HomeMy WebLinkAboutItem 52 - 08-14-2020 - Hearing Exhibit - KC 08 - Declaration of Nancy SuggITEM NO. 52
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Morbidity and Mortality Weekly Report
Weekly / Vol. 69 / No. 29 July 24, 2020
INSIDE
951 Identification of Substance-Exposed Newborns and
Neonatal Abstinence Syndrome Using ICD-10-CM —
15 Hospitals, Massachusetts, 2017
956 Evaluation of Online Risk Assessment To Identify
Rabies Exposures Among Health Care Workers —
Utah, 2019
960 Population Point Prevalence of SARS-CoV-2
Infection Based on a Statewide Random Sample —
Indiana, April 25–29, 2020
965 Estimated Community Seroprevalence of
SARS-CoV-2 Antibodies — Two Georgia Counties,
April 28–May 3, 2020
971 Notes from the Field: Effects of the COVID-19
Response on Tu berculosis Prevention and Control
Efforts — United States, March–April 2020
973 Notes from the Field: Characteristics of
Tetrahydrocannabinol–Containing E-cigarette, or
Vaping, Products Used by Adults — Illinois,
September–October 2019
976 QuickStats
Continuing Education examination available at
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U.S. Department of Health and Human Services
Centers for Disease Control and Prevention
Estimated County-Level Prevalence of Selected Underlying Medical Conditions
Associated with Increased Risk for Severe COVID-19 Illness —
United States, 2018
Hilda Razzaghi, PhD1; Yan Wang, PhD2; Hua Lu, MS2; Katherine E. Marshall, MPH1; Nicole F. Dowling, PhD1; Gabriela Paz-Bailey, MD, PhD1;
Evelyn R. Twentyman, MD1; Georgina Peacock, MD1; Kurt J. Greenlund, PhD2
Risk for severe coronavirus disease 2019 (COVID-19)–associ-
ated illness ( illness requiring hospitalization, intensive care unit
ICU] admission, mechanical ventilation, or resulting in death)
increases with increasing age as well as presence of underlying
medical conditions that have shown strong and consistent evi-
dence, including chronic obstructive pulmonary disease, cardiovas-
cular disease, diabetes, chronic kidney disease, and obesity (1–4).
Identifying and describing the prevalence of these conditions
at the local level can help guide decision-making and efforts to
prevent or control severe COVID-19–associated illness. Below
state-level estimates, there is a lack of standardized publicly avail-
able data on underlying medical conditions that increase the risk
for severe COVID-19–associated illness. A small area estimation
approach was used to estimate county-level prevalence of selected
conditions associated with severe COVID-19 disease among
U.S. adults aged 18 years (5,6) using self-reported data from
the 2018 Behavioral Risk Factor Surveillance System (BRFSS)
and U.S. Census population data. The median prevalence of any
underlying medical condition in residents among 3,142 counties
in all 50 states and the District of Columbia (DC) was 47.2%
range 22.0%–66.2%); counties with the highest prevalence
were concentrated in the Southeast and Appalachian region.
Whereas the estimated number of persons with any underlying
medical condition was higher in population-dense metropolitan
areas, overall prevalence was higher in rural nonmetropolitan areas.
These data can provide important local-level information about
the estimated number and proportion of persons with certain
underlying medical conditions to help guide decisions regarding
additional resource investment, and mitigation and prevention
measures to slow the spread of COVID-19.
BRFSS is an annual, random-digit–dialed landline and mobile
telephone survey of noninstitutionalized U.S. adults aged
18 years in all 50 states, DC, and U.S. territories. BRFSS col-
lects self-reported information on selected health behaviors and
conditions. Overall, 437,500 persons participated in the 2018
BRFSS survey, with a median weighted response rate of 49.9%.*
https://www.cdc.gov/brfss/index.html.
ATTACHMENT A
Exhibit No. KC008HEX-000650
Morbidity and Mortality Weekly Report
946 MMWR / July 24, 2020 / Vol. 69 / No. 29 US Department of Health and Human Services/Centers for Disease Control and Prevention
The MMWR series of publications is published by the Center for Surveillance, Epidemiology, and Laboratory Services, Centers for Disease Control and Prevention (CDC),
U.S. Department of Health and Human Services, Atlanta, GA 30329-4027.
Suggested citation: [Author names; first three, then et al., if more than six.] [Report title]. MMWR Morb Mortal Wkly Rep 2020;69:[inclusive page numbers].
Centers for Disease Control and Prevention
Robert R. Redfield, MD, Director
Anne Schuchat, MD, Principal Deputy Director
Chesley L. Richards, MD, MPH, Deputy Director for Public Health Science and Surveillance
Rebecca Bunnell, PhD, MEd, Director, Office of Science
Arlene Greenspan, PhD, Acting Director, Office of Science Quality, Office of Science
Michael F. Iademarco, MD, MPH, Director, Center for Surveillance, Epidemiology, and Laboratory Services
MMWR Editorial and Production Staff (Weekly)
Charlotte K. Kent, PhD, MPH, Editor in Chief
Jacqueline Gindler, MD, Editor
Paul Z. Siegel, MD, MPH, Guest Associate Editor
Mary Dott, MD, MPH, Online Editor
Terisa F. Rutledge, Managing Editor
Douglas W. Weatherwax, Lead Technical Writer-Editor
Glenn Damon, Soumya Dunworth, PhD,
Teresa M. Hood, MS, Donald G. Meadows, MA
Technical Writer-Editors
Martha F. Boyd, Lead Visual Information Specialist
Maureen A. Leahy, Julia C. Martinroe,
Stephen R. Spriggs, Tong Yang,
Visual Information Specialists
Quang M. Doan, MBA, Phyllis H. King,
Terraye M. Starr, Moua Yang,
Information Technology Specialists
MMWR Editorial Board
Timothy F. Jones, MD, Chairman
Michelle E. Bonds, MBA
Matthew L. Boulton, MD, MPH
Carolyn Brooks, ScD, MA
Jay C. Butler, MD
Virginia A. Caine, MD
Katherine Lyon Daniel, PhD
Jonathan E. Fielding, MD, MPH, MBA
David W. Fleming, MD
William E. Halperin, MD, DrPH, MPH
Jewel Mullen, MD, MPH, MPA
Jeff Niederdeppe, PhD
Patricia Quinlisk, MD, MPH
Patrick L. Remington, MD, MPH
Carlos Roig, MS, MA
William Schaffner, MD
Morgan Bobb Swanson, BS
The underlying medical conditions included in these
prevalence estimates were selected using the subset of the list of
conditions with the strongest and most consistent evidence† of
association with higher risk for severe COVID-19–associated
illness on CDC’s website as of June 25, 2020 (2) and for
which questions on the BRFSS aligned. These included
chronic obstructive pulmonary disease (COPD), heart
conditions, diabetes mellitus, chronic kidney disease
CKD), and obesity (defined as body mass index [BMI] of
30 kg per m2). Conditions from the list of those with mixed
and limited evidence§ of association with increased risk for
severe COVID-19 illness were not included (2). An analysis
of U.S. COVID-19 patient surveillance data found that
hospitalizations were six times higher, ICU admissions five
times higher, and deaths 12 times higher among patients with
underlying medical conditions, compared with those without
4); however, that analysis included a narrower definition of
obesity (BMI 40 kg per m2), and some, butnot all conditions
in both the strongest and most consistent evidence and mixed
and limited evidence lists.
Conditions with consistent evidence of increased risk for severe COVID-19–
associated illness from multiple small studies or a strong association from a
large study.
Conditions for which multiple studies have reached different conclusions about
risk associated with that condition. Those with limited evidence are those for
which consistent evidence has been reported from a small number of studies.
https://www.cdc.gov/coronavirus/2019-ncov/need-extra-precautions/evidence-
table.html.
BRFSS respondents were classified as having an underlying
medical condition if they answered “yes” to any of the follow-
ing questions: “Have you ever been told by a doctor, nurse, or
other health professional that you have COPD, emphysema,
or chronic bronchitis; heart disease (angina or coronary heart
disease, heart attack, or myocardial infarction); diabetes;
or chronic kidney disease?” Respondent-reported height
and weight were used to calculate BMI; respondents with
BMI 30 kg per m2 were considered to have obesity. A cre-
ated variable captured persons having any of these conditions.
Nationwide estimates of underlying medical conditions were
weighted to adjust for survey design. For county-level preva-
lence, estimates of each and of any condition were generated
using a multilevel regression and poststratification approach (5)
for 3,142 counties in all 50 states and DC. This approach has
been validated in comparison with direct BRFSS survey esti-
mates and local surveys for multiple chronic disease measures
at state and county levels (5,6). Briefly, a multilevel regression
model was constructed for each outcome using individual-level
age,¶ gender, race/ethnicity,** and educational-level†† data
Age was categorized into 13 age groups at 5-year intervals for ages 18 years.
Race/ethnicity was categorized as non-Hispanic white, non-Hispanic African
American, non-Hispanic American Indian or Alaska Native, non-Hispanic
Asian, non-Hispanic Native Hawaiian/other Pacific Islander, other single
non-Hispanic race, two or more non-Hispanic race groups, and Hispanic.
Education was categorized as less than high school, high school graduate,
some college or technical school, or college graduate.
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from the 2018 BRFSS, and data on county-level percentage
of the adult population living at <150% of the poverty level
from the 2014–2018 American Community Survey (ACS), a
survey sent to about 3.5 million addresses each month that
asks about topics not included on the decennial census, includ-
ing education and employment. The model parameters were
applied to 2018 Census county-level population estimates by
age, gender, and race/ethnicity to calculate the predicted prob-
ability of each outcome. Because the U.S. Census Bureau does
not provide county-level population data for education level
by age, sex, and race/ethnicity, a bootstrapping approach§§ was
used to impute it. The estimated prevalence was obtained by
multiplying the probability by the total population by county.
Model-based estimates for any condition were validated by
comparing them with the weighted direct survey estimates
from counties with sample size 500 (213) in BRFSS; the
Pearson correlation coefficient was 0.89. The county-level
estimates of having any underlying medical condition were
categorized into six county urban/rural classifications using
CDC’s National Center for Health Statistics definitions
large central metro/city, large fringe metro/suburb, medium
metro, small metro, micropolitan, noncore/rural) (7). The
overall weighted direct survey estimates were conducted using
SUDAAN (version 11; RTI International), and other analyses
were conducted using SAS (version 9.4; SAS Institute).
The nationwide prevalence of any of the five underlying
medical conditions among adults aged 18 years was 40.7%
95% confidence interval [CI]40.4%–41.0%) (Table 1).
The overall weighted prevalences of these conditions were
30.9% (obesity), 11.4% (diabetes), 6.9% (COPD), 6.8%
heart disease), and 3.1% (CKD).
Among 3,142 counties, the median estimated
modeled) county prevalence of any underlying medical
https://ww2.amstat.org/meetings/jsm/2016/onlineprogram/AbstractDetails.
cfm?abstractid=319359.
TABLE 1. Nationwide and model-based county-level (n = 3,142) estimates of prevalence and number of adults aged 18 years with selected
underlying medical conditions that might increase risk for severe COVID-19–associated illness — United States, 2018
Selected underlying
medical condition*
Nationwide prevalence†
95% CI)
Median county prevalence§
range)
Median county no. of adults†
range)
Any 40.7 (40.4, 41.0) 47.2 (22.0–66.2) 9,743 (41–2,877, 316)
Obesity (BMI 30 kg/m2) 30.9 (30.6, 31.2) 35.4 (15.2– 49.9) 7,174 (25–2,097,906)
Diabetes mellitus 11.4 (11.2, 11.6) 12.8 (6.1–25.6) 2,742 (11–952,335)
COPD 6.9 (6.7, 7.0) 8.9 (3.5–19.9) 1,962 (7–434, 075)
Heart disease 6.8 (6.7, 7.0) 8.6 (3.5–15.1) 1, 811 (7–434,790)
Chronic kidney disease 3.1 (3.0, 3.3) 3.4 (1.8– 6.2) 717 (3–237, 766)
Abbreviations: BMI = body mass index; CI = confidence interval; COPD = chronic obstructive pulmonary disease; COVID-19 = coronavirus disease 2019.
Diabetes mellitus includes both type 1 and type 2 diabetes. COPD includes emphysema and chronic bronchitis. Heart disease includes angina or coronary heart
disease, and heart attack or myocardial infarction.
Weighted direct estimates from the Behavioral Risk Factor Surveillance System, 2018.
Prevalence and number of adults estimated for 3,142 counties using a multilevel regression and poststratification approach applied to 2018 Behavioral Risk Factor
Surveillance System data.
condition was 47.2% (range 22.0%–66.2%); obesity,
35.4% (range 15.2%–49.9%); diabetes, 12.8%
range 6.1%–25.6%); COPD, 8.9% (range 3.5%–19.9%);
heart disease, 8.6% (range 3.5%–15.1%); and CKD, 3.4%
range 1.8%–6.2%) (Table 1).
Counties with the highest prevalences of any condition were
concentrated in Southeastern states, particularly in Alabama,
Arkansas, Kentucky, Louisiana, Mississippi, Tennessee,
and West Virginia, as well as some counties in Oklahoma,
South Dakota, Texas, and northern Michigan, among others
Figure) (Supplementary Table, https://stacks.cdc.gov/view/
cdc/90519). The estimated number of adults with any condi-
tion generally followed the population distribution, with higher
estimated numbers of persons with any underlying medical
conditions in more highly populated areas.
The estimated median prevalence of any condition generally
increased with increasing rurality, ranging from 39.4% in large
central metro counties to 48.8% in noncore counties (Table 2);
the estimated median number of persons with any underlying
condition ranged from 4,300 in noncore counties to 301,744
in large central metro counties.
Discussion
Three recent studies have reported that underlying medi-
cal conditions are highly prevalent among U.S. COVID-19
patients requiring hospitalization and ICU admission (3,4,8).
In this report, the median county prevalence of any of five
underlying medical conditions that increase the risk for severe
COVID-19–associated illness was 47.2%, and prevalences were
higher in counties in the southeastern United States and in
more rural counties. These county level estimates can be used
together with data on hospitalizations, ICU admissions, and
ventilator use among COVID-19 patients with underlying
conditions when planning for mitigation efforts and addi-
tional resource investment, including hospital beds, staffing,
ventilators, and other medical supplies that might be needed
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FIGURE. Model-based estimates of U.S. prevalence (A) and number (B) of adults aged 18 years with any selected underlying medical condition,*
by county — United States, 2018
52.3%–66.2%
48.8%–52.2%
45.8%–48.7%
41.8%–45.7%
22.0%–41.7%
32,707–2,877,316
13,529–32,706
7,033–13,528
3,358–7,032
41–3,357
A
B
Selected underlying conditions include chronic obstructive pulmonary disease, emphysema, or chronic bronchitis; heart disease (angina or coronary heart disease,
heart attack, or myocardial infarction); diabetes; chronic kidney disease; or obesity (body mass index 30 kg/m2).
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TABLE 2. Model-based estimates of prevalence and number of
persons aged 18 years with any select underlying medical
condition, by urban/rural county classification — United States, 2018
County
classification*
No. of
counties
Median county
prevalence
range)
Median county
no. of
persons (range)
Metropolitan
Large central metro†68 39.4
23.9–48.1)
301,744
43,770–2,877,316)
Large fringe metro§368 43.9
26.4–56.9)
34,221
1,611– 725, 284)
Medium metro¶372 45.5
22.0–61.7)
33,687
659–332,209)
Small metro** 358 45.8
27.8–62.2)
26,683
41–87,153)
Nonmetropolitan
Micropolitan††641 47.8
24.3–64.6)
13,979
176–59,820)
Noncore§§1,335 48.8
26.8–66.2)
4,300
47–29,469)
Based on 2013 Urban-Rural Classification Scheme for Counties from the
National Center for Health Statistics, CDC.
Large central metro counties in metropolitan statistical areas (MSAs) of
1 million population that 1) contain the entire population of the largest
principal city of the MSA, or 2) are completely contained within the largest
principal city of the MSA, or 3) contain 250,000 residents of any principal
city in the MSA.
Large fringe metro counties in MSA of 1 million population that do not
qualify as large central.
Medium metro counties in MSA of 250,000– 999,999 population.
Small metro counties are counties in MSAs of <250,000 population.
Micropolitan counties in MSAs.
Noncore counties not in MSAs.
to treat persons with underlying medical conditions, should
they become ill with COVID-19.
The percentage of the population (prevalence) and the esti-
mated numbers of adults with underlying medical conditions
provide information for planning and have implications for
health care resource utilization. Areas with comparatively lower
prevalences but large populations, such as metropolitan areas,
might still have large numbers of persons with underlying med-
ical conditions at increased risk for severe COVID-19 illness.
Conversely, areas with smaller populations but a comparatively
higher prevalence of persons with underlying medical condi-
tions might also have substantial need for additional resources
to treat severe COVID-19 illness. Health care in rural counties
is often underresourced,¶¶ and rural communities might have
limited access to adequate care, which could further increase
risk for poor COVID-19–associated outcomes. Prevalence
estimates help highlight counties with a higher relative need
for resources, whereas estimates of numbers of persons with
underlying medical conditions help identify overall need by
county; both can help decision-makers predict resource needs
and develop resource allocation plans.
https://www.aha.org/system/files/2019-02/rural-report-2019.pdf.
Summary
What is already known about this topic?
Older adults and those with chronic obstructive pulmonary
disease, heart disease, diabetes, chronic kidney disease, and
obesity are at higher risk for severe COVID-19–associated illness.
What is added by this report?
The median model-based estimate of the prevalence of any of
five underlying medical conditions associated with increased
risk for severe COVID-19–associated illness among U.S. adults
was 47.2% among 3,142 U.S. counties. The estimated number of
persons with these conditions followed population distribu-
tions, but prevalence was higher in more rural counties.
What are the implications for public health practice?
The findings can help local decision-makers identify areas at
higher risk for severe COVID-19 illness in their jurisdictions and
guide resource allocation and implementation of community
mitigation strategies.
The findings in this report are subject to at least five limita-
tions. First, estimates were based on BRFSS data and subject
to survey biases such as nonresponse, social desirability, and
recall and knowledge of having a particular condition. Second,
BRFSS data do not include all underlying medical conditions
that might increase risk for severe COVID-19 illness, such as
sickle cell disease, or information on organ transplant or disease
severity. Third, some of the underlying medical conditions
included in BRFSS might not exactly capture those conditions
with the strongest and most consistent evidence such as specific
heart conditions (e.g., cardiomyopathies and heart failure) or
specific type of diabetes. Further, because COVID-19 is a novel
disease and information regarding risk factors for severe illness
is evolving, additional underlying medical conditions might
be added in the future (as an example, cancer was added to
the list after these analyses were conducted). Fourth, BRFSS
data are collected for noninstitutionalized civilian persons and
exclude populations that might be particularly vulnerable to
severe COVID-19 illness, including those living in long-term
care facilities and incarcerated populations, and might therefore
not be representative for those groups. Finally, these estimates
might be imprecise because of the multilevel regression model-
ing process and county-level population estimation.
These findings can be used by state and local decision-
makers to help identify areas at higher risk for severe
COVID-19–associated illness because of underlying medical
conditions and guide resource allocation and implementa-
tion of prevention and mitigation strategies. Future analyses
could include weighting the contribution of each under-
lying medical condition according to the risk for severe
COVID-19–associated outcomes, as well as identifying and
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incorporating other aspects of vulnerability to both infec-
tion and severe outcomes to better estimate the number of
persons at increased risk for COVID-19. These findings
highlight the prevalence of underlying medical conditions at
the local (county) level that are important causes of morbid-
ity and mortality on their own and increase risk for severe
COVID-19–associated illness. These findings also emphasize
the importance of prevention efforts to reduce the prevalence
of these underlying medical conditions and their risk factors
such as smoking, unhealthy diet, and lack of physical activity.
Corresponding author: Hilda Razzaghi, Hrazzaghi@cdc.gov.
1CDC COVID-19 Response Team; 2Division of Population Health, National
Center for Chronic Disease Prevention and Health Promotion, CDC.
All authors have completed and submitted the International
Committee of Medical Journal Editors form for disclosure of potential
conflicts of interest. No potential conflicts of interest were disclosed.
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