Data Processing Improvements for Estimates of Health Insurance Coverage in the Current Population Survey Annual Social and Economic Supplement.

Estimates of health insurance coverage in the United States rely on household-based surveys, and these surveys seek to improve data quality amid a changing health insurance landscape. We examine postcollection processing improvements to health insurance data in the Current Population Survey Annual Social and Economic Supplement (CPS ASEC), one of the leading sources of coverage estimates. The implementation of updated data extraction and imputation procedures in the CPS ASEC marks the second stage of a two-stage improvement and the beginning of a new time series for health insurance estimates. To evaluate these changes, we compared estimates from two files that introduce the updated processing system with two files that use the legacy system. We find that updates resulted in higher rates of health insurance coverage and lower rates of dual coverage, among other differences. These results indicate that the updated data processing improves coverage estimates and addresses previously noted limitations of the CPS ASEC.

Improvements in Uninsurance Estimates for Fully Imputed Cases in the Current Population Survey Annual Social and Economic Supplement.

In 2019, the Current Population Survey Annual Social and Economic Supplement introduced updates to data processing, including to the imputation of health insurance for cases with no reported health insurance information. This article examines the impact on health insurance estimates of modernized imputation procedures that were part of a redesign of the Current Population Survey Annual Social and Economic Supplement. We use descriptive analysis and multinomial logistic regression to examine whether imputation biases estimates of health insurance coverage using data from the 2017 Current Population Survey Annual Social and Economic Supplement, which used legacy methods, and the 2017 Current Population Survey Annual Social and Economic Supplement Research File, which debuted the processing redesign. We find that cases with all of their health insurance information imputed using legacy methods were more likely to be uninsured or to be covered by multiple insurance types after adjusting for factors associated with having missing data. With the processing updates, fully imputed cases do not differ from other cases in their likelihood of being uninsured, having private coverage, having public coverage, or in having private and public coverage. Processing updates in the Current Population Survey Annual Social and Economic Supplement improved data quality by increasing the percent of people with any health insurance coverage and decreasing the percent of people with multiple types of coverage, especially among fully imputed cases.

Change and Stability in the Characteristics of the Population Without Health Insurance.

INTRODUCTION: The uninsured population faces greater health risks than the insured population. Although prior research has examined how the uninsured rate has changed for various sociodemographic groups, less is known about how the characteristics of the uninsured population have changed in recent years. METHODS: The analyses used 1-year American Community Survey data from 2013 through 2018 on the noninstitutionalized civilian population aged 19-64 years to examine trends in the characteristics of the U.S. uninsured population. Analyses also explored the importance of social and demographic change in the overall U.S. population by decomposing the change in the uninsured rate between 2013 and 2018. RESULTS: In 2018, the profile of the uninsured population differed from that of the noninstitutionalized civilian population aged 19-64 years with respect to a number of characteristics, including age, sex, and socioeconomic resources. Between 2013 and 2018, southern individuals and those with less than a high school education comprised a disproportionate share of the uninsured population. However, compositional changes did not drive the overall decline in the uninsured rate. CONCLUSIONS: Although prior research has considered changes in the uninsured rate for key sociodemographic groups, fewer studies have considered how these changes affected the composition of the uninsured population in the U.S. The profile of the uninsured population, which has changed over time, can help to inform interventions to target this group.

Regional Variation in the Predictive Validity of Self-Rated Health for Mortality.

Self-rated health (SRH) is a commonly used measure for assessing general health in surveys in the United States. However, individuals from different parts of the United States may vary in how they assess their health. Geographic differences in health care access and in the prevalence of illnesses may make it difficult to discern true regional differences in health when using SRH as a health measure. In this article, we use data from the 1986 and 1989-2006 National Health Interview Survey Linked Mortality Files and estimate Cox regression models to examine whether the relationship between SRH and five-year all-cause mortality differs by Census region. Contrary to hypotheses, there is no evidence of regional variation in the predictive validity of SRH for mortality. At all levels of SRH, and for both non-Hispanic white and non-Hispanic black respondents, SRH is equally and strongly associated with five-year mortality across regions. Our results suggest that differences in SRH across regions are not solely due to differences in how respondents assess their health across regions, but reflect true differences in health. Future research can, therefore, employ this common measure to investigate the geographic patterning of health in the United States.

The relationship between maternal education and reported childhood conditions.

Children of more-educated mothers tend to be healthier than children of less-educated mothers. However, in the United States, evidence for this relationship largely focuses on summary measures of health, such as subjective health status, birth weight, and height. Few studies have examined the relationship between mothers' education and children's reported conditions, the health metric that underlies many policy decisions concerning population health. Contrary to stylized facts about socioeconomic gradients in health, higher detection and reporting rates may lead to higher reporting rates among children of more-educated mothers, despite their better underlying health. This reporting pattern that might not mirror gradients for summary health measures. To examine this possibility, I investigate the association between maternal education and nine health conditions in the 1998-2014 National Health Interview Surveys (n = 176,097). I consider variation in the maternal education gradient across the specific reported conditions that children experience, paying particular attention to how patterns differ across children's ages. Results suggest that, unlike for the income gradient in child health, the relationship between maternal education and reported conditions varies in magnitude and direction across conditions. With some exceptions, the probability of reporting a diagnosed condition increases with maternal schooling. For some diagnoses, like asthma, this relationship is curvilinear, with an inverse gradient for children of the most educated mothers. However, the probability of reporting conditions that require neither diagnosis nor substantial parent-child involvement for detection tends to be flat across maternal education. Contrary to expectations, these relationships tend to be more pronounced for children who are 6 years of age or older than for younger children. These results expand understanding of the production and reporting of early-life health inequalities and illustrate limitations of an oft-used health metric. Reported conditions may underestimate socioeconomic inequalities in children's health.

Inequality and the association between involuntary job loss and depressive symptoms.

Although socioeconomic status (SES) has been to shown to be associated with susceptibility to involuntary job loss as well as with health, the ways in which individual SES indicators may moderate the job loss-health association remain underexplored. Using data from the Americans' Changing Lives study, we estimate the ways in which the association between job loss and depressive symptoms depends on five aspects of SES: education, income, occupational prestige, wealth, and homeownership. Our findings indicate that higher SES prior to job loss is not uniformly associated with fewer depressive symptoms. Higher education and lower prestige appear to buffer the health impacts of job loss, while financial indicators do not. These results have a number of implications for understanding the multidimensional role that social inequality plays in shaping the health effects of job loss.

Predictors of suicide relative to other deaths in patients with suicide attempts and suicide ideation: a 30-year prospective study.

BACKGROUND: Although there is a large literature that prospectively examines predictors of suicide, low base rates of suicide and imprecision of measurement hinder definitive conclusions from being drawn. METHOD: This study examined predictors of suicide relative to other types of death in a sample of 297 patients who had been hospitalized for suicide ideation or a suicide attempt between 1970 and 1975 and who were confirmed dead in 2005. Many predictors were measured using well-validated assessment instruments. RESULTS: Fifty-five patients had died by suicide. Univariate predictors of an increased risk for eventual suicide included younger age, completion of at least a high school degree, a diagnosis of a psychotic disorder, taking active precautions against discovery during the attempt, and a non-zero score on the suicide item of the Beck Depression Inventory, whereas African American ethnicity was associated with a decreased risk of eventual suicide. Variables that remained significant in a multivariate analysis included younger age, African American ethnicity, and taking active precautions against discovery during the attempt. Risk factors did not vary as a function of whether eventual suicide occurred less than or more than five years after the initial evaluation or by attempter v. ideator status. LIMITATIONS: Despite the attempt to maximize statistical power by following a high-risk sample for 30 years, the number of deaths by suicide was still relatively low. CONCLUSIONS: Taking active precautions against discovery of a suicide attempt has the potential to be an important predictor of eventual suicide and should be assessed by clinicians. Future prospective studies should assess predictors at multiple time points to gain a richer clinical picture of the circumstances surrounding deaths by suicide.