Coronavirus disease 2019 (COVID-19) introduced stressors to mental health, including loneliness stemming from social isolation, fear of contracting the disease, economic strain, and uncertainty about the future. We fielded a national survey measuring symptoms of psychological distress and loneliness among US adults in April 2020 and compared results with national data from 2018.

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Objective This study compares current public attitudes about drug addiction with attitudes about mental illness. Methods A web-based national public opinion survey (N=709) was conducted to compare attitudes about stigma, discrimination, treatment effectiveness, and policy support. Results Respondents hold significantly more negative views toward persons with drug addiction compared to those with mental illness. More respondents were unwilling to have a person with drug addiction marry into their family or work closely with them on a job. Respondents were more willing to accept discriminatory practices, more skeptical about the effectiveness of available treatments, and more likely to oppose public policies aimed at helping persons with drug addiction. Conclusions Drug addiction is often treated as a sub-category of mental illness, and health insurance benefits group these conditions together under the rubric of behavioral health. Given starkly different public views about drug addiction and mental illness, advocates may need to adopt differing approaches for advancing stigma reduction and public policy.

Difference-in-difference (DD) methods are a common strategy for evaluating the effects of policies or programs that are instituted at a particular point in time, such as the implementation of a new law. The DD method compares changes over time in a group unaffected by the policy intervention to the changes over time in a group affected by the policy intervention, and attributes the “difference-in-differences” to the effect of the policy. DD methods provide unbiased effect estimates if the trend over time would have been the same between the intervention and comparison groups in the absence of the intervention. However, a concern with DD models is that the program and intervention groups may differ in ways that would affect their trends over time, or their compositions may change over time. Propensity score methods are commonly used to handle this type of confounding in other non-experimental studies, but the particular considerations when using them in the context of a DD model have not been well investigated. In this paper, we describe the use of propensity scores in conjunction with DD models, in particular investigating a propensity score weighting strategy that weights the four groups (defined by time and intervention status) to be balanced on a set of characteristics. We discuss the conceptual issues associated with this approach, including the need for caution when selecting variables to include in the propensity score model, particularly given the multiple time point nature of the analysis. We illustrate the ideas and method with an application estimating the effects of a new payment and delivery system innovation (an accountable care organization model called the “Alternative Quality Contract” (AQC) implemented by Blue Cross Blue Shield of Massachusetts) on health plan enrollee out-of-pocket mental health service expenditures. We find no evidence that the AQC affected out-of-pocket mental health service expenditures of enrollees.

Context:Relatively little is known about the factors shaping public attitudes toward obesity as a policy concern. This study examines whether individuals' beliefs about the causes of obesity affect their support for policies aimed at stemming obesity rates. This article identifies a unique role of metaphor-based beliefs, as distinct from conventional political attitudes, in explaining support for obesity policies.Methods: This article used the Yale Rudd Center Public Opinion on Obesity Survey, a nationally representative web sample surveyed from the Knowledge Networks panel in 2006/07 (N = 1,009). The study examines how respondents' demographic and health characteristics, political attitudes, and agreement with seven obesity metaphors affect support for sixteen policies to reduce obesity rates. Findings:Including obesity metaphors in regression models helps explain public support for policies to curb obesity beyond levels attributable solely to demographic, health, and political characteristics. The metaphors that people use to understand rising obesity rates are strong predictors of support for public policy, and their influence varies across different types of policy interventions. Conclusions:Over the last five years, the United States has begun to grapple with the implications of dramatically escalating rates of obesity. Individuals use metaphors to better understand increasing rates of obesity, and obesity metaphors are independent and powerful predictors of support for public

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Serious psychological distress was reported by 13.6% of US adults in April 2020 vs 3.9% in 2018. 1 How psychological distress has changed over the course of the coronavirus disease 2019 (COVID-19) pandemic is unknown.

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