Monthly Cumulative Number and Percent of Persons Who Received ≥1 Influenza Vaccination Doses, by Flu Season, Age Group, and Jurisdiction

• Influenza vaccination coverage for children and adults is assessed through U.S. jurisdictions’ Immunization Information Systems (IIS) data, submitted from jurisdictions to CDC monthly in aggregate by age group. More information about the IIS can be found at https://www.cdc.gov/vaccines/programs/iis/about.html.

• Influenza vaccination coverage estimate numerators include the number of people receiving at least one dose of influenza vaccine in a given flu season, based on information that state, territorial, and local public health agencies report to CDC. Some jurisdictions’ data may include data submitted by tribes. Estimates include persons who are deceased but received a vaccination during the current season. People receiving doses are attributed to the jurisdiction in which the person resides unless noted otherwise. Quality and completeness of data may vary across jurisdictions. Influenza vaccination coverage denominators are obtained from 2020 U.S. Census Bureau population estimates.

• Monthly estimates shown are cumulative, reflecting all persons vaccinated from July through a given month of that flu season. Cumulative estimates include any historical data reported since the previous submission. National estimates are not presented since not all U.S. jurisdictions are currently reporting their IIS data to CDC. Jurisdictions reporting data to CDC include U.S. states, some localities, and territories.

• Because IIS data contain all vaccinations administered within a jurisdiction rather than a sample, standard errors were not calculated and statistical testing for differences in estimates across years were not performed.

• Laws and policies regarding the submission of vaccination data to an IIS vary by state, which may impact the completeness of vaccination coverage reflected for a jurisdiction. More information on laws and policies are found at https://www.cdc.gov/vaccines/programs/iis/policy-legislation.html.

• Coverage estimates based on IIS data are expected to differ from National Immunization Survey (NIS) estimates for children (https://www.cdc.gov/flu/fluvaxview/dashboard/vaccination-coverage-race.html) and adults (https://www.cdc.gov/flu/fluvaxview/dashboard/vaccination-adult-coverage.html) because NIS estimates are based on a sample that may not be representative after survey weighting and vaccination status is determined by survey respondent rather than vaccine records or administrations, and quality and completeness of IIS data may vary across jurisdictions. In general, NIS estimates tend to overestimate coverage due to overreporting and IIS estimates may underestimate coverage due to incompleteness of data in certain jurisdictions.

Age-standardised mortality rates for deaths involving coronavirus (COVID-19), non-COVID-19 deaths and all deaths by vaccination status, broken down by age group.

Flu vaccine uptake (%) in adults aged 65 and over, who received the flu vaccination between 1st September to the end of February as recorded in the GP record. The February collection has been adopted for our end of season figures from 2017 to 2018. All previous data is the same definitions but until the end of January rather than February to consider data returning from outside the practice and later in practice vaccinations.RationaleInfluenza (also known as Flu) is a highly infectious viral illness spread by droplet infection. The flu vaccination is offered to people who are at greater risk of developing serious complications if they catch the flu. The seasonal influenza programme for England is set out in the Annual Flu Letter. Both the flu letter and the flu plan have the support of the Chief Medical Officer (CMO), Chief Pharmaceutical Officer (CPhO), and Director of Nursing.Vaccination coverage is the best indicator of the level of protection a population will have against vaccine-preventable communicable diseases. Immunisation is one of the most effective healthcare interventions available, and flu vaccines can prevent illness and hospital admissions among these groups of people. Increasing the uptake of the flu vaccine among these high-risk groups should also contribute to easing winter pressure on primary care services and hospital admissions. Coverage is closely related to levels of disease. Monitoring coverage identifies possible drops in immunity before levels of disease rise.The UK Health Security Agency (UKHSA) will continue to provide expert advice and monitoring of public health, including immunisation. NHS England now has responsibility for commissioning the flu programme, and GPs continue to play a key role. NHS England teams will ensure that robust plans are in place locally and that high vaccination uptake levels are reached in the clinical risk groups. For more information, see the Green Book chapter 19 on Influenza.The Annual Flu Letter sets out the national vaccine uptake ambitions each year. In 2021 to 2022, the national ambition was to achieve at least 85 percent vaccine uptake in those aged 65 and over. Prior to this, the national vaccine uptake ambition was 75 percent, in line with WHO targets.Definition of numeratorNumerator is the number of vaccinations administered during the influenza season between 1st September and the end of February.Definition of denominatorDenominator is the GP registered population on the date of extraction including patients who have been offered the vaccine but refused it, as the uptake rate is measured against the overall eligible population. For more detailed information please see the user guide, available to view and download from https://www.gov.uk/government/collections/vaccine-uptake#seasonal-flu-vaccine-uptakeCaveatsRead codes are primarily used for data collection purposes to extract vaccine uptake data for patients who fall into one or more of the designated clinical risk groups. The codes identify individuals at risk, and therefore eligible for flu vaccination. However, it is important to note that there may be some individuals with conditions not specified in the recommended risk groups for vaccination, who may be offered influenza vaccine by their GP based on clinical judgement and according to advice contained in the flu letter and Green Book, and thus are likely to fall outside the listed Read codes. Therefore, this data should not be used for GP payment purposes.

Chicago residents who are up to date with influenza vaccines by ZIP Code, based on the reported home address and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE). “Up to date” refers to individuals aged 6 months and older who have received 1+ doses of influenza vaccine during the current season, defined as the beginning of July (MMWR week 27) through the end of the following June (MMWR week 26). Data Notes: Weekly cumulative totals of people up to date are shown for each combination ZIP Code and age group. Note there are rows where age group is "All ages" so care should be taken when summing rows. Weeks begin on a Sunday and end on a Saturday. Coverage percentages are calculated based on the cumulative number of people in each ZIP Code and age group who are considered up to date as of the week ending date divided by the estimated number of people in that subgroup. Population counts are obtained from the 2020 U.S. Decennial Census. For ZIP Codes mostly outside Chicago, coverage percentages are not calculated because reliable Chicago-only population counts are not available. Actual counts may exceed population estimates and lead to coverage estimates that are greater than 100%, especially in smaller ZIP Codes with smaller populations. Additionally, the medical provider may report a work address or incorrect home address for the person receiving the vaccination, which may lead to over- or underestimation of vaccination coverage by geography. All coverage percentages are capped at 99%. The Chicago Department of Public Health (CDPH) uses the most complete data available to estimate influenza vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Influenza vaccine administration is not required to be reported in Illinois, except for publicly funded vaccine (e.g., Vaccines for Children, Section 317). Individuals may receive vaccinations that are not recorded in I-CARE, such as those administered in another state, or those administered by a provider that does not submit data to I-CARE, causing underestimation of the number individuals who received an influenza vaccine for the current season. 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 when data are reported and how City of Chicago boundaries are defined. For all datasets related to influenza, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=flu . Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau 2020 Decennial Census

Chicago residents who are up to date with influenza vaccines by Healthy Chicago Equity Zone (HCEZ), based on the reported address, race-ethnicity, and age group of the person vaccinated, as provided by the medical provider in the Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE).

Healthy Chicago Equity Zones is an initiative of the Chicago Department of Public Health to organize and support hyperlocal, community-led efforts that promote health and racial equity. Chicago is divided into six HCEZs. Combinations of Chicago’s 77 community areas make up each HCEZ, based on geography. For more information about HCEZs including which community areas are in each zone see: https://data.cityofchicago.org/Health-Human-Services/Healthy-Chicago-Equity-Zones/nk2j-663f

“Up to date” refers to individuals aged 6 months and older who have received 1+ doses of influenza vaccine during the current season, defined as the beginning of July (MMWR week 27) through the end of the following June (MMWR week 26).

Data notes:

Weekly cumulative totals of people up to date are shown for each combination of race-ethnicity and age group within an HCEZ. Note that each HCEZ has a row where HCEZ is “Citywide” and each HCEZ has a row where age is "All" and race-ethnicity is “All Race/Ethnicity Groups” so care should be taken when summing rows. Weeks begin on a Sunday and end on a Saturday.

Coverage percentages are calculated based on the cumulative number of people in each population subgroup (age group by race-ethnicity within an HCEZ) who are up to date, divided by the estimated number of people in that subgroup. Population counts are from the 2020 U.S. Decennial Census. Actual counts may exceed population estimates and lead to >100% coverage, especially in small race-ethnicity subgroups of each age group within an HCEZ. All coverage percentages are capped at 99%. Summing all race/ethnicity group populations to obtain citywide populations may provide a population count that differs slightly from the citywide population count listed in the dataset. Differences in these estimates are due to how community area populations are calculated. The Chicago Department of Public Health (CDPH) uses the most complete data available to estimate influenza vaccination coverage among Chicagoans, but there are several limitations that impact our estimates. Influenza vaccine administration is not required to be reported in Illinois, except for publicly funded vaccine (e.g., Vaccines for Children, Section 317). Individuals may receive vaccinations that are not recorded in I-CARE, such as those administered in another state, or those administered by a provider that does not submit data to I-CARE, causing underestimation of the number individuals who received an influenza vaccine for the current season.

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 when data are reported and how City of Chicago boundaries are defined.

For all datasets related to influenza, see https://data.cityofchicago.org/browse?limitTo=datasets&sortBy=alpha&tags=flu .

Data Source: Illinois Comprehensive Automated Immunization Registry Exchange (I-CARE), U.S. Census Bureau 2020 Decennial Census

Health and Safety Code section 1288.7(a) requires California acute care hospitals to offer influenza vaccine free of charge to all healthcare providers (HCP) or sign a declination form if a HCP chooses not to be vaccinated. Hospitals must report HCP influenza vaccination data to the California Department of Public Health (CDPH), including the percentage of HCP vaccinated. CDPH is required to make this information public on an annual basis [Health and Safety Code section 1288.8 (b)].

California acute care hospitals are required to offer free influenza vaccine to HCP. Hospital HCP must receive an annual vaccine or sign a declination form. Hospitals collect vaccination data for all HCP physically working in the hospital for at least one day during influenza season, regardless of clinical responsibility or patient contact. Hospitals report HCP vaccination rates to the California Department of Public Health (CDPH) and CDPH publishes the hospital results annually. CDPH reports data separately for hospital employees, licensed independent practitioners such as physicians, other contract staff, and trainees and volunteers (Health and Safety Code section 1288.7-1288.8).

Detailed information about the variables included in each dataset are described in the accompanying data dictionaries for the year of interest.

For general information about NHSN, surveillance definitions, and reporting requirements for HCP influenza vaccination, please visit: https://www.cdc.gov/nhsn/hps/vaccination/index.html

To link the CDPH facility IDs with those from other Departments, including OSHPD, please reference the "Licensed Facility Cross-Walk" Open Data table at: https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk.

For information about healthcare personnel influenza vaccinations in California hospitals, please visit: https://www.cdph.ca.gov/Programs/CHCQ/HAI/Pages/HealthcarePersonnelInfluenzaVaccinationReportingInCA_Hospitals.aspx

Content

List of San Francisco Department of Public Health clinics offering flu vaccinations throughout the city in fall 2013.This dataset complies with the emerging Data Specification for Flu Shot Locations. For information about the specification, please see https://github.com/CityOfPhiladelphia/flu-shot-spec.For more information about SFDPH's Influenza Program www.sfcdcp.org/flu or call 311For more information about SFDPH's Open data Initiatives http://www.sfphes.org/resources/health-data or Contact Cyndy.comerford@sfdph.org

Context

This is a dataset hosted by the city of San Francisco. The organization has an open data platform found here and they update their information according the amount of data that is brought in. Explore San Francisco's Data using Kaggle and all of the data sources available through the San Francisco organization page!

• Update Frequency: This dataset is not updated.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by rawpixel on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Influenza A virus is a major respiratory pathogen in swine that leads to significant economic loss in the swine industry, and there is a critical need to improve on commercial vaccines. Traditional vaccines target the hemagglutinin (HA) portion of the IAV virus, lose protection to as viruses change, and may even lead to vaccine-associated enhanced respiratory disease (VAERD) after infection with a dissimilar influenza virus. A newer replicon particle (RP) vaccine platform targeting the influenza HA protein offers multiple advantages over traditional vaccines for swine, but have not yet been evaluated for the ability to avoid VAERD or the use of HA along with an additional viral vaccine target, such as neuraminidase (NA). In this work we demonstrated RP HA and NA influenza vaccines stimulate immune responses, protect from disease, and avoid VAERD following infection with a distantly related virus. This demonstrates the potential utility of RP vaccines against influenza and the importance in utilizing the NA in influenza vaccine design. Such improvement of IAV vaccines will reduce influenza disease and economic loss in commercial swine and reduce the risk of influenza transmission to people. These data include individual pig responses used in statistical analyses and figures to support the conclusions of the paper. Resources in this dataset:Resource Title: Data from: Bivalent hemagglutinin and neuraminidase influenza replicon particle vaccines protect without causing vaccine associated enhanced respiratory disease in swine. File Name: Data Repository.xlsx

The proteome of urine samples from quadrivalent influenza vaccine cohort were analyzed with self-contrasted method. Significantly changed urine protein at 24 hours after vaccination was enriched in immune-related pathways, although each person’s specific pathways varied. We speculate that this may be because different people have different immunological backgrounds associated with influenza. Then, urine samples were collected from several uninfected SARS-CoV-2 young people before and after the first, second, and third doses of the COVID-19 vaccine. The differential proteins compared between after the second dose (24h) and before the second dose were enriched in pathways involving in multicellular organismal process, regulated exocytosis and immune-related pathways, indicating no first exposure to antigen. Surprisingly, the pathways enriched by the differential urinary protein before and after the first dose were similar to those before and after the second dose. It is inferred that although the volunteers were not infected with SARS-CoV-2, they might have been exposed to other coimmunogenic coronaviruses. Two to four hours after the third vaccination, the differentially expressed protein were also enriched in multicellular organismal process, regulated exocytosis and immune-related pathways, indicating that the immune response has been triggered in a short time after vaccination. Multicellular organismal process and regulated exocytosis after vaccination may be a new indicator to evaluate the immune effect of vaccines. Urinary proteome is a terrific window to monitor the changes in human immune function.

Tabulated individual data points for data reported in the associated publication: Spackman E, Suarez DL, Lee CW, Pantin-Jackwood MJ, Lee SA, Youk S, Ibrahim S. Efficacy of inactivated and RNA particle vaccines against a North American Clade 2.3.4.4b H5 highly pathogenic avian influenza virus in chickens. Vaccine. 2023 Nov 30;41(49):7369-7376. doi: 10.1016/j.vaccine.2023.10.070. Epub 2023 Nov 4. PMID: 37932132.Description of methodsVirusesThe highly pathogenic avian influenza virus (HPAIV) isolate A/turkey/Indiana/22-003707-003/2022 H5N1 (TK/IN/22) and A/Gyrfalcon/Washington/41088/2014 H5N8 (GF/WA/14) isolate were each propagated and titrated in embryonating specific pathogen free (SPF) chicken eggs using standard procedures and titers were determined using the Reed-Muench method.VaccinesAn in-house vaccine was produced by de novo synthesizing the HA gene of TK/IN/22 that was modified to be low pathogenic (LP) and placing it in a PR8 backbone using rg methods as described . The vaccine (SEP-22-N9) contained 6 genes from PR8 and a de novo synthesized N9 NA from A/blue winged teal/Wyoming/AH0099021/2016 (H7N9). The rg virus was inactivated by treatment with 0.1% beta-propiolactone. Vaccines were produced with Montanide ISA 71 VG (Seppic Inc., Fairfield, NJ) adjuvant at ambient temperature in a L5M-A high shear mixer (Silverson Machines, Inc., East Longmeadow, MA) for 30sec at 1,000rpm, then for 3min at 4,000rpm using an emulsifying screen in accordance with the adjuvant manufacturer’s instructions.Sham vaccine was prepared in-house using sterile phosphate buffered saline as described above.Commercial vaccines were supplied by the manufacturers. The commercial inactivated vaccine (1057.R1 serial 590088) (rgH5N1) (Zoetis Inc., Parsippany, NJ) was produced with the GF/WA/14 (clade 2.3.4.4c HA gene) and the remaining 7 gene segments including the NA from PR8 (1). The Sequivity vaccine (serial V040122NCF) (RP) (Merck and Co. Inc., Rahway, NJ) is an updated version of their replication restricted alphavirus vector vaccine that expresses the TK/IN/22 H5 HA (modified to be low pathogenic LP).Challenge study designThree-week-old, mixed sex, SPF white leghorn chickens (Gallus gallus domesticus) were obtained from in-house flocks and were randomly assigned to vaccine groups.All vaccines were administered by the subcutaneous route at the nape of the neck. Commercial vaccines were given at the volumes instructed by the manufacturer (0.5ml each). In-house vaccine was given at a dose of 512 hemagglutination units per bird in 0.5ml. Three weeks post vaccination chickens were challenged with 6.7 log10 50% egg infectious doses (EID50) of TK/IN/22 in 0.1ml by the intrachoanal route.Oropharyngeal (OP) and cloacal (CL) swabs were collected from all birds at 2-, 4-, and 7-days post challenge (DPC). Swabs were also collected from dead and euthanized sham vaccinates at 1DPC.To evaluate antibody-based DIVA-VI tests, blood for serum was collected from the RP and SEP-22-N9 vaccinated groups at 7, 10 and 14DPC because the SEP-22-N9 vaccine does not elicit antibodies to N1 and the RP vaccine does not elicit antibodies to the N1 or NP proteins.Mortality and morbidity were recorded for 14DPC after which time the remaining birds were euthanized. If birds were severely lethargic or had neurological signs they were euthanized and were counted as mortality at the next observation time for mean death time calculations.Evaluation of antibody titers based on prime-boost order with the RP and inactivated vaccinesTo determine if there was a difference in antibody levels based on the order of vaccination with the RP vaccine and an inactivated vaccine, groups of 20 chickens (hatch-mates of the chickens in the challenge study) were given one dose of each vaccine three weeks apart (Supplementary Table 1). The first dose was administered at three weeks of age using the RP or SEP-22-N9 vaccine as described above. Then a second dose of either the same vaccine or the other vaccine was administered three weeks later (six weeks of age). All birds were bled for serum three weeks after the second vaccination (nine weeks of age). Antibody was quantified by hemagglutination inhibition (HI) assay as described below using the homologous antigen (TK/IN/22).Quantitative rRT-PCR (qRRT-PCR)RNA was extracted from OP and CL swabs using the MagMax (Thermo Fisher Scientific, Waltham, MA) magnetic bead extraction kit with the modifications described by Das et al., (2). Quantitative real-time RT-PCR was conducted as described previously (3) on a QuantStudio 5 (Thermo Fisher Scientific). A standard curve was generated from a titrated stock of TK/IN/22 and was used to calculate titer equivalents using the real time PCR instrument’s software.Hemagglutination inhibition assayHemagglutination inhibition assays were run in accordance with standard procedures. All pre-challenge sera were tested against the challenge virus. Sera from birds vaccinated with the rgH5N1 vaccine were also tested against the vaccine antigen, GF/WA/14. Titers of 8 or below were considered non-specific binding, therefore negative.Commercial ELISAPre-vaccination sera from 30 chickens were tested to confirm the absence of antibodies to AIV with a commercial AIV antibody ELISA (IDEXX laboratories, Westbrook, ME) in accordance with the manufacturer’s instructions. Pre- and post-challenge sera from the RP vaccine group (the only vaccine utilized here that does not induce antibodies to the NP) were also tested with this ELISA to characterize the detection of anti-NP antibodies post-challenge.Enzyme-linked lectin assay (ELLA) and neuraminidase inhibition (NI) to detect N1 antibody in serum from challenged chickensThe ELLA assay was performed in accordance with a previously published protocol with minor modifications (4). Absorbance data were fit to a non-linear regression curve with Prism 9.5 (GraphPad Software LLC, Boston, MA) to determine the effective concentration, and the 98% effective concentration (EC98) of the N1 source virus was subsequently used for NI assays.To detect N1 antibody with the optimized N1 NA concentrations, serum samples from the sham, SEP-22-N9, and RP vaccinated groups collected pre-challenge, 7, 10 and 14DPC, were heat inactivated at 56°C for one hour and diluted 1:20 and 1:40 using sample dilution buffer. Equal volumes of the N1 NA source virus at a concentration of 2X EC98 was added to each of the diluted serum samples. Then 100µl of the serum-virus mixture was added to the fetuin coated plates after the fetuin plates were washed as described above for the NA assay. Fetuin plates with the serum-virus mixture were then incubated overnight (approximately 17-19hr) at 37°C. The NA assay protocol described above was followed for the remaining NI assay steps.The percent NI activity of individual serum samples was determined by subtracting percent NA activity from 100. To calculate the percent NA activity, the average background absorbance value was subtracted from the sample absorbance value. The result was then divided by the average value of the NA source virus only (no serum) wells then multiplying by 100. A cut-off value for NI activity for positive detection of N1 antibody from chickens post-challenge was calculated by adding three standard deviations to the mean value obtained from pre-challenge sera of corresponding vaccine group for each dilution tested (1:20 and 1:40).References1. Kapczynski DR, Sylte MJ, Killian ML, Torchetti MK, Chrzastek K, Suarez DL. Protection of commercial turkeys following inactivated or recombinant H5 vaccine application against the 2015U.S. H5N2 clade 2.3.4.4 highly pathogenic avian influenza virus. Vet Immunol Immunopathol. 2017;191:74-9. Epub 2017/09/13. doi: 10.1016/j.vetimm.2017.08.001.2. Das A, Spackman E, Pantin-Jackwood MJ, Suarez DL. Removal of real-time reverse transcription polymerase chain reaction (RT-PCR) inhibitors associated with cloacal swab samples and tissues for improved diagnosis of Avian influenza virus by RT-PCR. Journal of Veterinary Diagnostic Investigation. 2009;21(6):771-8.3. Spackman E, Senne DA, Myers TJ, Bulaga LL, Garber LP, Perdue ML, et al. Development of a real-time reverse transcriptase PCR assay for type A influenza virus and the avian H5 and H7 hemagglutinin subtypes. Journal of Clinical Microbiology. 2002;40(9):3256-60.4. Bernard MC, Waldock J, Commandeur S, Strauss L, Trombetta CM, Marchi S, et al. Validation of a Harmonized Enzyme-Linked-Lectin-Assay (ELLA-NI) Based Neuraminidase Inhibition Assay Standard Operating Procedure (SOP) for Quantification of N1 Influenza Antibodies and the Use of a Calibrator to Improve the Reproducibility of the ELLA-NI With Reverse Genetics Viral and Recombinant Neuraminidase Antigens: A FLUCOP Collaborative Study. Front Immunol. 2022;13:909297. Epub 2022/07/06.

Background: Individuals with blood cancers have weakened immune systems due to their disease and treatments, putting them at higher risk of death for COVID-19. An effective vaccine is important to protect them from infection; however, major COVID-19 vaccine trials did not include this population. Therefore, it was not known if people with or treated for blood cancers develop a protective antibody response after vaccination, or if it would last as long as in the general population. Aims of the CITF-funded study: This study aimed to quantify the humoral immune response to the SARS-CoV-2 vaccine and to describe Grade 2-4 adverse events following immunization in patients with hematologic malignancies. Methods: This cohort study enrolled patients with hematologic malignancies aged 18 and older from hematology clinics in participating hospitals in Edmonton, Montreal, Ottawa, and Toronto. Participants completed an online questionnaire and provided a dried blood spot sample at baseline and during each follow-up—approximately 90, 180, 270, and 360 days after their second COVID-19 vaccine. Blood samples were also collected 28 days after any additional COVID-19 vaccination dose. Contributed dataset contents: The datasets include 751 participants who completed baseline questionnaires between August 2021 and August 2023. 95% of participants gave one or more blood samples for SARS-CoV-2 serology between September 2021 and February 2023. Variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, region, education, occupation), general health (smokes; chronic disease; height and weight; flu vaccine), SARS-CoV-2 vaccination, clinical characteristics, and serology.

Content

List of Chicago Department of Public Health free flu clinics offered throughout the city. For more information about the flu, go to http://bit.ly/9uNhqG.

Context

This is a dataset hosted by the City of Chicago. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore the City of Chicago using Kaggle and all of the data sources available through the City of Chicago organization page!

• Update Frequency: This dataset is updated annually.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Hush Naidoo on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Everyone 6 months and older should get the flu shot, especially people at high risk of complications from the flu and people who can pass the flu to those at high risk.

Background: There is a lack of data on the rates of infection with SARS-CoV-2 and risk factors for infection in healthcare workers (HCW), who are at high risk of exposure. This knowledge is important to mitigate risk factors and protect their communities in- and out-side the workplace by improving protective guidelines. Aims of the CITF-funded study: This study aimed to identify risk factors for contracting SARS-CoV-2 in the workplace, household, and personal environments to assess the variance in risk due to exposure. The objectives were 1) to determine the incidence of symptomatic and asymptomatic infection; 2) study the use and effectiveness of vaccines in HCWs; 3) determine the pattern of immune responses over time via serology; and 4) to measure the mental health impact of working during a pandemic. Methods: This cohort study enrolled full and part-time HCWs between the ages of 18 and 75 in Alberta, Nova Scotia, Ontario, and Quebec recruited via internal institutional advertisements and social media. A serum or dried blood spot sample was collected at enrolment, 30 days after receipt of each COVID-19 vaccine and each positive PCR or RAT, and every six months thereafter to assess IgG antibody levels. Participants completed questionnaires at enrolment regarding risk factors, vaccinations, past infections. Questionnaires were every 10 weeks to collect exposure data and as needed to collect vaccination and illness information. Contributed dataset contents: The datasets include 2164 participants who completed baseline questionnaires between June 2020 and March 2022. 87% of participants gave one or more blood samples for SARS-CoV-2 serology over this period. Variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, province, household, education, occupation), general health (smokes; asthma, lung disease, or other chronic disease diagnosis; height and weight; flu vaccine), SARS-CoV-2 vaccination, and serology.

This dataset represents preliminary estimates of cumulative U.S. RSV –associated disease burden estimates for the 2024-2025 season, including outpatient visits, hospitalizations, and deaths. Real-time estimates are preliminary and based on continuously collected surveillance data from patients hospitalized with laboratory-confirmed respiratory syncytial virus (RSV) infections. The data come from the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET), a surveillance platform that captures data from hospitals that serve about 8% of the U.S. population. Each week CDC estimates a range (i.e., lower estimate and an upper estimate) of RSV-associated disease burden estimates that have occurred since October 1, 2024.

Note: Data are preliminary and subject to change as more data become available. Rates for recent RSV-associated hospital admissions are subject to reporting delays; as new data are received each week, previous rates are updated accordingly.

Note: Preliminary burden estimates are not inclusive of data from all RSV-NET sites. Due to model limitations, sites with small sample sizes can impact estimates in unpredictable ways and are excluded for the benefit of model stability. CDC is working to address model limitations and include data from all sites in final burden estimates.

References

1. Reed C, Chaves SS, Daily Kirley P, et al. Estimating influenza disease burden from population-based surveillance data in the United States. PLoS One. 2015;10(3):e0118369. https://doi.org/10.1371/journal.pone.0118369 
2. Rolfes, MA, Foppa, IM, Garg, S, et al. Annual estimates of the burden of seasonal influenza in the United States: A tool for strengthening influenza surveillance and preparedness. Influenza Other Respi Viruses. 2018; 12: 132– 137. https://doi.org/10.1111/irv.12486
3. Tokars JI, Rolfes MA, Foppa IM, Reed C. An evaluation and update of methods for estimating the number of influenza cases averted by vaccination in the United States. Vaccine. 2018;36(48):7331-7337. doi:10.1016/j.vaccine.2018.10.026 
4. Collier SA, Deng L, Adam EA, Benedict KM, Beshearse EM, Blackstock AJ, Bruce BB, Derado G, Edens C, Fullerton KE, Gargano JW, Geissler AL, Hall AJ, Havelaar AH, Hill VR, Hoekstra RM, Reddy SC, Scallan E, Stokes EK, Yoder JS, Beach MJ. Estimate of Burden and Direct Healthcare Cost of Infectious Waterborne Disease in the United States. Emerg Infect Dis. 2021 Jan;27(1):140-149. doi: 10.3201/eid2701.190676. PMID: 33350905; PMCID: PMC7774540.
5. Reed C, Kim IK, Singleton JA,  et al. Estimated influenza illnesses and hospitalizations averted by vaccination–United States, 2013-14 influenza season. MMWR Morb Mortal Wkly Rep. 2014 Dec 12;63(49):1151-4. https://www.cdc.gov/mmwr/preview/mmwrhtml/mm6349a2.htm 
6. Reed C, Angulo FJ, Swerdlow DL, et al. Estimates of the Prevalence of Pandemic (H1N1) 2009, United States, April–July 2009. Emerg Infect Dis. 2009;15(12):2004-2007. https://dx.doi.org/10.3201/eid1512.091413  
7. Devine O, Pham H, Gunnels B, et al. Extrapolating Sentinel Surveillance Information to Estimate National COVID-19 Hospital Admission Rates: A Bayesian Modeling Approach. Influenza and Other Respiratory Viruses. https://onlinelibrary.wiley.com/doi/10.1111/irv.70026. Volume18, Issue10. October 2024.
8. https://www.cdc.gov/covid/php/covid-net/index.html">COVID-NET | COVID-19 | CDC 
9. https://www.cdc.gov/covid/hcp/clinical-care/systematic-review-process.html 
10. https://academic.oup.com/pnasnexus/article/1/3/pgac079/6604394?login=false">Excess natural-cause deaths in California by cause and setting: March 2020 through February 2021 | PNAS Nexus | Oxford Academic (oup.com)
11. Kruschke, J. K. 2011. Doing Bayesian data analysis: a tutorial with R and BUGS. Elsevier, Amsterdam, Section 3.3.5.

Background: Two-Spirit, lesbian, gay, bisexual, trans, queer, questioning, intersex, asexual, and other sexual- and gender-marginalized people experience health inequities that may have resulted in greater impact of COVID-19 disease and public health measures. Aims of the CITF co-funded study:The study aimed to conduct an online survey of 2SLGBTQQIA+ individuals across urban and non-urban regions in Canada to evaluate the impact of COVID-19 on their health, wellness, stigma, and material security. Methods: This cross-sectional study recruited individuals who identified as a gender or sex minority or a sexual orientation minority, were at least 15 years old, and resided in Canada during the COVID-19 pandemic. Participants were recruited via media advertisements. Researchers collected information via an online survey offered in English, French, and Spanish, and used a community-based approach to engage participants. Participants over the age of 18 were given the option to provide a self-collected dried blood spot (DBS) via a mailed kit to test for SARS-CoV-2. Contributed dataset contents: The datasets include 4013 participants who completed baseline interviews between April 2022 and September 2022. 30% of participants gave one blood sample for SARS-CoV-2 serology between March 2022 and February 2023. Variables include data in the following areas of information: demographics (age, gender, race-ethnicity and indigeneity, province), general health (chronic conditions; flu vaccine), SARS-CoV-2 vaccination, and serology.

BackgroundInfluenza vaccination uptake among United States adults aged 65 years or older remains suboptimal and stagnant. This study aims to evaluate the prevalence of influenza vaccination and examine sociodemographic disparities within a nationally representative sample.MethodsThis study is a cross-sectional study. We used the data from the Behavioral Risk Factor Surveillance System spanning the years 2011 to 2022. Logistic regression models were used to assess potential associations between influenza vaccination uptake and sociodemographic characteristics. Concentration indexes were also calculated to measure the socioeconomic inequalities on influenza vaccination uptake.ResultsThe study included 1,391,440 adults aged 65 years and older, with 62.87% reporting having received an influenza vaccination. The weighted prevalence of influenza vaccination uptake showed a slight increase, ranging from 59.05% in 2011–2013 to 67.49% in 2020–2022. Higher vaccination rates were observed among non-Hispanic Whites [63.16%; odds ratio (OR) 1.38, (95% CI 1.33–1.42)], individuals with education above high school [63.89%; OR 1.16, (95% CI 1.12–1.19)], and those with an income above $50,000 [65.86%; OR 1.47, (95% CI 1.43–1.50)]. Compared to non-Hispanic Black people with an income below $25,000 and education less than high school, the ORs were significantly higher among non-Hispanic whites [2.12, (95% CI 1.97–2.28)], non-Hispanic Black people [1.30, (95% CI 1.18–1.44)], and Hispanics [1.40, (95% CI 1.24–1.59)] earning above $50,000 and education above high school. Those who received an influenza vaccination tended to be concentrated in the high-income group and high-education group.ConclusionThere are substantial racial and socioeconomic disparities in influenza vaccination uptake among individuals aged 65 years or older. Health policy maybe urgently needed to reduce these avoidable inequalities.

IntroductionThe key to understanding the COVID-19 correlates of protection is assessing vaccine-induced immunity in different demographic groups. Young people are at a lower risk of COVID-19 mortality, females are at a lower risk than males, and females often generate stronger immune responses to vaccination.MethodsWe studied immune responses to two doses of BNT162b2 Pfizer COVID-19 vaccine in an adolescent cohort (n = 34, ages 12–16), an age group previously shown to elicit significantly greater immune responses to the same vaccine than young adults. Adolescents were studied with the aim of comparing their response to BNT162b2 to that of adults; and to assess the impacts of other factors such as sex, ongoing SARS–CoV–2 infection in schools, and prior exposure to endemic coronaviruses that circulate at high levels in young people. At the same time, we were able to evaluate immune responses to the co-administered live attenuated influenza vaccine. Blood samples from 34 adolescents taken before and after vaccination with COVID-19 and influenza vaccines were assayed for SARS–CoV–2-specific IgG and neutralising antibodies and cellular immunity specific for SARS–CoV–2 and endemic betacoronaviruses. The IgG targeting influenza lineages contained in the influenza vaccine were also assessed.ResultsRobust neutralising responses were identified in previously infected adolescents after one dose, and two doses were required in infection-naïve adolescents. As previously demonstrated, total IgG responses to SARS–CoV-2 Spike were significantly higher among vaccinated adolescents than among adults (aged 32–52) who received the BNT162b2 vaccine (comparing infection-naïve, 49,696 vs. 33,339; p = 0.03; comparing SARS-CoV–2 previously infected, 743,691 vs. 269,985; p

Percentage of individuals in Norway vaccinated with adjuvanted (AS03) A/H1N1 2009 pandemic influenza vaccine during the A/H1N1 2009 influenza virus pandemic.

This dataset contains information from a population-based survey, which investigated human exposure to live poultry, and population psychological response and behavioral changes of the community members during two waves of influenza A(H7N9) epidemics in Southern China in 2013-2014. The dataset including 3 files. * One file named "population_wt.csv" contained population information of the studied sites; * One file named "H7N9 survey China_Que stionarie_eng.doc" was the survey questionaire; * The third file named "dataset_H7N9.csv" contained datasets acquired during the two waves of A(H7N9) epidemics,a data frame with 1657 observations on the following 44 variables. Survey ##a numeric vector: where the subject live## 1= the first wave () 2= the second wave () Place ##a numeric vector: where the subject live## 5=Guangzhou 10=Zijin County, Heyuan City SG3 ##a numeric vector: the gender of the subject## 1=Female 2=Male SG4_b ##a numeric vector: the age group of the subject, unit=years## 1=18-24 2=25-34 3=35-44 4=45-54 5=55-64 6=65+ SG6 ##a numeric vector: the marital status of the subject## 1=Single 2=Married 3=Divorced /separated 4=Widowed 5=Refuse to answer SG8 ##a numeric vector: the educational attainment of the subject## 1=Illiteracy 2=Primary school 3=Middle school 4=High school 5=College and above SG12 ##a numeric vector: the average income of the subject, unit=Chinese Yuan## 1=Less than l,000 2=1,001—2,000 3=2,001—3,000 4=3,001—4,000 5=4,001—6,000 6=6,001—8,000 7=8,001—10,000 8=10,001—2,000 9=15,001—20,000 10=20,001—30,000 11=More than 30,001 12=No income 13=Don’t know 14=Refuse to answer AX1_a ##a numeric vector: the anxiety level of the subject, I feel rested ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_b ##a numeric vector: the anxiety level of the subject, I feel content ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_c ##a numeric vector: the anxiety level of the subject, I feel comfortable ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_d ##a numeric vector: the anxiety level of the subject, I am relaxed ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_e ##a numeric vector: the anxiety level of the subject, I feel pleasant ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_f ##a numeric vector: the anxiety level of the subject, I feel anxious ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_g ##a numeric vector: the anxiety level of the subject, I feel nervous ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_h ##a numeric vector: the anxiety level of the subject, I am jittery ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_i ##a numeric vector: the anxiety level of the subject, I feel “high strung” ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So AX1_j ##a numeric vector: the anxiety level of the subject, I feel over-excited and “rattled” ## 1=Not at all 2=Sometimes 3=Moderately So 4=Very Much So BF4b##a numeric vector indicating the subject's rate of worriness towards H7N9 avian flu, 1 being very mild to 10 being very severe## EM1 ##a numeric vector: How often did you go to wet markets in the past year ## 1=1-2/year 2=3-5/year 3=6-11/year 4=1-3/month 5=1-2/week 6=3-5/week 7=Almost every day 8=Almost not EM2 ##a numeric vector: How often did you buy poultry in wet markets in the past year ## 1=1-2/year 2=3-5/year 3=6-11/year 4=1-3/month 5=1-2/week 6=3-5/week 7=Almost every day 8=Almost not EM3 ##a numeric vector: Did you usually pick up the poultry for examination before deciding to buy it ## 1=Yes 2=No 3=Sometime “yes”, sometime “no” EM4 ##a numeric vector: Where was the live poultry slaughtered when you bought it? ## 1=Always in wet market 2=Usually in wet market 3=Usually in my household 4=Always in my household 5=Other places EM5 ##a numeric vector: Have your habit of buying live poultry changed since the first human H7N9 case was released in the past month ## 1=Yes, not buying since then 2=No, still buying and eating live poultry 3=Still buying but less than before EM6 ##a numeric vector: Would you support permanent closure of live poultry markets in order to control avian influenza epidemics ## 1=Strongly agree 2=Agree 3=Not agree 4=Strongly disagree 5=Don’t know EM8 ##a numeric vector: Have your raised live poultry in your backyard in the past year ## 1=Yes 2=No BF1 ##a numeric vector indicating risk perception of the subject: How likely do you think it is that you will contract H7N9 avian flu over the next 1 month ## 1=Never 2=Very unlikely 3=Unlikely 4=Evens 5=Likely 6=Very likely 7=Certain BF2a ##a numeric vector indicating risk perception of the subject: What do you think are your chances of getting H7N9 avian flu over the next 1 month compared to other people outside your family of a similar age ## 1=Not at all 2=Much less 3=Less 4=Evens 5=More 6=Much more 7=Certain BF3_l ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by the body contact with patients ## 1=Yes 2=No 3=Don’t Know BF3_m ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by touching objects that have been contaminated by the virus ## 1=Yes 2=No 3=Don’t Know BF3_n ##a numeric vector indicating knowledge of the subject: H7N9 avian flu is spread by the close contact with chickens in a wet market ## 1=Yes 2=No 3=Don’t Know BF4 ##a numeric vector: If you were to develop flu-like symptoms tomorrow, would you be... ## 1=Not at all worried 2=Much less worried than normal 3=Worried less than normal 4=About same 5=Worried more than normal 6=Worried much more than normal 7=Extremely worried BF4a ##a numeric vector indicating risk perception of the subject: In the past one week, have you ever worried about catching H7N9 avian flu ## 1=No, never think about it 2=Think about it but it doesn’t worry me 3=Worries me a bit 4=Worries me a lot 5=Worry about it all the time BF5a ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with seasonal flu in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF5b ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with H5N1 avian flu in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF5c ##a numeric vector indicating risk perception of the subject: How does H7N9 avian flu compare with SARS in terms of seriousness ## 1=Much higher 2=A little higher 3=Same 4=A little lower 5=Much lower 6=Don’t Know BF7 ##a numeric vector evaluating the current performance of the national government in controlling H7N9 avian flu, (0=extremely poor, 5=moderate, 10=excellent) ## BF7a ##a numeric vector evaluating the current performance of the provincial/city government in controlling H7N9 avian flu, (0=extremely poor, 5=moderate, 10=excellent) ## PM2 ##a numeric vector indicating the preventive behavior of the subject, covering the mouth when sneeze or cough ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (no sneeze or cough) PM3 ##a numeric vector indicating the preventive behavior of the subject, washing hands after sneezing, coughing or touching nose ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (no sneeze or cough) PM3a ##a numeric vector indicating the preventive behavior of the subject,washing hands after returning home ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know 6=Not applicable (never go out) PM4 ##a numeric vector indicating the preventive behavior of the subject,using liquid soap when washing hands ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know PM5 ##a numeric vector indicating the preventive behavior of the subject,wearing face mask ## 1=Always 2=Usually 3=Sometimes 4=Never 5=Don’t know PM7 ##a numeric vector:If free H7N9 flu vaccine is available in the coming month, would you consider receiving it ## 1=Yes 2=No 3=Not sure 4=Don’t know

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