**Core Concept**
The 95% confidence interval (CI) is a statistical measure that provides a range of values within which a population parameter is likely to lie. In this context, it's used to estimate the prevalence of a disease in a population. The formula to calculate the 95% CI for a proportion is: CI = p ± (Z * √(p(1-p)/n)), where p is the sample proportion, Z is the Z-score corresponding to the desired confidence level, and n is the sample size.

**Why the Correct Answer is Right**
To calculate the 95% CI, we first need to find the Z-score corresponding to 95% confidence, which is approximately 1.96. Given the sample size (n = 100) and the estimated prevalence (p = 10% or 0.1), we can plug these values into the formula. The standard error (SE) is √(p(1-p)/n) = √(0.1(1-0.1)/100) = √(0.09/100) = √0.0009 = 0.03. Then, the margin of error (ME) is Z * SE = 1.96 * 0.03 = 0.0588. Finally, the 95% CI is estimated prevalence ± ME, which is 0.1 ± 0.0588.

**Why Each Wrong Option is Incorrect**
* **Option A:** This option is not provided, so we'll skip it.
* **Option B:** This option is not provided, so we'll skip it.
* **Option C:** This option is not provided, so we'll skip it.
* **Option D:** This option is not provided, so we'll skip it.

**Clinical Pearl / High-Yield Fact**
When calculating the 95% CI for a proportion, remember that the margin of error decreases as the sample size increases, but the Z-score remains constant for a given confidence level.

**Correct Answer:** 0.0412 to 0.1588.