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Analyzing disparity trends for health care insurance coverage among non-elderly adults in the US: evidence from the Behavioral Risk Factor Surveillance System, 1993–2009

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Abstract

Objective

To explore the changing disparities in access to health care insurance in the United States using time-varying coefficient models.

Data

Secondary data from the Behavioral Risk Factor Surveillance System (BRFSS) from 1993 to 2009 was used.

Study design

A time-varying coefficient model was constructed using a binary outcome of no enrollment in health insurance plan versus enrolled. The independent variables included age, sex, education, income, work status, race, and number of health conditions. Smooth functions of odds ratios and time were used to produce odds ratio plots.

Results

Significant time-varying coefficients were found for all the independent variables with the odds ratio plots showing changing trends except for a constant line for the categories of male, student, and having three health conditions. Some categories showed decreasing disparities, such as the income categories. However, some categories had increasing disparities in health insurance enrollment such as the education and race categories.

Conclusions

As the Affordable Care Act is being gradually implemented, studies are needed to provide baseline information about disparities in access to health insurance, in order to gauge any changes in health insurance access. The use of time-varying coefficient models with BRFSS data can be useful in accomplishing this task.

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Notes

  1. The nature of the question in the BFSS survey simply asks the respondent if they are currently enrolled in a health care plan. There is no follow-up question on what type of insurance that they hold and therefore it would not be possible to identify who would be using Medicaid.

  2. The 12th grade of secondary schools is typically the last year of high school.

  3. BRFSS includes among the “health variables” also the self-assessed health (SAH). Even though SAH has been widely used in previous studies examining the relationship between health and socioeconomics, SAH is a subjective measure of health that may involve biases in the measurement of disparities (see [5, 11, 20] for a discussion of biases associated with self-assessed health). In order to support the reliability of our measure of health insurance disparities, we employed a more objective (even though self-reported) functional measure of health: the number of health conditions.

  4. Although BRFSS is complex survey data, this analysis did not use weights to adjust for this. The addition of weights was very computationally heavy for the model and sample size used and would not have been feasible. However, since we are interested in coefficient or OR trends, the weights are not essential, particularly since the weighing variables are present in the model. A check of a simple model (with only one time-varying coefficient) with weights has shown very little effect on the results of the VCM (results are not shown). Descriptive statistics presented in Table 1 are consistent with this choice and consequently should be taken as references to better understand the models here proposed and not as unbiased estimates of the variables reported.

  5. The models were fit using the R software program version 3.0.1 with the mgcv package and the bam function, which is designed for fitting models with big data [41].

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Acknowledgments

The authors would like to thank the reviewers for their valuable comments on an earlier version of this paper.

Author information

Authors and Affiliations

  1. Demographic and Health Surveys Program, ICF International, Rockville, MD, USA

    Shireen Assaf

  2. Department of Economics, Ca’ Foscari University of Venice, Venice, Italy

    Stefano Campostrini

  3. Department of Economics and Management, University of Pavia, Via San Felice, 5, 27100, Pavia, Italy

    Cinzia Di Novi

  4. Health, Econometrics and Data Group, University of York (UK). LCSR, National Research University Higher School of Economics, Russian Federation, Moscow, Russia

    Cinzia Di Novi

  5. Northrop Grumman Corporation, Atlanta, GA, USA

    Fang Xu

  6. Department of Biostatistics and Bioinformatics, Rollins School of Public Health Emory University, Atlanta, USA

    Carol Gotway Crawford

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  1. Shireen Assaf
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  2. Stefano Campostrini
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  3. Cinzia Di Novi
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  4. Fang Xu
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Correspondence to Cinzia Di Novi.

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Assaf, S., Campostrini, S., Di Novi, C. et al. Analyzing disparity trends for health care insurance coverage among non-elderly adults in the US: evidence from the Behavioral Risk Factor Surveillance System, 1993–2009. Eur J Health Econ 18, 387–398 (2017). https://doi.org/10.1007/s10198-016-0806-1

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  • DOI: https://doi.org/10.1007/s10198-016-0806-1

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