As In The Module 8 Discussion, We Will Consider The Town

As In The Module 8 Discussion M8d1 We Will Consider The Town Of Spr

As in the Module 8 Discussion (M8D1), we will consider the town of Springfield, where there are 100,000 adults. Consider a different genetic disease, which affects only 0.1% of the U.S. population. The test for this disease is also 98% accurate. All the adults in Springfield were also tested for this disease. Use a table to organize your results, like those in the text (e.g., Table 3.7 on page 175) or like one you or a fellow discussion group member may have employed in the discussion. Include this table in your write-up. While referring to this table, answer the following questions: How many of the residents of Springfield are likely to have the disease? How many of the people who actually have the disease get a positive test result? How many of the people who do not have the disease get a positive test result? Of the people who get a positive test result, how many of them have the disease? Convert this to a percentage: What percent of people who get a positive result actually have the disease? Compare your results with the problem you solved in the discussion activity (M8D1). Specifically, focus on the percent of people who get a positive result that actually have the disease. Remember that both genetic tests were 98% accurate. Why were the percentages so different? State a conclusion about how the rarity of a disease affects testing results.

Paper For Above instruction

The analysis of diagnostic testing, especially in cases involving rare diseases, highlights critical concepts in epidemiology and biostatistics, notably the importance of understanding test accuracy and disease prevalence. In this study, we examine how a test's characteristics influence the likelihood that a person with a positive test actually has the disease, a concept quantified by positive predictive value (PPV). The scenario describes a town of Springfield with 100,000 adults, and aims to explore the implications when testing for a rare genetic disease affecting only 0.1% of the population. The test in question is 98% accurate, emphasizing its high sensitivity and specificity—though their interpretation in the context of prevalence is crucial.

Creating a contingency table helps visualize the testing outcomes and understand the distribution of true positives, false positives, true negatives, and false negatives. This analysis begins with estimating the number of individuals in Springfield who actually have the disease. Given a prevalence of 0.1%, approximately 100 residents out of 100,000 are expected to be truly affected. Among these, with test sensitivity of 98%, about 98 individuals will be correctly identified as positive (true positives). Conversely, among the 99,900 individuals without the disease, the false positive rate (2%) results in approximately 1,998 individuals falsely testing positive.

The next step involves calculating the number of positive test results overall. The total positives include the true positives (98) and false positives (1,998), summing to 2,096. The positive predictive value (PPV), a key metric in clinical decision-making, indicates the proportion of positive results that are true positives. Using these numbers, PPV is calculated as 98 divided by 2,096, roughly 4.67%. This remarkable figure demonstrates that despite high test accuracy, the rarity of the disease significantly reduces the probability that a positive test truly indicates the presence of disease.

Comparing this to more common conditions reveals why prevalence profoundly impacts the interpretation of diagnostic tests. When the disease is rare, most positive results are false positives because the number of false positives exceeds the true positives. This is a classic example of the base rate fallacy, where ignoring disease prevalence causes overestimation of the likelihood of disease given a positive result. It underscores the importance for clinicians to consider disease prevalence alongside test accuracy when interpreting diagnostic results.

In conclusion, the rarity of a disease greatly affects the predictive value of diagnostic tests. The lower the prevalence, the lower the positive predictive value, even with highly accurate tests. This phenomenon emphasizes that screening programs must weigh the benefits of early detection against the risks of false positives, which can lead to unnecessary anxiety, further testing, or treatment. From a public health perspective, selecting appropriate testing strategies involves understanding how disease prevalence influences predictive values, ensuring resources are used efficiently, and minimizing harmful consequences of false positives. Ultimately, awareness of these principles enhances decision-making in clinical settings and guides policy development for screening programs.

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