Key Concepts About Population Counts

Calculating population counts for a given condition from NHANES follows these steps:   

 

  1. Calculate the percentage who have the outcome or characteristic by age, sex, or race/ethnicity subgroups, in which you are interested.  You will output these results to a SAS or Stata file.

 

Info iconIMPORTANT NOTE

Note: Age standardization of the prevalence estimates is NOT performed because the population counts should be based on the crude (unadjusted) prevalence in the population.

 

  1. Use the relevant population totals from the Current Population Surveys (CPS) to determine population estimates in NHANES.  Since NHANES is a nationally representative survey of the non-institutionalized U.S. population, population estimates are based on the CPS totals for this aspect of the U.S. population.  Use CPS totals for the midpoint of each survey cycle.  CPS-based population tables for NHANES by race/ethnicity, gender and age are located at: http://www.cdc.gov/nchs/nhanes/response_rates_CPS.htm.

 

Info iconIMPORTANT NOTE

Note: Population totals generated in NHANES can only be representative of the number of individuals with the health condition in the non-institutionalized U.S. population.

 

  1. If you wish to report multiple age, gender or race/ethnic subgroups, you can combine these population totals.  It also is possible to combine NHANES survey cycles.  For example, to combine two survey cycles (e.g., 2001-2002 and 2003-2004), you must use the midpoint of each cycle, and combine them as follows:  (NHANES 2001-2002 population totals) + (NHANES 2003-2004 population totals) in order to get a population total for 2001-2004.  Similarly, you would do this for each of the age-, sex-, or race/ethnicity groups you wanted to combine to get a population total for that group. 
  2.  

    Info iconIMPORTANT NOTE

    Note: The only exception would be when combining NHANES 1999-2000 with 2001-2002 data.  As stated in the weighting module, these survey years used a different reference population for sampling, so population totals for 1999-2002 are provided by NCHS.

     

    Once CPS totals are combined, results should be output to a file.

  1. Multiply the prevalence of the health condition of interest by the corresponding CPS-based population total to obtain an estimate of the number of non-institutionalized U.S. individuals with the condition. To calculate age-, sex-, or race/ethnicity- specific population estimates, multiply the prevalence of the health condition in each sub-domain by the CPS population total for the respective sub-domain. 

Since the non-institutionalized CPS population totals are used to calculate the final sampling weights for the NHANES survey, you may wonder why you cannot just sum the final sampling weights for all sample persons with the health condition of interest, in order to arrive at population estimates for the health condition.  For example, the total population estimate for a given health condition from the interviewed sample should equal the sum of the final interview weights for that health condition within the demographic domains among all interviewed persons.  However, if there are a significant number of exclusions or missing data for a health condition, summing the weights will not produce an accurate population estimate.  Therefore, using this method is NOT RECOMMENDED.  The differences in population estimates by the calculated method versus the summed weight method are illustrated in the table below.

 

Comparison of Population Estimates using Calculated and Summed Methods
Sample Domain % U.S. Population Correct Estimate Incorrect Estimate
Total 29.2% 57,859,000 55,362,000
Male 27.3% 25,844,000 24,855,000
Female 30.9% 32,039,000 30,506,000
Non-Hispanic Blacks 37.0% 8,103,000 7,277,000
Mexican American 17.1% 2,409,000 2,182,000

 

Info iconIMPORTANT NOTE

DO NOT use the summed weight method to determine population estimates for a given health condition because the potential for exclusions or missing data for that health condition may lead to population underestimates.  

 

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