

one or more other exposures or characteristics, are not affected to

the same degree by exposure of concern

What happens when two exposure act through separate

means to produce disease?the relative impact of either of them is greater in that segment of

the population in which the other exposure is absent

What happens when two factors have the capacity to act

together in a single causal pathway leading to disease?The incidence of that disease in persons in whom both factors are

present would be more than sum of the two rates produced by

either factors presence alone.

VARIATION IN SIZE OF RELATIVE RISK ACROSS SUBGROUP

Look for variation in RR or AR

What happens when incidence of disease differs in two

subgroups? If RR associated with another exposure is the same in each of the

subgroups, the corresponding AR will differ, possibly to an

important degreeDifferences in RR associated with an exposure between the

subgroups simply may be a reflection of the exposures adding to

the risk by the same amount in each of the subgroup

AGE AS POSSIBLE EFFECT MODIFIER

Can we compare mean (or median) ages of cases whoh do

or do not have a particular exposure or characteristic?No. It can be misleading as the same difference in mean age at

diagnosis can be produced by complerely different patters of effect

modiciation (Eg, pg 430)

DOES IMPROVING SENSITIVITY OF EPIDEMIOLOGIC STUDIES

DECREASE THEIR SPECIFICITY?

Yes

LIMITATIONS OF EPIDEMIOLOGIC STUDIES

Under what circumstances are we unable to identify etiologic factor

through non-randomized trial?

Issue Strategy

1. Magnitude of increased risk

produced by factor is too small

to be reliably identified

Examine exposure-disease

association within subgroups

of the population (based on

the presence or level of other

risk factors) in whom the

relative impact of exposure is

likely to be greatest

2. Magnitude of increased r isk

is theoretically not too small,

but

a. there is insufficient variation

among individuals within a

population regarding

presence/level of the factor

b. We are unable to distinguish

the effect of the factor from

that of other correlated factors

c. Practical problems:

i) No valid measure of past

presence or past levels of factor

ii) Lengthy induction/latent

period

Identify population within

which there is variation

Conduct the study inpopulation in which the

confounding factor is not so

highly correlated with

exposure in question

Identify exposure records, or

stored samples (as in nested

case control study)


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18. SCREENING

WHEN CAN SCEENING BE JUSTIFIED?Disease is an important public health problem

The natural history of the disease presents a suitable window of

opportunity for screening (long time window period, or already

receiving care at the right time)

Effective treatment is available, and capable of favorably altering

the diseases natural history. Alternatively, an effective way to

prevent spread to other people is at hand.

Treatment or interventions to prevent spread to others, are moreeffective if initiated in the pre-symptomatic stage than when

initiated in symptomatic patient

A suitable screening test is available: reasonably inexpensive and

safe, acceptable to the population screened and able to discriminate

between disease and non-diseased

ASSESSING SCREENING TEST PERFORMANCE

Sensitivity and Specificity

Predictive value of the test

What does prevalence of the disease affect on?

Predictive value (especially, positive predictive value)

What is the implication of the fact that PV+ value of

screening test can be quite low in screened populations

with low disease prevalence?It can affect how a positive screening test result should be

interpreted and perhaphs how this information is communicated to

the screenee

Persons with a positive screening test result must unsually be

evaluated further to determine whether the result was a true

positive or a false positive

It affects choice of a target population for screening. Subgroups inwhich prevalence is highest can yield both more cases per screening

test and more true positives per positive screening test

LIKELIHOOD RATIO

EVALUATING THE EFFECTIVENESS OF SCREENING

Does treatment given at early detection lead to a more

favorable outcome than treatment given when the cancer

is clinically manifest?

Randomized Trail and Cohort (Follow up) studiesIn non randomized trail, is there potential confounding that true

benefit or lack associated with use of the test is distorted?

Which group to compare?Screened vs unscreened group

Is there a lead time bias ?

Most of the cancer haveWhat is appropriate group to compare?

Mortality experience, not of cases alone, but of the screened

group with that of an unscreened group, with both groups

monitored from the time of screening

Is there length bias sampling?patients who have a long preclinical but detectable phase of

disease are more readily found via screening than are patients with

that disease whose preclinical phase is short.

Those tend to have better survival in absence of treatment

What are things to consider if it is ecological study?

Reliable data

size ofpopulation in each time period large enough


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evidence to indicate, absence of screening, the mortality rates

would not have fallen

Things to consider if it is case control studyAre persons selected as ill or diseased to he extent that diagnosis

would occur in absence of screening?

Are controls representative of the population that generated the

cases with respect to the presence or evel of screening activity?

A control that is restricted to earlier or less severe forms of the

condition under study is not appropriate (eg early stage cancer)

While control would not exclude persons with early or milddisease, it would include them only in proportion to their numbers

in the population

Are there confounding factors? That are associated with

screening an late state of disease and mortality


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19. OUTBREAK INVESTIGATION

What are purposes of outbreak investigation?

Limit scope of severity and immediate threat to public health

Prevent future outbreaks

Identify new vehicles of infection

Monitor the success of intervention program

STEPS IN AN OUTBREAK INVESTIGATION

1. Verify the accuracy of disease reports

Confirm the diagnosis1. Determine existence of an outbreak

Compare observed vs. expected in a preliminary investigation

2. Establish a case definition

May need to be modified as more information is available

When appropriate, classify by confirmed, probable, or possible

3. Identify additional cases

4. Conduct descriptive epidemiology

5. Generate and test hypotheses (e.g., disease causation, risk

factors, transmission)

6. Monitor course of the outbreak and reassess strategies

7. Carry out lab and environmental investigations

8. Implement disease control measures

9. Communicate findings

Detection: How are Outbreaks identified ?

Step 1: Verify the Accuracy of Disease Reports

Establish the accuracy of the data (report)

Know your data sources

Confirm the diagnosis

Review clinical findingsdo they make sense?

Review laboratory results and methods

Interview cases and potential cases

Consult with subject matter experts

Is it an outbreak?

Rule out a pseudo-outbreak.

Consider other reasons for an increase in reports

For example, changes in

Reporting procedures

Case definitions

Awareness among reporters

Habits of reporters (referral bias)

Diagnostic tests used and their characteristics (esp. PPV)

Size of population

Step 2: Determine the Existence of Outbreak

Compare observed vs. expected number of cases

Observed: number of cases reported during this event

Expected: number of cases you would normally expect (in

comparable period of time)

Background rate: typical rate of disease among affected population;

consult historical surveillance data, scientific literature, and disease

registries

Use rates to make comparisons

Frequency of cases relative to population size

Is there a real increase in the rate of observed cases beyond what is

expected?

Is outbreak investigation Necessary?

When should a potential outbreak be investigated?

Considerations include:

Severity of illness

Communicability

Potential ongoing health threat

Need to learn more about agent new or novel

Public concern and political considerations

Available resources

Step 3: Establish a case definition

Require standardized case definition

Case definition should include criteria for

o Person, Place, Timeo Clinical criteria (should be simple and objective)

Use CDC or CSTE case definition when possible

Do not include potential risk factor in case definition

Classify cases

Can have definite, probable and possible caseso Useful for tracking cases

o Useful in estimating burden of illness

In larger outbreaks, not necessary to confirm every case

Step 4 Identify additional caseEnhanced surveillance:

Active

Health departments actively solicit reports from:

Health care providers and health care facilities

Clinical and public health laboratories

Discrete populations (e.g., exposed persons)

Passive

Non-direct way of increasing awareness

Targeted communications

Step 5: Conduct Descriptive Epidemiology

Who where and when?Use the Data

Use descriptive epidemiology to characterize the outbreak by

person, place, and time.

For new conditions, you may need to produce description before

creating case definition.

Data can be used to refine case definition.

New clinical features?

What population is being affected?

Review of Descriptive Epidemiology Terms

Incubation period

Time between exposure to infectious agent and the

first signs/symptoms of clinical disease

Index case

Initial case/patient who may have become the source of exposure

for other cases or first affected case

Primary cases

Cases who were exposed to the source (agent)

Secondary cases

Cases who were exposed by a primary case inperson-to-person

spread

Descriptive Epidemiology: Time

The epi curve displays the distribution of cases

over time (and can display more).

Can be used to:

Estimate magnitude and time trend

Determine exposure period

Help predict course of epidemic

Suggest the type of epidemic

Point source (exposure at one point in time)

Common (continuous) source (exposures continue over time)

Propagated (exposure to the source by initial cases, followed by

secondary cases infected from person-to-person spread)

How to create an Epi Curve


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Descriptive Epidemiology : Place

Plot locations of exposure

Descriptive Epidemiology: Person

Define population at risk Age

Gender

Occupation

Social features

Medical history

Travel history

Step 6 (a) : Generate hypotheses

Step 6 (b) Test Hypotheses

Analytical Epidemiology

Different methods (study designs) for comparing groups

Two study designs used in outbreak investigation

1.Cohort studies

Well-defined groups of exposed and non-exposed individuals

Track and compare disease (or outcome) among exposed and

non-exposed individuals

2.Case-Control studies

Compare individuals with a disease (cases) to those without

the disease (controls)

Examine differences in exposures or r isk factors

Selecting an Appropriate Study Design

Case Control studies: Control Selection

Controls should be similar to cases with respect to opportunities for

exposure

Cannot have the disease in any form

Must represent the population from which cases came (e.g.,

same age group)

Strategies for control selection

Random sample

Friend or neighbor controls

Meal companions

Step 7 Monitor Outbreak and Reassess Strategies

Refine hypotheses if necessary

Have other potential explanations been overlooked?

Sequential case-control studies

Narrow down exposures to identify risk factor

Surveillance Data Needs During outbreaks

Step 8: Environmental and lab investigations

Complement epidemiological investigations

Environmental investigations

Examine and sample food, water sources, buildings,

materials, or environmental surfaces

Provide information about:

Exposure to agent

Contamination during food preparation, or manufacturing

Exposure during recreational activities Document contaminated environment

Do trace back investigations

Step 9: Control and Prevention Measures

Implement Control and Prevention Policies

Policy development and implementation

Food safety

Guidance on procedures

Guidance on food handling

Policies about food preparation

Shellfish harvesting

Exclusion of ill children from daycare settings

Petting zoos; pet turtle bans; salmonella and psittacosis warnings at

pet shops, etc. Isolation and quarantine

Immunization policy

Step 10: Communicate Findings

Provide ongoing current and accurate information to:

Staff within your team and agency

Environmental health officer, public information officer,

department administration

Other health agencies

Local and state health departments, CDC, and Indian Health

Service

Governmental agencies and jurisdictions

Health care providers and facilities

The public: media, schools, businesses

Communicate with the Public

prelim guide (autosaved)

Documents