Change in unemployment by social vulnerability among United States counties with rapid increases in COVID-19 incidence-July 1-October 31, 2020.

OBJECTIVE: During the COVID-19 pandemic, the unemployment rate in the United States peaked at 14.8% in April 2020. We examined patterns in unemployment following this peak in counties with rapid increases in COVID-19 incidence.
METHOD: We used CDC aggregate county data to identify counties with rapid increases in COVID-19 incidence (rapid riser counties) during July 1-October 31, 2020. We used a linear regression model with fixed effect to calculate the change of unemployment rate difference in these counties, stratified by the county's social vulnerability (an indicator compiled by CDC) in the two months before the rapid riser index month compared to the index month plus one month after the index month.
RESULTS: Among the 585 (19% of U.S. counties) rapid riser counties identified, the unemployment rate gap between the most and least socially vulnerable counties widened by 0.40 percentage point (p<0.01) after experiencing a rapid rise in COVID-19 incidence. Driving the gap were counties with lower socioeconomic status, with a higher percentage of people in racial and ethnic minority groups, and with limited English proficiency.
CONCLUSION: The widened unemployment gap after COVID-19 incidence rapid rise between the most and least socially vulnerable counties suggests that it may take longer for socially and economically disadvantaged communities to recover. Loss of income and benefits due to unemployment could hinder behaviors that prevent spread of COVID-19 (e.g., seeking healthcare) and could impede response efforts including testing and vaccination. Addressing the social needs within these vulnerable communities could help support public health response measures.

Introduction

In the United States, the COVID-19 pandemic has resulted in more than 62,000,000 reported cases and more than 840,000 associated deaths as of January 13, 2022 [1]. Although the pandemic may have affected most people living in the United States in some way, the impact has not been equal across communities [2, 3]. Societal factors such as poverty, lack of access to transportation, crowded households, racial and ethnic inequalities, work-related hardship or risk (e.g., unemployment, underemployment, and distribution of essential and/or public facing jobs), and other social conditions, affect a community’s ability to cope with a disaster [4] like the COVID-19 pandemic. Social vulnerability is a term that refers to the potential negative effects on communities caused by stresses like disease outbreaks [5]. Counties that are more socially vulnerable are more likely to have high COVID-19 incidence [3]. For example, racial and ethnic minority groups have had disproportionately high numbers of COVID-19 cases and associated hospitalizations and deaths [6-8]. Counties with a higher percentage of people living in crowded housing are more likely to experience rapidly increasing COVID-19 incidence [3].

In addition to the direct negative health impacts of COVID-19, the pandemic also has impacted the economy: the unemployment rate peaked at 14.8% in April 2020 when more than 6,000,000 people filed initial claims of unemployment insurance in a week [9]. Persons from racial and ethnic minority groups are overrepresented in the services industries such as leisure and hospitality that were disproportionally impacted by the economic downturn caused by COVID-19 [10-12]. Rapid increase of COVID-19 incidence in a community may lead to business closures and worker layoffs, and the impact on socially vulnerable communities may be greater than less vulnerable communities [13].

In this study, we sought to examine changes in unemployment rates among counties with rapid increases in COVID-19 incidence (rapid riser counties). Since March 8, 2020, the Centers for Disease Control and Prevention (CDC) has used county-level case counts and standard criteria to identify counties with rapidly increasing COVID-19 incidence, known as rapid riser counties. These criteria reflect a rapid increase of COVID-19 incidence within a short period of time as a method to focus public health efforts in these communities with disproportionately high COVID-19 rates [14]. Specifically, we described unemployment changes in rapid riser counties by the CDC social vulnerability index (SVI), an index that measures the potential negative effects on communities caused by external stresses on human health and helps local officials identify socially vulnerable communities that may need support before, during, or after disasters [5]. The SVI has been used in prior county-level COVID-19 studies [3, 15].

Materials and methods

Using CDC aggregate county data (a primary case reporting feed utilized for federal response and managed by CDC) of reported daily COVID-19 case counts during July 1–October 31, 2020, we identified rapid riser counties, defined as those that met all of the following standardized criteria [14] on the day assessed: 1) >100 new cases in the most recent 7 days, 2) >0% change in 7-day incidence, 3) a decrease of <60% or an increase in the most recent 3-day incidence over the preceding 3-day incidence, and 4) a 7-day incidence/30-day incidence ratio >0.31 of COVID-19; and, met one or both of the following triggering criteria: 1) >60% change in the most recent 3-day incidence, or 2) >60% change in the most recent 7-day incidence of COVID-19. These criteria were developed through a collaborative process involving multiple federal agencies [14]. For this analysis, we categorized a county as a rapid riser if the county met the standardized daily rapid riser criteria on at least three days in the week; we chose this approach in order to minimize misclassification due to reporting errors (e.g., as might occur if a county reported COVID-19 case counts in multi-day batches instead of daily). We defined the index month based on the month when a county first experienced a rapid rise in COVID-19 incidence; and employed a mid-month cut-point where, if the first rapid riser alert was on or after the 15th day of the month, the next month was assigned as the index month. Pre-index months were defined as the 2 months preceding the index month. Post-index months were defined as the index month plus the following month.

We extracted county-level SVI data from the most recent database, CDC/ATSDR SVI database 2018 [5]. The overall SVI and the four SVI themes were used: 1) socioeconomic status including “below poverty”, “unemployed”, “income”, and “no high school diploma”; 2) household composition & disability including “aged 65 years or older”, “aged 17 years or younger”, “civilian with a disability”, and “single-parent households”; 3) minority status & languages including “minority” and “speak English ‘less than well’”; and 4) housing type & transportation including “multi-unit structures”, “mobile homes”, “crowding”, “no vehicle”, and “group quarters”. Both overall SVI and four SVI themes for each of the counties included were categorized by quartiles based on SVI and the SVI themes in the 3,141 US counties with complete data (99.9% of US counties or county equivalents). One U.S. county was excluded due to missing SVI data. The fourth quartile represents the most socially vulnerable U.S. counties, and the first quartile represents the least socially vulnerable U.S. counties.

We obtained monthly unemployment rates at the county level from January through November 2020 from the U.S. Bureau of Labor Statistics (BLS) [16]. BLS defines unemployment rate as the percent of unemployed persons of the civilian labor force [17]. The county-level unemployment rate gap was defined as the rate difference comparing first SVI quartile (referent) to other quartiles, overall and by SVI themes. We selected the July–October 2020 timeframe to identify rapid riser counties to understand the impact of county-level COVID-19 rapid rise on unemployment rates after the initial phase of the COVID-19 pandemic. For each rapid riser county, we examined unemployment rates within a period of four months (i.e., two months before an index month, the index month, and the following month). We calculated the difference of the average unemployment rate gap between pre-index months and post-index months, using a linear regression model (Eq 1). Five separate regression models—one by overall SVI and four SVI themes were estimated. The coefficients of the interaction terms for each SVI quartile represent the changes in the unemployment rate gap. The standard error was clustered at the county level while controlling for month- and county-fixed effects as well as the number of days a county met rapid riser criteria within the 60 days after counties were first flagged as a rapid riser. The statistical analysis was performed using Stata 15 (College Station, TX). Where Unemployment Rate represents the unemployment rate of county c during time t, SVIQ2 SVIQ3, and SVIQ4 indicates the status of the second, third, and fourth quartile of the SVI for county c, Exposure is the rapid rise status for county c during time t, RR intensity, is the number of days a county met rapid riser criteria within 60 days after counties were first flagged as rapid riser for county c during time t, Country and Month represent county- and month- fixed effects.

Since the unemployment rate appears in both side of the equation (The overall SVI and the SVI socioeconomic status theme include unemployment rate), sensitivity analyses by excluding unemployment from the overall SVI and SVI socioeconomic status theme were conducted to see if this would significantly impact the results.

This activity was reviewed by CDC and was conducted consistent with applicable federal law and CDC policy. 45 C.F.R. part 46, 21 C.F.R. part 56; 42 U.S.C. Sect. 241(d); 5 U.S.C. Sect. 552a; 44 U.S.C. Sect. 3501 et seq.

Results

From July 1 through October 31, 2020, 585 (19%) of 3,141 U.S. counties were considered a COVID-19 rapid riser county for the first time (S1 Fig): 243 (42%) in July, 134 (23%) in August, 73 (12%) in September, and 135 (23%) in October. Among them, 29% were in the Midwest region, 2% were in the Northeast region, 58% were in the South region, and 11% were in the West region. Forty seven percent of those rapid riser counties are from metropolitan areas, and 53% of them are from non-metropolitan areas. Regarding unemployment rates among these 585 counties from January through November 2020, average unemployment rates peaked in April and then started to decrease (Fig 1). There was an unemployment rate gap between the most socially vulnerable counties and least socially vulnerable counties before March 2020. The gap narrowed in April when the unemployment rate reached its highest level and widened again since July 2020.

Fig 1

Monthly unemployment rate by Social Vulnerability Index (SVI) quartile in 585 U.S. counties identified as a rapid riser in COVID-19 incidence† --- United States, January-November, 2020.

†Rapid riser counties were defined as those that met all of the following criteria: 1) >100 new cases in recent week, 2) >0% change in the 7-day incidence, 3) >-60% change in the 3-day incidence, and 4) a 7-day incidence / 30-day incidence ratio >0.31. In addition, rapid riser counties met one or both of the following triggering criteria: 1) >60% change in 3-day incidence, or 2) >60% change in 7-day incidence. For this analysis, we categorized a county as a rapid riser if the county met the standardized daily rapid riser criteria on at least three days in the week.

Average unemployment rate in the rapid riser counties changed over the time period examined in the analysis by overall SVI quartiles (Fig 2). The unemployment rate gap widened by 0.40 percentage points (95% CI: 0.15%, 0.65%) between the most and the least socially vulnerable counties when comparing rates between pre-index months and post-index months (Table 1). Comparing the most socially vulnerable counties to the least, for the socioeconomic status SVI theme, the unemployment rate gap widened by 0.36 percentage point (95% CI: 0.11%, 0.61%); and for the racial and ethnic minority and English proficiency SVI theme, the unemployment rate gap widened by 0.38 percentage point (95% CI: 0.06%, 0.70%) between the most and the least socially vulnerable counties. No significant changes were found with respect to household composition and disability SVI theme or the housing type and transportation SVI theme. The sensitivity analyses results are not significantly different from the results from our main analysis and the conclusion remains the same (S1 Table).

Fig 2

County-level unemployment rate before and after† a rapid rise in COVID-19 incidence§, by Social Vulnerability Index (SVI) quartiles --- United States.

† Before rapid rise is defined as the 2 months preceding the rapid rise index month. After rapid rise is defined as the rapid rise index month plus the following month. § Rapid riser counties were defined as those that met all of the following criteria: 1) >100 new cases in recent week, 2) >0% change in the 7-day incidence, 3) >-60% change in the 3-day incidence, and 4) a 7-day incidence / 30-day incidence ratio >0.31. In addition, rapid riser counties met one or both of the following triggering criteria: 1) >60% change in 3-day incidence, or 2) >60% change in 7-day incidence. For this analysis, we categorized a county as a rapid riser if the county met the standardized daily rapid riser criteria on at least three days in the week.

Table 1

Unemployment rate gap changes (β) by Social Vulnerability Index (SVI), overall and by svi theme, among rapid riser counties (N = 585) before and after a rapid rise in COVID-19 incidence --- United States.

Social Vulnerability Index (SVI) Quartile, by SVI Theme	Unemployment Rate Gap Change		Number of counties
	β§	(95% CI)
Overall SVI
Q1 (lowest vulnerability)	Reference		96
Q2	-0.04	(-0.29, 0.20)	137
Q3	-0.12	(-0.39, 0.14)	156
Q4 (highest vulnerability)	0.40**	(0.15, 0.65)	196
SVI socioeconomic status theme
Q1(lowest vulnerability)	Reference		115
Q2	-0.001	(-0.26, 0.26)	132
Q3	0.22	(-0.02, 0.46)	175
Q4 (highest vulnerability)	0.36**	(0.11, 0.61)	163
SVI household composition & disability theme
Q1(lowest vulnerability)	Reference		133
Q2	-0.06	(-0.31, 0.18)	132
Q3	0.03	(-0.24, 0.29)	151
Q4 (highest vulnerability)	0.15	(-0.11, 0.41)	169
SVI minority status & language theme
Q1(lowest vulnerability)	Reference		72
Q2	0.24	(-0.02,0.51)	172
Q3	0.41**	(0.14,0.68)	197
Q4 (highest vulnerability)	0.38*	(0.06,0.70)	144
SVI housing type & transportation theme
Q1(lowest vulnerability)	Reference		68
Q2	-0.16	(-0.46, 0.13)	131
Q3	0.01	(-0.27, 0.29)	185
Q4 (highest vulnerability)	0.06	(-0.23, 0.34)	201

Boldface indicates statistical significance (*p<0.05, **p<0.01).

†Rapid riser counties were defined as those that met all of the following criteria: 1) >100 new cases in recent week, 2) >0% change in the 7-day incidence, 3) >-60% change in the 3-day incidence, and 4) a 7-day incidence / 30-day incidence ratio >0.31. In addition, rapid riser counties met one or both of the following triggering criteria: 1) >60% change in 3-day incidence, or 2) >60% change in 7-day incidence. For this analysis, we categorized a county as a rapid riser if the county met the standardized daily rapid riser criteria on at least three days in the week.

¶ Before rapid rise is defined as the 2 months preceding the index rapid rise month. After rapid rise is defined as the index month of rapid rise plus the following month.

§ The coefficient of regression.

Discussion

In U.S. counties experiencing rapid increases in COVID-19 incidence after the initial unemployment peak in April, unemployment rate gaps widened between the most and the least socially vulnerable counties when comparing rates between pre-index months and post-index months. The average unemployment rate gap between the most and the least socially vulnerable counties before the index month was 2.64%. An unemployment gap of 0.40 percentage point represents about a 15% increase. By the SVI themes, the unemployment rate gap widened significantly for counties with the lowest socioeconomic status and highest proportion of racial and ethnic minority residents and lowest English proficiency. These findings show that the already existing unemployment gap worsened when COVID-19 rapid rise occurred in communities.

However, we did not find significant changes for the household composition & disability theme and housing type & transportation theme, which suggests that these two SVI themes may be less relevant to the widening of the unemployment gap after a county experienced the COVID-19 incidence rapid rise compared to the other two SVI themes.

Previously published research showed that communities with higher social vulnerability are more likely to become rapid risers of COVID-19 incidence [3]. Our findings suggest that aftereffects of rises in incidence in these counties can include adverse economic impacts on the respective communities. The widened unemployment gap after COVID-19 incidence rapid rise between the most and least socially vulnerable counties suggests that it may take longer for socially and economically disadvantaged communities to recover their economy. Our findings corroborate previous studies that people in racial and ethnic minority groups and with lower income were disproportionally affected economically by COVID-19 [12, 18].

This analysis is subject to at least six limitations. First, counties with smaller populations may be less likely to meet the rapid riser criteria; thus, this analysis may not be representative of less densely populated counties. Second, because we limited the analysis to first-time rapid riser counties that occurred in a time period after the U.S. unemployment peak in April 2020, we did not assess the effects in counties that were identified as rapid risers prior to July 2020 when many large urban areas were first identified as rapid riser. Almost two-thirds of US counties are non-metropolitan and a little over fifty percent of the counties examined in our sample are non-metropolitan counties. Thus, these results may not be representative of the entire United States. Third, the unemployment rate may not fully match with daily rapid rises in COVID-19 incidence since unemployment rate was reported on a monthly basis. Fourth, other policies or external shocks may happen around the same time as the COVID-19 incidence rapid rise occurred, so our estimate may be subject to bias. Fifth, the SVI does not cover every aspect of the vulnerability of a community. For instance, it does not reflect the nature of work in a community such as the proportion of essential and/or public facing jobs. Finally, the SVI is an indicator of the socio-economic conditions of a county in 2018 and it is possible that those conditions may have changed since 2018, so our findings may not reflect those potential changes. In addition, the unemployment rate appears in both sides of the regression equation, which may lead to bias of the estimate. We re-estimated our results by excluding the unemployment rate from the SVI, and the results are similar to those from the main analysis.

The findings of this study underscore the importance of socioeconomic inequality in a public health crisis such as during the COVID-19 pandemic. Existing socioeconomic inequality is associated with the risk for a county to become a rapid riser in COVID-19 incidence [3], and also increases the challenge for more socially vulnerable counties to recover from any economic downturns occurring during the pandemic. Loss of income and of benefits due to unemployment could also hinder behaviors that slow or prevent spread of COVID-19. For instance, unemployed individuals would have less access to health care and are less likely to receive needed medical care [19, 20], which would impede response efforts including testing and vaccination [20, 21]. In socially vulnerable communities disproportionately affected by COVID-19 and experiencing high rates of unemployment, such as those with high proportion of racial and ethnic minority groups and non-English speakers residents, addressing the social needs (e.g., access to healthcare [19], food [22], health insurance [23], non-English language resources for economic benefits, health care, and accurate COVID-19 information [24, 25]) to help support public health measures such as testing, contact tracing, and vaccination may be needed during any infectious disease pandemic or public health crisis.

This file contains an additional supplementary figure.

US rapid riser county map. The map was generated using SAS 9.4; basemap: UNITED STATES—COUNTIES: Copyright(C) 1996. SAS Institute Inc. Created and last modified 06/25/2015. The data displayed in the map are available at: Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. CDC/ATSDR Social Vulnerability Index [2018] Database [US]. https://www.atsdr.cdc.gov/placeandhealth/svi/data_documentation_download.html Accessed on [January 10th, 2022]. https://usafacts.org/visualizations/coronavirus-covid-19-spread-map.

(DOCX)

Click here for additional data file.

This file contains an additional supplementary table.

Sensitivity analysis table.

(DOCX)

Click here for additional data file.

1 Dec 2021

Submitted filename: PLOS1 Article.docx

Click here for additional data file.

4 Feb 2022

Revisions and Response to Reviewer Comments on

“Change in Unemployment by Social Vulnerability among United States Counties with Rapid Increases in COVID-19 Incidence — July 1–October 31, 2020”

(Manuscript # PONE-D-21-27081)

Dear Dr. Nelson,

Thank you for the comments and careful review of our manuscript. We appreciate the suggestions to improve it based on yours and the reviewers’ feedback. We believe the comments from you and the reviewers have helped to strengthen our work and we describe how we addressed each below. We have carefully considered all of the comments and made the corresponding revisions. Revisions in the manuscript were highlighted in yellow.

Reviewer #1:

1. I thank the authors for an interesting, important, and timely article. I have included here a few comments and questions that I think will help improve the article.

�  Thank you for your positive comments!

2. Introduction

a. Might be worth updating the introduction sentence based on more recent COVID numbers (could update this closer to publication timeline)

�  Thank you for your suggestion! We have updated with COVID numbers from January 13, 2022(Lines 69-70) and can update again when closer to publication.

“In the United States, the COVID-19 pandemic has resulted in more than 62,000,000 reported cases and more than 840,000 associated deaths as of January 13, 2022”

b. In the third sentence on the way that a community can cope with a disaster, it’s not just unemployment – perhaps rephrase to talk about other work-related factors: underemployment, distribution of essential and/or public facing jobs (which in turn put different groups of people at higher risk for COVID), as well as unemployment.

�  Thank you for your suggestion! We revised the sentence to include other work-related factors (Lines 73-74): Now it reads as below:

“Societal factors such as poverty, lack of access to transportation, crowded households, racial and ethnic inequalities, work-related hardship or risk (e.g., unemployment, underemployment, and distribution of essential and/or public facing jobs), and other social conditions, affect a community’s ability to cope with a disaster like the COVID-19 pandemic”

c. Unemployment is also tied to negative health impacts. Perhaps rephrase the first sentence of the second paragraph to get at the direct health impacts of COVID as well as the indirect (which impact employment, which in turn impact health �  e.g., someone has to leave their work, and their work is how they received health insurance so now they are uninsured and management of chronic health conditions deteriorates, as well as the impact of being unemployed on stress levels which in turn impacts health)

�  Thank you for your comments and suggestions! We agree that there are direct and indirect negative health impacts of COVID-19. Our objective with the first sentence of the second paragraph was to summarize the direct negative health impacts of COVID-19 discussed in the previous paragraph to bridge to the second paragraph that relates unemployment to the impact on the economy. We included the indirect negative health impact of COVID-19 through unemployment in the discussion section in lines 236 to 242.

For clarity, we added the word “direct” to this sentence (Line 82). Now it reads as below:

“In addition to the direct negative health impacts of COVID-19”

d. It would help to say a little more about the SVI in the introduction. Perhaps a few additional references of how it’s been used?

�  Thank you for your suggestion! We revised the last sentence of third paragraph to include a brief description about SVI and added a few additional references in lines 96 to 98(#3, #5 and #15). Now it reads as below:

“Specifically, we described unemployment changes in rapid riser counties by the CDC social vulnerability index (SVI), an index that measures the potential negative effects on communities caused by external stresses on human health and helps local officials identify socially vulnerable communities that may need support before, during, or after disasters (5). The SVI has been used in prior county-level COVID-19 studies (3, 15).”

(3) Dasgupta S, Bowen VB, Leidner A, Fletcher K, Musial T, Rose C, et al. Association Between Social Vulnerability and a County's Risk for Becoming a COVID-19 Hotspot - United States, June 1-July 25, 2020. MMWR Morb Mortal Wkly Rep. 2020;69(42):1535-41.

(5) Centers for Disease Control and Prevention. CDC Social Vulnerability Index 2021 [Available from: https://www.atsdr.cdc.gov/placeandhealth/svi/index.html.

(15) Hughes MM, Wang A, Grossman MK, Pun E, Whiteman A, Deng L, et al. County-Level COVID-19 Vaccination Coverage and Social Vulnerability - United States, December 14, 2020-March 1, 2021. MMWR Morb Mortal Wkly Rep. 2021;70(12):431-6.

3. Methods

a. You use SVI data from 2018 – is this the most recently available? Can you comment (or maybe you do in the discussion) about how this may differ from the situation in 2020? (I see this commented on in the limitations in the discussion, but perhaps acknowledging this time gap in the methods too?

�  Thank you for raising the questions! The SVI is periodically updated; the 2018 data are the most recent. We revised the method section to indicate that this is the most recently available database (Line 118). Now it reads as below:

“We extracted county-level SVI data from the most recent database, CDC/ATSDR SVI database 2018.”

b. You mention that there were 3.141 US counties with complete data – how many counties total in the US? What is the %complete?

�  Thank you for your question. There are 3,142 counties (or county equivalents) in the US. The SVI data accounts for 99.9% of the US counties. We revised the sentence in the Methods section (Line 126). Now it reads as below:

“Both overall SVI and four SVI themes for each of the counties included were categorized by quartiles based on SVI and the SVI themes in the 3,141 US counties with complete data (99.9% of US counties or county equivalents).”

4. Discussion

a. Can you comment on you might hypothesize how things might differ in other periods? For example, during the winter peak, initial peak, or once the vaccine became widely available?

�  Thank you for your comment! We agree that different times during the pandemic (e.g., winter peak, post-vaccine) would likely have different results. We cannot hypothesize how these different situations might affect the results. We think this is an interesting idea and may consider a follow up analysis and manuscript.

b. One thing that I don’t think is reflected in the SVI is that not all jobs are created equal. That is, especially in times of COVID, some jobs put people at increased risk due to the nature of the work. For example, public facing jobs, essential jobs – grocery stores, bus drivers, etc. – these people are deemed working, but face increased risk compared to people that can work from home. We see racial and ethnic inequities based on who works in these jobs (see for example https://www.mass.gov/doc/characterizing-ma-workers-in-select-covid-19-essential-services-food-stores-and-urban-transit/download. Perhaps worth mentioning something about this limitation of the SVI?

�  Thank you for your comment and appreciate your perspective! We agree that SVI does not cover every aspect of the vulnerability of a community. We added this to the limitation section (Lines 227-229). Now it reads:

“Fifth, the SVI does not cover every aspect of the vulnerability of a community. For instance, it does not reflect the nature of work in a community such as the proportion of essential and/or public facing jobs.”

Reviewer #2:

1. This is a thoughtful and interesting analysis, examining factors associated with unemployment for rapid COVID uptake counties in the US. Thank you for examining this issue.

�  Thank you for your positive comments!

2. I was struck by potential differences between rural and urban counties and among different US regions - these potentially important differences are not currently reflected in this analysis. If possible, I would suggest additional analyses to explore these results in data subgroups (urban and rural counties; different regions). Furthermore a map of US counties color-coded to share descriptive results (eg. only high riser counties shaded; outlining from bold to thin to reflect levels of increase for unemployment; color shading for significant results - Q4 SVI SES, Q3/Q4 minority+language) could be very useful in helping local and/or state decision-makers. I also acknowledge that a figure like this could be part of additional manuscripts exploring this issue.

�  Thank you for your comments and suggestions! We agree that there may be potential differences between rural and urban counties. For this analysis, however, we only examined the counties that firstly become the rapid riser during July 1-October 31, 2020 in this manuscript. We selected this timeframe to understand the impact of county-level COVID-19 rapid rise on unemployment rates after the initial phase of the COVID-19 pandemic in April when the unemployment peak was universal because the entire country was shut down. During this timeframe, many large urban counties were not included in our sample because many were rapid risers at the beginning of the pandemic. Thus, the urban category from this timeframe may not be representative of all urban areas. It would be misleading to make a conclusion on the urban/rural differences based on our current sample. We clarified the second limitation regarding representativeness of large urban areas (Lines 219-223):

“Second, because we limited the analysis to first-time rapid riser counties that occurred in a time period after the U.S. unemployment peak in April 2020, we did not assess the effects in counties that were identified as rapid risers prior to July 2020 when many large urban areas were first identified as rapid riser. Almost two-thirds of US counties are non-metropolitan and a little over fifty percent of the counties examined in our sample are non-metropolitan counties. Thus, these results may not be representative of the entire United States.”

To your second suggestion, we have made a US map with rapid riser counties shaded and used different color for counties with different quartile of overall-SVI. We added the figure into our supplementary document: S1 Fig.

3. For the Discussion, I think it’s quite crucial to emphasize the increased need for non-English language resources in terms of unemployment benefits, employment opportunities, general healthcare, and COVID information in particular considering the impact on non-English language speakers reflected here.

�  Thank you for your comments! We agree that the need for non-English language resources is crucial. We added a sentence to discuss non-English speaker residents’ social needs in lines 242 to 246. Now it reads as below:

“In socially vulnerable communities disproportionately affected by COVID-19 and experiencing high rates of unemployment such as those with high proportion of racial and ethnic minority and non-English speaker residents, addressing the social needs (e.g., access to health care, food, health insurance, non-English language resources for economic benefits, health care, and accurate COVID-19 information) to help support public health measures such as testing, contact tracing, and vaccination may be needed during any infectious disease pandemic or public health crisis.”

4. I saw just a few minor typos. Please reread for copy editing.

�  Thank you! We have proofread it and corrected the typos.

5. Overall an excellent study. Thank you!

�  Thank you for your positive comments!

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

10 Mar 2022

Change in Unemployment by Social Vulnerability among United States Counties with Rapid Increases in COVID-19 Incidence — July 1–October 31, 2020

PONE-D-21-27081R1

Dear Dr. Tang,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Candace C. Nelson

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Thank you for addressing the comments and suggestions of the Reviewers. After a close review of this manuscript, I have one further request: Please remove the percentage signs ("%") from the beta values and corresponding confidence intervals listed in Table 1, Beta values can sometimes be interpreted as percentage change (which is true in this case), and it's fine to do so in the text to help the reader interpret the results, but they are not actually percentages and shouldn't be represented as such in the table.

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: Thank you for addressing the comments of the reviewers! I think the manuscript has been improved and is an important contribution to the scientific literature.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: Emily Sparer-Fine

24 Mar 2022

PONE-D-21-27081R1

Change in unemployment by social vulnerability among United States counties with rapid increases in COVID-19 incidence — July 1–October 31, 2020

Dear Dr. Tang:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Candace C. Nelson

Academic Editor

PLOS ONE