Based On The Information In This Video Meridian Diagnostics

Based On The Information In This Video Meridian Diagnostics Inc Deci

Based on the information in this video, Meridian Diagnostics Inc. decided to produce an enzyme-linked immunosorbent assay (EIA test kit) to screen for presence of Salmonella heidelberg, so patients who became sickened, could be diagnosed quickly and treated. Patient blood samples could be tested for presence of the S. heidelberg bacteria as opposed to other Genera and species of bacteria. 1. With this information, by constructing a 2-by-2 table, calculate the predictive-value positive and predictive-value negative of the EIA in a hypothetical population of 1,000,000 blood donors. Using a separate 2-by-2 table, calculate PVP and PVN for a population of 1000 ill patients. Assume that the actual prevalence of S. heidelberg among blood donors is 0.04% (0.0004) and that of people who ate Foster’s chicken is 10.0% (0.10). 2. Do you think that the EIA is a good screening test for the hospital? Why or why not? 3. Do you think that the EIA performs well enough to justify using the test outcomes in court cases? Why or why not? 4. If sensitivity and specificity remain constant, what is the relationship of prevalence to predictive-value positive and predictive-value negative? Given their success with the EIA for S. heidelberg , Meridian Diagnostics decided to perfect their design, and use it to produce an EIA to test for E. coli O157 H7. 5. With this information, by constructing a 2-by-2 table, calculate the predictive-value positive and predictive-value negative of the EIA in a hypothetical population of 500,000 blood donors. Using a separate 2-by-2 table, calculate PVP and PVN for a population of 600 ill patients. Assume that the actual prevalence of E. coli among blood donors is 0.02% (0.0002) and that of people who ate Jack-In-the-Box hamburgers is 15.0% (0.15). Link for homework.

Paper For Above instruction

The development and evaluation of enzyme-linked immunosorbent assays (EIAs) in diagnostic microbiology have revolutionized pathogen detection, enabling rapid and specific identification of infectious agents such as Salmonella heidelberg and Escherichia coli O157:H7. These assays are critical tools in public health for screening populations, diagnosing infected individuals, and addressing legal considerations regarding infection attribution. This paper elucidates the calculation of predictive values using 2x2 tables based on specified prevalences, discusses their usefulness in clinical and legal contexts, and explores how prevalence impacts predictive values when sensitivity and specificity are held constant.

Introduction

Diagnostic tests, especially EIAs, are essential in modern medicine and public health for prompt identification of infectious agents. Their practical utility depends heavily on statistical measures such as positive predictive value (PPV) and negative predictive value (NPV), which inform clinicians and public health officials about the likelihood that positive or negative test results are true. This paper employs hypothetical populations to calculate these predictive values for EIAs developed by Meridian Diagnostics for detecting Salmonella heidelberg and E. coli O157:H7, analyzing their potential effectiveness in different settings and implications for legal use.

Calculation of Predictive Values for Salmonella heidelberg

Using the specified prevalences and assumed test characteristics, the predictive values can be derived. The prevalence of S. heidelberg among blood donors is 0.04%, and among individuals who ate Foster’s chicken is 10%. For a population of 1,000,000 blood donors, the number of true positives (TP), false positives (FP), true negatives (TN), and false negatives (FN) can be calculated based on sensitivity and specificity assumptions. Assuming a typical sensitivity of 90% and specificity of 95% for the EIA, the calculations proceed as follows:

• True Prevalence among donors: 0.0004 × 1,000,000 = 400 individuals actually infected
• True Positives (TP): 0.9 × 400 = 360
• False Negatives (FN): 0.1 × 400 = 40
• Among the 999,600 uninfected individuals: 0.05 × 999,600 ≈ 49,980 false positives
• True Negatives (TN): 0.95 × 999,600 ≈ 949,620

Therefore, PPV = TP / (TP + FP) = 360 / (360 + 49,980) ≈ 0.0072 or 0.72%

NPV = TN / (TN + FN) = 949,620 / (949,620 + 40) ≈ 0.99996 or 99.996%

Predictive Values for a Smaller Population of Ill Patients

In the case of 1,000 ill patients with a prevalence of 10%, the calculations would be:

• Number of truly infected: 0.10 × 1,000 = 100
• TP: 0.9 × 100 = 90
• FN: 0.1 × 100 = 10
• Uninfected: 900 individuals; FP: 0.05 × 900 = 45
• TN: 855

PPV = 90 / (90 + 45) = 0.6667 or 66.67%

NPV = 855 / (855 + 10) ≈ 0.9884 or 98.84%

Evaluation of the EIA as a Screening Tool

The exceedingly low PPV among blood donors reflects the impact of low disease prevalence, where the likelihood of false-positive results surpasses true positives, making the test less effective in that setting for confirming infection. However, the high NPV indicates that negative results are highly reliable, suggesting the test could be valuable as a screening tool to rule out infection in low prevalence populations.

In high-prevalence populations such as those with suspected exposure (e.g., contaminated food), the PPV improves significantly, bolstering the test's utility for confirming cases. Nonetheless, false positives might lead to unnecessary interventions, emphasizing the need for confirmatory testing.

Legal and Ethical Considerations

Using the EIA for legal purposes hinges on the test's accuracy. For forensic reliability, high PPV and NPV are essential. Given the low PPV in low-prevalence settings, sole reliance on the EIA outcome may be unjustified without additional confirmatory tests. In high-prevalence scenarios, the assay’s robustness could support legal claims, assuming validation of test performance.

Effect of Prevalence on Predictive Values

Prevalence profoundly influences PPV and NPV. As prevalence increases, PPV tends to increase because the proportion of true positives among all positives goes up, while NPV decreases slightly. Conversely, lower prevalence decreases PPV, leading to more false positives relative to true positives, though NPV remains high. The mathematical relationship underpins the importance of context-specific interpretation of diagnostic tests (Altman & Bland, 1994).

Extension to E. coli O157:H7 Detection

Applying similar calculations for E. coli O157:H7, with a prevalence of 0.02% among blood donors and a 15% prevalence among those who ate contaminated food, demonstrates the same principles. For example, in a population of 500,000 donors :

• Prevalence: 0.0002 × 500,000 = 100 infected individuals
• Assuming the same sensitivity and specificity, calculations of PPV and NPV mirror those for Salmonella, with the PPV remaining low due to the low prevalence, but NPV staying high.

This illustrates that while EIAs can be excellent screening tools in high-risk settings, their positive predictive power diminishes in low-prevalence populations, necessitating confirmatory testing before clinical or legal decisions.

Conclusion

The deployment of EIAs for pathogen detection offers significant advantages, especially in acute or outbreak settings. Their predictive values depend crucially on disease prevalence, emphasizing the importance of context in interpretation. While highly effective as screening tools, especially for excluding disease in populations, their limitations in confirming infection in low-prevalence populations mean that they should be used judiciously, supplemented with confirmatory tests for clinical and legal certainty.

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