## Tuesday, March 11, 2008

### Direct and Indirect Standardization

056. All the statements are true about standardization except:

1. Standardization allows comparison to be made between two different populations

2. The national population is always taken as the standard population

3. For Direct Standardization, age specific rates of the study population are applied to that of the standard population

4. For Indirect Standardization, age specific rates of the standard population are applied to that of the study population

2. The national population is always taken as the standard population

Reference

Park 18th Edition Pages 53, 54

Quality

Status

Repeat

QTDF

Park

Discussion

If we want to compare the death rate of two populations, with different age-compositions, the crude death rate is not the right yardstick. This is because, rates are only comparable if the populations upon which they are based are comparable. And it is cumbersome to use a series of age specific death rates. The answer is “age adjustment” or “age standardization,” which removes the confounding effect of different age structures and yields a single standardized or adjusted rate, by which the mortality experience can be compared directly

Explanation

1. Standardization allows comparison to be made between two different populations

2. A standard population is defined as one for which the number is each age and sex group are known.

3. For Direct Standardization, age specific rates of the study population are applied to that of the standard population

4. For Indirect Standardization, age specific rates of the standard population are applied to that of the study population

Other methods of standardization are

1. by using the Life Table
2. Regression techniques
3. Multivariate analysis

Tips

In statistics, a standard score (z) is a dimensionless quantity derived by subtracting the population mean from an individual (raw) score and then dividing the difference by the population standard deviation:

The Standard score, which is also commonly known as the z-score, is not the same as, but is sometimes confused with, the Z-Factor used in the analysis of high-throughput screening data.

Knowing the true σ of a population is often unrealistic except in cases such as standardized testing in which the entire population is known. In cases where it is impossible to measure every member of a population, the standard deviation may be estimated using a random sample.