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Updates to COVID -19 epidemiology
Advisory Committee on Immunization Practices
September 19, 2025
1U.S. Centers for Disease Control and Prevention
COVID -NET monitors COVID -19 hospitalizations across
parts of the U.S.
•RESP -NET includes COVID -NET, RSV -NET, FluSurv -NET
•Collaboration between state and local health
departments and CDC
•Includes data from >300 hospitals in 185 counties
across 13 states, covering about 10% of the U.S.
population
COVID -NET: https://www.cdc.gov/covid/php/covid -net/index.html . 2
Types of COVID -NET data
1.Population -based rates of COVID -19–
associated hospitalizations
-Counts every laboratory -confirmed COVID -
19–associated hospitalization among
people living in COVID -NET counties
-Includes all hospitalizations that meet the
COVID -NET case definition:
•Laboratory -confirmed SARS -CoV-2-
positive test result
•Resident of COVID -NET catchment area
-Collects some data (age, sex, race/ethnicity,
site, test/admission dates) for all cases
32.Clinical data (including outcomes,
underlying medical conditions,
treatment, discharge diagnoses)
-Obtained via detailed medical chart
reviews from anonymous, random sample
of hospitalizations (since reviewing every
case is not possible)
•Monthly random sample from each of six
age groups at each site designed to
represent the broader population
-Clinical analyses limited to hospitalizations
due to COVID -19
Purpose of COVID -NET data
1.Population -based rates of
COVID -19–associated hospitalizations
42.Clinical data (including outcomes,
underlying medical conditions,
treatment, discharge diagnoses)
•Monitor laboratory -confirmed COVID -19-
associated hospitalizations among children
and adults
•Provide decision -makers and public with
broad and timely (weekly) understanding of
general trends
‒Rates published weekly on public
dashboard since 2020
•Estimate and compare disease burden over
time
•Respond to rising rates•Categorize hospitalizations that are due to
COVID -19
•Better understand hospitalization trends
and who is most at risk
•Track severity of illness
•Examine how many people hospitalized due
to COVID -19 have underlying medical
conditions
•Provide insight into treatments used
Defining Hospitalizations in COVID -NET
5
1. Definition of COVID -NET hospitalizations for
population -based rates
How does COVID -NET define a COVID -19–associated hospitalization?
•A hospitalization (case) is counted if:
-The person lives in a defined COVID -NET surveillance catchment area AND
-Tests positive for SARS -CoV-2 (using a laboratory -based molecular, antigen
or serology test) within 14 days before or during hospitalization
6
1. Definition of COVID -NET hospitalizations for
population -based rates (cont’d)
Why use this definition?
•Designed to monitor overall trends in hospitalizations
-Simple approach works across hundreds of hospitals
-Available in near real -time
-Same definition used to monitor hospitalizations for
other pathogens (RSV, influenza) in the U.S. and
worldwide
•Developed by infectious disease experts
•Balances accuracy, speed, and broad coverage
7This approach is used
internationally* to conduct
COVID -19 hospitalization
surveillance, including in
Australia, Canada,
Denmark, France,
Germany, India, Ireland,
Italy, Netherlands,
New Zealand, South
Africa, Spain, Sweden, the
United Kingdom, and the
European Union.
*Australia: PAEDS, Denmark; SSI national registers, France: SI -VIC, Germany: SurvNet, India: NCRC, Ireland: HPSC, Italy: Sorvegl ianza Integrata COVID -19,Netherlands: NICE/RIVM, South
Africa: DATCOV, Spain: RENAVE, Sweden: FoHM national reporting, the United Kingdom: NHS COVID -19 Hospital Activity data, and the European Union: ERVISS.
2. Definition of hospitalizations due to COVID -19 used
for clinical analyses
How does COVID -NET define hospitalizations “due to COVID -19”?
•“With” vs. “for” (due to) debate related to COVID -19 hospitalizations
•Early in the pandemic, hospitals screened every patient when they arrived
at the hospital
•This captured hospitalizations among patients who tested positive for SARS -
CoV-2 admitted for other reasons (e.g., surgery, labor and delivery)
•To address this, COVID -NET developed an algorithm to identify cases for
which COVID -19 was the likely primary reason for admission, hereafter
referred to as “hospitalizations due to COVID -19”
-Algorithm uses chief complaint and history of present illness
-Data for all current and previous surveillance periods back to March 2020 are
posted monthly on public dashboard
COVID -NET Interactive Data Dashboard: https://www.cdc.gov/covid/php/covid -net/index.html 8
Why classifying hospitalizations as “with” or “due to”
COVID -19 is not simple
•It can be difficult to identify a single cause for hospitalization
•A positive SARS -CoV-2 test can influence the decision to admit someone
with medical conditions/comorbidities
•Additionally, presence of medical conditions/comorbidities can influence
the decision to admit someone who tests positive
•SARS -CoV-2 testing, treatment, discharge diagnosis codes, and other
clinical data elements can all misclassify hospitalizations with respect to
whether or not they are due to COVID -19
-ICD-10-CM codes in the U.S. are designed for administrative and billing purposes, not
surveillance, and may overcount or undercount
‒COVID -19 code may be used just to indicate positive SARS -CoV-2 test*
9ICD-10-CM: International Classification of Disease, Tenth Revision, Clinical Modification
*ICD -10-CM Official Guidelines for Coding and Reporting FY 2025 -- UPDATED October 1, 2024 (October 1, 2024 - September 30, 2025 ): https://stacks.cdc.gov/view/cdc/158747 . Inpatient
guidance: If the diagnosis documented at the time of discharge is qualified as “probable,” “suspected,” “likely,” “questionable,” “poss ible,” “still to be ruled out,” “compatible with,”
“consistent with,” or other similar terms indicating uncertainty, code the condition as if it existed or was established.
How COVID -NET defines hospitalizations due to COVID -
19 using likely primary reason for admission
*Complaints with any attribution to specific/known etiology (i.e. appendicitis, imaging -
identified, substance abuse/overdose/withdrawal) or related to: trauma; biliary or
cirrhosis; cancer/mass/leukemia/tumor; cellulitis/abscess/localized infection; foreign body,
genitourinary, back/extremity/joint pain, surgical complication, medication reaction,
preeclampsia or gestational hypertension, ingestion/poisoning, medical device, scheduled
treatment/chemotherapy 10Is chief complaint/history of present
illness (HPI) related to:
•Obstetrics/labor and delivery
•Inpatient surgery or procedure (e.g.,
joint or heart valve replacement)
•Psychiatric admission needing acute
medical care
•Trauma (e.g., car crash, fracture), or
•Hospitalized at birth (newborn)?All laboratory -confirmed COVID -19–
associated hospitalizations Is chief complaint/ HPI reason for admission noted as:
•Fever or respiratory illness
•COVID -19–like illness, or
•Suspicion for COVID -19?
NO
Reason for admission reviewed by 2 physicians;
3rd physician resolves disagreements
YES
EXCLUDENO
Indicates SARS -CoV -2 was
incidental finding or
hospitalization unlikely
related to COVID -19*Indicates
hospitalization
likely related
to COVID -19YES
Hospitalization due to
COVID -19
87% of all recent hospitalizations among SARS -CoV-2-positive patients were
due to COVID -19 based on reason for admission
Data are posted publicly: https://www.cdc.gov/covid/php/covid -net/index.html . Likely reason for admission due to COVID -19 is defined as SARS -CoV-2-positive test ≤14 days before/during
hospitalization AND chief complaint or history of present illness in medical record indicates fever, respiratory illness, COV ID-19-like illness, or suspicion for COVID -19-like illness. 1189
69839187
0102030405060708090100
0–17 years 18–49 years 50–64 years ≥65 years OverallWeighted percent of COVID -NET hospitalizations
Age groupPercent of COVID -19-associated hospitalizations due to COVID -19 based on reason
for admission, by age group and surveillance season —
COVID -NET, October 2022 –May 2025
2022 –2023 2023 –2024 2024 –2025•Percent of COVID -19–associated
hospitalizations due to COVID -19
have increased over time
•No longer widespread screening
of asymptomatic patients
•Percent of COVID -NET
hospitalizations due to COVID -19
increases with age among adults
•Adults ages ≥65 years account for
70% of COVID -19–associated
hospitalizations, of which 91% are
considered due to COVID -19 based
on reason for admission.70% of COVID -19–
associated hospitalizations
How COVID -NET goes beyond billing codes
•Not all partner hospitals are able to provide final ICD -10-CM codes
-Coding may be delayed or limited
-Coding may be stored only in inaccessible billing systems
•COVID -NET surveillance officers also review the discharge summary for each patient
-Capture conditions that were not present at admission but developed during the hospital stay
-Gives fuller picture of patient’s hospitalization and reduces some of the bias that comes from
relying only on billing codes
12
COVID -NET hospitalizations classified as due to COVID -19 or
with COVID -19 using two different approaches
1366,125 COVID -19 hospitalizations among patients of
all ages during October 2023 –September 2024
7,279 (11%) random sample of
hospitalizations with chart review
81% 52%
88%
*Respiratory -related defined as acute respiratory distress syndrome (ARDS), acute respiratory failure, asthma exacerbation, bron chiolitis, bronchitis, chronic obstructive pulmonary disorder (COPD) exacerbation, or
pneumonia as indicated by the abstracted condition from discharge summary or the presence of an ICD -10-CM discharge diagnosis co de.COVID -19
ICD-10-CM
code
COVID -19 ICD -10-CM
code or sepsis or
respiratory -related*
discharge diagnosisSepsis or respiratory -
related* discharge
diagnosis
Neither COVID -19 ICD -
10-CM code nor sepsis
or respiratory -related*
discharge diagnosis12%ICD -10 codes and discharge diagnoses approach
66,125 COVID -19 hospitalizations among patients of
all ages during October 2023 –September 2024
7,279 (11%) random sample of
hospitalizations with chart review
85%
COVID -19 likely
reason for admission
Non -COVID -19 reason
for admission15%COVID -NET reason for admission approach
84%
COVID -19
ICD-10-CM
code
57%
Pulmonary
or sepsis
discharge
diagnosis91%
Either
COVID -19
ICD-10-CM
code OR
pulmonary
or sepsis
discharge
diagnosis64%
COVID -19
ICD-10-CM
code
Key Takeaways
•Two approaches for defining hospitalizations due to COVID -19
-COVID -19 identified as reason for admission
-COVID -19-related discharge diagnoses
•Similar proportions of patients classified using both methods:
-88% had COVID -19 ICD -10-CM code or sepsis or respiratory diagnosis
-85% identified by COVID -NET as COVID -19 being likely primary reason for admission
•91% of hospitalizations using COVID -NET’s reason for admission approach had a COVID -19 ICD -10-CM
code or sepsis or respiratory diagnosis
•COVID -NET’s reason for admission approach is more conservative than examining
discharge diagnoses
•COVID -NET’s methods for identifying hospitalizations due to COVID -19 balances
timeliness, accuracy, and representativeness
14
Population -based rates of COVID -19–
associated hospitalizations
15
Rates of COVID -19–associated hospitalization are
highest among the youngest and oldest age groups
A COVID -19–associated hospitalization is defined as laboratory -confirmed SARS -CoV-2 in a person who (a) lives in a defined COVID -NET surveillance catchment area AND (b) tests positive
for SARS -CoV-2 (using a laboratory -based molecular, antigen or serology test) within 14 days before or during hospitalization. 16223
117
298 82774195653
0100200300400500600700
<6 months 6–11 months 1–4 years 5–11 years 12–17 years 18–49 years 50–64 years 65–74 years ≥75 yearsHospitalizations per 100,000 population
Age groupPopulation -based COVID -19–associated hospitalization rates (per 100,000 population), by age group —
COVID -NET, October 2024 –September 2025
Rates are presented per 100,000 population and indicate the cumulative
12-month age group -based risk of COVID -19–associated hospitalization.
Among adults hospitalized due to COVID -19, 15% were
admitted to the intensive care unit (ICU)
1725
1917
14
145
051015202530
0–17 years 18–49 years 50–64 years ≥65 yearsWeighted percent of hospitalizations
Age groupWeighted percent (with 95% confidence intervals) of patients hospitalized due to COVID -19 with interventions
and outcomes, by age group — COVID -NET, June 2024 –May 2025
ICU admission In-hospital death<1
The figure displays the proportion of adults hospitalized due to COVID -19 based on reason for admission with interventions and o utcomes, by age group — COVID -NET, June 2024 –May
2025. Error bars denote 95% confidence intervals (95% CI). Data are limited to hospitalizations where COVID -19 is a likely primary reason for admission. Deaths do not include other COVID -
19-related deaths that might occur after a patient is discharged to hospice or deaths that occur soon after hospital discharge t hat could be attributable to COVID -19-related illness.During this period, 84% of all adults hospitalized due to COVID -19 who died in -hospital were ages ≥50 years.
Burden of COVID -19-Associated
Hospitalizations among Infants
18
Rates of respiratory virus -associated hospitalizations vary
by age group and pathogen.
1902004006008001000120014001600
<1 1–4 5–11 12–17 18–49 50–64 65–74 ≥75Hospitalizations per 100,000 population
Age group, in yearsCumulative rates of COVID -19–, influenza -, and respiratory syncytial virus
(RSV) -associated hospitalizations — RESP -NET, October 2024 –September 2025
COVID-19 Influenza RSV
Rates for all three pathogens (COVID -19, influenza, and respiratory syncytial virus [RSV]) are laboratory -confirmed. Data source : https://www.cdc.gov/resp -net/dashboard/
Note that rates are not adjusted for testing or limited to admissions where the respiratory infection is the reason for admis sion. Influenza surveillance was conducted October 2024 –April
2025.
Cumulative COVID -19-associated hospitalization rates are highest
among adults aged ≥75 years, followed by infants aged <6 months
and adults ages 65 –74 years.
Weekly rates of COVID -19–associated hospitalizations per 100,000 population by age group —COVID -NET, October 2024 –September 2025
Note that rates are not adjusted for testing. Rates are not limited to admissions where the respiratory infection is the like ly primary reason for admission. 20050100150200250
Oct 2024 Nov 2024 Dec 2024 Jan 2025 Feb 2025 Mar 2025 Apr 2025 May 2025 Jun 2025 Jul 2025 Aug 2025 Sep 2025Hospitalizations per 100,000 population
Surveillance week end date
<6 months 6–11 months 1–4 years 5–11 years
12–17 years 18–49 years 50–64 years 65–74 years50–64 years:
1011–4 years: 29
18–49 years: 27
12–17 years: 8
5–11 years: 8<6 months: 223
65–74 years: 195
6–11 months: 117
50–64 years: 740200400600800Hospitalizations per
100,000 population≥75 years: 653
Relative risks of hospitalization due to
COVID -19 among adults by chronic
condition
21
Background
•Update to peer -reviewed manuscript by Ko, et al. published in Clinical
Infectious Diseases in 2021*
* Ko J, Danielson ML, Town M, et al. Risk Factors for COVID -19-Associated Hospitalization: COVID -NET and BRFSS. https://doi.org/10.1093/cid/ciaa1419 . 22
Data Sources
•COVID -NET (number of hospitalizations due to COVID -19)
-October 2022 –September 2023
-98 counties across 13 states
•Behavioral Risk Factor Surveillance System (BRFSS) (chronic conditions)
-Largest continuously conducted health survey system in the world (hundreds of
thousands of interviews among community -dwelling U.S. adults each year)
-2022 survey
•National Center for Health Statistics (population data)
-2020 U.S. Census data
23
Overview of the Analysis
•Obtain weighted counts of persons hospitalized due to COVID -19 with and
without underlying conditions (COVID -NET)
-Limited to hospitalizations with COVID -19 as likely primary reason for admission
-Limited to community -dwelling adults to match BRFSS parameters
•Calculate weighted counts of community -dwelling adults with and
without chronic diseases of interest in participating states (BRFSS)
•Generate proportion of state population residing in the COVID -NET county
catchment area (Census)
•Calculate adjusted rate ratios of hospitalization rates with vs. without
chronic conditions
24
Chronic conditions examined
•Coronary artery disease
•History of stroke
•Diabetes mellitus
•Chronic kidney disease
•Chronic obstructive pulmonary disease (COPD)
•Asthma
•Obesity (body mass index [BMI] 30 –<40 kg/m²)
•Severe obesity (BMI ≥40 kg/m²)
•Current smoker
25
The prevalence of most chronic conditions among adults
hospitalized due to COVID -19 was generally higher than the
prevalence observed in the general population.
Error bars denote 95% uncertainty intervals (95% CI). Obesity is defined as BMI 30 –<40 kg/m². Severe obesity is BMI ≥40 kg/m². 26
Weighted prevalence (with 95% uncertainty intervals) of chronic medical conditions among adults
hospitalized due to COVID -19 compared to community -dwelling adults in COVID -NET states, 2022 –2023Weighted prevalence (%)
Among adults, most chronic conditions examined
increased the risk of being hospitalized due to COVID -19
COPD: chronic obstructive pulmonary disease. Obesity is defined as BMI 30 –39 kg/m². Severe obesity is BMI ≥40 kg/m². Note that v ertical axis is presented in logarithmic scale. 27Rate ratio (with 95% uncertainty intervals) comparing rates of COVID -19 hospitalization among
community -dwelling adults with a chronic medical condition to those without, by age group —
COVID -NET states, October 2022 –September 2023
•Risk conferred by
several conditions
appear to decline
with age (CAD,
diabetes, obesity).
This might be a
consequence of:
•Inability to
adjust for
comorbidities in
the models
•Low population
prevalence of
some conditions
in some age
groups
Risk for hospitalization due to COVID -19 increases
with the number of chronic conditions and age.
§ Model includes number of conditions, age group, sex, and race or ethnicity group.
¶ Number of conditions is a sum of chronic conditions (asthma, COPD, chronic kidney disease, coronary artery disease, diabete s, history of stroke, severe obesity, and current smoking).
Note that vertical axis is presented in logarithmic scale. 28•Multiple chronic medical
conditions and older age
were the strongest risk
factors for COVID -19
hospitalization among
adults.
•Hospitalization rates were
18.5 -times as high among
adults ages ≥75 years
compared to 18 –49 years.
•The greatest risk factor
examined in this analysis
Strengths and Limitations
•Data are gathered from robust COVID -19 hospitalization public health
surveillance system
•Ability to compare underlying conditions using state -level prevalence
•Capability to examine and compare specific underlying conditions,
prevalence of multiple conditions and age group as risk factors
•Limitations
-Results are preliminary and under review
-Analysis is limited to community -dwelling adults
-Adjustments for some comorbidities could not be made due to sparse data,
especially in younger groups.
-Unable to look at differences in rate ratios across outcomes (e.g., ICU admission)
or race and ethnicity categories due to sparse data in some categories.
29