Study Design

1. Presented by : Sirjana Tiwari
MPH(PHSM)
Pokhara University
2017
Study Designs in Ecologic Studies: Multiple Group Study and
Statistical Analysis
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2.  Correlation studies
 Examine association between exposure and disease measures on
the population level
 Unit of measurement = population groups
 Eg: Behavioral Risk Factor surveillance system
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3. Variables in an ecologic analysis
aggregate measures
environmental measures
global measures
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4. variables
 Aggregate measures are summaries (e.g. means or proportions) of
observations derived from individuals in each group
e.g. the proportion of smokers
the proportion of median family income.
 Environmental measures are physical characteristics of the place in
which members of each group live or work
e.g. air-pollution level or hours of sunlight
 Global measures are attributes of groups or places for which there is no
distinct analogue at the individual level.
e.g. population density
level of social disorganization
the existence of a specific law
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5. STUDY DESIGNS
1. The groups of an ecologic study may be identified
a. by place (multiple-group design),
b. by time (time-trend design)
c. by combination of place and time (mixed design).
2. The method of exposure measurement
a. an ecologic design is called exploratory if the primary exposure of
potential interest is not measured,
b. analytic if the primary exposure variable is measured and included
in the analysis. In practice, this dimension is a continuum, since
most ecologic studies are not conducted to test a single hypothesis
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6. TYPE OF ECOLOGIC STUDY DESIGNS
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Based upon Groups of an
Ecologic study
Based upon the Method of exposure
measurement in Ecological study
a. by place (multiple-group
design)
Exploratory Analytical/Etiological
b. by time (time-trend design) Exploratory Analytical/Etiological
c. by combination of place and
time (mixed design)
Exploratory Analytical/Etiological

7. Multiple-Group Study(by place)
a. Exploratory study
 We compare the rate of disease among many regions during the same
period
 For example, the age-adjusted cancer mortality rates in the U.S. by county for
the period 1950-69 for oral cancers, found a striking difference in geographic
patterns by-sex:
 Among men, the mortality rates were greatest in the urban Northeast,
 among women, the rates were greatest in the South east.
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8. Example: An Multiple group studies of obesity
and income inequality
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The relation between male obesity and
income inequality in 21 rich countries.
The relation between female obesity and income
inequality in 21 rich countries.

9. example
 It also involve the comparison of rates between migrants and their offspring
and residents of their countries of emi gration and immigration.
 For example, if US immigrants from Japan have rates of a disease similar to US
whites but much lower than Japanese residents, the difference may be due to
environmental or behavioral risk factors operating during adulthood.
 However, the interpretation of results from these studies is often limited by
differences between countries in the classification and detection of disease or
cause of death
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10. Multiple-Group Study(by place)
a. Exploratory study
mapping
 A simple comparison of rates across regions is done in Mapping.
 First, regions with smaller numbers of observed cases show greater
variability in the estimated rate; thus the most extreme rates tend to be
observed for those regions with the fewest cases.
 Second, nearby regions tend to have more similar rates than do distant
regions (i.e. autocorrelation) because unmeasured risk factors tend to cluster in
space.
 Statistical methods for dealing with both problems have been developed by
fitting the data to an autoregressive spatial model and using empirical Bayes
techniques to estimate the smoothed rate for each region
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11. Multiple-Group Study(by place)
b. Analytical study
In this type of study, we assess the ecologic association between the average
exposure level or prevalence and the rate of disease among many groups.
The unit of analysis is a geopolitical region.
For example, Hatch & Susser (29) examined the association between
background gamma radiation and the incidence of child hood cancers
between 1975 and 1985 in the region surrounding a nuclear power plant.
The authors found positive associations between radiation level and the
incidence of leukemia (an expected finding) as well as solid tumors (an
unexpected finding).
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12.  Data analysis in this type of multiple-group study usually involves fitting
the data to a mathematical model.ie.
 linear relative rate
 exponential relative rate
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13. Statistical analysis
1. 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, as described below.
2. Use the fitted regression model to predict the disease rate for a population in
which everyone is exposed. Call expose rate R 1. Similarly, predict the rate for
a population in which nobody is exposed, and call unexposed rate R 0.
3. Estimate relative risk as R 1/R 0 and attributable risk as R 1 − R 0.
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14.  Look at overall association between exposure and outcome
measure for ecological units being compared
Scatter plot
Pearson correlation coefficient (r)-overall measure of
correlation
Measures degree of linear relationship between the
continuous variable
-1<r<+1
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15. Regression analysis
Figure : Power of the repeated measurements multilevel regression analysis as a function of the
magnitude of the regression coefficient, n=60 persons with 40 repeated measurements15

16. 16
 In multiple-group ecologic studies, we regress the group-specific
disease rates (Y) on the group-specific exposure prevalence's (X).
 (Note that, throughout this chapter, uppercase letters are used to
represent ecologic variables and their estimated regression
coefficients; lowercase letters are used to represent individual-level
variables and their estimated regression coefficients

17. Linear model
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 The most common model form for analyzing ecologic data is the
linear model. Ordinary least-squares methods can be used to
produce the following prediction equation: Ŷ = B0 + B1X, where B0
and B1 are the estimated intercept and slope. An estimate of the
biologic effect of the exposure (at the individual level) can be
derived from the regression results
 The predicted disease rate (Ŷx = 1) in a group that is entirely
exposed is B0+B1(1) = B0+B1, and the predicted rate (Ŷx = 0) in a
group that is entirely unexposed is B0+B1(0) = B0. Therefore, the
estimated rate difference is B0+B1 - B0 = B1, and the estimated rate
ratio is (B0 + B1)/B0 = 1 + B1/B0.

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 Alternatively, fitting a log-linear (exponential) model to the data
yields the following prediction equation: ln(Ŷ) = B0 + B1X or Ŷ =
exp(B0+B1X). Applying the same method as used for linear models,
the estimated rate ratio is Ŷx = 1/Ŷ x = 0 = exp (B1).

19. Ordinary least-squares linear regression
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 Durkheim's (1951) examination of religion and suicide in four
groups of Prussian provinces between 1883 and 1890.
 The groups were formed by ranking 13 provinces according to the
proportion (X) of the population that was Protestant.
 Using ordinary least-squares linear regression, we estimate the
suicide rate (Ŷ, events per 105 person-years) in each group to be
3.66 + 24.0(X).
 Therefore, the estimated rate ratio, comparing Protestants with
other religions, is 1 + (24.0/3.66) = 7.6. Note in Figure that the fit of
the linear model appears to be excellent (R2 = 0.97)

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 Suicide rate (Y, events per 105 person-years) by proportion Protestant (X)
for four groups of Prussian provinces, 1883–1890. The four observed
points (X, Y) the fitted line is based on unweighted least-squares
regression.

21. Empirical Bayes method
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 It is the one of the most important application of conditional
probability which gives the information about the occurrence of one
event to predict the probability of another events.
 Ie.
 Empirical Bayes model is use to calculate sensitivity, specificity,
predictive value positive and predictive value negative

22. Spatial autocorrelation
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 spatial autocorrelation test is designed for of long-term, large-scale
ecological datasets
 This test foster significant future advances in understanding
population regulation, metapopulation dynamics and other areas of
population ecology.

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Fig: Synchrony in relative mean density of mountain hares (Lepus timidus) among 11 provinces in
Finland measured over 39 years (1946–1984) plotted against the distance between the
geographical centers of the provinces. The total number of comparisons is 55, corresponding to
the number of pairwise comparisons possible between 11 sets of data. Note that synchrony
declines with increasing distance between provinces (r 5 –0.57)

24. Mathematical model
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 Mathematical models developed to predict and control behavior in applied
settings, and they have guided research in other areas of psychology.
 A good mathematical model can provide a common framework for
understanding what might otherwise appear to be diverse and unrelated
behavioral phenomena.
 It is important for those who develop mathematical models of behavior to find
ways (such as verbal analogies, pictorial representations, or concrete examples)
to communicate the key premises of their models to non specialists.

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
Fig: the percentage of JEAB articles that presented at least one
equation to describe the relation between an independent variable
and a dependent variable

26. Reference
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9780195150780-chapter-12
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