Inventory of US mental health trends during the COVID-19 pandemic

The outbreak of the novel coronavirus (COVID-19) that broke out in Wuhan, China in 2019, quickly spread to the rest of the world, causing more than 4.2 million infections and about 85 thousand deaths in its first year in 2020. . the World Health Organization (WHO) declared COVID-19 a global pandemic in March 2020. After that, many countries began adopting data collection protocols from municipalities and local provinces to help them make informed decisions to curb the spread of the causative agent severe acute respiratory tract syndrome coronavirus 2 (SARS‑CoV‑2). However, the limited availability of information led many countries to implement large-scale control measures, such as lockdowns.

The state and federal governments in the United States have implemented several mandates to control the spread of SARS‑CoV‑2. However, the federal government took a hands-off approach where individual states can decide how to limit the spread of the disease. There were different mandates, ranging from school and work closures to stay at home. In addition, the increase in COVID-19 cases and deaths led to more pandemic-related concerns among people. Policies that determine pandemic-related decisions can negatively impact mental health, leading to depression and anxiety. As a result, a jump in mental health was reported in the second quarter of 2020 compared to the last quarter of 2019.

Research: Understanding mental health trends during the US COVID-19 pandemic using network analysis. Image credit: rudall30/Shutterstock

Several studies indicated that lockdowns and many COVID-19-related policies could increase the burden on mental health, especially for vulnerable groups. However, some policies have been shown to have a positive impact on both psychological and physical health. In addition, vaccines were also reported to reduce mental health problems after their first introduction in late 2020. However, much of the population was hesitant to receive vaccination and continued to experience similar mental health problems. Therefore, dynamic interpretation of data over time is essential, concise and clear.

A previous study by Bulai and Amico conducted a network analysis to determine the COVID-19 interactions between different regions of Italy and the impact of Italian government policies to control the spread of the disease. They used six indicators to form a correlation network known as “Covidome,” which showed the north-south clustering of Italy’s regions. In addition, they also saw a significant difference in Covidome fluctuations between the first and second wave of pandemics based on political choices between different regions.

A new study posted on the pre-print server medRxiv*focused on applying clustering and network analysis to determine connectivity across states and using COVID-19-related mental health indicators to understand how COVID-19 affects mental health in the United States.

About the study

The study was based on research findings from the Delphi Group at Carnegie Mellon University. Survey questions ranged from the economic impact of COVID-19 and physical health to behavioral questions and mental health. The answers of the participants were aggregated, collected and made public. Three indicators that indicated the impact of COVID-19 on mental health were the percentage of participants who experienced anxiety in the past seven days, the percentage of participants who were worried about their finances in the next month, and the percentage of participants who experienced anxiety in the past seven days. felt depressed. Information was also obtained on daily confirmed cases and deaths of COVID-19.

The results collected from the survey are categorized into two time periods, the first ranging from September 8, 2020 to March 2, 2021 and the second from March 2, 2021 to January 10, 2022. Plotting daily COVID-19 cases, hospitalizations, and deaths three waves indicated. On this basis, the data is split into three different time periods, namely from April 1 to July 1, 2021, from July 2 to November 11, 2021 and from November 2, 2021 to January 10, 2022. In addition to the individual states, there are four geographic regions (south, western, northeast, and midwestern United States and references from political parties were used to identify any mental health trends among states that are clustered as a result of similar politically or geographically established communities. In addition, the survey results on mental health indicators were plotted based on political affiliation and geographic regions.

The policies put in place to contain the COVID-19 outbreak were categorized as financially concerned or related to depression and anxiety. Clustering and correlation networks were used to determine the connectivity of states. Finally, dynamic connectome analysis was used to understand the relationship between government policies, mental health indicators, and their relationship between political parties and geographic regions. Both correlation values ​​and eigenvector centrality values ​​were analyzed for mental health indicators. The minimum and maximum correlation values ​​were determined for each period and verified using the eigenvector centrality values.

Study findings

The results did not clearly distinguish between communities for the three mental health indicators, except for a slight clustering in the southern region. A loyalty matrix constructed using the three mental health indicators showed three main clusters, with the southern geographic region being the most interesting. However, the southern region did not include Arkansas, West Virginia, and Virginia, while it included Nevada, North Carolina, and California, which are non-southern states.

The lowest minimum and highest maximum correlation values ​​were observed in the northeastern region in the first period for the feeling anxious variable. In the second period, the northeast showed the lowest correlation values, while the south showed the highest. The West had the lowest correlation values, while the Midwest had the highest.

In the first period, the minimum and maximum correlation values ​​for depressive feelings were observed in the Midwestern region. In the second period, the maximum value was observed in the south and the minimum in the northeast. During the last period, northeastern states had the highest correlation and western states the lowest.

The financial concerns variable had a higher maximum correlation in the first period and a decreasing correlation in the subsequent periods. In the first period, the Midwest had the lowest correlation values, while the South had the highest values. During the second period, the south had the highest values ​​and the northeast had the lowest values. Finally, the values ​​for the third period were observed to be lowest in the northeast and highest in the west.

On the variable feelings of fear, the Midwest showed minimal eigenvector centrality and maximal values ​​in the Northeast. In the second period, maximum values ​​were observed in the south and minimum values ​​in the northeast. The south had the lowest values ​​during the third period, while the northeast had the highest.

There were only two regions with minimum and maximum eigenvector centrality values ​​for the variable feeling depressed. The Midwest was found to have maximum values ​​for the first period and minimum and maximum values ​​for the second period. During the third and second periods, maximum values ​​were observed in the northeast, while minimum values ​​were observed during the third period.

For the variable finance concerns, it was observed that the Midwest had the maximum values ​​and the South the minimum values ​​in the first period. For the second period, the south was observed to have both minimum and maximum values. Finally, the West had maximum values ​​for the third period and the Midwest had minimum values.

In addition, correlation values ​​increased for the Democratic states, but not for the Republican states. For the variable feelings of anxiety, both maximum and minimum correlation values ​​were observed in the Democratic states in the first period. For the second period, the maximum values ​​were observed in the Republican states and the minimum values ​​in the Democratic states. For the third period, the minimum values ​​were observed in the Republican states and the maximum values ​​in the Democratic states.

The Republican states were found to have minimum values ​​for the feel depressed variable for all three time periods. The maximum correlation values ​​were observed in the Democratic states for the first and third periods, while in the Republican states for the second period. In addition, for the finance concerns variable, all maximum and minimum values ​​were observed in the Republican states for all time periods.

For the variable feelings of anxiety, minimum eigenvector centrality values ​​were observed in the Republican states and the maximum values ​​in the Democratic states during the first wave. Both maximum and minimum values ​​were observed in the Republican states for the second wave, while in the Democratic states for the third wave.

For the feel depressed variable, minimal eigenvector centrality values ​​were observed in the Republican states in the first and second periods and the Democratic states in the third period. On the other hand, the maximum eigenvector centrality values ​​were observed in the Democratic states in the first and second periods and in the Republican states in the second period. For the variable concerns about finances, all maximum and minimum eigenvector centrality values ​​were observed in the Republican states.

Therefore, the current study showed a similar trend for finance concerns and anxiety in the Republican and Southern states between March 3, 2021 and January 10, 2022. indicator. In addition, the variables of depression and anxiety overlapped with the increase in COVID-19 cases, hospitalizations and deaths, and the spread of the Delta variant.

Limits

The study has certain limitations. First, fear can be caused by other sources, such as media attention and negative COVID-19 experience. Second, most lockdown measures had ended during the data collection of this study. Third, certain states relaxed restrictions that could affect participants’ response to the survey. Fourth, the proximity of specific policies makes it difficult to determine their impact on mental health. Fifth, the study may not be sensitive to changes in short-term correlation. Finally, the interpretation of the variable feelings of depression is difficult.

*Important announcement

medRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered conclusive, should guide clinical practice/health-related behavior or be treated as established information.

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