Grid Is Attached; Questionnaires Were Mailed To 5,000 People

Grid Is Attachedquestionnaires Were Mailed To 5000 People Who Were S

Questionnaires were mailed to 5,000 people who were randomly selected. Each person was asked to list age, sex, smoking habits, and respiratory symptoms during the preceding seven days. About 80% of the questionnaires were completed and returned, resulting in a final sample of 4,000 respondents. Among these, there were 1,100 smokers, 1,000 respondents with respiratory symptoms, 700 reporting both symptoms and smoking, 2,600 neither smoking nor respiratory symptoms, and 400 who smoked but had no symptoms. The task is to calculate the odds ratio of having respiratory symptoms (disease) in relation to smoking (exposure), interpret the results, and illustrate the calculations in a grid format.

Paper For Above instruction

The study under consideration investigates the association between smoking and respiratory symptoms using a structured epidemiological approach. By collecting data through a questionnaire with a representative sample, the research aims to quantify the strength of the relationship between exposure (smoking) and disease (respiratory symptoms) through calculating the odds ratio, a common measure used in case-control and cross-sectional studies for assessing such associations.

To analyze this relationship, a 2x2 contingency table or grid was used to organize the data, and the odds ratio (OR) was computed from this table. This method allows for straightforward interpretation of whether smoking is associated with higher odds of respiratory symptoms, which can be crucial for public health recommendations and interventions.

Constructing the Data Grid

The available data is as follows:

- Total respondents: 4,000

- Smokers: 1,100

- Respiratory symptoms: 1,000

- Both smoking and symptoms: 700

- Neither smoking nor symptoms: 2,600

- Smoked but no symptoms: 400

Based on this information, the remaining counts for the different categories can be calculated as follows:

- Smokers with symptoms: 700 (given)

- Smokers without symptoms: 400 (given)

- Non-smokers with symptoms: 1,000 (total with symptoms) - 700 = 300

- Non-smokers without symptoms: 2,600 (neither smoking nor symptoms) + remaining non-smoking individuals without symptoms.

Since total respondents are 4,000 and the total smokers are 1,100, the non-smokers are 4,000 - 1,100 = 2,900. Of these, 400 are smokers without symptoms, so non-smokers without symptoms = 2,900 - 400 = 2,500. Also, total with symptoms are 1,000, so non-smokers with symptoms = total with symptoms - smokers with symptoms = 1,000 - 700 = 300. Given these, the contingency table (grid) is:

| | Respiratory Symptoms | No Respiratory Symptoms | Total |

|----------------------|------------------------|------------------------|--------|

| Smoker | 700 | 400 | 1,100 |

| Non-Smoker | 300 | 2,500 | 2,800 |

| Total | 1,000 | 2,900 | 4,000 |

Calculating the Odds Ratio (OR)

The odds of respiratory symptoms among smokers:

- Odds_smokers = (Number of smokers with symptoms) / (Number of smokers without symptoms) = 700 / 400 = 1.75

The odds of respiratory symptoms among non-smokers:

- Odds_non-smokers = 300 / 2,500 = 0.12

The odds ratio (OR) is therefore:

OR = Odds_smokers / Odds_non-smokers = 1.75 / 0.12 ≈ 14.58

Interpretation of the Odds Ratio

An odds ratio of approximately 14.58 suggests that individuals who smoke are about 14.6 times more likely to experience respiratory symptoms than those who do not smoke. This denotes a strong association between smoking and respiratory symptoms within this sample. It implies that smoking significantly increases the likelihood of developing respiratory symptoms, highlighting the importance of smoking cessation programs as part of respiratory disease prevention strategies.

Conclusion

The calculation and interpretation underscore the significant role smoking plays in respiratory health. The high odds ratio supports public health policies aimed at reducing smoking prevalence. It also emphasizes the need for clinicians to screen patients for smoking status and respiratory symptoms actively, as the linkage between the two is demonstrably strong. These findings align with existing literature confirming smoking as a major risk factor for respiratory illnesses.

References

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  • Harvard School of Public Health. (2022). The Impact of Smoking on Respiratory Health. Harvard.edu.
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  • Rothman, K. J., & Greenland, S. (2018). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.
  • U.S. Department of Health and Human Services. (2014). The Health Consequences of Smoking—50 Years of Progress. Centers for Disease Control and Prevention.
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