Poverty, crowded housing, and other community attributes associated with social vulnerability
increase a community’s risk for adverse health outcomes during and following a public
health event (
1
). CDC uses standard criteria to identify U.S. counties with rapidly increasing coronavirus
disease 2019 (COVID-19) incidence (hotspot counties) to support health departments
in coordinating public health responses (
2
). County-level data on COVID-19 cases during June 1–July 25, 2020 and from the 2018
CDC social vulnerability index (SVI) were analyzed to examine associations between
social vulnerability and hotspot detection and to describe incidence after hotspot
detection. Areas with greater social vulnerabilities, particularly those related to
higher representation of racial and ethnic minority residents (risk ratio [RR] = 5.3;
95% confidence interval [CI] = 4.4–6.4), density of housing units per structure (RR = 3.1;
95% CI = 2.7–3.6), and crowded housing units (i.e., more persons than rooms) (RR = 2.0;
95% CI = 1.8–2.3), were more likely to become hotspots, especially in less urban areas.
Among hotspot counties, those with greater social vulnerability had higher COVID-19
incidence during the 14 days after detection (212–234 cases per 100,000 persons for
highest SVI quartile versus 35–131 cases per 100,000 persons for other quartiles).
Focused public health action at the federal, state, and local levels is needed not
only to prevent communities with greater social vulnerability from becoming hotspots
but also to decrease persistently high incidence among hotspot counties that are socially
vulnerable.
Daily county-level COVID-19 case counts were obtained through USAFacts (https://usafacts.org/),
which compiles data reported by state and local health departments.* Beginning on
March 8, 2020, hotspot counties were identified daily using standard criteria
†
(
2
). County-level social vulnerability data were obtained from the 2018 CDC SVI, which
was developed to identify communities with the most needs during and following public
health events. Scores for overall SVI, along with four vulnerability subcomponents
pertaining to 1) socioeconomic status, 2) household composition and disability, 3)
representation of racial and ethnic minority groups and English proficiency, and 4)
housing type and transportation, were generated using 15 population-based measures.
§
Scores for the overall and subcomponent measures were presented as percentile rankings
by county, with higher scores indicating greater vulnerability. SVI scores were categorized
as quartiles based on their distribution among all U.S. counties. Urbanicity of counties
was based on the National Center for Health Statistics 2013 urban-rural classification
scheme
¶
(
3
).
Counties meeting hotspot criteria at least once during March 8–July 25 were described
by urbanicity and social vulnerability based on the first date of hotspot detection.
All other analyses were limited to hotspots identified during June 1–July 25. Among
all 3,142 U.S. counties, RRs with 95% CIs were calculated using bivariate log-binomial
models to assess differences in the probability of being identified as a hotspot during
June 1–July 25 by SVI quartile, overall and for the four SVI subcomponents; analyses
were also stratified by urbanicity.** Based on these results, the probability of hotspot
identification was further examined by specific measures of social vulnerability related
to the representation of the following groups in each county: racial and ethnic minority
residents, English proficiency, housing type, and transportation; counties were categorized
as at or above or below the national median values.
Among the 747 counties meeting hotspot criteria during June 1–July 25, 689 (92%) were
classified as new hotspots.
††
Among these 689 counties, the median COVID-19 incidence
§§
was calculated over the 14 days after hotspot identification and compared with incidence
during the same period among 689 randomly selected non-hotspot counties matched by
three urbanicity categories. Among new hotspot counties, incidence was also compared
by SVI quartile.
¶¶
All analyses were conducted using SAS (version 9.4; SAS Institute) and R (version
4.0.2; The R Foundation). P-values <0.05 were considered statistically significant.
The percentage of hotspots in nonmetropolitan areas increased from 11% during March–April
to 40% during June–July (Figure 1). The percentage of hotspots in the highest SVI
quartile increased from 22% during March–April to 42% during June–July (Figure 1).
FIGURE 1
Daily number of counties identified as hotspots, by urbanicity (A)* and by quartiles
of overall social vulnerability index score (B), based on first date of hotspot identification
(N = 905 counties)
†
,
§
— United States, March 8–July 25, 2020
* According to the 2013 National Center for Health Statistics Urban-Rural Classification
Scheme for counties, counties can be grouped into one of six categories based on population
size, including large central metropolitan, large fringe metropolitan, medium metropolitan,
small metropolitan, micropolitan, and noncore areas. For this analysis, results were
presented in three categories: large central metropolitan and large fringe metropolitan
(large metropolitan), medium and small metropolitan, and micropolitan and noncore
areas (nonmetropolitan).
† Overall social vulnerability scores were percentile rankings ranging from 0–1, with
higher values indicating greater social vulnerability. Scores were categorized into
quartiles based on distribution among all U.S. counties.
§ Each county only appears once and is represented based on the first date of hotspot
identification during March 8–July 25, 2020.
The figure is a histogram showing the daily number of counties identified as hotspots,
by urbanicity (A) and by quartiles of overall social vulnerability index score (B),
based on first date of hotspot identification (N = 905 counties), in the United States,
during March 8–July 25, 2020.
During June 1–July 25, 747 (24%) U.S. counties (representing 60% of the U.S. population)
were identified as hotspots (Table). Counties with higher social vulnerability, particularly
vulnerabilities related to the representation of racial and ethnic minority residents,
English proficiency, housing type, and transportation, had a higher probability of
being identified as a hotspot. For example, the risk for becoming a hotspot was 37.3
(95% CI = 20.1–69.3) times as high among areas in the highest quartile of vulnerability
related to representation of racial and ethnic minority residents and English proficiency
and 3.4 (95% CI = 2.7–4.2) times as high among areas in the highest quartile of vulnerability
related to housing type and transportation, compared with areas in the lowest quartile
for these vulnerabilities. These vulnerability subcomponents were more strongly associated
with hotspot identification in less urban areas. Counties with median percentage or
higher of racial and ethnic minority residents (RR = 5.3; 95% CI = 4.4–6.4), housing
structures with ≥10 units (RR = 3.1 [2.7–3.6]), and crowded housing units (i.e., more
persons than rooms) (RR = 2.0; 95% CI = 1.8–2.3) were more likely to become hotspots.
TABLE
Associations between social vulnerability measures* and hotspot identification, overall
and by urbanicity
†
(N = 3,142 total counties) —United States, June 1–July 25, 2020
Social vulnerability
All counties
Large metropolitan counties
Medium and small metropolitan counties
Nonmetropolitan counties
Overall
Hotspots
Overall
Hotspots
Overall
Hotspots
Overall
Hotspots
No.
No. (row %)
RR (95% CI)¶
No.
No. (row %)
RR (95% CI)¶
No.
No. (row %)
RR (95% CI)¶
No.
No. (row %)
RR (95% CI)¶
Overall (row %)
3,142
747 (24)
—
436
227 (52)
—
372
190 (51)
—
1,976
195 (10)
—
Overall social vulnerability
Q1 (lowest vulnerability)
786
109 (14)
Reference
171
68 (40)
Reference
152
34 (22)
Reference
463
7 (2)
Reference
Q2
784
176 (22)
1.6 (1.3–2.0)
122
68 (56)
1.4 (1.1–1.8)
205
96 (47)
2.1 (1.5–2.9)
457
12 (3)
1.7 (0.7–4.4)
Q3
785
198 (25)
1.8 (1.5–2.2)
99
59 (60)
1.5 (1.2–1.9)
212
98 (46)
2.1 (1.5–2.9)
474
41 (9)
5.7 (2.6–12.6)
Q4 (highest vulnerability)
786
263 (33)
2.4 (2.0–2.9)
44
32 (73)
1.8 (1.4–2.4)
161
97 (60)
2.7 (2.0–3.7)
581
134 (23)
15.3 (7.2–32.3)
Social vulnerability related to socioeconomic status
Q1 (lowest vulnerability)
785
167 (21)
Reference
180
95 (53)
Reference
176
62 (35)
Reference
429
10 (2)
Reference
Q2
786
197 (25)
1.2 (1.0–1.4)
144
72 (50)
0.9 (0.8–1.2)
218
107 (49)
1.4 (1.1–1.8)
424
18 (4)
1.8 (0.9–3.9)
Q3
784
188 (24)
1.1 (0.9–1.4)
81
47 (58)
1.1 (0.9–1.4)
201
97 (48)
1.4 (1.1–1.8)
502
44 (9)
3.8 (1.9–7.4)
Q4 (highest vulnerability)
786
194 (25)
1.2 (1.0–1.4)
31
13 (42)
0.8 (0.5–1.2)
135
59 (44)
1.2 (0.9–1.6)
620
122 (20)
8.4 (4.5–15.9)
Social vulnerability related to household composition and disability
Q1 (lowest vulnerability)
786
240 (31)
Reference
228
115 (50)
Reference
215
103 (48)
Reference
343
22 (6)
Reference
Q2
786
163 (21)
0.7 (0.6–0.8)
122
70 (57)
1.1 (0.9–1.4)
181
66 (36)
0.8 (0.6–1.0)
483
27 (6)
0.9 (0.5–1.5)
Q3
784
181 (23)
0.8 (0.6–0.9)
58
33 (57)
1.1 (0.9–1.5)
190
98 (52)
1.1 (0.9–1.3)
536
50 (9)
1.5 (0.9–2.4)
Q4 (highest vulnerability)
786
163 (21)
0.7 (0.6–0.8)
28
9 (32)
0.6 (0.4–1.1)
144
58 (40)
0.8 (0.7–1.1)
614
96 (16)
2.4 (1.6–3.8)
Social vulnerability related to racial and ethnic minority residents and English proficiency
Q1 (lowest vulnerability)
788
10 (1)
Reference
55
5 (9)
Reference
111
3 (3)
Reference
622
2 (0)
Reference
Q2
783
86 (11)
8.7 (4.5–16.5)
91
22 (24)
2.7 (1.1–6.6)
179
37 (21)
7.6 (2.4–24.2)
513
27 (5)
16.4 (3.9–68.5)
Q3
785
279 (36)
28.0 (15.0–52.2)
104
63 (61)
6.7 (2.8–15.6)
242
142 (59)
21.7 (7.1–66.6)
439
74 (17)
52.4 (12.9–212.4)
Q4 (highest vulnerability)
786
372 (47)
37.3 (20.1–69.3)
186
137 (74)
8.1 (3.5–18.8)
198
143 (72)
26.7 (8.7–81.9)
402
92 (23)
71.2 (17.6–287.3)
Social vulnerability related to housing type and transportation
Q1 (lowest vulnerability)
786
87 (11)
Reference
159
70 (44)
Reference
139
14 (10)
Reference
488
3 (1)
Reference
Q2
786
149 (19)
1.7 (1.3–2.2)
112
57 (51)
1.2 (0.9–1.5)
158
60 (38)
3.8 (2.2–6.4)
516
32 (6)
10.1 (3.1–32.7)
Q3
785
218 (28)
2.5 (2.0–3.2)
87
52 (60)
1.4 (1.1–1.7)
219
117 (53)
5.3 (3.2–8.9)
479
49 (10)
16.6 (5.2–53.0)
Q4 (highest vulnerability)
785
293 (37)
3.4 (2.7–4.2)
78
48 (62)
1.4 (1.1–1.8)
214
134 (63)
6.2 (3.7–10.3)
493
111 (23)
36.6 (11.7–114.5)
Individual components of social vulnerability related to racial and ethnic minority
residents and English proficiency§
Percentage of racial and ethnic minority residents (median = 16.1%)
Less than median
1,569
118 (8)
Reference
149
37 (25)
Reference
301
54 (18)
Reference
1,119
27 (2)
Reference
At or above median
1,567
629 (40)
5.3 (4.4–6.4)
287
190 (66)
2.7 (2.0–3.6)
429
271 (63)
3.5 (2.7–4.5)
857
168 (20)
8.1 (5.5–12.1)
Percentage who speak English less than well (median = 0.7%)
Less than median
1,458
130 (9)
Reference
129
23 (18)
Reference
273
47 (17)
Reference
1,056
60 (6)
Reference
At or above median
1,684
617 (37)
4.1 (3.4–4.9)
307
204 (66)
3.7 (2.6–5.4)
457
278 (61)
3.5 (2.7–4.6)
920
135 (15)
2.6 (1.9–3.5)
Individual components of social vulnerability related to housing type and transportation§
Percentage of housing structures with ≥10 units (median = 2.9%)
Less than median
1,554
179 (12)
Reference
111
29 (26)
Reference
234
39 (17)
Reference
1,209
111 (9)
Reference
At or above median
1,588
568 (36)
3.1 (2.7–3.6)
325
198 (61)
2.3 (1.7–3.2)
496
286 (58)
3.5 (2.6–4.7)
767
84 (11)
1.2 (0.9–1.6)
Percentage of housing units that are mobile home units (median = 10.9%)
Less than median
1,559
440 (28)
Reference
328
186 (57)
Reference
424
210 (50)
Reference
807
44 (5)
Reference
At or above median
1,583
307 (19)
0.7 (0.6–0.8)
108
41 (38)
0.7 (0.5–0.9)
306
115 (38)
0.8 (0.6–0.9)
1,169
151 (13)
2.4 (1.7–3.3)
Percentage of households with more persons than rooms (median = 1.9%)
Less than median
1,513
235 (16)
Reference
213
88 (41)
Reference
350
112 (32)
Reference
950
35 (4)
Reference
At or above median
1,629
512 (31)
2.0 (1.8–2.3)
223
139 (62)
1.5 (1.2–1.8)
380
213 (56)
1.8 (1.5–2.1)
1,026
160 (16)
4.2 (3.0–6.0)
Percentage of households without vehicle access (median = 5.7%)
Less than median
1,571
333 (21)
Reference
271
138 (51)
Reference
346
130 (38)
Reference
954
65 (7)
Reference
At or above median
1,571
414 (26)
1.2 (1.1–1.4)
165
89 (54)
1.1 (0.9–1.3)
384
195 (51)
1.4 (1.1–1.6)
1,022
130 (13)
1.9 (1.4–2.5)
Percentage of persons living in institutionalized group quarters (median = 2%)
Less than median
1,569
348 (22)
Reference
273
149 (55)
Reference
334
122 (37)
Reference
962
77 (8)
Reference
At or above median
1,573
399 (25)
1.1 (1.0–1.3)
163
78 (48)
0.9 (0.7–1.1)
396
203 (51)
1.4 (1.2–1.7)
1,014
118 (12)
1.5 (1.1–1.9)
Abbreviations: CI = confidence interval; RR = risk ratio.
* Scores for all social vulnerability measures represented percentile rankings by
county, ranging from 0–1, with higher scores indicating greater vulnerability. Scores
were categorized into quartiles based on distribution among all U.S. counties.
† Because of limited sample size, the National Center for Health Statistics urban/rural
categories were collapsed into large metropolitan (which includes large central metropolitan
and large fringe areas), medium and small metropolitan, and nonmetropolitan (micropolitan
and noncore) areas.
§ Cutoffs for individual components of social vulnerability related to housing type
and transportation were based on median values.
¶ P-values for Fisher’s exact tests yielded statistically significant findings (p<0.05)
for all 95% CIs excluding the null value.
At the time of identification, incidence among new hotspot counties was 97 cases per
100,000 persons; in contrast, incidence in non-hotspot counties was 27 cases per 100,000
persons (p<0.001). Fourteen days later, hotspot county incidence was 140 cases per
100,000, and incidence in non-hotspot counties was 40 cases per 100,000 persons (p<0.001)
(Figure 2). During the 14 days after hotspot detection, the absolute change in incidence
in hotspot counties was higher than that in non-hotspot counties (p<0.001). Among
hotspot counties, incidence was higher for counties with higher social vulnerability
and particularly high in the highest quartile of social vulnerability on the day identified
as a hotspot (212 cases versus 35–56 per 100,000 for other quartiles; p<0.001) and
14 days after being identified as a hotspot (234 cases versus 82–131 per 100,000;
p<0.001) (Figure 2).
FIGURE 2
COVID-19 incidence* during the 14 days after identification as a hotspot, compared
with counties not identified as hotspots
†
(A) (N = 1,378 counties), and COVID-19 incidence, by quartile of social vulnerability
index among hotspot counties
§
(B) (N = 689 counties) — United States, June 1–July 25, 2020
* Cases per 100,000 persons; calculated based on 7-day moving window (total number
of cases over the last 7 days per 100,000 population) during the 14 days after hotspot
identification to smooth expected variation in daily case counts.
† To compare incidence in hotspot and non-hotspot counties, a random sample of non-hotspot
counties (1:1 ratio) was matched to hotspot counties by urbanicity and assigned the
same date of reference.
§ Overall social vulnerability scores were percentile rankings ranging from 0–1, with
higher values indicating more social vulnerability. Scores were categorized into quartiles
based on distribution among all U.S. counties.
The figure is a line chart showing COVID-19 incidence during the 14 days after identification
as a hotspot, compared with counties not identified as hotspots (A) (N = 1,378 counties),
and COVID-19 incidence, by quartile of social vulnerability index among hotspot counties
(B) (N = 689 counties), in the United States, during June 1–July 25, 2020.
Discussion
In this analysis, counties with more social vulnerabilities, particularly those with
a higher percentage of racial and ethnic minority residents, high-density housing
structures, and crowded housing units, were at higher risk for becoming a COVID-19
hotspot, especially in less urban areas. Among hotspot counties, areas with more social
vulnerability had significantly higher incidence than did other counties. These findings
have implications for efforts to prevent counties with social vulnerability from becoming
COVID-19 hotspots, including prioritizing vaccination access,*** and for implementing
public health action in counties that become hotspots.
Consistent with previous findings (
4
–
6
), these results show that COVID-19 disproportionately affects racial and ethnic minority
groups, who might also experience more socioeconomic challenges.
†††
Communities with higher social vulnerability have a higher percentage of racial and
ethnic minority residents, who might be more likely to have essential jobs requiring
in-person work and live in potentially crowded conditions (
7
,
8
). These circumstances could put racial and ethnic minority residents at risk for
COVID-19 through close contact with others. Incorporating the needs of populations
that are socially vulnerable into community mitigation plans is essential for limiting
COVID-19 transmission. Specifically, implementing recommended prevention efforts at
facilities requiring in-person work (e.g., meat processing facilities and grocery
stores), including temperature or symptom screening, mask mandates, social distancing
practices, and paid sick leave policies encouraging ill workers to remain home, might
reduce transmission risk among populations that are vulnerable at workplaces (
9
). In addition, plain-language and culturally sensitive and relevant public health
messaging should be tailored based on community needs, communicated by local leaders,
and translated into other languages in areas with many nonnative English speakers
(
9
).
Additional support from federal, state, and local partners is needed for communities
with social vulnerabilities and at risk for COVID-19, particularly for persons living
in crowded or high-density housing conditions. Initiatives to provide temporary housing,
food, and medication for COVID-19 patients living in crowded housing units could be
considered to permit separation from household members during infectious periods.
§§§
As expected, hotspot counties had significantly higher COVID-19 incidence at the time
of detection than did non-hotspot counties. Hotspot counties also had a higher absolute
change in incidence during the 14 days after identification, demonstrating real and
meaningful increases in incidence in these counties and underscoring the importance
of implementing robust public health responses in these counties. Among hotspot counties,
areas with the highest social vulnerability had significantly higher incidence, indicating
an urgent need to prioritize public health action in these counties to curb COVID-19
transmission. Hotspot data informed deployment of multiagency response teams from
CDC, the Federal Emergency Management Agency, the Office of the Assistant Secretary
for Preparedness and Response, and the Office of the Associate Secretary for Health,
to 33 locations in 21 states during June 29–July 24. These COVID-19 Response Assistance
Field Teams (CRAFTs) learned from state and local leaders about local response efforts
and assessed how federal assistance could augment local efforts to reduce the impact
of the COVID-19 pandemic. Areas with high social vulnerability need continued support
in developing and implementing mitigation strategies and strengthening contact tracing
programs to quickly identify and isolate COVID-19 cases and limit transmission.
The findings in this report are subject to at least three limitations. First, associations
between social vulnerability and risk for COVID-19 infection using person-level data
could not be assessed; it was also not possible to assess confounding by factors such
as employment. Second, changes in testing availability and laboratory reporting might
have affected COVID-19 incidence estimates and hotspot detection. Finally, the hotspot
criteria might have limited the ability to detect hotspots in counties with smaller
populations.
Building on previous work (
10
), these findings underscore the need for federal, state, and local partners to work
with community leaders to support areas with high social vulnerability and prevent
them from becoming COVID-19 hotspots. These findings also demonstrate the need to
reevaluate factors related to high incidence for earlier detection of hotspot counties,
particularly in areas with high social vulnerabilities; among hotspot counties, these
results demonstrate the need to prioritize immediate public health action in counties
with the highest social vulnerability, especially in less urban areas.
Summary
What is already known about this topic?
Communities with higher social vulnerabilities, including poverty and crowded housing
units, have more adverse outcomes during and following a public health event.
What is added by this report?
Counties with greater social vulnerability were more likely to become areas with rapidly
increasing COVID-19 incidence (hotspot counties), especially counties with higher
percentages of racial and ethnic minority residents and people living in crowded housing
conditions, and in less urban areas. Hotspot counties with higher social vulnerability
had high and increasing incidence after identification.
What are the implications for public health practice?
Focused public health action is urgently needed to prevent communities that are socially
vulnerable from becoming COVID-19 hotspots and address persistently high COVID-19
incidence among hotspot areas that are socially vulnerable.