**Core Concept**
The given question relates to the interpretation of receiver operating characteristic (ROC) curves in the context of diagnostic test evaluation. ROC curves are graphical representations of the trade-off between sensitivity (true positive rate) and 1-specificity (false positive rate) at different threshold settings for a binary classification problem.

**Why the Correct Answer is Right**
When the curve changes from blue to red, it indicates an improvement in the accuracy of the diagnostic test. The red curve is closer to the top-left corner of the graph, indicating higher sensitivity and specificity. This is because the red curve represents a better balance between true positives and true negatives, allowing for more accurate identification of individuals with the disease (high sensitivity) while minimizing false alarms (high specificity). The improvement in the curve's position is due to the test's increased ability to correctly classify patients as diseased or non-diseased.

**Why Each Wrong Option is Incorrect**
**Option B:** Both sensitivity and specificity would not decrease simultaneously, as this would imply a worse diagnostic test performance, contradicting the observed improvement in the ROC curve.
**Option C:** Sensitivity and specificity cannot increase and decrease simultaneously; the improvement in one would necessitate a corresponding improvement in the other.
**Option D:** A decrease in sensitivity would imply a worse test performance, reducing the true positive rate, which is not the case here.

**Clinical Pearl / High-Yield Fact**
When interpreting ROC curves, a better-performing test is characterized by a curve that is closer to the top-left corner of the graph, indicating higher sensitivity and specificity. This is a crucial concept in evaluating the diagnostic accuracy of medical tests.

**✓ Correct Answer: A. Both Sensitivity & Specificity increase**