Large numbers of NIU in NHIS 2010-2022


I am using NHIS 2010-2022 data to study morbidities, and I found there are many NIU cases for morbidity variables.

For example, the variable CANCEREV (Ever told had cancer), the universe is “Sample adults age 18+” for all years. However, among adults aged 18+, 47.5% is still “niu” cases as below across years 2010-2022. Why are there so many NIU cases?

I read a question from another user below my question, but the explanation from NHIS staff was still not clear. Thanks.

CANCEREV | Freq. /Percent
niu | 366,605 /47.50
no | 361,951 /46.90
yes | 42,879 /5.56
refused | 219 /0.03
don’t know | 157 /0.02

The “sample adult” component of the universe statement is not entirely intuitive; it does not refer to all sampled adults, but to a singular adult per family/household who was selected to be the sample adult. This blog post is incredibly helpful (specifically figure 1) for describing the Sample Adult concept.

For 1997-2018, a sample adult is the one adult per family (with the possibility of multiple families residing in the same household) who was selected at random by the computerized survey instrument to answer additional health-related questions. In IPUMS NHIS, for variables based on questions asked of sample adults and/or sample children, the universe statement in the variable description refers to “sample adults” and/or “sample children” rather than “persons”. That means that members of the household who were not selected to be asked these additional questions will have NIU values for sample variables. Another redesign of the survey in 2019 reduced the survey to a single sample adult and child within a household, with only basic demographics collected on all remaining household residents.

Sample adults can be identified with the variable ASTATFLG. These NIU cases will be filtered out when subsetting your sample to respondents with ASTATFLG = 1. For further information, I recommend reviewing the IPUMS NHIS user note on sample design as well as the note on the 2019 survey redesign.

1 Like

Thanks so much for your detailed explanations. They are helpful.