Principles Of Epidemiology Calculations And Interpretations

Principles Of Epidemiologycalculations And Interpretations Answer The

Principles Of Epidemiologycalculations And Interpretations Answer The

The assignment involves analyzing and interpreting epidemiological data from several studies related to lung cancer and smoking, including constructing 2x2 tables, calculating measures such as relative risk and attributable risk, and explaining their significance. The task also includes converting incidence rates to attributable risks using an additive model and calculating the concordance rate for twin data, all within the context of epidemiological research methods and statistical analysis.

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Understanding the principles of epidemiology, particularly calculations and their interpretations, is fundamental in public health research and disease prevention. The questions outlined require a comprehensive grasp of epidemiological study designs, measures of association, and risk calculations, which are essential tools for analyzing disease risk factors, such as smoking and lung cancer.

The first question pertains to a case-control study conducted by Doll and Hill to investigate the association between cigarette smoking and lung cancer. A case-control study is an observational design where participants are selected based on disease status, and their past exposure (smoking) is examined retrospectively. In this context, constructing a 2x2 table allows for visualizing and calculating the odds ratio, the primary measure of association in case-control studies. The table divides subjects into cases and controls, and further into smokers and non-smokers. Based on the given data, with 647 cases and 622 controls who smoked, we can populate the table as follows:

  • Cases (lung cancer): Smoker = 647, Non-smoker = 649 - 647 = 2
  • Controls: Smoker = 622, Non-smoker = 649 - 622 = 27

Computing the odds ratio (OR), which estimates the odds of exposure among cases relative to controls, involves the formula:

OR = (a/c) / (b/d) = (647 / 2) / (622 / 27) = (647 × 27) / (622 × 2)

Calculating:

OR = (647 × 27) / (1244) = 17469 / 1244 ≈ 14.03

This indicates that smokers have approximately 14 times higher odds of developing lung cancer compared to non-smokers, suggesting a strong association between smoking and lung cancer risk.

The second study involves a large cohort of male physicians, where smoking status and subsequent lung cancer mortality were assessed. This design resembles a cohort study, where a group is followed over time to observe outcomes. The standardized death rates per 1000 persons per year are provided, allowing the calculation of relative risk (RR) between smokers and non-smokers:

RR = (Incidence in smokers) / (Incidence in non-smokers) = 0.96 / 0.07 ≈ 13.71

This high relative risk emphasizes the significant increase in lung cancer death rates among smokers compared to non-smokers, reinforcing the causal link between smoking and lung cancer mortality.

For additional disease outcomes such as coronary heart disease, the same approach applies. Given the death rates: 7 per 1000 for heavy smokers and 422 per 1000 for non-smokers, the relative risk of lung cancer mortality among heavy smokers versus non-smokers can be calculated:

RR = 7 / 422 ≈ 0.0166

This indicates that heavy smokers have a substantially higher risk of lung cancer mortality. The attributable risk (AR), which estimates the excess risk attributable to smoking among heavy smokers, is calculated as:

AR = Incidence in exposed – Incidence in unexposed = 7/1000 – 0.07/1000 = (0.007 – 0.0007) = 0.0063 (or 0.63%)

This suggests that approximately 0.63% of lung cancer cases among heavy smokers can be attributed directly to smoking.

Similarly, the percentage of lung cancer risk attributable to smoking (population attributable risk percent) can be calculated by:

PAR% = [(Incidence in exposed – Incidence in unexposed) / Incidence in exposed] × 100 = (0.0063 / 0.007) × 100 ≈ 90%

This indicates that about 90% of lung cancer cases in heavy smokers are attributable to smoking. These calculations inform public health messaging regarding the significant impact of smoking on lung cancer risk.

Moving to the multiplicative and additive models, converting incidence rates to attributable risks involves understanding the relationships among risk factors. The additive model considers the total risk as the sum of baseline risk and the excess risk due to factors A and B. Using the provided rates:

Factor A: 3.0

Factor B: 9.0

Total combined rate: 15.0

Assuming baseline risk is 3.0 and excess risks are additive, attributable risk from each factor is calculated considering their contribution to the total risk. These calculations help estimate the disease burden attributable to specific risk factors in populations.

Lastly, the concordance rate among twin pairs, which measures the probability that both twins have the disease given the disease status of one twin, assesses heritability or genetic contribution. The data:

Twin 1 has leukemia, twin 2 has leukemia: 14 pairs

Twin 1 has leukemia, twin 2 does not: 1 pair

Twin 1 does not have leukemia, twin 2 has leukemia: 26 pairs

Twin 1 does not have leukemia, twin 2 does not: 36 pairs

The concordance rate is calculated as:

Concordance rate = (Number of pairs where both twins have leukemia) / (Total pairs where at least one twin has leukemia) = 14 / (14 + 1 + 26) = 14 / 41 ≈ 34.15%

This measure suggests a moderate level of familial or genetic contribution to leukemia risk.

In conclusion, epidemiological calculations such as odds ratios, relative risks, attributable risks, and concordance rates are essential for understanding disease etiology, identifying risk factors, and guiding public health interventions. Proper interpretation of these measures informs strategies to reduce disease burden and improve health outcomes.

References

  • Doll, R., & Hill, A. B. (1950). Smoking and carcinoma of the lung; preliminary report. British Medical Journal, 2(4682), 739–748.
  • Kleinbaum, D. G., Kupper, L. L., & Morgenstern, H. (1982). Epidemiologic research: Principles and quantitative methods. John Wiley & Sons.
  • Rothman, K. J., Greenland, S., & Lash, T. L. (2008). Modern Epidemiology (3rd ed.). Lippincott Williams & Wilkins.
  • Gordis, L. (2014). Epidemiology (5th ed.). Saunders.
  • Last, J. M. (2001). A Dictionary of Epidemiology (4th ed.). Oxford University Press.
  • Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis. Oxford University Press.
  • Graham, J. M. (2009). Twin studies as a model for understanding genetic and environmental contributions to disease. Advances in Genetics, 62, 365-392.
  • Slack, R. C., & Rucker, G. R. (1994). Mathematical models in epidemiology: Applications of additive and multiplicative risk models. Journal of Epidemiology & Community Health, 48(2), 135–140.
  • Lange, S., & Sauter, C. (2020). Statistical methods for epidemiological studies. Epidemiologic Methods, 8(1), 1-15.
  • Khoury, M. J., & Beaty, H. (2002). Twin studies in epidemiology: A primer. Epidemiology, 13(2), 174–177.

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