The COVID-19 pandemic increased the rate of mental health disorders, as well as demand for mental health services. It remains unclear, however, the extent to which it impacted access to mental health care. Using data from an audit field experiment, which ran from January to May 2020 and overlapped with the onset of the COVID-19 pandemic, we examine the impact of COVID-19 on access to mental health care appointments in the United States. We find that increased intensity of COVID-19–measured by daily cases, daily fatalities, and weekly excess deaths–is associated with decreased access to mental health care appointments.
more »
« less
Reconciling Seemingly Contradictory Results from the Oregon Health Insurance Experiment and the Massachusetts Health Reform
A headline result from the Oregon Health Insurance Experiment is that emergency room (ER) utilization increased. A seemingly contradictory result from the Massachusetts health reform is that ER utilization decreased. I reconcile both results by identifying treatment effect heterogeneity within the Oregon experiment and extrapolating it to Massachusetts. Even though Oregon compliers increased their ER utilization, they were adversely selected relative to Oregon never takers, who would have decreased their ER utilization. Massachusetts expanded coverage from a higher level to healthier compliers. Therefore, Massachusetts compliers are comparable to a subset of Oregon never takers, which can reconcile the results.
more »
« less
- Award ID(s):
- 1350132
- PAR ID:
- 10484761
- Publisher / Repository:
- MIT Press Direct
- Date Published:
- Journal Name:
- Review of Economics and Statistics
- Volume:
- 105
- Issue:
- 3
- ISSN:
- 0034-6535
- Page Range / eLocation ID:
- 646 to 664
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Traditional implementations of federated learning for preserving data privacy are unsuitable for longitudinal health data. To remedy this, we develop a federated enhanced fuzzy c-means clustering (FeFCM) algorithm that can identify groups of patients based on complex behavioral intervention responses. FeFCM calculates a global cluster model by incorporating data from multiple healthcare institutions without requiring patient observations to be shared. We evaluate FeFCM on simulated clusters as well as empirical data from four different dietary health studies in Massachusetts. Results find that FeFCM converges rapidly and achieves desirable clustering performance. As a result, FeFCM can promote pattern recognition in longitudinal health studies across hundreds of collaborating healthcare institutions while ensuring patient privacy.more » « less
-
Mental health issues have long posed a challenge on university campuses. While no population is immune, research has shown that students from marginalised backgrounds can have higher rates of mental health issues and suffer worse outcomes as a result. These discrepancies have been attributed to everything from different cultural norms to the micro-aggressions and other barriers that students from marginalised populations face on university campuses. With the onset of COVID-19 in the United States, many residential universities switched to a remote learning model, fundamentally changing the relationship between students, campus, family support. This work uses survey data from students in the United States to explore how COVID-19 affected mental health issues among students from different backgrounds. While the pandemic drastically increased rates of depressive disorder among all respondents, discrepancies between mental health rates for women and Hispanic/Latinx compared to men and White respondents either decreased or disappeared. Additionally, respondents identifying as Asians were less likely to screen positive for several mental health conditions than White, Non-Hispanic respondents. These findings may point to important new insights about the ways in which engineering education undermines some groups’ mental health.more » « less
-
Abstract People believe they should consider how their behavior might negatively impact other people, Yet their behavior often increases others’ health risks. This creates challenges for managing public health crises like the COVID-19 pandemic. We examined a procedure wherein people reflect on their personal criteria regarding how their behavior impacts others’ health risks. We expected structured reflection to increase people's intentions and decisions to reduce others’ health risks. Structured reflection increases attention to others’ health risks and the correspondence between people's personal criteria and behavioral intentions. In four experiments during COVID-19, people (N = 12,995) reported their personal criteria about how much specific attributes, including the impact on others’ health risks, should influence their behavior. Compared with control conditions, people who engaged in structured reflection reported greater intentions to reduce business capacity (experiment 1) and avoid large social gatherings (experiments 2 and 3). They also donated more to provide vaccines to refugees (experiment 4). These effects emerged across seven countries that varied in collectivism and COVID-19 case rates (experiments 1 and 2). Structured reflection was distinct from instructions to carefully deliberate (experiment 3). Structured reflection increased the correlation between personal criteria and behavioral intentions (experiments 1 and 3). And structured reflection increased donations more among people who scored lower in cognitive reflection compared with those who scored higher in cognitive reflection (experiment 4). These findings suggest that structured reflection can effectively increase behaviors to reduce public health risks.more » « less
-
The increased use of algorithms to support decision making raises questions about whether people prefer algorithmic or human input when making decisions. Two streams of research on algorithm aversion and algorithm appreciation have yielded contradicting results. Our work attempts to reconcile these contradictory findings by focusing on the framings of humans and algorithms as a mechanism. In three decision making experiments, we created an algorithm appreciation result (Experiment 1) as well as an algorithm aversion result (Experiment 2) by manipulating only the description of the human agent and the algorithmic agent, and we demonstrated how different choices of framings can lead to inconsistent outcomes in previous studies (Experiment 3). We also showed that these results were mediated by the agent's perceived competence, i.e., expert power. The results provide insights into the divergence of the algorithm aversion and algorithm appreciation literature. We hope to shift the attention from these two contradicting phenomena to how we can better design the framing of algorithms. We also call the attention of the community to the theory of power sources, as it is a systemic framework that can open up new possibilities for designing algorithmic decision support systems.more » « less
An official website of the United States government

