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www.carteeh.org Lecture #27: - PPT Presentation

Observational Analytical Epidemiological Studies Raed Khalid Alotaibi Imam Abdulrahman Bin Faisal University rkalotaibiiauedusa 966508284310 Declaration of any conflicts of interest ID: 933305

time disease exposed exposure disease time exposure exposed studies study total case determine outcome epidemiology incidence control cohort outcomes

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Slide1

www.carteeh.org

Slide2

Lecture #27: Observational Analytical Epidemiological Studies

Raed Khalid Alotaibi

Imam Abdulrahman Bin Faisal University

rkalotaibi@iau.edu.sa

+966508284310

Declaration of any conflicts of interest

Lecture Track(s): HT

Slide3

Experimental Design and Causal Inference

A procedure conducted by changing the level of the variable of interest in a controlled environment to examine the effect on an outcome.

Slide4

Experimental Design and Causal Inference

Slide5

Study Designs and Types

Slide6

Study Designs and Types

Descriptive

Analytic

Comparison group

No

Yes

Nature

Observational

Observational or Experimental

Answers the …

What, who, where and when

Why and how

Describes the …

Place, person and time

Strengths of association between X and Y

Hypothesis

Generates hypothesis

Test hypothesis

Examples

Case studies, cross-sectional, and ecological

Experimental, cohort, case-control and case-crossover

Slide7

Cohort Studies

Disease

No

Disease

Total

Incidence Rates of Disease

Exposed

A

B

A+B

A / (A+B)

Non-Exposed

C

D

C+D

C / (C+D)

Slide8

Cohort Studies

Slide9

Case-Control Studies

Disease

No

Disease

Exposed

A

B

Non-Exposed

C

D

Total

A+C

B+D

Odds of exposure

A / C

B / D

Odds Ratio

(A/C) / (B/D)

Slide10

Case-Control Studies

Slide11

Cross Sectional Studies

Disease

No

Disease

Total

Exposed

A

B

A+B

Non-Exposed

C

D

C+D

Total

A+C

B+D

A+B+C+D

Slide12

Cross Sectional Studies

Disease

No

Disease

Total

Exposed

A

B

A+B

Non-Exposed

C

D

C+D

Total

A+C

B+D

A+B+C+D

Disease

No

Disease

Total

Exposed

5

14

19

Non-Exposed

11

42

53

Total

16

56

72

Slide13

Time Series

Sequence of health-related measurements

Regular time intervals

Is there a temporal pattern indicating an association?

Slide14

Time Series

The Epidemic curve:

Exposure source

Exposure durationLikely exposure periodIncubation periodMagnitude of outcome

Time trend in outcome

Slide15

Time Series - Example

Per capita cigarette consumption and male lung cancer incidence in the United States over the last century.

Source:

Doss, M., 2016. Changing the paradigm of cancer screening, prevention, and treatment. 

Dose-Response

14

(4), p.1559325816680539.

Slide16

Summary

Study Design

Description

Strength

Weakness

Cross sectional

“Snapshot” in time. Both exposure and disease status are collected. May be used to measure frequency and prevalence of disease or exposure.

Easy and inexpensive to conduct.

Can measure multiple exposure and outcomes simultaneously.

Control over measurements and study population

Can determine prevalence

Temporality cannot be determined

Potential bias if low response rate

Not optimal for rare exposure or outcomes

Cannot determine incidence or relative risk

Case-Control

Individuals with outcome are compared to those without the outcome in terms of their exposure status.

Effective for rare outcomes

Less expensive and time consuming to cohort studies.

Determines the odds ratio (in rare outcomes the odds ratio is a good estimate of the relative risk)

Potential recall bias

Cannot determine incidence or prevalence

Only one outcome may be studied

Cohort

Study starts with exposure status and individuals are followed up. Outcome rates are compared between exposed and non-exposed. Can be either retrospective or prospective.

“Gold standard” for observational studies

Temporality established (time sequence of events

Able to determine incidence and relative risk

Expensive to conduct

Time consuming

Not optimal for rare outcomes

Time Series

A sequence of health-related outcomes or measurements are gathered over several time periods.

Determine temporality or sequence of events

Determine change in health-related events over time

In infectious disease outbreaks may be able to determine the point source of infection

Generate hypothesis of causality

Ecological bias

Potential age, cohort, and period effect.

Slide17

Sources and references

Merrill, R.M., 2008. Environmental epidemiology: principles and methods. Jones & Bartlett Learning.

https://www.cdc.gov/training/quicklearns/exposure/index.html

Doss, M., 2016. Changing the paradigm of cancer screening, prevention, and treatment. Dose-Response, 14(4), p.1559325816680539.

Kannel

, W.B. and McGee, D.L., 1979. Diabetes and cardiovascular disease: the Framingham study. Jama, 241(19), pp.2035-2038.

Belanger, C.F.,

Hennekens

, C.H., Rosner, B. and

Speizer

, F.E., 1978. The nurses’ health study. Am J

Nurs

, 78(6), pp.1039-1040.

Zmirou

, D., Gauvin, S., Pin, I.,

Momas

, I., Sahraoui, F., Just, J., Le

Moullec

, Y.,

Bremont

, F.,

Cassadou

, S., Reungoat, P. and Albertini, M., 2004. Traffic related air pollution and incidence of childhood asthma: results of the Vesta case-control study. Journal of Epidemiology & Community Health, 58(1), pp.18-23.Vert, C., Sánchez-Benavides, G., Martínez, D.,

Gotsens

, X.,

Gramunt

, N.,

Cirach

, M.,

Molinuevo

, J.L.,

Sunyer

, J.,

Nieuwenhuijsen

, M.J.,

Crous-Bou

, M. and Gascon, M., 2017. Effect of long-term exposure to air pollution on anxiety and depression in adults: A cross-sectional study. International journal of hygiene and environmental health, 220(6), pp.1074-1080.

Schwartz, J. and Marcus, A., 1990. Mortality and air pollution j

london

: a time series analysis. American journal of epidemiology, 131(1), pp.185-194.

Slide18

Rothman, K.J., Greenland, S. and Lash, T.L. eds., 2008. Modern epidemiology. Lippincott Williams & Wilkins.Gordis 2014. Epidemiology, Philadelphia, PA, Saunders/Elsevier.Szklo, M. and Nieto, F.J., 2014. Epidemiology: beyond the basics. Jones & Bartlett Publishers.

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