A Study Is Run To Estimate The Incidence Of Atrial Fibrillat

A Study Is Run To Estimate the Incidence Of Atrial Fibrillation Af I

A study is run to estimate the incidence of atrial fibrillation (AF) in men and women over the age of 60. Development of atrial fibrillation was monitored over a 10-year follow-up period. The data are summarized below. Developed AF Did Not Develop AF Men Women Compute the Odds Ratio of AF incidence comparing men and women. (Round to 2 decimal places)

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The objective of this study is to compare the incidence of atrial fibrillation (AF) between men and women over the age of 60 by calculating the odds ratio (OR). The odds ratio is a measure of association that quantifies the strength of the relationship between two categorical variables—in this case, gender and the development of AF. Given the data, we will compute the OR to determine whether men have higher or lower odds of developing AF compared to women during the 10-year follow-up period.

Suppose the data collected are organized in a 2x2 contingency table as follows:

To clarify, the odds for men developing AF are calculated as a/c, and the odds for women developing AF are calculated as b/d. The odds ratio (OR) is then:

OR = (a/c) ÷ (b/d) = (a × d) / (b × c)

This formula compares the odds of developing AF in men to the odds in women.

Based on hypothetical numbers consistent with typical studies, let's assume the following data:

• Men who developed AF: 120
• Men who did not develop AF: 880
• Women who developed AF: 80
• Women who did not develop AF: 920

Using these figures, the calculation for the odds ratio is:

OR = (120 × 920) / (80 × 880) = 110,400 / 70,400 ≈ 1.567

Rounding to two decimal places, the OR is approximately 1.57.

Interpretation of the odds ratio indicates that men have about 1.57 times the odds of developing AF compared to women over the 10-year period. Since the OR is greater than 1, this suggests a higher risk for men. However, further statistical analysis, such as calculating confidence intervals, is necessary to determine if this association is statistically significant.

In epidemiological research, odds ratios are valuable for understanding associations, especially in case-control studies. While relative risk (risk ratio) provides a more direct measure of risk, ORs are often used because they are easier to calculate in retrospective studies and when the outcome of interest is rare. Nevertheless, the interpretation of ORs should be contextualized with confidence intervals to assess precision and significance.

This illustrative example underscores the importance of accurately collecting data and performing proper statistical analyses to inform clinical and public health decisions. Identifying groups at higher risk enables targeted interventions, which can improve health outcomes among vulnerable populations, such as older adults at risk of atrial fibrillation.

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