The Death Rate Per 100,000 For Lung Cancer Is 7 Among Nonsmo

The Death Rate Per 100000 For Lung Cancer Is 7 Among Non Smokers And

The provided content presents a set of epidemiological data and questions related to disease risk assessment, causal inference, and the evaluation of diagnostic tests. The core assignment involves analyzing these data points, understanding measures such as etiologic fractions, risk differences, relative risks, predictive values, and diagnostic test validity, and applying appropriate epidemiological formulas and concepts to interpret the information accurately.

Paper For Above instruction

The assessment of disease causality and diagnostic accuracy lies at the heart of epidemiological research and public health intervention. This paper explores various epidemiological measures, including etiologic fractions, risk difference, relative risks, and diagnostic test parameters, using the data provided to demonstrate their application and interpretation.

First, we analyze the disease data related to lung cancer and coronary thrombosis concerning smoking status. The death rates per 100,000 among smokers and non-smokers are given as 7 versus 71 for lung cancer and 422 versus 599 for coronary thrombosis. The prevalence of smoking in the population is reported as 55%. These data allow calculation of the population etiologic fraction (PEF) — the proportion of disease in the population attributable to smoking — and evaluation of causal relationships.

Population etiologic fraction, also known as the population attributable fraction, quantifies the proportion of all cases that could be prevented if exposure (smoking) were eliminated. It can be calculated using the formula:

PEF = [P(E) × (RR - 1)] / [P(E) × (RR - 1) + 1]

where P(E) is the prevalence of exposure and RR is the relative risk associated with exposure. Using the data, the relative risks for lung cancer and coronary thrombosis are computed as the ratio of death rates among smokers to non-smokers:

  • Lung cancer: RR = 71 / 7 ≈ 10.14
  • Coronary thrombosis: RR = 599 / 422 ≈ 1.42

Substituting into the etiologic fraction formula:

  • For lung cancer:

    PEF = (0.55 × (10.14 - 1)) / (0.55 × (10.14 - 1) + 1) ≈ (0.55 × 9.14) / (0.55 × 9.14 + 1) ≈ 5.027 / 6.527 ≈ 0.77

  • For coronary thrombosis:

    PEF = (0.55 × (1.42 - 1)) / (0.55 × (1.42 - 1) + 1) ≈ (0.55 × 0.42) / (0.55 × 0.42 + 1) ≈ 0.231 / 1.231 ≈ 0.19

These calculations suggest that about 77% of lung cancer cases among smokers could be attributed to smoking, whereas roughly 19% of coronary thrombosis cases could be attributed to smoking. Comparing these with the options provided, the most accurate matches are 0.83 for lung cancer and 0.18 for coronary thrombosis, considering some variability in estimation.

Next, considering the sample 2 by 2 table, the risk difference or attributable risk measures the absolute difference in disease risk between exposed (factor positive) and unexposed groups. The formulas are:

  • Risk in exposed: A / (A + B)
  • Risk in unexposed: C / (C + D)
  • Attributable risk: (A / (A + B)) - (C / (C + D))

Matching this with the options, the correct choice is:

(A / (A + B)) - (C / (C + D))

The epidemiological implication of the data on smoking-related diseases indicates that smoking is strongly causally linked to lung cancer, with a high relative risk and attributable fraction, while its link to coronary thrombosis is less pronounced but still significant. The relative risk and etiologic fractions suggest a much higher causal association between smoking and lung cancer than with coronary disease, aligning with the established scientific consensus that smoking is a primary risk factor for lung cancer.

In terms of diagnostic tests, specificity and sensitivity are critical parameters. Specificity measures the proportion of disease-free persons correctly identified by the test (true negatives), and sensitivity measures the proportion of diseased persons correctly identified (true positives). The question concerning specificity asks for the expression of this parameter given the test results. The specificity is calculated as:

Specificity = True negatives / (True negatives + False positives)

from the table, if the disease appears in individuals with negative test results solely among those without the disease, then:

  • Specificity = D / (C + D)

Given the options, the value 80% (or 0.80) is consistent with common specificity measures. For sensitivity, the focus is on detecting diseased individuals, which is computed as:

  • Sensitivity = A / (A + C)

Regarding improving sensitivity, lowering the cut-off point increases the probability of testing positive in diseased individuals, thus increasing sensitivity. Therefore, the correct approach is to:

Lower the cut point below 50 units

The question about reliability and validity, based on the described figure, asserts that high reliability corresponds to consistent test results across repeated trials or different measurement conditions. Symbols representing high reliability in the given options indicate that the measure produces stable results, which aligns with the understanding that high reliability is characterized by repeatability and consistency, often visualized by the presence of strong correlations or consistent scoring.

Finally, regarding validity, a test that fails to accurately assess an intended construct or outcome exhibits low validity. For a test assessing age-related changes in bone density, failure to detect real changes indicates low concurrent validity, which measures the correlation between the test and a gold standard at the same point in time. Conversely, low predictive validity would relate to poor capacity to predict future outcomes, which is not specified here.

References

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