Public health week 9

Corelation

A mutual relationship or connection between two or more variables

Corelation coefficient (r)

( r) statistical measure qauntifiying the strength and direction of a relationship between two variables

Value of ( r) ranges

From -1 to +1

Values closer to +1 indicate

Strong positive relationship

Values closer to -1 indicate

Strong negative (inverse) relationship

Values near 0 indicate

Little or no relationship

Positive corelation

Both variables move in same direction

Negative corelation

Both variable move in opposite directions

No corelation

No predictable relationship between variables

Limitations of correlation

Corelation DOES NOT mean causation

Random error

Unpredictable variations in measurements causing deviations in either direction around the true value

Validity

Study produces measurements that are free from systematic error or bias

Bias

Systematic deviation from truth, causing measurement errors in the same direction repeatedly

Selection bias

Occurs when the sampled population differs in ways related to the exposure and outcome from those who did not participate

Information bias

Arises from innacurate or misclassified data collection from study subjects

Confounding

Happens when an extraneous their variable influences both the exposure and the outcome, confusing the relationship between them

Internal validity

Correctness of measurements made within the study sample itself

External validity

How well the study’s findings apply or generalize to larger or different populations beyond the study group

Reliability

Consistency or precision of measurements; minimal random error

Incidence rate

The number of new cases if a health event occuring in a defined population during a specified time period

Prevalence rate

The total number of existing cases of a health event at a given point in time

Mortality rate

The rate at which sea occurs in a population

Case fatality rate (CFR)

The proportion of deaths within a group of individuals diagnosed with a specific medical condition over the course of disease

To infer a casual relationship between exposure and an out come; three conditions must be met

• Alteration of the cause altera the effect

• Temporality

• Association at the individual level

Population / ecological studies

Studies comparing groups rather than individuals

Case-Control studies

Study where individuals with a disease are compared to those without to see if past exposure differs between groups

Cohort studies

studies that follow groups of individuals with and without an exposure overtime to see who develops outcome

Randomized controlled trials (RCTs)

Study where individuals are randomly assigned to exposure or control group