1. Descriptive Epidemiology
Dr. KANUPRIYA CHATURVEDI

2. How we view the world….. Pessimist: The glass is half empty.
Optimist: The glass is half full.
Epidemiologist: As compared to what?

3. Epidemiology is...

4. Epidemiology is... "The worst taught course in Medical school."
Medical Student

5. Epidemiology is... "The science of making the obvious obscure."
Clinical Professor

6. Epidemiology is... "The science of long division....
I'=[(480)(log2)(10E6)]/[(9.1)(0.955po)+0.45n]"
Statistician

7. Definition of Epidemiology*
"The STUDY of the DISTRIBUTION and DETERMINANTS of HEALTH-RELATED STATES in specified POPULATIONS, and the application of this study to CONTROL of health problems."
*Last, J.M A Dictionary of Epidemiology, 2nd ed.

8. Epidemiology: Definition
Dynamic study of the
Determinants
Occurrence
Distribution
Control
Pattern
Of health and disease in a population

9. Epidemiology EPI DEMO LOGOS
Upon,on,befall People,population,man the Study of
The study of anything that happens to people
“That which befalls man”

10. Definition of Epidemiology
A quantitative basic science, built on a working knowledge of probability, statistics and sound research methods.
A method of causal reasoning, based on developing and testing biologically plausible hypothesis pertaining to occurrence and prevention of morbidity and mortality.
A tool for public health action to promote and protect the public's health based on science, causal reasoning, and a dose of practical common sense.

11. Epidemiology is a Quantitative Discipline
Measures of frequency
Counts and rates
Measures of association
Relative risk
Odds ratio
Statistical inference
P-value
Confidence limits

12. Clinician Epidemiologist
Patient’s diagnostician
Investigations
Diagnosis
Therapy
Cure
Community’s diagnostician
Investigations
Predict trend
Control
Prevention

13. Epidemiology Describes health events cause and risk factors of disease
clinical pattern of disease
Identify syndromes
Identify control and/or preventive measures

14. So, Epidemiology Is the basic science of public health
Provides insight regarding the nature, causes, and extent of health and disease
Provides information needed to plan and target resources appropriately

15. Descriptive Analytic Experimental Kinds of Epidemiology
Study of the occurrence and distribution of disease
Further studies to determine the validity of a hypothesis concerning the occurrence of disease.
Deliberate manipulation of the cause is predictably followed by an alteration in the effect not due to chance

16. Overview of epidemiologic design strategies
Descriptive
Populations{Correlational studies}
Individual
Case report
Case series
Cross sectional studies
Analytic studies
Observational
Case control
Cohort
Retrospective
Prospective
Interventional/Experimental
Randomized controlled trial
Field trial
Clinical trial

17. Descriptive vs. Analytic Epidemiology
Used when little is known about the disease
Rely on preexisting data
Who, where, when
Illustrates potential associations
Analytic
Used when insight about various aspects of disease is available
Rely on development of new data
Why
Evaluates the causality of associations
Both are important!

18. Descriptive Studies
Relatively inexpensive and less time-consuming than analytic studies, they describe,
Patterns of disease occurrence, in terms of,
Who gets sick and/or who does not
Where rates are highest and lowest
Temporal patterns of disease
Data provided are useful for,
Public health administrators (for allocation of resources)
Epidemiologists (first step in risk factor determination)

19. Descriptive Epidemiology
Correlational studies
Case reports
Case series
Cross sectional studies

20. Correlational Studies (Ecological Studies)
Uses measures that represent characteristics of entire populations
It describes outcomes in relation to age, time, utilization of services, or exposures
ADVANTAGES
We can generate hypotheses for case-control studies and environmental studies
We can target high-risk populations, time-periods, or geographic regions for future studies

21. Correlational Studies
LIMITATIONS
Because data are for groups, we cannot link disease and exposure in individual
We cannot control for potential confounders
Data represent average exposures rather than individual exposures, so we cannot determine a dose-response relationship
Caution must be taken to avoid drawing inappropriate conclusions, or ecological fallacy

22. Patterns of disease Occurrence : Correlation of Population statistics
Ecologic ( correlation ) studies
Used as first step in determining association plot : disease (population) burden [ Y axis ] vs. prevalence of “risk factor” [ X axis ] e.g. smoking vs. lung cancer
-- correlation coefficient : r ; + 1 to -1
Quantifies linear relationship between exposure & disease

23. Case Reports (case series)
Report of a single individual or a group of individuals with the same diagnosis
Advantages
We can aggregate cases from disparate sources to generate hypotheses and describe new syndromes
Example: hepatitis, AIDS
Limitations
We cannot test for statistical association because there is no relevant comparison group
Based on individual exposure {may simply be coincidental}

24. Case report/Case series(contd.)
Important interface between clinical medicine & epidemiology
Most common type of studies published in medical journals{1/3rd of all}
e.g. Frisbee finger , break dancing neck
AIDS ~ b/w oct1980-may81, 5 cases of P.carinii pneumonia were diagnosed among previously healthy young homosexual males in L.A.

25. Cross-Sectional Studies (prevalence studies)
Measures disease and exposure simultaneously in a well-defined population
Advantages
They cut across the general population, not simply those seeking medical care
Good for identifying prevalence of common outcomes, such as arthritis, blood pressure or allergies
Limitations
Cannot determine whether exposure preceded disease
It considers prevalent rather than incident cases, results will be influenced by survival factors
Remember: P = I x D

26. Cross-Sectional Studies
Can be used as a type of analytic study for testing hypothesis, when;
Current values of exposure variables are unalterable over time
Represents value present at initiation of disease
E.g. eye colour or blood group
If risk factor is subject to alterations by disease, only hypothesis formulation can be done

27. The epidemiologic approach: Steps to public health action
DESCRIPTIVE
What (case definition)
Who (person)
Where (place)
When (time)
How many (measures)
ANALYTIC
Why (Causes)
How (Causes)
MEASURES
Counts
Times
Rates
Risks/Odds
Prevalence
METHODS
Design
Conduct
Analysis
Interpretation
ALTERNATIVE
EXPLANATIONS
Chance
Bias
Confounding
INFERENCES
Epidemiologic
Causal
ACTION
Behavioural
Clinical
Community
Environmental

28. Key questions
Why now?
Why here?
Why in this group?

29. Descriptive Epidemiology
Study of the occurrence and distribution of disease
Terms:
Time
Place
Person

30. What are the three categories of descriptive epidemiologic clues?
□ Person: Who is getting sick?
□ Place: Where is the sickness occurring?
□ Time: When is the sickness occurring?
PPT = person, place, time

31. Time
Secular
Periodic
Seasonal
Epidemic

32. Secular Trend
The long-time trend of disease occurrence

33. Tetanus – by year, USA,
During 2000, a total of 35 cases of tetanus were reported. The percentage of cases among persons aged years
Has increased in the last decade. Note: A tetanus vaccine was first available in 1933.

34. Possible Reasons for Changes in Trends
Artifactual
Errors in numerator due to
Changes in the recognition of disease
Changes in the rules and procedures for classification of causes of death
Changes in the classification code of causes of death
Changes in accuracy of reporting age at death
Errors in the denominator due to error in the enumeration of the population

35. Possible Reasons for Changes in Trends (cont.)
Real
Changes in age distribution of the population
Changes in survivorship
Changes in incidence of disease resulting from
Genetic factors
Environmental factors

36. Other phrases
Cyclic trends ~ recurrent alterations in occurrence , interval or frequency of disease
Secular cyclicity
Levels of immunizations
Build up of susceptibles
e.g. Hep A-7 yr cycle,Measles-2yr cycle
Short term cyclicity
Chickenpox,salmonella(yearly basis)

37. Periodic Trend
Temporal interruption of the general trend of secular variation

38. Whooping Cough - Four-monthly admissions, 1954-1973

39. Seasonal
A cyclic variation in disease frequency by time of year & season.
Seasonal fluctuations in,
Environmental factors
Occupational activities
Recreational activities

40. Pneumonia-Influenza Deaths – By year, 1934-1980
Seasonal Trend
Pneumonia-Influenza Deaths – By year,

41. Epidemic
An increase in incidence above the expected in a defined geographic area within a defined time period

42. Endemic, Epidemic and Pandemic
Endemic - The habitual presence (or usual occurrence) of a disease within a given geographic area
Epidemic - The occurrence of an infectious disease clearly in excess of normal expectancy, and generated from a common or propagated source
Pandemic - A worldwide epidemic affecting an exceptionally high proportion of the global population
Number of Cases
of Disease
Time

43. Time clustering Time Place Cluster/disease cluster
A group of cases occur close together & have a well aligned distribution pattern {in terms of time and place}
Cluster analysis-used for rare or special disease events.

44. Time/Place clustering analysis using the Poisson model {Poisson spatial/nearest neighbor distribution}
Poisson probability distribution is an inferential statistics probability measure.
Describes objects/events as they are distributed geographically.
Geographical area divided into a series of equal square areas.
Randomization i.e. each case has equal probability of falling into each square.
If clustering occurs, probability of cause-effect relationship goes up & vice versa.

45. Place Geographic Area Example Action Level Investigation
Control
Prevention
Home – Patient ill
Restaurant – Food Eaten
Farm – Eggs Infected
Diagnosis is Made
Contact occurred between agent and host
Source became infected

47. Person Age Hobbies Sex Pets Occupation Travel
Immunization status Personal Habits
Underlying disease Stress
Medication Family unit
Nutritional status School
Socioeconomic factors Genetics
Crowding Religion

48. Descriptive epidemiology : Patterns of Disease Occurrence
distribution of disease in populations numerator ( “event” count ) / denominator ( group “at risk” )
by “person” : age , race / ethnicity , gender , occupation , education , marital status , genetic marker , sexual preference
by “place” : residence (urban vs. rural) , worksite , social event
by “time” : week , month , year ; sporadic , seasonal , trends --- incubation period ; latency

49. Sources of information
Census data
Vital statistical records
Employment health examinations
Clinical records from hospitals
National figures on food consumption , medications, health events etc

50. Epidemiologic ( scientific ) Approach
1. Identify a PROBLEM : clinical suspicion ; case series ; review of medical literature
2. Formulate a HYPOTHESIS ( asking the right question ) ; good hypotheses are: Specific, Measurable, and Plausible
3. TEST that HYPOTHESIS ( assumptions vs. type of data )
4. always Question the VALIDITY of the result(s) : Chance ; Bias ; and Causality

51. Epidemiologic Study: threats to Validity
Chance : role of random error in outcome measure(s) ( p - value ; power of the study and the confidence interval ) --- largely determined by sample size
Bias : role of systematic error in outcome measure(s)
Selection bias - subjects not representative
Information bias - error(s) in subject data / classification
Confounding - 3rd variable (causal) assoc. w/ both X and Y

52. What is a hypothesis? An educated guess an unproven idea
based on observation or reasoning, that can be proven or disproven through investigation.

53. What goes into a hypothesis?
Characteristics of the disease
The illness
Established modes of transmission
Distribution
In time
By place
By person

54. Hypothesis formulation
4 methods {derived from 5 canons of inductive reasoning by John Stuart Mill}
Method of difference
Method of agreement
Method of concomitant variation
Method of analogy

55. Measures Morbidity: Refers to the presence of disease in a population
Mortality: Refers to the occurrence of death in a population

56. Methods for Measuring
How do we determine disease frequency for a population?
Rate = Frequency of defined events in specified population for given time period
Rates allow comparisons between two or more populations of different sizes or of a population over time

57. Compute Disease Rate Number of persons at risk = 5,595,211
Number of persons with disease = 17,382
Rate = 17,382 persons with heart disease 5,595,211 persons
= heart disease / resident / year

58. Rates
Rates are usually expressed as integers and decimals for populations at risk during specified periods to make comparisons easier.
heart disease / resident / year x 100,000
= heart disease / 100,000 residents / year

59. Prevalence vs. Incidence
Prevalence is the number of existing cases of disease in the population during a defined period.
Incidence is the number of new cases of disease that develop in the population during a defined period.

60. Incidence
Incidence rate is a measure of the probability of the event among persons at risk.

61. Incidence Rates Population denominator:
IR = # new cases during time period X K
specified population at risk

62. Example (Incidence Rate)
During a six-month time period, a total of 53 nosocomial infections were recorded by an infection control nurse at a community hospital. During this time, there were 832 patients with a total of 1,290 patient days. What is the rate of nosocomial infections per 100 patient days?

63. Mortality Rates A special type of incidence rate
Number of deaths occurring in a specified population in a given time period

64. Use of Mortality rates
Mortality rates are used to estimate disease frequency when…
incidence data are not available,
case-fatality rates are high,
goal is to reduce mortality among screened or targeted populations

65. Mortality Rates: Examples
Crude mortality: death rate in an entire population
Rates can also be calculated for sub-groups within the population
Cause-specific mortality: rate at which deaths occur for a specific cause

66. Mortality Rates: Examples
Case-fatality: Rate at which deaths occur from a disease among those with the disease
Maternal mortality: Ratio of death from childbearing for a given time period per number of live births during same time period

67. Mortality Rates: Examples
Infant mortality: Rate of death for children less than 1 year per number of live births
Neonatal mortality: Rate of death for children less than 28 days of age per number of live births

68. Prevalence
Prevalence: Existing cases in a specified population during a specified time period (both new and ongoing cases)
Prevalence is a measure of burden of disease or health problem in a population

69. Prevalence
Prevalence: The number of existing cases in the population during a given time period.
PR = # existing cases during time period
population at same point in time
Prevalence rates are often expressed as a percentage.

70. Factors Influencing Prevalence
Decreased by:
Shorter duration of disease
High case-fatality rate from disease
Decrease in new cases (decrease in incidence)
In-migration of healthy people
Out-migration of cases
Improved cure rate of cases
Increased by:
Longer duration of the disease
Prolongation of life of patients without cure
Increase in new cases
(increase in incidence)
In-migration of cases
Out-migration of healthy people
In-migration of susceptible people
Improved diagnostic facilities
(better reporting)

71. Basic Measures of Association
Relative risk& odds ratio
We often need to know the relationship between an outcome and certain factors (e.g., age, sex, race, smoking status, etc.)
Used to guide planning and intervention strategies

72. 2 x 2 contingency table for Calculation of Measures of Association
Outcome 
Exposure
Present
Absent
TOTAL a b
a+b c d
c+d
a+c
b+d
a+b+c+d
Note: “Exposure” is a broad term that represents any factor that may be related to an outcome.

73. Relative Risk Ratio of the incidence rates between two groups
Can only be calculated from prospective studies (cohort studies)
Interpretation
RR > 1: Increased risk of outcome among “exposed” group
RR < 1: Decreased risk, or protective effects, among “exposed” group
RR = 1: No association between exposure and outcome

74. Calculation of Relative Risk
incidence rate among exposed
RR =
incidence rate among non-exposed

75. Calculation of Relative Risk
Outcome 
Exposure
Present
Absent
TOTAL a b
a+b c d
c+d
a+c
b+d
a+b+c+d
Relative Risk =

76. Relative Risk Case Study
Smoking and low birth weight
Birth Weight
Smoking status
<2500 g
>2500 g
TOTAL
Smoker
120
240
360
Non-smoker 60
580
640
180
820
1000

77. Answers to Relative Risk Case Study
1. Incidence of LBW among smokers
2. Incidence of LBW among non-smokers
3. Relative risk for having a LBW baby among smokers versus non-smokers

78. Understanding Probability and Odds
Probability: Chance or risk of an event occurring (a proportion)
Probability= no. of times an event occurs
no. of times an event can occur
Odds: ratio of the probability of an event occurring to the probability of an event not occurring
Odds = P/(1-P)

79. Calculation of Odds Ratio
Outcome 
Exposure
Present
Absent
TOTAL a b
a+b c d
c+d
a+c
b+d
a+b+c+d
Odds Ratio =

80. Odds Ratio The odds ratio (OR) is a ratio of two odds.
The OR can be calculated for all three study designs
Cross-sectional
Case-control
Cohort.

81. Various approaches to Odds ratio
Cross product/odds ratio
2 x 2 contingency table (ad/bc)
Prevalence odds ratio
cross sectional studies
Exposure odds ratio( odds of exposure in diseased vs. nondiseased)
In rare cases or exotic diseases
Disease odds/Rate odds ratio(odds of getting a disease if exposed or unexposed)
Cohort & cross sectional
Risk odds ratio
Cross sectional ,cohort & case control

82. Odds Ratio
For cohort & cross sectional studies: OR is a ratio of the odds of the outcome in exposed persons to the odds of the outcome in non-exposed persons.
For case-control studies: OR is a ratio of the odds of exposure in cases to the odds of exposure in controls.
Provides an estimate of the relative risk when the outcome is rare

83. Interpretation of Odds Ratio
OR > 1: Increased odds of exposure among those with outcome
OR < 1: Decreased odds, or protective effects, among those with outcome
OR = 1: No association between exposure and outcome

84. Keeping the Terms Straight
“Risk ratio” = “relative risk”
“Relative odds” = “odds ratio”
Remember – the key is recognizing the terms “risk” and “odds”

85. Appropriateness of Measures
Remember that the relative risk can only be calculated in prospective studies
Odds ratio can be calculated for any design
Cohort / prospective
Case-control
Cross-sectional

86. Inference
The relative risk and odds ratio provide the magnitude of difference between some factor and an outcome
How do we know if the magnitude is statistically significant?

87. Confidence Intervals
A confidence interval is a range of values that is likely (e.g., 95%) to contain the true value in the underlying population

88. The 10 Steps of Outbreak Investigation
Prepare for field work
Establish the existence of an outbreak
Verify the diagnosis
Define & identify cases
Perform descriptive epidemiology
Develop hypotheses
Perform analytic epidemiology
Refine hypotheses & conduct additional studies
Implement control & prevention measures
Communicate findings

89. Objectives of Descriptive Epidemiology
To evaluate trends in health and disease and allow comparisons among countries and subgroups within countries
To provide a basis for planning, provision and evaluation of services
To identify problems to be studied by analytic methods and to test hypotheses related to those problems