A Screening Program Uses A Home Urine Testing Kit To Detect
A Screening Program Uses A Home Testing Urine Kit To Detect Disease X
A screening program uses a home testing urine kit to detect disease X. You decide to test the kit in your community of 1,000 adults. You know that the prevalence of disease X in your community is 20% meaning that 200 have the disease at any given point in time. The test correctly identified 150 individuals with the disease while it was able to correctly identify 700 individuals without the disease. Requirements: Draw a 2x2 table. IMPORTANT to Calculate the sensitivity, specificity, PPV, and NPV of the screening test. In 1 or 2 paragraphs, provide an interpretation of your results. Your paper should: be 1-2 pages in length. properly cite research sources. show how you calculated your answers. be free of spelling and grammar errors.
Paper For Above instruction
The purpose of this analysis is to evaluate the effectiveness of a home testing urine kit in detecting disease X within a community setting. Using a hypothetical community sample of 1,000 adults, with a known disease prevalence of 20%, we will construct a 2x2 contingency table based on the provided data to compute key diagnostic accuracy metrics: sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). These metrics are essential to understand how well the screening tool performs in identifying true cases and predicting disease status.
Construction of the 2x2 Table
Given Data:
- Total population = 1,000 adults
- Prevalence of disease X = 20%, so true cases = 200
- Test correctly identified 150 individuals with the disease (true positives)
- Test correctly identified 700 individuals without the disease (true negatives)
From these, we can calculate the false negatives and false positives:
- False negatives (missed disease cases) = True positives subtracted from total actual cases = 200 - 150 = 50
- False positives (incorrectly identified disease in healthy individuals) = Total negatives minus true negatives: Total negatives = total population - true cases = 800; true negatives = 700; thus, false positives = 800 - 700 = 100
Based on this, the 2x2 table is constructed as follows:
| Test Positive | Test Negative | Total | |
|---|---|---|---|
| Actual Disease | 150 (True Positives) | 50 (False Negatives) | 200 |
| Actual No Disease | 100 (False Positives) | 700 (True Negatives) | 800 |
| Total | 250 | 750 | 1000 |
Calculations of Diagnostic Metrics
Using the 2x2 table, the following diagnostic performance measures are calculated:
- Sensitivity = True Positives / (True Positives + False Negatives) = 150 / (150 + 50) = 150 / 200 = 75%
- Specificity = True Negatives / (True Negatives + False Positives) = 700 / (700 + 100) = 700 / 800 = 87.5%
- Positive Predictive Value (PPV) = True Positives / (Test Positives) = 150 / (150 + 100) = 150 / 250 = 60%
- Negative Predictive Value (NPV) = True Negatives / (Test Negatives) = 700 / (700 + 50) = 700 / 750 ≈ 93.33%
Interpretation of Results
These results indicate that the home testing urine kit for disease X has a sensitivity of 75%, meaning it correctly identifies three-quarters of those with the disease. The specificity of 87.5% suggests that the test is fairly effective at correctly ruling out individuals without the disease. The positive predictive value of 60% means that when the test is positive, there is a 60% chance the individual actually has the disease, which is moderate and indicates a reasonable level of reliability but some false positives. Conversely, the negative predictive value of approximately 93.33% suggests that a negative test result is highly reliable for ruling out disease X in individuals tested.
In practical terms, the test performs relatively well in detecting true cases and correctly excluding healthy individuals, making it a useful tool in community screening efforts. However, the moderate PPV emphasizes the need for confirmatory testing following positive results to avoid unnecessary anxiety or treatment. The high NPV supports its utility as a rule-out screening method, effectively reassuring individuals who test negative. Overall, while the test is promising, further enhancements could improve its predictive values and reduce false positives, which are critical factors for effective community health screening programs.
References
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