Essays on Smoking, Drinking and Obesity: Evidence from a Randomized Experiment

This dissertation consists of three chapters analyzing risky health behaviors utilizing data from the Lung Health Study (LHS), a randomized smoking cessation program. The first two chapters of this dissertation analyze the effects of smoking on alcohol consumption and BMI, respectively. The third chapter studies whether and how much the objective smoking information, which is defined by clinicians, may be misreported. The first chapter examines the effect of smoking on alcoholic beverage consumption. The epidemiology literature suggests that both behaviors affect similar brain regions and are commonly consumed together. So far, the economics literature has presented inconclusive causal evidence on the relationship. Building on the theory of rational addiction, I estimate the relationship between smoking and alcohol consumption using several different smoking measures. I report four salient findings. First, self-reported and clinically verified smoking variables suggest that quitting smoking lowers alcoholic beverages consumption by 11.5%. Second, cigarette consumption dating back 12 months affects alcohol consumption, and those with the highest past 12 months average cigarette consumption see the largest increase in alcohol consumption. Third, I find that the length of quitting affects future alcohol consumption as well. Continuously abstaining from smoking for 12 months reduces alcoholic beverage consumption by 27.5% per week. Fourth, non-smoking for 12 months also reduces the probability of drinking any alcoholic beverages by 31%. The second chapter aims to identify the causal effect of smoking on body mass index (BMI). Since nicotine is a metabolic stimulant and appetite suppressant, quitting or reducing smoking could lead to weight gain. Using randomized treatment assignment to instrument for smoking, we estimate that quitting smoking leads to an average long-run weight gain of 1.8-1.9 BMI units, or 11-12 pounds at the average height. These results imply that the drop in smoking in recent decades explains 14% of the concurrent rise in obesity. Semi-parametric models provide evidence of a diminishing marginal effect of smoking on BMI, while subsample regressions show that the impact is largest for younger individuals, females, those with no college degree, and those with healthy baseline BMI levels. The third chapter analyzes and compares self-reported and clinically verified smoking information. Descriptive statistics show that about 8% of clinically verified smokers self-report that they do not smoke (under-report participation), and that smoking cessation treatment group participants misreport smoking participation 2 to 1 relative to control group participants. In our first methodological approach we regard the objectively verified smoking measure as the gold standard. We estimate linear probability models and find that being male and married increases the probability of misreporting by 10 percentage points. Additionally, older participants are more likely to misreport smoking status, while those using nicotine gum and with a higher BMI are less likely to misreport. However, all variables can only explain a small fraction of the variation that explains misreporting. Our second methodological approach takes an agnostic view on whether the clinically verified smoking information is accurate. We utilize BMI, Carbon Monoxide (CO), and Cotinine level information to inform whether a person is a smoker. We estimate a Bayesian mixture model to account for the heterogeneity in BMI, CO and Cotinine levels after a substantial decrease in post treatment smoking participation. All of our models show that smokers are more likely assigned to the low BMI, high CO and high Cotinine level distributions. Among those classified as misreporters, we find that 30% have a very high probability of being part of the non-smoking distributions. As a result, we believe that objectively- verified smoking measure may not be better than the self-reported measure

What Happens to Texans’ Insurance Coverage When Medicaid and Marketplace Pandemic-Era Policies End?

Policy ReportThe COVID-19 related public health emergency (PHE) led to federal legislation that changed the landscape of Medicaid and Marketplace insurance coverage. Beginning in 2020, policy responses led to increasing Medicaid enrollment due to federal rules preventing Medicaid disenrollment, and increased Marketplace participation through generous subsidies extended to the majority of the working age population without access to employer provided coverage. In this brief, we describe and summarize the implications of the federally declared PHE and federal legislation for health insurance coverage during the 2020-2022 period in Texas at the state and county level, estimate the implications for insurance coverage once the PHE ends, and provide estimated aggregate fiscal impacts. Texas had the nation’s highest uninsurance rate at 18.4% in 2019, but since January 2020, total Texas Medicaid caseload has increased by 41% or 1.6 million people (as of June 2022), and about 750,000 individuals have newly enrolled in Marketplace coverage, likely substantially decreasing the number uninsured. The Medicaid policies have provided a net financial windfall to the state of $3.5 billion since January 2020. With the eventual end of the PHE, our conservative estimates expect that 550,000 to 700,000 individuals will lose Medicaid coverage, increasing the uninsurance rate by at least 2 percentage points or about 10%. Attention to policies and administrative actions that support ongoing insurance enrollment can help ensure that the large gains to insurance coverage achieved during the PHE can be sustained. Policies and administrative actions that would help ensure the historic gains in coverage are maintained include reducing red-tape costs of processing renewals and redeterminations by streamlining eligibility systems (including the use of information already available to the state), using the capacity of managed care and health insurance navigator organizations for outreach and processing, and taking advantage of increased federal matches for Medicaid expansion.Episcopal Health Foundatio

The Effect of Predictive Analytics-Driven Interventions on Healthcare Utilization

Among high-risk Medicare Advantage members with congestive heart failure, a proactive outreach program driven by a claims-based predictive algorithm reduced the likelihood of an emergency department (ED) or specialist visit in one year by 20% and 21%, respectively. The average number of visits dropped as well, with a 40% reduction in the volume of ED visits and a 27% reduction in the volume of cardiology visits after the first year

Marketplace Health Insurance & the Public Health Emergency: Implications for Texas

Texas has the highest uninsurance rate in the country. The COVID-19 related public health emergency led to the American Rescue Plan Act, federal legislation that increased premium subsidies for private Marketplace health insurance plans and expanded eligibility criteria. Since early 2021, total Texas Marketplace enrollment has increased by 70% or 750,000 people. In recent work, the authors used public data to estimate the gains in Marketplace coverage attributable to the policy, the remaining share of uninsured individuals who may remain eligible for subsidized Marketplace coverage, and projected losses in coverage when these temporary policies expire at the end of 2025. This article is a companion to the December 2023, issue of The Takeaway on Medicaid and the Public Health Emergency.Episcopal Health Foundatio

The Three-Year Impact of the Affordable Care Act on Disparities in Insurance Coverage

Objective: To estimate the impact of the major components of the ACA (Medicaid expansion, subsidized Marketplace plans, and insurance market reforms) on disparities in insurance coverage after three years. Data Source: The 2011-2016 waves of the American Community Survey (ACS), with the sample restricted to nonelderly adults. Design: We estimate a difference-in-difference-in-differences model to separately identify the effects of the nationwide and Medicaid expansion portions of the ACA using the methodology developed in the recent ACA literature. The differences come from time, state Medicaid expansion status, and local area pre-ACA uninsured rates. In order to focus on access disparities, we stratify our sample separately by income, race/ethnicity, marital status, age, gender, and geography. Principal Findings: After three years, the fully implemented ACA eliminated 43% of the coverage gap across income groups, with the Medicaid expansion accounting for this entire reduction. The ACA also reduced coverage disparities across racial groups by 23%, across marital status by 46%, and across age-groups by 36%, with these changes being partly attributable to both the Medicaid expansion and nationwide components of the law. Conclusions: The fully implemented ACA has been successful in reducing coverage disparities across multiple groups