1. Epidemiological Study Designs
Muhammad Tahir, MPH,MSc Epi & Bio
Acknowledgment:
Ms. Tazeen Saeed Ali
Assistant Prof. AKUSON

2. Objectives By the end of the session the participants will be able
Examine the purposes, structure, strengths and weaknesses of the different types of research designs.
Compare difference between these approaches
Review the definition of epidemiology, objectives, and history.
Compare the different phases of natural history of disease transmission.
Explain the integration of epidemiological designs in to community health nursing practice.

3. Background Rationale and Characteristics of epidemiological Research
Need to understand disease causation
Need to describe disease occurrence
Used to generate and test hypothesis, and evaluate health interventions
Design Characteristics
Specific goals and objectives
Methodology

4. Background Goals of Epidemiology and Public Health
Scientific research is the process of proposing and testing postulates and hypothesis in a specific field that may be or not related to public health
Evaluation research is concerned with the decision making process to implement, continue or adopt a new program that may be or not in the public health field
Epidemiological studies can be and has been used in both research approaches

5. Applications of Epidemiological Designs
Clinical Trials
testing new drugs or clinical management approaches
determining prognosis
Cohort
natural history of disease
etiology of disease - test hypothesis on causation

6. Applications of Epidemiological Designs
Case-Control
natural history of disease
etiology - test hypothesis on causation
Case report, case series, X-sectional,
describe disease occurrence
generate hypothesis

7. Research Designs in the Health Field
Observational Designs
Analytic
Cohort studies
Case-control studies
Cross-sectional studies
Descriptive
Case report
Case series
Ecological or correlation studies

8. Research Designs in the Health Field
Experimental Designs
Laboratory experiments
Clinical Trials
Field Trials
Intervention Trials
Quasi-Experimental
Field

9. Epidemiological Designs
Descriptive
objective is to describe the patterns and trends
help to generate hypothesis
help to plan programs
measure frequency of disease or other health outcome (occurrence)
measures determinants (risk factors) and effects on health outcomes
risk factors and effects may be measured over time

10. Case Report
What?
the profile of a single patient is reported in detail by one or more clinicians
Example
In 1961, a published case report of a 40 year-old women who developed pulmonary embolism after beginning use oral contraceptive

11. Case Series
What?
An individual case report that has been expanded to include a number of patients with a given disease
Example
In Los Angeles, five young homosexuals men, previously healthy, were diagnosed with pneumocyst cariini pneumonia in a 6-month period

12. Case Series
Clinical case-series: usually a coherent and consecutive set of cases of a disease (or similar problem) which derive from either the practice of one or more health care professionals or a defined health care setting, e.g. a hospital or family practice.
A case-series is, effectively, a register of cases.
Analyse cases together to learn about the disease.
Clinical case-series are of value in epidemiology for:
Studying symptoms and signs
Creating case definitions
Clinical education, audit and research

13. Cross-sectional
often interest is to describe frequency and pattern of either disease or health-related outcome occurrence.
existing traits, be it disease or health-related outcome, are measured at same time.
Usually data collected in a survey. door to door, mail or telephone interview and measurement.
neither cases nor comparison group, if exist, are pre-selected (post hoc selection).

14. Example - X-sectional Prevalence of Pap Smear

15. Cross-Sectional Study
In special circumstances can be analytic
neither cases nor comparison group are pre-selected. post hoc selection.
existing traits, be it exposure or health outcome, are measured at same time. therefore, assessment of temporality in found association is not possible. There are exceptions.

16. Cross-Sectional Study

17. Cross-Sectional Study

18. Cross-Sectional Study

19. Cross-Sectional Study

20. Prevalence
Proportion of individuals in a population with disease at a specific point of time
Provides estimate of the probability or risk at one will be ill at a point in time
Provides an idea of how severe a problem may be
Useful for planning health services (facilities, staff)

21. Formula for prevalence:
Number of existing cases of disease
P = at a given point in time
Total population at risk
2176 subjects with asthma encounter
P = = .07
31005 subjects
= 7 asthmatics per 100 subjects
= 7 %

22. Types of Prevalence
Point prevalence: number of cases that exist at a given point in time
Lifetime prevalence: proportion of the population that has a history of a given disorder at some point in time
Period prevalence: number of cases that exist in a population during a specified period of time

23. Ecological or Correlation
Ecological Studies
whole population is the unit of analysis
relationship between exposure and outcome at the individual level is missing (incomplete design)
ecological fallacy
Correlation Studies
same as ecological
aim to show strength of the ecological association

24. Ecological fallacy: example
Imagine a study of the rate of coronary heart disease in the capital cities of the world relating the rate to average income.
Within the cities studied, coronary heart disease is higher in the richer cities than in the poorer ones.
We might predict from such a finding that being rich increases your risk of heart disease.
In the industrialised world the opposite is the case - within cities such as London, Washington and Stockholm, poor people have higher CHD rates than rich ones.
The ecological fallacy is usually interpreted as a major weakness of ecological analyses.
Ecological analyses, however, informs us about forces which act on whole populations.

25. Epidemiological Designs
Analytic
main objective is to test hypothesis of relationship between exposure to a risk factor and disease or other health outcome
a measure of association is estimated
the magnitude, precision and statistical significance of the association is determined

26. Case Control Study
select population of cases and controls that are comparable
using historical data, determinant (exposure or risk factor) is measured retrospectively among case and controls
exposure and level of exposure measurement results compared between cases and controls to test a-priori hypothesis

27. Case-Control Study

28. Case-Control Study

29. Case-Control Study

30. Case-Control Study

31. Odds Ratio Breast No Breast Cancer Cancer Alcohol 70 100
No alcohol
a x d (70) (140)
b x c (50) (100)
* Used for case control studies because persons are selected based on disease status so you can’t calculate risk of getting disease
OR = =
= 2.0

32. RR or OR RR = 1 Risk in exposed is equal to risk in non exposed
RR > 1 Risk in exposed is greater than risk in non exposed
RR < 1 Risk in exposed is less than risk in non exposed

33. Cohort or Follow-up Study
determinant (risk factor) is measured in pre-selected cohort to identify exposed and non-exposed
cohort is followed-up
effect or health outcome (disease) is measured over time
at the end of study period results are compared between exposed and non-exposed to test a-priori hypothesis

34. follow-up period

35. end of follow-up

36. Cohort study
exposed
not exposed

37. Incidence among non-exposed
Cohort study
exposed
Incidence among
exposed RR
not exposed
Incidence among non-exposed

38. Cohort Study

39. Cohort Study

40. Cohort Design Study begins here disease Factor present no disease
population
free of
disease
disease
Factor
absent
no disease
Cohort Design
present
future
time
Study begins here

41. Cohort Study

42. Cohort Study

43. Relative Risk
Measure of association between incidence of disease and factor being investigated
Ratio of incidence rate for persons exposed to incidence rate for those not exposed
Incidence rate among exposed
RR =
Incidence rate among unexposed
Estimate of magnitude of association between exposure and disease

44. Formula for relative risk:
Incidence rate among exposed
RR =
Incidence rate among unexposed
a / (a + b)
RR =
c / (c+ d)

45. Difference Measures Attributable risk
# of cases among the exposed that could be eliminated if the exposure were removed
= Incidence in exposed - Incidence in unexposed
Population attributable risk percent
Proportion of disease in the study population that could be eliminated if exposure were removed
Incidence in total population - Incidence in unexposed
incidence in total population =

46. Attributable risk
Relative risk: RR

47. RR=
60 /100
40 /100
= 60/100 x 100/40= 1.5

48. The rate (proportion) of a disease or other outcome in exposed individuals that can be attributed to the exposure
Attribute: one of the feature of a disease Or cause of a disease

50. Formula for Attributable Risk
Incidence in exposed group - incidence in non exposed group
Incidence in exposed group
(60/100) - (40/100)
(60/100) = =
= 0.33 = 33 %
Attributable risk indicates the prevention or cessation of smoking or risk factor facilitates the decrease in the burden of lunge cancer

51. Research Designs Controlled experiments (Experimental)
determinant or intervention (treatment) is planned
control (non-treatment) group is free from the intervention
subjects selected into intervention (treatment) and control (non-treatment) groups by randomization

52. Examples Laboratory experiments Clinical trials Field trials
effects of anti-cancer drugs in mice
Clinical trials
effect of anti-cancer drugs in humans ( volunteers)
Field trials
a-priori hypothesis about effect of intervention is assessed
subjects of study not patients. usually healthy individuals in the community
because probability of disease is small, a large number of subjects are needed Salk and Sabin vaccine trials
Intervention Trials
similar to field trial
however, intervention is available at a group or community level.
fluoridation of tap water

53. Experimental Design time outcome Intervention no outcome Study
RANDOMIZATION
Intervention
no outcome
Study
population
outcome
Control
no outcome
Experimental Design
baseline
future
time
Study begins here (baseline point)

54. Experimental Advantages
Best evidence study design
No selection bias (using blinding)
Controlling for possible confounders
Comparable Groups (using randomization)

55. Quasi-Experimental
Does not meet all of the requirements necessary for controlling the influence of extraneous variables.
Most common criteria not met is random assignment.
The operative word is plausible. With a well-designed quasi experiment, other interpretations, while possible, are highly implausible.
Sometimes there are ethical or logistical constraints that make it impossible for you to conduct a true experimental study. Thus, the quasi-experimental design can be your best alternative. Generally, a quasi-experiment exists whenever causal conclusions cannot be drawn because of one or more rival hypothesis for the observed effects.

56. Questions