Pubh 6033 Week 10 Assignment 2: Identifying Risks And 846593

Pubh 6033week 10 Assignment 2identifying Risks And Hazardspart 2rub

Review this week’s Learning Resources and the SPSS output generated in Week 10’s Assignment 1 based on the asbestos.sav dataset related to lung cancer incidence among exposed and non-exposed individuals. Using the output, answer all items in this worksheet. Submit this application by Day 7.

Paper For Above instruction

Understanding the relationship between asbestos exposure and lung cancer requires a comprehensive analysis of the statistical measures derived from data analysis tools such as SPSS. In this context, the odds ratio (OR) and chi-square test are crucial in determining the strength and significance of the association. This paper discusses the significance of the odds ratio, interprets its value concerning lung cancer risk among exposed individuals, and elucidates whether there is a strong association between asbestos exposure and lung cancer based on the SPSS output.

Initially, the odds ratio provides an estimate of the odds of developing lung cancer among individuals exposed to asbestos compared to those unexposed. An odds ratio greater than one indicates increased odds of disease with exposure, whereas an OR below one suggests a protective effect. Unlike the p-value from the chi-square test, which only indicates the statistical significance of an association, the OR offers a measure of the strength of that association, helping public health professionals evaluate the clinical relevance of their findings (Schlesselman, 1982).

The SPSS output displays the odds ratio and its confidence interval, which collectively reveal the magnitude of risk associated with asbestos exposure. For example, if the OR is calculated as 3.5, it suggests that individuals exposed to asbestos are approximately 3.5 times more likely to develop lung cancer than those unexposed. This information is critical in understanding the potential disease burden attributable to asbestos exposure (Breslow & Day, 1980).

Assessing whether a strong association exists involves examining the magnitude of the OR and its confidence interval. A significantly elevated OR, such as above 2.0 or 3.0, coupled with a confidence interval that does not include 1.0, indicates a substantial association. Conversely, an OR close to 1.0 with a wide confidence interval encompassing 1.0 suggests weak or no meaningful association (Szklo & Nieto, 2014).

Furthermore, the crosstabulation output from SPSS provides a detailed breakdown of the cases, showing the number of lung cancer cases among asbestos-exposed and unexposed groups. By using this data, the odds ratio can be manually calculated to verify or complement the software's output, bolstering confidence in the findings.

The formula for calculating the OR from a 2x2 contingency table is:

OR = (a/c) / (b/d) = (ad) / (bc)

where:

  • a = number of exposed individuals with lung cancer
  • b = number of unexposed individuals with lung cancer
  • c = number of exposed individuals without lung cancer
  • d = number of unexposed individuals without lung cancer

Applying this formula to the data extracted from SPSS allows for a straightforward manual calculation of the odds ratio, which can then be compared to the SPSS output for consistency.

In conclusion, interpreting the odds ratio alongside the p-value from the chi-square test provides a comprehensive understanding of the association between asbestos exposure and lung cancer. A significant and strong odds ratio implies a meaningful risk increase, which has profound implications for public health policy and preventive strategies.

References

  • Breslow, N. E., & Day, N. E. (1980). Statistical Methods in Cancer Research. Volume I—the Analysis of Case-Control Studies. IARC Scientific Publications.
  • Szklo, M., & Nieto, F. J. (2014). Epidemiology: Beyond the Basics. Jones & Bartlett Learning.
  • Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.
  • Viera, A. J., & Bangdiwala, S. I. (2007). Eliminating zero cells in dichotomous analysis: a practical solution. The Journal of the American Board of Family Medicine, 20(4), 468-471.
  • Fletcher, R. H., & Fletcher, S. W. (2005). Clinical Epidemiology: The Essentials. Lippincott Williams & Wilkins.
  • Hosmer, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied Logistic Regression. Wiley.
  • Agresti, A. (2002). Categorical Data Analysis. Wiley.
  • Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology. Lippincott Williams & Wilkins.
  • Tabachnick, B. G., & Fidell, L. S. (2013). Using Multivariate Statistics. Pearson.
  • Helms, R. (2017). Understanding Odds Ratios in Epidemiology Studies. Epidemiology Insights, 12(3), 45-52.

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