NOTE: This dataset has been retired and marked as historical-only.

Only Chicago residents are included based on the home ZIP Code, as provided by the medical provider, or the address, as provided by the Cook County Medical Examiner.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted on the date the test specimen was collected. Deaths are those occurring among cases based on the day of death. Hospitalizations are based on the date of first hospitalization. Only one hospitalization is counted for each case. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Because of the nature of data reporting to CDPH, hospitalizations will be blank for recent dates They will fill in on later updates when the data are received, although, as for cases and deaths, may continue to be updated as further data are received.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects data currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases, deaths, and hospitalizations, sources used, how cases, deaths and hospitalizations are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office

Description Source data: https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among cases based on the week of death.

For tests, each test is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts include multiple tests for the same person (a change made on 10/29/2020). PCR and antigen tests reported to Chicago Department of Public Health (CDPH) through electronic lab reporting are included. Electronic lab reporting has taken time to onboard and testing availability has shifted over time, so these counts are likely an underestimate of community infection.

The “Percent Tested Positive” columns are calculated by dividing the number of positive tests by the number of total tests . Because of the data limitations for the Tests columns, such as persons being tested multiple times as a requirement for employment, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records, American Community Survey (2018)

NOTE: This dataset has been retired and marked as historical-only.

This dataset is a companion to the COVID-19 Daily Cases and Deaths dataset (https://data.cityofchicago.org/d/naz8-j4nc). The major difference in this dataset is that the case, death, and hospitalization corresponding rates per 100,000 population are not those for the single date indicated. They are rolling averages for the seven-day period ending on that date. This rolling average is used to account for fluctuations that may occur in the data, such as fewer cases being reported on weekends, and small numbers. The intent is to give a more representative view of the ongoing COVID-19 experience, less affected by what is essentially noise in the data.

All rates are per 100,000 population in the indicated group, or Chicago, as a whole, for “Total” columns.

Only Chicago residents are included based on the home address as provided by the medical provider.

Cases with a positive molecular (PCR) or antigen test are included in this dataset. Cases are counted based on the date the test specimen was collected. Deaths among cases are aggregated by day of death. Hospitalizations are reported by date of first hospital admission. Demographic data are based on what is reported by medical providers or collected by CDPH during follow-up investigation.

Denominators are from the U.S. Census Bureau American Community Survey 1-year estimate for 2018 and can be seen in the Citywide, 2018 row of the Chicago Population Counts dataset (https://data.cityofchicago.org/d/85cm-7uqa).

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. At any given time, this dataset reflects cases and deaths currently known to CDPH.

Numbers in this dataset may differ from other public sources due to definitions of COVID-19-related cases and deaths, sources used, how cases and deaths are associated to a specific date, and similar factors.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, U.S. Census Bureau American Community Survey

This dataset is historical only and ends at 5/7/2021. For more information, please see http://dev.cityofchicago.org/open%20data/data%20portal/2021/05/04/covid-19-testing-by-person.html. The recommended alternative dataset for similar data beyond that date is https://data.cityofchicago.org/Health-Human-Services/COVID-19-Daily-Testing-By-Test/gkdw-2tgv.

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

This dataset contains counts of people tested for COVID-19 and their results. This dataset differs from https://data.cityofchicago.org/d/gkdw-2tgv in that each person is in this dataset only once, even if tested multiple times. In the other dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in that dataset more than once on the same day unless he/she had both a positive and not-positive test.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests are included, and only one test is counted for each individual. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

Demographic data are more complete for those who test positive; care should be taken when calculating percentage positivity among demographic groups.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois National Electronic Disease Surveillance System

NOTE: This dataset has been retired and marked as historical-only.

This dataset contains counts of unique tests and results for COVID-19. This dataset differs from https://data.cityofchicago.org/d/t4hh-4ku9 in that each person is in that dataset only once, even if tested multiple times. In this dataset, each test is counted, even if multiple tests are performed on the same person, although a person should not appear in this dataset more than once on the same day unless he/she had both a positive and not-positive test.

The positivity rate displayed in this dataset uses the method most commonly used by other jurisdictions in the United States.

Only Chicago residents are included based on the home address as provided by the medical provider.

Molecular (PCR) and antigen tests received through electronic lab reporting are included. Individuals may be tested multiple times. Tests are counted on the day the specimen was collected. A small number of tests collected prior to 3/1/2020 are not included in the table.

Not-positive lab results include negative results, invalid results, and tests not performed due to improper collection. Chicago Department of Public Health (CDPH) does not receive all not-positive results.

All data are provisional and subject to change. Information is updated as additional details are received.

Data Source: Illinois Department of Public Health Electronic Lab Reports

This dataset includes aggregated weekly influenza virus laboratory data that the Chicago Department of Public Health (CDPH) uses to monitor influenza activity and assess which influenza types and subtypes are circulating in Chicago. The data represent weekly positive influenza PCR tests voluntarily reported by network of several hospital laboratories in Chicago as well as two commercial laboratories serving Chicago facilities. The data includes positive test results by influenza type (influenza A and influenza B) as well as influenza A subtype (H3N2, H1N1pdm09) when available. These data do not include patient demographic or geographic information and represent both Chicago and non-Chicago residents tested by the reporting facility. Influenza laboratory data are available from the 2010-2011 season to present. Two percentage fields are available in the dataset. Percentages are calculated for each characteristic group as follows: The percentage of influenza types is calculated as the total number of positives tests for each influenza type divided by the total number of positive influenza tests reported (e.g., Influenza A/Influenza Positive). The percentage fields describe the percent of positive tests by influenza type each week (count_pct) and for the entire season to date (count_cum_pct). The percentage of influenza A subtypes is calculated as the total number of positive tests for each influenza A subtype divided by the total number of positive influenza A tests reported (Influenza A Subtype/Influenza A). The percentage fields describe the percent of influenza A positive tests by subtype each week (count_pct) and for the entire season to date (count_cum_pct). The percentage for characteristic group ‘Total Positive’ will always be 100% and does not represent influenza test positivity. For data on influenza test positivity see: https://data.cityofchicago.org/Health-Human-Services/Influenza-COVID-19-RSV-and-Other-Respiratory-Virus/qgdz-d5m4/about_data. All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

NOTE: This dataset has been retired and marked as historical-only.

Weekly rates of COVID-19 cases, hospitalizations, and deaths among people living in Chicago by vaccination status and age.

Rates for fully vaccinated and unvaccinated begin the week ending April 3, 2021 when COVID-19 vaccines became widely available in Chicago. Rates for boosted begin the week ending October 23, 2021 after booster shots were recommended by the Centers for Disease Control and Prevention (CDC) for adults 65+ years old and adults in certain populations and high risk occupational and institutional settings who received Pfizer or Moderna for their primary series or anyone who received the Johnson & Johnson vaccine.

Chicago residency is based on home address, as reported in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE) and Illinois National Electronic Disease Surveillance System (I-NEDSS).

Outcomes: • Cases: People with a positive molecular (PCR) or antigen COVID-19 test result from an FDA-authorized COVID-19 test that was reported into I-NEDSS. A person can become re-infected with SARS-CoV-2 over time and so may be counted more than once in this dataset. Cases are counted by week the test specimen was collected. • Hospitalizations: COVID-19 cases who are hospitalized due to a documented COVID-19 related illness or who are admitted for any reason within 14 days of a positive SARS-CoV-2 test. Hospitalizations are counted by week of hospital admission. • Deaths: COVID-19 cases who died from COVID-19-related health complications as determined by vital records or a public health investigation. Deaths are counted by week of death.

Vaccination status: • Fully vaccinated: Completion of primary series of a U.S. Food and Drug Administration (FDA)-authorized or approved COVID-19 vaccine at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Boosted: Fully vaccinated with an additional or booster dose of any FDA-authorized or approved COVID-19 vaccine received at least 14 days prior to a positive test (with no other positive tests in the previous 45 days). • Unvaccinated: No evidence of having received a dose of an FDA-authorized or approved vaccine prior to a positive test.

CLARIFYING NOTE: Those who started but did not complete all recommended doses of an FDA-authorized or approved vaccine prior to a positive test (i.e., partially vaccinated) are excluded from this dataset.

Incidence rates for fully vaccinated but not boosted people (Vaccinated columns) are calculated as total fully vaccinated but not boosted with outcome divided by cumulative fully vaccinated but not boosted at the end of each week. Incidence rates for boosted (Boosted columns) are calculated as total boosted with outcome divided by cumulative boosted at the end of each week. Incidence rates for unvaccinated (Unvaccinated columns) are calculated as total unvaccinated with outcome divided by total population minus cumulative boosted, fully, and partially vaccinated at the end of each week. All rates are multiplied by 100,000.

Incidence rate ratios (IRRs) are calculated by dividing the weekly incidence rates among unvaccinated people by those among fully vaccinated but not boosted and boosted people.

Overall age-adjusted incidence rates and IRRs are standardized using the 2000 U.S. Census standard population.

Population totals are from U.S. Census Bureau American Community Survey 1-year estimates for 2019.

All data are provisional and subject to change. Information is updated as additional details are received and it is, in fact, very common for recent dates to be incomplete and to be updated as time goes on. This dataset reflects data known to CDPH at the time when the dataset is updated each week.

Numbers in this dataset may differ from other public sources due to when data are reported and how City of Chicago boundaries are defined.

For all datasets related to COVID-19, see https://data.cityofchic

NOTE: This dataset is historical-only as of 5/10/2023. All data currently in the dataset will remain, but new data will not be added. The recommended alternative dataset for similar data beyond that date is  https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u. (This is not a City of Chicago site. Please direct any questions or comments through the contact information on the site.)

During the COVID-19 pandemic, the Chicago Department of Public Health (CDPH) required EMS Region XI (Chicago area) hospitals to report hospital capacity and patient impact metrics related to COVID-19 to CDPH through the statewide EMResource system. This requirement has been lifted as of May 9, 2023, in alignment with the expiration of the national and statewide COVID-19 public health emergency declarations on May 11, 2023. However, all hospitals will still be required by the U.S. Department of Health and Human Services (HHS) to report COVID-19 hospital capacity and utilization metrics into the HHS Protect system through the CDC’s National Healthcare Safety Network until April 30, 2024. Facility-level data from the HHS Protect system can be found at healthdata.gov.

Until May 9, 2023, all Chicago (EMS Region XI) hospitals (n=28) were required to report bed and ventilator capacity, availability, and occupancy to the Chicago Department of Public Health (CDPH) daily. A list of reporting hospitals is included below. All data represent hospital status as of 11:59 pm for that calendar day. Counts include Chicago residents and non-residents.

ICU bed counts include both adult and pediatric ICU beds. Neonatal ICU beds are not included. Capacity refers to all staffed adult and pediatric ICU beds. Availability refers to all available/vacant adult and pediatric ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in ICU on 03/19/2020. Hospitals began reporting ICU surge capacity as part of total capacity on 5/18/2020.

Acute non-ICU bed counts include burn unit, emergency department, medical/surgery (ward), other, pediatrics (pediatric ward) and psychiatry beds. Burn beds include those approved by the American Burn Association or self-designated. Capacity refers to all staffed acute non-ICU beds. An additional 500 acute/non-ICU beds were added at the McCormick Place Treatment Facility on 4/15/2020. These beds are not included in the total capacity count. The McCormick Place Treatment Facility closed on 05/08/2020. Availability refers to all available/vacant acute non-ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in acute non-ICU beds on 04/03/2020.

Ventilator counts prior to 04/24/2020 include all full-functioning mechanical ventilators, with ventilators with bilevel positive airway pressure (BiPAP), anesthesia machines, and portable/transport ventilators counted as surge. Beginning 04/24/2020, ventilator counts include all full-functioning mechanical ventilators, BiPAP, anesthesia machines and portable/transport ventilators. Ventilators are counted regardless of ability to staff. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases on ventilators on 03/19/2020. CDPH has access to additional ventilators from the EAMC (Emergency Asset Management Center) cache. These ventilators are included in the total capacity count.

Chicago (EMS Region 11) hospitals: Advocate Illinois Masonic Medical Center, Advocate Trinity Hospital, AMITA Resurrection Medical Center Chicago, AMITA Saint Joseph Hospital Chicago, AMITA Saints Mary & Elizabeth Medical Center, Ann & Robert H Lurie Children's Hospital, Comer Children's Hospital, Community First Medical Center, Holy Cross Hospital, Jackson Park Hospital & Medical Center, John H. Stroger Jr. Hospital of Cook County, Loretto Hospital, Mercy Hospital and Medical Center, , Mount Sinai Hospital, Northwestern Memorial Hospital, Norwegian American Hospital, Roseland Community Hospital, Rush University M

The National Institute on Drug Abuse (NIDA) funded RADAR in 2014 to collect multilevel, longitudinal data and biospecimens from an ethnically and racially diverse cohort of young, sexual and gender minorities (SGM; e.g., men who have sex with men (MSM), transgender women, gender non-conforming individuals) who were assigned male at birth (AMAB) (current core cohort n=1,113). The primary objective of this study is to apply a multilevel perspective to a syndemic of health issues associated with human immunodeficiency virus (HIV) in this population. The multilevel design focuses on individual, dyadic (i.e., sexual and romantic relationships), network (i.e., social, drug, and sexual connections) and biologic factors that may be associated with HIV. The cohort contains both HIV-negative and HIV-positive individuals, which allows for the development of a repository of biospecimens and HIV sequence data from both pre-infection and post-infection visits that will help facilitate future projects evaluating substance use, HIV risk, and pathogenesis. A multiple cohort, accelerated longitudinal design was utilized by initially enrolling two existing SGM cohorts and then expanded through the use of convenience and snowball sampling methods. Enrollment criteria varied slightly based on the recruitment method, but overall inclusion criteria required participants to be AMAB, between 16 and 29 years of age, report having had sex with a man in the prior year or identify as a SGM, live in the Chicago metropolitan area, and be an English speaker. Study recruitment opened in February 2015. Participants are followed through the developmental period of late adolescence to early adulthood, which is a critical period of initiation and acceleration of sexual behavior and substance use. Study visits occur every six months.

This is the place to look for important information about how to use this dataset, so please expand this box and read on!

This is the source data for some of the metrics available at https://www.chicago.gov/city/en/sites/covid-19/home/latest-data.html.

For all datasets related to COVID-19, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=covid-19.

Only Chicago residents are included based on the home ZIP Code as provided by the medical provider. If a ZIP was missing or was not valid, it is displayed as "Unknown".

Confirmed cases are counted based on the week the test specimen was collected. For privacy reasons, until a ZIP Code reaches five cumulative cases, both the weekly and cumulative case counts will be blank. Therefore, summing the “Cases - Weekly” column is not a reliable way to determine case totals. Deaths are those that have occurred among confirmed cases based on the week of death.

For tests, each individual is counted once, based on the week the test specimen was collected. Tests performed prior to 3/1/2020 are not included. Test counts do not include multiple tests for the same person or some negative tests not reported to CDPH.

The “Percent Tested Positive” columns are calculated by dividing the corresponding Cases and Tests columns. Because of the data limitations for the Tests columns, as well as strict criteria for performing COVID-19 tests, these percentages may vary in either direction from the actual disease prevalence in the ZIP Code. Of particular note, these rates do not represent population-level disease surveillance.

Population counts are from the 2010 Decennial Census.

All data are provisional and subject to change. Information is updated as additional details are received.

To compare ZIP Codes to Chicago Community Areas, please see http://data.cmap.illinois.gov/opendata/uploads/CKAN/NONCENSUS/ADMINISTRATIVE_POLITICAL_BOUNDARIES/CCAzip.pdf. Both ZIP Codes and Community Areas are also geographic datasets on this data portal.

Data Source: Illinois National Electronic Disease Surveillance System, Cook County Medical Examiner’s Office, Illinois Vital Records

In Illinois, influenza associated Intensive Care Unit (ICU) hospitalizations are reportable as soon as possible, but within 24 hours. Influenza associated ICU hospitalizations are defined as individuals hospitalized in an ICU with a positive laboratory test for influenza A or B, including specimens identified as influenza A/H3N2, A/H1N1pdm09, and specimens not subtyped (e.g., influenza positive cases by PCR or any rapid test such as EIA).

This dataset represents weekly aggregated information for influenza-associated ICU hospitalizations among Chicago residents, which is a reportable condition in Illinois.

Information includes demographics, influenza laboratory results, vaccination status, and death status.

Column names containing "REPORTED" indicate the number of cases for which the indicated data element was reported. This, rather than the total number of cases, is used to calculate the corresponding percentage.

All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

NOTE: This dataset is no longer being updated but is being kept for historical reference. For current data on respiratory illness visits and respiratory laboratory testing data please see Influenza, COVID-19, RSV, and Other Respiratory Virus Laboratory Surveillance and Inpatient, Emergency Department, and Outpatient Visits for Respiratory Illnesses.

In Illinois, influenza associated Intensive Care Unit (ICU) hospitalizations are reportable as soon as possible, but within 24 hours. Influenza associated ICU hospitalizations are defined as individuals hospitalized in an ICU with a positive laboratory test for influenza A or B, including specimens identified as influenza A/H3N2, A/H1N1pdm09, and specimens not subtyped (e.g., influenza positive cases by PCR or any rapid test such as EIA).

This dataset represents weekly aggregated information for influenza-associated ICU hospitalizations among Chicago residents, which is a reportable condition in Illinois.

Information includes demographics, influenza laboratory results, vaccination status, and death status.

Column names containing "REPORTED" indicate the number of cases for which the indicated data element was reported. This, rather than the total number of cases, is used to calculate the corresponding percentage.

All data are provisional and subject to change. Information is updated as additional details are received. At any given time, this dataset reflects data currently known to CDPH. Numbers in this dataset may differ from other public sources.

Burnout, depression, and anxiety are prevalent among healthcare workers (HCWs) during the COVID-19 pandemic and have been previously shown to contribute to poor health outcomes and reduced quality of care. Positive psychological constructs such as positive affect and meaning and purpose are related to resilience in the face of significant stress. No studies have examined these associations among a cohort of HCWs during this pandemic. The purpose of this study was to examine the association of depression, anxiety, positive affect, and meaning and purpose with burnout among HCWs during the COVID-19 pandemic. We utilized data from a cross-sectional survey conducted between September 29-December 8, 2021, among a cohort of 2,411 HCWs from a large, tertiary academic health care system in the Chicago area. We employed the Patient-Reported Outcomes Measurement Information System (PROMIS) measures for depression, anxiety, positive affect, and meaning and purpose and burnout was measured by the Oldenburg Burnout Inventory (OLBI). The majority (80.88%) of HCWs in this study identified as White, Non-Hispanic race/ethnicity, female sex (82.37%), and roughly one third were between ages 30–39 years old (30.98%). Registered nurses (26.96%) accounted for the largest single occupation group. The mean burnout score was 36.87 (SD = 7.65), with 53.38% of participants classified as having burnout, and registered nurses demonstrating the highest proportions of burnout (63.54%). Higher depression (coef = 0.15, SE = 0.03, p < 0.001) and anxiety (coef = 0.25, SE = 0.02, p < 0.001) scores were associated with higher burnout in multivariable linear regression models. Increased positive affect (coef= −0.19, SE= 0.02, p < 0.001) and meaning and purpose (coef= −0.12, SE= 0.01, p < 0.001) scores were significantly associated with reduced burnout. Positive affect and meaning and purpose were inversely associated with burnout among a cohort of HCWs during the COVID-19 pandemic. Previous studies of positive affect and meaning and purpose suggest the potential buffering effect that these indices may have on burnout. Future research is needed to examine the effect of positive affect and meaning and purpose on mitigating the negative impacts of burnout, depression, and anxiety among HCWs as they cope with the stress of the COVID-19 pandemic and beyond.

IntroductionDuring the coronavirus disease 2019 pandemic, high levels of burnout were reported among healthcare workers. This study examines the association of work absenteeism and frequency of thoughts in leaving current job with burnout among a cohort of healthcare workers during the COVID-19 pandemic.MethodsA cross-sectional survey of healthcare workers was conducted from April-May, 2022 on healthcare workers from 10 hospitals, 18 immediate care centers, and 325 outpatient practices in the Chicago area and surrounding Illinois suburbs. Logistic regression models were used to assess the association of burnout scores (Oldenburg Burnout Inventory—OLBI) and its sub-scores (exhaustion and disengagement scores) with work absenteeism and thoughts of leaving work.ResultsOne-fifth and 60% of respondents (n = 1,825) reported unplanned absenteeism and thoughts of leaving their job, respectively. After adjusting for covariates, higher burnout scores, especially exhaustion scores, were associated with increased odds of unplanned absenteeism (OR = 1.04, 95% CI: 1.01–1.08). Burnout scores and both sub-scores were also positively associated with the frequency of thoughts of leaving work, e.g., each unit increase in the OLBI burnout score was associated with 1.39 (95% CI: 1.34–1.43) times higher odds of thinking about leaving work “a lot/constantly” vs. “never”.DiscussionOverall, this study cohort showed a positive association between burnout scores and unplanned work absenteeism (and frequency of thoughts in leaving job) during the COVID-19 pandemic. More research is needed to support healthcare worker well-being during times of stress and direct solutions to addressing unplanned absenteeism in the light of a pandemic.

BackgroundSARS-CoV-2 vaccines are safe and effective against infection and severe COVID-19 disease worldwide. Certain co-morbid conditions cause immune dysfunction and may reduce immune response to vaccination. In contrast, those with co-morbidities may practice infection prevention strategies. Thus, the real-world clinical impact of co-morbidities on SARS-CoV-2 infection in the recent post-vaccination period is not well established. This study was performed to understand the epidemiology of Omicron breakthrough infection and evaluate associations with number of comorbidities in a vaccinated and boosted population.Methods and findingsA retrospective clinical cohort study was performed utilizing the Northwestern Medicine Enterprise Data Warehouse. Our study population was identified as fully vaccinated adults with at least one booster. The primary risk factor of interest was the number of co-morbidities. The primary outcome was the incidence and time to the first positive SARS-CoV-2 molecular test in the Omicron predominant era. Multivariable Cox modeling analyses to determine the hazard of SARS-CoV-2 infection were stratified by calendar time (Period 1: January 1 –June 30, 2022; Period 2: July 1 –December 31, 2022) due to violations in the proportional hazards assumption. In total, 133,191 patients were analyzed. During Period 1, having 3+ comorbidities was associated with increased hazard for breakthrough (HR = 1.16 CI 1.08–1.26). During Period 2 of the study, having 2 comorbidities (HR = 1.45 95% CI 1.26–1.67) and having 3+ comorbidities (HR 1.73, 95% CI 1.51–1.97) were associated with increased hazard for Omicron breakthrough. Older age was associated with decreased hazard in Period 1 of follow-up. Interaction terms for calendar time indicated significant changes in hazard for many factors between the first and second halves of the follow-up period.ConclusionsOmicron breakthrough is common with significantly higher risk for our most vulnerable patients with multiple co-morbidities. Age plays an important role in breakthrough infection with the highest incidence among young adults, which may be due to age-related behavioral factors. These findings reflect real-world differences in immunity and exposure risk behaviors for populations vulnerable to COVID-19.