Lecture 2: Ecologic Studies and CrossSectional Studies Measures of disease occurrence and associatio

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Lecture 2Ecologic Studies and Cross-Sectional
StudiesMeasures of disease occurrence and
association

• Dr. Dick Menzies
• June 6th, 2007

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Ecologic studies

• Basic design
• Exposures at group level
• Diseases (outcomes) at group level
• Association is between group characteristics, and
  disease occurrence in the group
• Study populations
• Any level or type of grouping of subjects
• Country, Province, city, neighbourhood, city
  block
• KEY - group should be relatively homogeneous
• At least for exposure of interest
• Differences between groups should be more than
  differences within groups

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Ecologic studies - exposures

• Exposures are measured at group level
• National/regional data - Per capita income,
  consumption of alcohol, cigarettes
• Reported illnesses (if 1 disease leads to
  another)
• Environmental data climate, air pollution
• Census data income, education, housing
• Down to city block
• KEY exposures should be uniform for all of
  group

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Ecologic studies comparisons

• Temporal Disease changes over time
• Adv same population (genetics, lifestyles)
• Disadv Many other things can change
• Spatial Different incidence of disease in
  different places
• Adv same time so temporal changes less
• Disadv may be many other differences

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Ecologic studies analysis

• Correlation (can answer)
• Is there a relationship between X and Y?
  (agreement)
• Is it statistically significant?
• What direction (positive or negative)?
• Regression (can answer)
• Is there a relationship between X and Y?
• Is it statistically significant?
• What direction (positive or negative)?
• What is magnitude of effect?
• Y a bX

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R 1.0Perfect correlation (rare)
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R 0.6Strong correlation, but not perfect
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R -0.8Strong correlation, but negative
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R 0No correlation at all
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R 0 Is the correlation zero for the same
reason?
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Correlation tells us about agreement of
tests Regression - estimates change of X per unit
change in Y
Figure 10-2. Scatterplot of C1q-binding complexes
and IgG-containing complexes (Adapted from N Engl
J Med 1978 298 126.)
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Ecologic study - example

• Skin test sensitivity to coccidiomycosis and
  place of residence
• What might be a source of error?
• What does it tell us an individuals skin test?

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Ecologic studies - the observation

• National mortality rates from Cardio-vascular
  disease

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Ecologic study CHD mortality

• National mortality rates from Cardio-vascular
  disease differ widely
• What could account for observed differences?
• Some plausible hypotheses
• How to test these hypotheses?

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Ecologic study air pollution

• Observation Rates of lung cancer lower in rural
  areas than urban.
• Why what are plausible hypotheses
• Confounding - what else might be different?
• How to control for this?

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Ecologic studies and Tuberculosis
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Decreasing coal use and decreasing TB USA 1953
2003
Coal use ( ) and TB incidence ( ) .
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Increasing coal use and increasing TB China 1978
- 2004
Total coal combustion ( ) Notified cases
of of TB ( ).
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Ecologic studies summary of problems

• Exposures Assumed uniform for all individuals,
  but this is rarely true
• Smoking or alcohol NOT at all
• Income Depends on size of group
• Environmental More uniform
• Fluoridation of water - ?Uniform exposure?
• KEY heterogeneity within population should be
  less than between populations
• Confounding HUGE problem.
• Temporal many things change over time
• Spatial many other differences between places

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Cross-Sectional or Prevalence Studies

• Snap shot of disease and exposures at the same
  time in a population
• Like a case control study
• measures disease that is present/has occurred
• Measures exposures that are present/have
  occurred.
• Key difference - strategy to identify controls
• This is a stronger design for selection of
  controls
• In some ways a hybrid design
• Like a case-control for disease
• Like a cohort for controls

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Uses / and advantages

• Define risk factors for a disease
• Personal demographic, life-style
• Medical (lipids, BP, other meds)
• Environmental
• Occupation
• Define prevalence of disease and of exposures
• Since whole population studied
• Defines population impact of a given exposure.
• Useful for Health policy, planning health
  services utilization, public health programmes

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Limitations

• Not useful for
• Etiologic research (cannot be sure of cause and
  effect)
• Temporal trends increasing prevalence may
  reflect greater incidence, or longer duration
  (better survival), or changes in population
  (aging, or selective in or out-migration)
• Problems of cross-sectional surveys in general
• Higher prevalence may be associated with factors
  because
• Causes higher incidence of disease BAD
• Or, longer duration GOOD

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Diseases best studied

• Diseases studied should be reasonably common, ie
  high prevalence
• Otherwise study will involve too many controls
  without condition - this is inefficient
• Chronic disease with long duration (Higher
  prevalence)
• Or, acute disease with very high incidence

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Study Populations for Prevalence surveys

• General population samples
• Without specific exposures
• This is difficult and so not done commonly
• One method is random digit dialing (telephone
  list) or random household selection (using GIS)
• Staged cluster sampling
• Must have a complete list of persons, communities
  to select sample

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Study Populations for prevalence surveys

• Proxy General Population
• School populations
• Primary school will be more complete than high
  school
• Requires 90-100 of children in school to be
  representative - but still ignores older,
  younger, child-less
• Work-forces - eg Electricians, Nurses
• Not as representative of total population (age,
  SES, education, healthy worker effect)
• But can be used for non-occupational determinants
  - (as well as occupational determinants)

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Study Population - Exposure based

• Workforce Studies
• Workforce studies for occupational exposures
• eg Asbestos workers and Lung Ca or mesothelioma
• Health care workers and TB
• But healthy worker effect
• And some characteristics might be quite specific
  to work force
• Special Populations
• Prisoners, military, mental institutions
• Useful for studying selected exposures in these
  populations

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In/out migration of populations

• Any special population represents some selection
  to get in, and to get out
• In-migration to a work-force, or a geographic
  area
• May be more healthy (workers, Army)
• Or, could be less (Arizona)
• Out-migration often highly selective
• Health effects of exposures
• Chronic diseases of any kind leave work-force

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Selecting the Study Population

• Census survey - means survey all of the
  population
• Feasible if - you are the government
• or - you have a small group
• Sample surveys random selection
• Individuals - from the entire population
• Need a LIST of population
• Cluster sampling select population from within
  sub-groups
• Need a LIST of all groups
• Take all persons in selected units
• Eg., workers on certain wards in hospitals or
  residents of certain neighbourhoods

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Study methods - Detecting the disease

• Case definition
• Must be very clear,
• Same as other published studies
• Include mild or asymptomatic cases ?
• Diagnostic method
• Questionnaires - Have you been diagnosed with
  ?
• Quicker, cheaper
• Validity has questionnaire been used before??
• Direct - sero-prevalence, diabetes, lipids, TST
• Will this be practical, feasible, acceptable
• Will it be valid?

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Measures of Disease Occurrence - Prevalence

• Prevalence number of persons with condition or
  disease at a given point in time
• Prevalence is really a ratio
• Numerator number of persons with disease
• Denominator all persons in population
• Prevalence can be expressed as
• At a given point in time - eg, January 1st, 2004
• Or on entry to university or military service
• Or can be for a period or time, eg., prevalence
  during medical school or a five year period of
  time

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Measures of Disease Occurrence - Incidence

• Incidence number who develop Disease X in a
  population initially free of Disease X in YY
  time.
• Numerator persons with newly developed Disease
  X
• Denominator persons without Disease X at the
  beginning of the period of study
• Time - YY weeks, months, years
• Incidence NDiseaseX in TimeYY / Ntotal
• Births and deaths are a form of incidence
• Birth rate, mortality rate

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Relationship between Incidence and Prevalence

• Prevalence Incidence x Duration
• This holds ONLY when
• Incidence is stable
• Duration is also stable
• These conditions are often not true
• Eg., HIV incidence is changing
• Duration is also changing with new effective
  therapy

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Prevalence and incidence - examples

• Asthma prevalence /incidence
• Prevalence of Asthma in population 6
• Incidence of asthma (past 20 years) 0.2/year
• Duration 6/0.2 30 years
• Public health officials need estimate of ART
  needed for next 5 years
• Incidence of HIV infection 0.1 annually
• (stable for past 10 years)
• Median survival with ART 20 years
• Prevalence now, requiring ART 2
• Prevalence requiring ART in 5 years?

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Prevalence and Incidence ExampleCalculating
ART required

• Prevalence now 2
• Prevalence in 5 years 2 0.1 X 5
• 2.5
• Minus deaths 2 X 1/20 0.1
• 2.4
• Need average period prevalence 2.2
• (Times ART meds needed per person for 5 years)

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Types of Incidence and Prevalence Measures
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Types of Incidence and Prevalence Measures
(contd)
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Specific definitions

• Prevalence (P) Persons with disease/Total
  population this is a ratio (NOT A RATE)
• Point Prevalence number of persons with disease
  at a specific point in time
• Period Prevalence number of persons with
  disease during a specific period of time
• Annual Prevalence number of persons with
  disease over one year.
• Sero-Prevalence number of persons with
  serologic evidence of disease or infection or
  exposure

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Incidence or prevalence?

• Number of homicides in Montreal in 2005
• Number of homicide detectives in Montreal in 2005
• Number of new homicide detectives hired by Mtl
  police force in 2005
• Female homicide detectives in Montreal on Dec 31
  2005

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Exposure Assessment - Misclassification

• Often exposures measured retrospectively
• Major weakness in cross-sectional studies
• Especially diseases with long latency
• Or prevalent disease with long duration
• Inaccurate recall random misclassification
• Strategies measure current exposures
• Use objective measures age, sex, BP, weight
• Or easily remembered
• Smoking history
• Pregnancies and children
• Occupation

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Is current exposure representative?

• Children with prevalent asthma
• Cross-sectional study finds their current home
  environment has LESS allergens than controls
• Children with New (incident) asthma
• Prospective study finds current home environment
  has MORE allergens than controls
• Why the discrepant findings?

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Exposure Assessment Recall bias

• Cases with disease remember exposures better
• Often prompted by their doctors
• Example - Fetal malformations both parents
  spend all their time remembering things.
• So almost all exposures significantly more
  common in diseased
• Control by measure disease directly at time of
  survey, and assess exposure before disease status
  known. (eg screening for HIV, TST)
• If disease ascertained by questionnaire use more
  objective exposure measures

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What will recall bias tend to do?

• Acts to confirm known associations
• Patients have heard of exposures causing their
  disease
• Doctors repeatedly ask Are you sure you never
  worked with asbestos for Mesothelioma
• May create associations when people have a
  belief
• Hydro-electric power lines and depression
• UFFI urea-formaldehyde foam and asthma

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Misclassification

• Random poor measurement
• Questionnaire just cannot remember
• Direct measurements inaccurate tests
• Reduces chance of finding real associations
• Non-random biased measurement
• Questionnaire recall bias
• Direct measurement If there are important
  benefits or problems from a finding
• Will produce biased estimates
• No association when there is one
• Or exaggerated, or even reverse associations

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Precision vs Bias

• Precision Repeated measures get the same
  results little variability in measures
• Bias Ability of measurement to get close to
  real value
• Ideal measure is both precise and without bias

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Random Misclassification
Real Value
Actual Measures
Actual measures are more or less than Real value
but evenly distributed above or below.
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Example of Bias in Measurement
Real Value
Actual Measures
Actual measures are all less than real value.
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Precision Degree of Variation in Measurement
Real Value
Actual measures
Actual measures are all clustered tightly around
real values.
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Imprecision in Measurement
Real Value
Actual measures are spread out widely but
centered around real value. (Imprecise, but
unbiased)
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Measures of Disease AssociationPrevalence Odds
Ratios

• Summary measure of disease association in
  prevalence studies
• General formula
• odds of exposure given disease
• odds of exposure given no disease
• Like case control studies, prevalence studies
  identify subjects on the basis of disease status.

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Measures of disease association Prevalence Odds
Ratio

• In a prevalence survey, 60 individuals were found
  to have diabetes out of 1,000 surveyed
• Prevalence of diabetes total 6
• Prevalence of diabetes among obese persons
  27/200 13.5
• Prevalence of diabetes in non obese persons
  33/773 4.3

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Prevalence Odds Ratio, contd

• Express the findings as prevalence odds
• i.e., odds of exposure if disease
• or, odds of obesity if diabetes 27/33 0.81
• Odds of obesity if not diabetes 200/740 0.27
• Prevalence odds ratio (POR) 0.81/0.27 3.0
• For cross-sectional or prevalence studies the
  prevalence odds ratio is the same as the ratio of
  the prevalence of disease in persons with and
  without the risk factor

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POR Effect of random mis-classification

• CHILDHOOD obesity and adult diabetes

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POR Effect of random mis-classification

• What the survey obtained

Odds of childhood obesity if diabetic 29/31
0.93 Odds of childhood obesity if not
diabetic 378/562 0.67 POR 0.93/0.67 1.4
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POR Non-random mis-classification

• Adult diabetics more likely to recall obesity in
  childhood

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POR Non-random mis-classification

• Recall bias Adult diabetics more likely to
  report CHILDHOOD obesity than non-diabetics

Odds of childhood obesity if diabetic 45/15
3.0 Odds of childhood obesity if not
diabetic 200/740 0.27 POR 3.0 / 0.27 11.4
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Longitudinal prevalence studies

• In some cross-sectional studies inferences can be
  made about incidence, as if a cohort design was
  used
• When population has spectrum of years of
  exposure/age
• Tuberculin or HIV sero-prevalence survey
• Years of work as health professional
• However, this design still has same problems of
  retrospective exposure assessment

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Longitudinal prevalence study - example

• Results of HIV sero-prevalence in males and
  females in different age groups

• What can one say about incidence
• By age?
• By age and sex?

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Longitudinal prevalence study - example

• Estimated annual incidence in each age/sex
  category

• Incidence higher in younger females, but then
  males catch up