The document outlines key concepts and applications of epidemiology, emphasizing its role in understanding disease distribution and determinants, causation, and measurement of disease and mortality. It also details various types of epidemiological studies, screening methods, and biostatistical approaches essential for analyzing health data and making clinical decisions. Furthermore, it discusses common sources of error in studies and the importance of proper sampling and bias minimization in research.

1. Epidemiology-A Bird’s Eye View
Dr Shyam Ashtekar
June 2016
ashtekar.shyam@gmail.com
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2. CDC
• CDC is center for Disease Control Atlanta in US
• Our CDC- COUNT-DIVIDE-COMPARE
6/16/2016 2

3. Ten Areas of
Epidemiology
Epidemiolo
gy
Concepts
Uses of
epidemiolo
gy
association
and
causation
Disease &
mortality
measurem
ents
methods-
descriptive
, analytical,
trials,
reviewsScreening
for disease
Infectious
Disease
Epidemiolo
gy
Bio stat in
epidemiolo
gy
Sources of
error-Bias
Application
s-clinical,
preventive,
HS, HP,
NCCD
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4. 1.Concepts
Distribution &
Determinants
6 Questions
What-where-whom-
when are
Descriptive issues
Why and How are
analytical issues
It is applied to a
group/community/
population
Attempts to
establish relation of
cause & effect, risk
association
Main toolbox of PSM
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5. 2. Uses of epidemiology
Disease
Knowledge
• Understand Causes, risk factors, prognosis, syndromes
• Natural History, rise and fall of disease
Interventions
• Assess Diagnostic and Treatment tools
• Investigate and control epidemics
Community
level use
• Diagnose Health & illness of communities
• Plan & evaluate preventive programs
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6. 3 (A) Causation!
Hills’ Criteria
• Temporal precedence
• Independence of association
• Strength of association (RR,
OR)
• Biological plausibility
• Consistency across studies
• Coherence
• Dose response relation
• Reversibility *
• Good study design necessary
Cause..
• Sufficient cause
• Necessary cause
• Contributory cause
• Multifactorial cause
• Direct, indirect
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7. 3 (B)Association (Risk factor)
• Direct
• Indirect
• Multifactorial
• Enabling, contributory
• Spurious (false)
• Confounding factors
(present both on cause
and effect side)
• We test strength of
association-OR, RR, AR,
Risk difference etc
Effect-Disease
Cause
RF1
RF..n
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8. 4. Disease & Mortality Measurements
• Disease Incidence
• Disease Prevalence
• Mortality rates
• Life expectancy
• Proportional mortality rates
• Age-adjusted rates
• DALY index
• Rates
(number/time)
• Ratios (n/n)
• Proportions
(n/N)%
• Percentages
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9. 6/16/2016 9
5.Types of epidemiological studies

10. 6/16/2016 10
An overview of epidemiological studies and their features
Type Approach Method Type of study/name Outcome/Result Study population or Subjects
REVIE
W
Analytical
(Compares)
Computational
standardization
Meta-Analysis A combined parameter based on re-
computing/standardization
Many studies on one issue
Review of studies Systematic review Summarized view of methods/results Many studies
EXPERMENTAL
Experimental
(Compares)
Experiment/
Trial/Treatment/
Intervention
With Controls and
Randomization
RCT-, Double Blind Effect of an intervention Cases in two randomized groups
RCT- Double Blind,
RCT-DB-Cross over
Effect of an intervention Cases in two randomized groups
Community Trial Effect of an intervention Defined communities
Field Trial Effect of an intervention Area Population
Non Randomized trials Natural Experiments Effect of experiment Area affected
Before & After trials Comparison with same group, before & after Same group
Uncontrolled trials Measures Change in same group Same group
OBSERVATIONAL
Analytical
(search for
determinants
(Compares)
Cohort analyses
Exposure to Effect
Cohort/Prospective Relative Risk/Attributable Risk (RR/AR).
Can study more variables
Group with common features,
having current Exp and No Exp
Cohort-Retrospective Relative Risk/Attributable Risk(RR/AR) Group with common features ,
having Past Exp and No Exp
Analyses effect to
exposure
Case-Control study Odds Ratio (OR) /cross product of risk in
exposed and non-exposed individuals
Cases and matched non-cases
called controls
Exposure and effect in
same time frame
Analytical Cross-
Sectional (??)
Comparative Prevalence, hypothesis of
association of two or more variables
A sample of population
Descriptive
(No
comparison)
Only describe whom-
when-where-what
Descriptive Cross
sectional
Facts/description about
agent/host/environment, prevalence rate
Individuals from a defined
group, to be studied.
Ecological study Rates/magnitude of what/where/when Reports/surveillance /Census
Surveillance Noise level, changes in time A defined population
Follow up studies Incidence/ time trend A defined population
Case Series magnitude of the problem Physician Records of many cases
Case Report/
practitioner’s views
Suspicion/suggestion Single case or series, views
P
o
w
e
r
o
f
s
t
u
d
y
d
e
s
i
g
n

11. 6. Screening pop at risk for disease
• Diagnostic test applied to
apparently healthy
population at risk
– Mass Screening test
– High risk Screening
– Multi-phasic screening
• Test should be Acceptable,
simple, repeatable, feasible,
low cost, valid (sensitive,
specific, accurate), high yield!
• Disease should be
common, important, treatable,
of good prognosis, sufficient
lead time/screening time
• Analysis done by 2*2
table with odds
ratio/cross product (Not
RR)
• Issues of borderline
values
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12. 7 (A) Infectious Disease Epidemiology
• Infection- infestation
contamination,
• Infectious, contagious,
communicable
• Epidemic, sporadic, endemic,
pandemic, zoonotic, exotic
• Epizootic, enzootic
• Nosocomial, opportunistic
• Control, elimination, eradication
• Source, reservoir, cases, carriers,
animal reservoir
• Carriers (incubatory, healthy,
convalescent)
• Direct and Indirect
transmission, vectors
• Parasitism (successful?)
• Incubation, infectious period,,
communicable period,
generation time
• Epidemic curve, serial period,
primary case, secondary case,
attack rate, case fatality rate
• Immunity, active passive, herd,
susceptible, resistant
• Isolation, quarantine, source
reduction
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13. 7 (B) Infectious Disease Epidemiology
Investigation of an epidemic-
• Verification of diagnosis,
• Confirmation of epidemic,
• Defining pop at risk,
• Case-finding, lab tests
• Relevant ecology info,
water/sanitation/pollution etc
• Data collection
• Data analysis,
• Build and test hypothesis,
• Investigate all people at risk of disease,
• Suggest control measures
• Report!
• Epidemic Curve
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14. 8 (A) Bio stat in epidemiology
Objectives-Estimation, comparison,
Hypothesis, inference about
risk/effectiveness of intervention
Proper sampling, sampling size,
Types of variables-qualitative
(nominal, ordinal, categorical),
quantitative (discrete numbers or
continuous data)
Proper recording and data analysis
Normal distribution -Measures of
variation, dispersion, central
tendency, skewness, kurtosis..
Probability of events-independent,
dependent, combined
Tests for quantitative data (Z test,
Diff between Means, Anova, T
test for small samples, paired T
test)
Tests for qualitative data (Z test
Diff for between proportions, Chi
Square test, ranking-tests)
Correlation and regression for
relation/rate of change between
two variables
Tests for significance at p<0.05
Type 1 and type 2 errors
Graphic representation
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15. 8 (B) Bio stat in epidemiology :2*2
tables
Important Concepts
• Case control-Odds ratio
(cross product) ad/bc
• Cohort- Relative risk (RR)
• Screening tests: Sensitivity :
a/(a+c), specificity :d/(b+d)
• Categorical data-
frequencies of disease in
exposed and non-exposed
groups- Chi
ᵡ
2=∑(O-E)2/E
Use of 2*2 tables (a,b,c,d cells)
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Disease
Present
Disease
absent
Total
Exposure Or
test +ve
a b a+b
No Exposure
Or
test-ve
c d c+d
Total a+c b+d

16. Bio stat (C) Types of sampling
Probability (Randomized)
sampling
• Simple random
• Systematic random sampling
• Stratified sampling
• Multi-stage sampling
• Multi-phase sampling
• Cluster sampling
• (What is randomization..)
Non-probability
/Non Randomized
• Purposive sampling
• Quota sampling
• Convenience
sampling
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17. 9. Sources of error-in studies
• Sampling Error-improve by size & selection
• Bias in Selection of subjects
• Measurement bias (method, observer,
instrument, participant, biological variation)
• Confounding factors (present in both cause
and effect)
• Validity factors-sensitivity and specificity
• Type 1 & type 2 error
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18. 10. Applications
Clinical studies
• About comparing /assessing
diagnostic tests
• Compare, assess treatment
regimes
Other studies
• Assessment of health
services,
• Health policy and program
evaluation
• Study of non-communicable
diseases
• Preventive trials
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19. How you will use it
• It should get into your
thinking ..processing of
information
• It is a way of looking at
things-clinical, social,
scientific approach.
Even philosophy of life
• In clinical practices
• Taking clinical decisions
• Keeping records,
analysis
• Reading research
papers, books
• ASKING Questions of
one self
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20. Thanks
Dr Shyam Ashtekar
Assistant Professor, PSM dept,
SMBT medical college, Nandi Hills,
Dhamangaon Ta. Igatpuri Maharashtra
ashtekar.shyam@gmail.com
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