National Health and Nutrition Examination Survey: California and Los Angeles County, Estimation Methods and Analytic Considerations, 1999-2006 and 2007-2014.

Background California is the most populated state and Los Angeles County is the most populated county in the United States. National Health and Nutrition Examination Survey (NHANES) sample weights and variance units were developed for these places to obtain subnational estimates. Objective This report describes the California and Los Angeles County NHANES 1999-2006 and 2007-2014 samples, including the creation of the sample weights and variance units and descriptions of the resulting data files. Some analytic guidelines are provided. Results Eight years of NHANES data were combined for each data file to provide an adequate sample size and reduce disclosure risks. Because Los Angeles County has been a self-representing primary sampling unit, sample weights for Los Angeles County were relatively straightforward. However, a modelbased approach was used to create sample weights for California. The relatively large proportion of Mexican- American and other Hispanic persons in California, coupled with the different NHANES 1999-2014 sample design requirements for oversampling these groups within the small number of NHANES locations selected each cycle, led to a relatively large size of these groups in the California and Los Angeles County NHANES files. For example, 1,137 and 374 of the 3,353 Mexican-Americans persons in NHANES 2007-2014 were in the California and Los Angeles County samples, respectively. Conclusion The California and Los Angeles County NHANES 1999-2006 and 2007-2014 samples are available in the National Center for Health Statistics Research Data Center.

Effects of Address Coverage Enhancement on Estimates from Address-Based Sampling Studies.

For over a decade, address-based sampling (ABS) frames have often been used to draw samples for multistage area sample surveys in lieu of traditionally listed (or enumerated) address frames. However, it is well known that the use of ABS frames for face-to-face surveys suffer from undercoverage due to, for example, households that receive mail via a PO Box rather than being delivered to the household's street address. Undercoverage of ABS frames has typically been more prominent in rural areas but can also occur in urban areas where recent construction of households has taken place. Procedures have been developed to supplement ABS frames to address this undercoverage. In this article, we investigate a procedure called Address Coverage Enhancement (ACE) that supplements the ABS frame with addresses not found on the frame, and the resulting effects the addresses added to the sample through ACE have on estimates. Weighted estimates from two studies, the Population Assessment of Tobacco and Health Study and the 2017 US Program for the International Assessment of Adult Competencies, are calculated with and without supplemental addresses. Estimates are then calculated to assess if poststratifying analysis weights to control for urbanicity at the person level brings estimates closer to estimates from the supplemented frame. Our findings show that the noncoverage bias was likely minimal across both studies for a range of estimates. The main reason is because the Computerized Delivery Sequence file coverage rate is high, and when the coverage rate is high, only very large differences between the covered and not covered will result in meaningful bias.

An Investigation of Nonresponse Bias and Survey Location Variability in the 2017-2018 National Health and Nutrition Examination Survey.

Over the past two decades, a steady decline in response rates on national face-to-face surveys has been documented, with steeper declines observed in recent years. The impact of nonresponse on survey estimates is inconsistent and depends on the correlation between response propensity and the survey estimates. To better understand the impact of declining response rates on the 2017-2018 National Health and Nutrition Examination Survey (NHANES), potential nonresponse bias (NRB) was investigated. NRB was assessed using three approaches: (a) studying variation within the respondent set; (b) benchmarking and comparisons to external data; and (c) comparing alternative weighting adjustments. Because NHANES only samples 30 counties in every 2-year cycle, the sample of counties in any given cycle may be an outlier on some characteristics. Such sampling variability may compound the effects of NRB. For this reason, the representativeness of the 2017-2018 NHANES counties was examined by comparing: (a) the characteristics of the 2017-2018 sampled counties with those from prior cycles; (b) each sampled county with the average of all the counties in the sampling stratum from which that county was selected; and (c) the 2017-2018 counties with 5,000 other samples that could have been drawn under the same sample design using a simulation study. The NRB analyses showed that the 2017-2018 NHANES sample had a lower proportion of college graduates and higher-income individuals compared with prior cycles. Additionally, the 2017-2018 NHANES counties had lower proportions of college graduates and lower mean incomes compared with counties from prior cycles and counties not selected in 2017-2018, which exacerbated the effects of NRB. Weighting adjustments used in prior cycles were not sufficient to address the bias in the 2017-2018 NHANES. Instead, enhanced weighting adjustments for education and income reduced the bias resulting from nonresponse and location sampling variability.