It always seems impossible until it is done. Nelson Mandela
Idea Transcript
Community medicine Dr.Suhailla
Analytic Epidemiology Determining the Etiology of Disease
Causality and Causal Relationships ● Must have statistical significance ● Association may be either positive orν ν negative (if positive, the association is higher than expected; if negative the association is lower than expected)
Causal Association ●Strength of association ●Dose-Response Relationship ●Consistency of the Association ●Temporally Correct Association ●Specificity of the Association ●Coherence with Existing Information
Sources of Data ● Primary data – information collected directly by the researcher ● Secondary data – data that has already been collected and stored for analysis
Types of Surveys ●Administrative surveys, medical records, vital records and statistical data ●Telephone surveys ●Self-administered surveys ●Personal interviews
Measurement Issues ●Measurement is an attempt to assign numbers to observations according to a set of rules ●Variables can be categorical or continuous ●Intent is to translate observations into a system that allows assessment of the hypothesis
Types of Categorical Variables ●Nominal variables – assigns name orν number purely on arbitrary basis (e.g., race, sex) ●Ordinal variables – measures assignedν from (typically) a lesser to greater value ●Interval variables – scale that assigns aν number to an observation based on a constant unit of measurement
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Reliability and Validity Issues ● Reliability – the extent to which a measurement has stability and homogeneity ● Validity – represents the precision to which the measure is truly measuring the phenomena being measured (measure must be reliable to be valid) Content Validity – the extent to which the measure reflects the full concept being studied Criterion Validity – assessed by comparing the test measure of the phenomenon
Sensitivity ♠ Sensitivity (Se) – measures how accurately the test identifies those with the
condition or trait, i.e., correctly identifies or captures true positives ♠ High sensitivity is needed: -When early treatment is important -When identification of every case is important
Specificity ♠ Specificity (Sp) – measures howν accurately the test identifies those without the condition or trait, i.e., correctly identifies or excludes the true negatives. ♠ High specificity is needed when: -Re-screening is impractical -When reducing false positive is important
The Research Cycle Theory Operational Hypothesis
Empirical findings
Statistical Tests
Observations and Measurements
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Types of Studies ●Cross-sectional – prevalence rates that may suggest association (good for developing theory, but no causal association) ●Retrospective (Case-control) – good forν rare diseases and initial etiologic studies ●Prospective (cohort, longitudinal, followup) – yields incidence rates and estimates for risk. Better for causal association. ●Experimental (intervention studies) –strongest evidence for etiology
Considerations for Study ■Design Stage of hypothesis development
■Nature of disease ■Nature of Exposure ■Nature of study population ■Context of research
Cross-Sectional Studies ●Single point in time (snapshot studies) ●Risk factors and disease measured at the same time ●Determines prevalence ratios
Cross-Sectional Study Design Cases
Exposed Sample Population
Non cases Non exposed
cases
Non cases
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Advantages and Disadvantages of Cross Sectional Studies Advantages Disadvantage ●Give general description or scope of problem
●No calculation of risk Temporal sequence is unclear ●Not good for rare diseases
●Useful in health service evaluation and planning ●Baseline for prospective study ●Identifies cases and controls for retrospective study ●Low-cost
●Selective survival can lead to bias ●Selective recall can lead to bias ●Cohort effect may be misleading
Prospective Study Design Disease free persons are classified on exposure at beginning of follow-up period then tracked to ascertain the occurrence of disease. Question of Study: Do persons with the factor of interest develop or avoid the disease more frequently than those without the factor or exposure.
Prospective Study Criteria -Obtain Incidence data -Obtain the incidence among the exposed A/A+B -Obtain incidence among the non-exposed to determine relative risk C/C+D -Determine Relative Risk [A/(A+B)]/[C/(C+D)]
Advantages and Disadvantages of Prospective Studies Advantages ■Provides good assessment of temporal sequence ■Evaluate before onset of disease and watch for disease
Disadvantages ■Selection bias ■Loss to follow-up ■Expensive
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Retrospective Study Design Subjects are selected on the basis of disease status: either cases or controls then classified on the basis of past exposure Question of Study: Do persons with the outcome of interest (cases) have the exposure characteristic (or history of exposure) more frequently than those without the outcomes (controls)
Retrospective Study Design Exposure positive A
Case
Exposure negative B
Exposure positive C Controls
Exposure negative D
Retrospective Study Method ●Compare the odds of exposure among the cases with the odds of exposure among ●the controls Odds of Exposure Among Cases = [A/(A+C)]/[C/A+C)] or A/C ●Odds of Exposure Among Controls =[B/(B+D)]/[D/B+D)] or B/D ●Get Odds Ratio or odds of expose among cases/Odds of exposure among controls (A/C)/(B/D)
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Advantages and Disadvantages of Retrospective Studies Advantages Advantages - Less expensive than cohort (retrospective) Studies - Quicker than cohort - Can identify more than one exposure - Good for rare diseases - Well design leads to good etiologic investigation Disadvantages - Selective Survival - Selective recall - Temporal sequence not as clear - Not suited for rare exposures - Gives an indirect measure of risk - More susceptible to bias - Limited to single outcome
Experimental Studies ●Uses an intervention in which the investigator manipulates a factor and measures the outcome ●Elements of a complete experiment ■ Manipulation of data ■ Use of a control group ■ Ability to randomize subjects to treatment groups
Advantages and Disadvantages of Experimental Studies Advantages ● Prospective direction ● Ability to randomize subjects ● Temporal sequence of cause and effect ● Can control extraneous variables ● Best evidence of causality Disadvantages ● Contrive situation ● Impossible to control human behavior
Blinding in Experimental Studies The importance of blinding depends on the needed outcome. Less important if the outcome is clear. Non-blinded – both subject & investigator know the treatment allocation Single-blinded – investigator knows, subject does not know Double-blinded – neither investigator and subject
Sources of Bias ● During selection of participants ● Absence of blinding allocation can lead to differential classification ● Other sources of miscalculation ● Withdrawals, ineligible sources, loss to follow-up ● Premature termination
Selection Bias ■ Cases and controls, or exposed and non exposed individuals were selected is such that an apparent association is observed - even if there is no association. ■ Biased selection - taking from a pool in which we know the risk is higher is selection bias. ■ Small sample size or small response size
Information Bias Methods of information about the subjects in the study are inadequate and results show information gathered regarding exposures and/or disease is incorrect. ■Reporting bias ■Abstracting records ■Bias in interviewing ■Bias from surrogate interviews ■Surveillance bias ■Recall bias 7
Other Issues ■Confounding Variables ●To prove that Factor A is a result of disease B,we say that a third factor, Factor X is a Confounder if the following is true: ●Factor X is a known risk factor for Disease B. ●Factor X is associated with Factor A bit is not a result of Factor A.