Guidance for Pooling Multiple Years of NHIS Data

By Julia A. Rivera Drew


Depending on their research question, analysts will commonly pool multiple years of the National Health Interview Survey (NHIS) data together in order to increase sample sizes of particular subpopulations of interest, such as bisexual adults, immigrants, or pregnant women. The complex design of the NHIS, however, requires analysts to take additional steps to correctly construct and analyze pooled NHIS datasets. Moreover, planned changes to the NHIS design implemented in 2019, as well as changes made in response to the COVID-19 pandemic, require additional special handling to correctly analyze datasets combining multiple years of NHIS data. The objectives of this blog post are to: (1) share tips to correctly construct and analyze pooled NHIS datasets and (2) identify resources for more information.

Tips to Correctly Construct and Analyze Pooled NHIS Datasets

1. Create a pooled sampling weight to use with your pooled dataset.

In general, when pooling multiple years of NHIS data together, you will need to create a new sampling weight to use with the pooled sample. To create this new sampling weight, divide the appropriate sampling weight by the number of years within each distinct sample design period. For example, if one wished to estimate the number of children living in families with low or very low food security (FSSTAT) using pooled 2020-2021 NHIS data (e.g., similar to this report), one would need to create a new sampling weight by dividing the sampling weight identified under the “weights” tab for FSSTAT, SAMPWEIGHT, by the number of years pooled together from the same sampling design period (in this case, two). The sum of the pooled weights would then represent the average annual population size for the pooled time period, rather than the total cumulative population size for the pooled time period. For any given combination of variables, refer to information under the “weights” tab for the variables included in your analysis to help select the appropriate sampling weight. The distinct NHIS sample design periods are 1963-1974, 1975-1984, 1985-1994, 1995-2005, 2006-2015, 2016-2018, and 2019-present.

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Using the MEPS-HC to Study Change in Adult Mental Health

By Julia A. Rivera Drew and Natalie Del Ponte

The Medical Expenditure Panel Survey-Household Component, or MEPS-HC, data are an invaluable resource for studying short-term trajectories in health, including adult mental health. An integrated series of the MEPS-HC data is available at IPUMS MEPS. Collected on the Self-Administered Questionnaire and, starting in 2019, the Preventive Self-Administered Questionnaire, the MEPS-HC includes two validated adult mental health scales. The Kessler Psychological Distress Scale (K6) and the two-item Patient Health Questionnaire depression screener (PHQ-2) are asked twice per panel, during interview rounds 2 and 4 (see Table 1). There are also two validated scales measuring health-related quality of life (HRQOL) that capture several interrelated health domains, including mental health. These include the Short Form-12 (SF-12) in 2000-2016 and the Veterans RAND-12 (VR-12) starting in 2017 (see Table 2 for VR-12 measures). For more information on the SF-12, see the section on SF-12 scoring on MCS and for more information on the VR-12, see ADDAYA.

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An Age-Based Approach to Disability

By Solvejg Wastvedt and Yash Singh

Disability is dynamic: it evolves over time and interacts with environmental and societal factors. Due to the complex nature of disability, researchers conceptualize and measure disability differently depending on their research question and available data. For instance, an identical condition might evolve differently for a child facing food insecurity compared to one that has stable access to food. Similarly, a physical limitation for a worker in New York City may have a vastly different impact compared to a similar worker in rural Iowa. Disability can be viewed as the relational concept between individuals with physical or mental impairment and the environment.1 2 This complexity makes measuring disability a challenging task. The following post aims to help researchers understand and use disability measurements available in IPUMS data collections.

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