The novel coronavirus disease (COVID-19), which emerged in Wuhan, China, in 2019, soon spread to the rest of the world, causing more than 4.2 million infections and about 85,000 deaths in the first year of 2020. As a result, the World Health Organization (WHO) declared COVID-19 a global pandemic in March 2020. Subsequently, many countries began adopting protocols to collect data from municipalities and local counties to help them make informed containment decisions of the spread of the causative agent of severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). However, the limited availability of information has led many countries to implement large-scale control measures such as lockdowns.
State and federal governments in the United States have implemented several mandates to manage the spread of SARS‑CoV‑2. However, the federal government has chosen an approach that allows individual states to decide how to limit the spread of the disease. There were various mandates ranging from school and work closures to stay-at-home orders. In addition, the increase in cases and deaths of COVID-19 has led to increased anxiety among people related to the pandemic. Policies determining decisions related to the pandemic may adversely affect mental health, leading to depression and anxiety. As a result, the second quarter of 2020 saw a spike in mental illness compared to the last quarter of 2019.
Study: Understanding Mental Health Trends During the COVID-19 Pandemic in the United States Using Network Analysis. Image credit: rudall30 / Shutterstock
Several studies have shown that the lockdown and many policies related to COVID-19 may increase the burden on mental health, especially for vulnerable groups. However, some policies have been shown to positively affect both psychological and physical health. In addition, vaccines have also been reported to reduce mental health problems since their initial release in late 2020. However, a large portion of the population is hesitant to get vaccinated and continues to experience similar levels of mental distress. Therefore, the dynamic interpretation of the data is vital in a short and clear time.
A previous study by Bulai and Amico applied network analysis to determine the interactions of COVID-19 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 the “Covidome”, which shows the clustering of Italy’s regions in a north-south direction. Moreover, they also observed a significant difference in Covidome fluctuations between the first and second pandemic waves based on political choices between different regions.
New study published on the preprint server medRxiv* aimed to apply clustering and network analysis to determine connectivity between states and used mental health indicators related to COVID-19 to understand how COVID-19 is affecting mental health in the United States.
About the research
The study is based on results from Carnegie Mellon University’s Delphi Group survey. The survey questions ranged from the economic impact of COVID-19 and physical health to behavioral guidelines and mental health. Participant responses were summarized, collated and made publicly available. Three measures that show the impact of COVID-19 on mental health are the percentage of participants who have experienced feelings of anxiety in the past seven days, the percentage of participants who feel worried about their finances for the next month, and the percentage of participants who that he had been feeling depressed for the past seven days. Information on daily confirmed cases and deaths from COVID-19 was also received.
The results collected from the survey were categorized into two time frames, the first ranging from September 8, 2020 to March 2, 2021, and the second from March 2, 2021 to January 10, 2022. Daily COVID-19 Cases Schedule , hospitalizations and deaths indicated three waves. Based on these, the data was split into three different periods, April 1 to July 1, 2021, July 2 to November 11, 2021, and November 2, 2021 to January 10, 2022. In addition to individual states, four geographic regions (South, West, Northeast, and Midwest) of the United States and reference to political parties were used to determine any trends in mental health among states clustered as a result of similar political or geographically established communities. In addition, survey results for mental health indicators were plotted by political preference and geographic region.
Policies implemented to control the COVID-19 outbreak were categorized as worrying about finances 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 policy, mental health indicators, and their relationship between political parties and geographic regions. Both correlation values and eigenvector central values were analyzed for mental health indicators. The minimum and maximum correlation values were determined for each period and checked using the central values of the eigenvector.
The results did not show a clear community distinction for the three mental health indicators, except for a slight clustering in the southern region. The loyalty matrix constructed using the three mental health indicators shows three main groups, of which the southern geographic region is 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 not southern states.
The lowest minimum and the highest maximum correlation values are observed from the Northeast region in the first period for the anxiety variable. In the second period, the northeast shows the lowest correlation values, while the south shows the highest. The West has the lowest correlation values, while the Midwest has the highest.
During the first period, the minimum and maximum correlation values for feeling depressed were observed in the Midwest region. In the second period, the maximum value is observed in the south, and the minimum in the northeast. Over the latter period, northeastern states have the highest correlation and western states have the lowest.
The concern about finances variable has a higher maximum correlation in the first period and a decreasing correlation in subsequent periods. In the first period, the Midwest region had the lowest correlation values, while the South had the highest values. In the second period, the south has the highest values and the northeast the lowest. Finally, the values of the third period are observed to be lowest in the northeast and highest in the west.
For the anxiety variable, the Midwest shows minimum eigenvector centrality and maximum values in the Northeast. During the second period, maximum values are observed in the south and minimum values in the northeast. The south has the lowest values in the third period, while the northeast has the highest.
There were only two regions with minimum and maximum eigenvector centrality values for the feeling depressed variable. The Midwest is observed to have maximum values for the first period and minimum and maximum values for the second period. Maximum values are observed in the northeast during the third and second periods, and minimum values in the third period.
For the variable worrying about finances, the Midwest is observed to have maximum values and the South as minimum values in the first period. For the second period, the south is observed with minimum and maximum values. Finally, the West had maximum values for the third period and the Midwest had minimum values.
Furthermore, the correlation values increased for Democratic states but not for Republican states. For the anxiety variable, both the maximum and minimum correlation values were observed in democratic states for the first period. For the second period, maximum values are observed in Republican states and minimum values in Democratic states. For the third period, the minimum values are observed in the republican states and the maximum values in the democratic states.
Republican states were observed to have the lowest values for all three time periods for the feeling depressed variable. Maximum correlation values are observed in Democratic states for the first and third periods, while in Republican states for the second period. Additionally, for the finance-concerned variable, all maxima and minima were observed in Republican states for all periods.
For the anxiety variable, the minimum values of eigenvector centrality were observed in Republican states and the maximum values were observed in Democratic states during the first wave. Both the maximum and minimum values were observed in Republican states for the second wave, while in Democratic states for the third wave.
For the feeling depressed variable, minimum eigenvector centrality values are observed in Republican states in the first and second periods and in Democratic states in the third period. On the other hand, the maximum values of eigenvector centrality are observed in Democratic states in the first and second periods and Republican states in the second period. For the worry about finances variable, all maximum and minimum values of eigenvector centrality were observed in Republican states.
Therefore, the current study demonstrates a similar trend for worry about finances and feelings of anxiety among Republicans and Southern states between March 3, 2021 and January 10, 2022. However, no identifiable communities that resemble political parties or geographic regions for sentiment are reported of depression indicator. Furthermore, the depressive and anxious feelings variables overlapped with increased COVID-19 cases, hospitalizations and deaths, and the prevalence of the Delta variant.
The study has certain limitations. First, anxiety may be caused by other sources, such as media exposure and negative experiences with COVID-19. Second, most blocking measures had ended at the time of data collection for this study. Third, some countries were loosening restrictions that could have affected the response of survey participants. Fourth, the proximity of specific policies makes it difficult to determine their impact on mental health. Fifth, the study may not be sensitive to short-term changes in the correlation. Finally, the interpretation of the depression feelings variable is difficult.
medRxiv publishes preliminary scientific reports that are not peer-reviewed and therefore should not be considered conclusive, guide clinical practice/health-related behavior, or be treated as established information.