Sample survey data can help estimate average values for large areas, but it's harder for smaller areas like cities. The usual methods give unreliable results due to small sample sizes. To improve accuracy, a model called linear mixed model (LMM) is used, which combines data from related areas to make better estimates. This model gives more precise results with smaller errors. The model also helps calculate confidence intervals to measure accuracy. Overall, using LMM can help estimate values for small areas more accurately, especially for things like mortality rates.