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.

What is the FluPRINT database?

The FluPRINT represents fully integrated and normalized immunology measurements from eight clinical studies taken from 740 individuals undergoing influenza vaccination with inactivated or live attenuated seasonal influenza vaccines from 2007 to 2015 at the Stanford Human Immune Monitoring Center.

The FluPRINT dataset contains information on more than 3,000 parameters measured using mass cytometry, flow cytometry, phosphorylation-specific cytometry, multiplex cytokine assays, clinical lab tests (hormones and complete blood count), serological profiling and virological tests. In the dataset, vaccine protection is measured using a hemagglutination inhibition (HAI) assay, and following FDA guidelines individuals are marked as high or low responders depending on the HAI antibody titers after vaccination.

Want to know more?

To understand how the FluPRINT dataset was generated and validated, and how to use it, please refer to our open-access paper published in Scientific Data journal:

Tomic, A., Tomic, I., Dekker, C.L. et al. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Sci Data 6, 214 (2019). https://doi.org/10.1038/s41597-019-0213-4

For additional exploration, please check out the project’s website: www.fluprint.com, where you can also explore the FluPRINT dataset on the following link: https://fluprint.com/#/database-access.

If you want to host your own FluPRINT database, please follow our instructions provided on the Github repository: https://github.com/LogIN-/fluprint.

How to use FluPRINT?

Here, you can download the entire FluPRINT database export as an SQL file, or as a CSV file. Additionally, we included the file with the SQL query to obtain those files.

Files are provided in two formats: zip and 7zip (7z). 7zip is a free and open-source file archiver available for download here: https://www.7-zip.org.

In the FluPRINT database, there are 4 tables: donor, donor_visits, experimental_data, and medical_history.
The exact description of each table is available in the FluPRINT paper.

Briefly, in the table donor, each row represents an individual with information about the clinical study in which an individual was enrolled (study ID and study internal ID), gender, and race. The second table, named donor_visits describes information about the donor’s age, cytomegalovirus (CMV) and Epstein-Barr virus (EBV) status, Body Mass Index (BMI), and vaccine received on each clinical visit. Information about vaccine outcome is available as geometric mean titers (geo_mean), the difference in the geometric mean titers before and after vaccination (delta_geo_mean), and the difference for each vaccine strain (delta_single). In the last field, each individual is classified as a high and low responder (vaccine_resp). On each visit, samples were analyzed and information about which assays were performed (assay field) and value of the measured analytes (units and data) are stored in the experimental_data table. Finally, the medical_history table describes information connected with each clinical visit about the usage of statins (statin_use) and if influenza vaccine was received in the past (influenza vaccine history), if yes, how many times (total_vaccines_received). Also, we provide information on which type of influenza vaccine was received in the previous years (1 to 5 years prior to enrolment in the clinical study). Lastly, information about influenza infection history and influenza-related hospitalization is provided.

How to cite FluPRINT?

If you use FluPRINT in an academic publication, please use the following citation:

Tomic, A., Tomic, I., Dekker, C.L. et al. The FluPRINT dataset, a multidimensional analysis of the influenza vaccine imprint on the immune system. Sci Data 6, 214 (2019). https://doi.org/10.1038/s41597-019-0213-4

Contact Information

If you are interested to find out more about the FluPRINT, or if you experience any problems with downloading files, please contact us at info@adrianatomic.com.

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

List of sites that guarantee specific, non-flu vaccines will be in-stock and available for the time period indicated in each record. For flu shot locations, go to https://data.cityofchicago.org/Health-Human-Services/Flu-Shot-Locations-2014-Present/w3hg-pyhz. Note for using this dataset that it is always recommended that individuals contact their healthcare provider or closest pharmacy to check on vaccine availability prior to walking in for vaccination. Each vaccine has a filtered view showing only records from that vaccine and blank filter conditions that can be used to filter further by time. These filtered views can be used for almost all purposes as if they were datasets, although any additional criteria added by a user would need to be saved to a new filtered view in order to use it in this manner. This dataset approximately follows https://github.com/codeforamerica/flu-shot-spec/blob/master/data-format.csv and is designed for use by https://github.com/tkompare. For more information about CDPH Vaccines/Shots/Immunizations, go to https://www.chicago.gov/city/en/depts/cdph/provdrs/health_protection_and_response/svcs/immunization1.html.

While vaccine hesitancy has become a salient issue, few studies have examined the influence of international collaboration and vaccine developments on people’s attitudes toward vaccines. The international collaboration especially with China has been an integral part of the field of influenza. In recent years, attitudes toward vaccines and China are both heavily politicized in the U.S. with a deepening partisan divide. Republicans are more likely than Democrats to be vaccine hesitant, and they are also more likely to view China negatively. At the same time, the U.S. has economic, security and medical collaboration with Japan and most Americans display a very positive view of the country. Thus, does a more international collaboration or more country specific vaccine development have an influence on U.S. vaccine hesitancy? This study conducts a survey-embedded question-wording experiment to assess the roles of US-China and US-Japan collaboration and partisanship in people’s willingness to get the flu vaccine. Despite the previously successful and effective US-China collaboration, this study finds that respondents especially Republicans are much less likely to receive a US-China flu vaccine than a US-Japan or US alone. Interestingly, both Democrats and Republicans are as willing to receive a US-Japan vaccine as US alone. These results point to critical roles of partisanship and international relations.

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

Rank, number of deaths, percentage of deaths, and age-specific mortality rates for the leading causes of death, by age group and sex, 2000 to most recent year.

Effective September 27, 2023, this dataset will no longer be updated. Similar data are accessible from wonder.cdc.gov.

Deaths involving COVID-19, pneumonia, and influenza reported to NCHS by sex, age group, and jurisdiction of occurrence.

This file contains the complete set of data reported to 122 Cities Mortality Reposting System. The system was retired as of 10/6/2016. While the system was running each week, the vital statistics offices of 122 cities across the United States reported the total number of death certificates processed and the number of those for which pneumonia or influenza was listed as the underlying or contributing cause of death by age group (Under 28 days, 28 days - 1 year, 1-14 years, 15-24 years, 25-44 years, 45-64 years, 65-74 years, 75-84 years, and - 85 years). U:Unavailable. - : No reported cases.* Mortality data in this table were voluntarily reported from 122 cities in the United States, most of which have populations of >100,000. A death is reported by the place of its occurrence and by the week that the death certificate was filed. Fetal deaths are not included. Total includes unknown ages. More information on Flu Activity & Surveillance is available at http://www.cdc.gov/flu/weekly/fluactivitysurv.htm.

Provisional counts of the number of death occurrences in England and Wales due to coronavirus (COVID-19) and influenza and pneumonia, by age, sex and place of death.

The Respiratory Virus Hospitalization Surveillance Network (RESP-NET) is a network that conducts, active, population-based surveillance for laboratory confirmed hospitalizations associated with Influenza, COVID-19, and RSV. The RESP-NET platforms have overlapping surveillance areas and use similar methods to collect data. Hospitalization rates show how many people in the surveillance area are hospitalized with influenza, COVID-19, and RSV compared to the total number of people residing in that area.

Data will be updated weekly. Data are preliminary and subject to change as more data become available.

Attitudes to the political, social and economic Transformation . Topics: Economic situation; economic transformations; development of private business, privatization of land and of large enterprises; buying and selling land; willingness to work for a private company; direction of foreign policy; freedom of expression of political views; return to socialism vs. develop capitalism; role of social groups; trust in family and relatives, oneself, neighbors, fellow citizens, god, colleagues, church, astrologers, mass media; police, communist party, political parties, "Rukh", nationalists, Verkhovna Rada (parliament), armed forces, government, president, private entrepreneurs, mangers of large state enterprises, trade unions (traditional and new); membership in organizations; leisure activities; newspapers read last week; interests in politics; capable political leaders; strong leader vs. democracy; multiparty system; political parties and movements, that deserve power; important political movements; participation and voting in the Verkhovna Rada (parliament) Elections (March 1998); trust in deputy elected in one´s district; opinion about the President Kuchma; preferred role of the president; preferred priority in the policies of the president; general political situation in Ukraine and Russia; joining the union of Russia and Belarus; Russian language as a official language; satisfaction with one´s own present position in society, one´s own contribution to society and with that what one gets from society; predominant influence on one´s own life; satisfied with outlook on life; mood last days; social position in society; ability to live under changing social conditions as regards to health, working, clothing, housing, economic knowledge, confidence in one´s own abilities, medical assistance, fashionable clothing, basic furniture, contemporary political knowledge, resolve in pursuing one´s goals, legal protection for defending one´s rights and interests, ability to have an adequate vacation, having a second, unofficial job, buying the most necessary products, initiative and independence in solving daily problems, adequate leisure time, opportunity to work to full potential, opportunity to eat according to one´s own tastes; general health condition; suffering from any chronic illnesses; frequency of catching a cold/flu last year; frequency of being sick; stressful situations during last year; consequences of the Chornobyl catastrophe for one´s own health; satisfaction with quality of life in one´s resident; close relatives living outside Ukraine; leaving current residence (influential factors); preferred place to live; satisfaction with living conditions; current living conditions; number of rooms; size of family; number of people living together in one room; equipment in the household; possession of goods; second resident; domestic animals/pets; material level of the family´s life (scale); second income; income group; salary last month and anything left for next months; responsibility for delayed payments of wages; average income of the last month; monthly income (per person) providing average life of one´s own family; monthly average income (per person) counted as poor/rich; changes of material conditions for medical services, vacation, leisure time, reliable information about events in Ukraine and in the world, raising children, freedom to express views, participation in cultural events, environmental situation, personal security, protection from the whims of bureaucrats and bodies of power, security of employment; frequency of hooliganism and robberies in one´s own district; decision which encroached on people´s interests and actions against it; probability of mass protest actions and participation in them; political protests; death penalty; attitudes towards ethnic groups; violation of ethnic groups; maintain of peace and order; frequency of changing place of employment; work in public or private sector; job satisfied; religious confession; nationality; native language; spoken languages; language of the interview. Einstellung zur politischen, sozialen und ökonomischen Transformation. Themen: Ökonomische Situation; ökonomische Transformation; Entwicklung der Privatwirtschaft; Privatisierung von Grund und Boden sowie großer Unternehmen; Kauf und Verkauf von Land; Bereitschaft in einem privaten Betrieb zu arbeiten; Ausrichtung der Außenpolitik; Meinungsfreiheit; Rückkehr zum Sozialismus oder Entwicklung des Kapitalismus; Rolle sozialer Gruppen; Vertrauen in Institutionen; Freizeitaktivitäten; Zeitungslesen letzte Woche; Interesse an Politik; fähige politische Führer; starker Führer vs. Demokratie; Mehrparteiensystem; politische Parteien und Bewegungen, die die Macht verdienen; wichtige politische Bewegungen; Wahlbeteiligung und Wahlverhalten bei der Parlamentswahl 1998; Vertrauen in gewählten Abgeordneten des Distrikts; Meinung über Prof. Kuchma; bevorzugte Rolle des Präsidenten; bevorzugte Prioritäten der Politik des Präsidenten; allgemeine politische Situation in der Ukraine und in Russland; Beitritt der Vereinigung von Weißrussland und Russland; Russisch als offizielle Sprache; Zufriedenheit mit der eigenen Position in der Gesellschaft, dem eigenen Beitrag zur Gesellschaft und mit dem, was man von der Gesellschaft bekommt; vorherrschender Einfluss auf das eigene Leben; Zufriedenheit mit den Lebensaussichten; Stimmung in den letzten Tagen; soziale Position in der Gesellschaft; Fähigkeit, unter sich verändernden Bedingungen zu leben; Häufigkeit von Erkältungskrankheiten im letzten Jahr; Krankheitshäufigkeit; Stress im letzten Jahr; Folgen der Tschernobyl-Katastrophe für die eigene Gesundheit; Zufriedenheit mit der Lebensqualität in der eigenen Wohnumgebung; enge Verwandte im Ausland; Bereitschaft den Wohnort zu wechseln; bevorzugter Wohnort; Zufriedenheit mit Lebensbedingungen; Anzahl der Wohnräume; Familiengröße; Haushaltsgröße; Haushaltsausstattung; Besitz von Gütern; zweiter Wohnsitz; Haustiere; materielles Lebensniveau der Familie (Skala); Zweiteinkommen; Einkommensgruppe; Einkommen des letzten Monats und was davon übrig ist; Verantwortlichkeiten für verspätete Zahlung; Durchschnittseinkommen des letzten Monats; monatliches Einkommen (pro Person); Durchschnittsleben für Familie gewährleisten; arm/reich; veränderte Bedingungen für die medizinische Versorgung; Urlaub; Freizeit; zuverlässige Informationen über die Ereignisse in der Ukraine und der Welt; Kindererziehung; Meinungsfreiheit; Teilnahme an kulturellen Veranstaltungen; Umweltsituation; persönliche Sicherheit; Schutz vor Behördenwillkür; Arbeitsplatzsicherheit; Häufigkeit von Überfällen in der Wohnumgebung; Übergriffe auf die Interessen der Menschen und Aktionen dagegen; Wahrscheinlichkeit von Massenprotesten und Teilnahme daran; politischer Protest; Todesstrafe; Haltung gegenüber ethnischen Gruppen; Menschenrechtsverletzungen ethnischer Gruppen; Aufrecherhaltung von Ruhe und Ordnung; Häufigkeit des Wechsels der Arbeitsstelle; Arbeit im öffentlichen oder privaten Sektor; Arbeitszufriedenheit; Religion; Nationalität; Muttersprache; weitere Sprachen; Interviewsprache. Quota sample (combined with route selection). Average bias from current social statistics does not exceed 2.0 percent. Quotenstichprobe (kombiniert mit Random Route). Durchschnittliche Abweichung von amtlicher Statistik nicht mehr als 2%.

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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The study aims to describe patients hospitalized for severe acute respiratory illness (SARI) due to COVID-19 (SARI-COVID) in Brazil according to demographic characteristics and comorbidities up to the 21st Epidemiological Week of 2020. The study aimed to compare these characteristics with those of patients hospitalized for SARI due to influenza in 2019/2020 (SARI-FLU) and with the Brazilian general population. The proportions of demographic characteristics, comorbidities, and pregnant and postpartum women among patients hospitalized for SARI-COVID and SARI-FLU were obtained from the SIVEP-Gripe database, and the estimates for the Brazilian population were obtained from the population projections performed by Brazilian Institute of Geography and Statistics, Information System on Live Birth data, and nationwide surveys. Compared to the Brazilian population, patients hospitalized for SARI-COVID showed a higher proportion of males, elderly individuals and those aged 40 to 59 years, comorbidities (diabetes mellitus, cardiovascular disease, chronic kidney disease, and chronic lung diseases), and pregnant/postpartum women. Compared to the general population, Brazilians hospitalized for SARI-FLU showed higher prevalence rates of ages 0 to 4 years or over 60 years, white race/color, comorbidities (diabetes, chronic kidney disease, asthma, and other chronic lung diseases), and pregnant/postpartum women. The data suggest that these groups are evolving to more serious forms of the disease, so that longitudinal studies are extremely relevant for investigating this hypothesis and supporting appropriate public health policies.

Percent of tests positive for a pathogen is one of the surveillance metrics used to monitor respiratory pathogen transmission over time. The percent of tests positive is calculated by dividing the number of positive tests by the total number of tests administered, then multiplying by 100 [(# of positive tests/total tests) x 100]. These data include percent of tests positive values for the detection of severe acute respiratory virus coronavirus type 2 (SARS-CoV-2), the virus that causes COVID-19 and Respiratory syncytial virus (RSV) reported to the National Respiratory and Enteric Virus Surveillance System (NREVSS), a sentinel network of laboratories located through the US, includes clinical, public health and commercial laboratories; additional information available at: https://www.cdc.gov/surveillance/nrevss/index.html. Influenza results include clinical laboratory test results from NREVSS and U.S. World Health Organization collaborating laboratories; more details about influenza virologic surveillance are available here: https://www.cdc.gov/flu/weekly/overview.html. Data represent calculations based on laboratory tests performed, not individual people tested. RSV and COVID-19 are limited to nucleic acid amplification tests (NAATs), also listed as polymerase chain reaction tests (PCR). Participating laboratories report weekly to CDC the total number of RSV tests performed that week and the number of those tests that were positive. The RSV trend graphs display the national average of the weekly % test positivity for the current, previous, and following weeks in accordance with the recommendations for assessing RSV trends by percent (https://academic.oup.com/jid/article/216/3/345/3860464). All data are provisional and subject to change.

Background: Vitamin D supplementation improves the immune function of human body and can be a convenient way to prevent influenza. However, evidence on the protective effect of vitamin D supplementation on influenza from Randomized Controlled Trials (RCTs) is inconclusive.Methods: RCTs regarding the association between vitamin D supplementation and influenza were identified by searching PubMed, Cochrane library, Embase and Chinese Biomedical Database (CBM) from inception until present (last updated on 10 November 2021). Studies that reported dosages and durations of vitamin D supplementation and number of influenza infections could be included. Heterogeneity was assessed using Cochran's Q test and I2 statistics, the meta-analysis was conducted by using a random-effects model, the pooled effects were expressed with risk ratio (RR) with 95% confidence interval (95% CI).Results: 10 trials including 4859 individuals were ultimately eligible after scanning. There was no evidence of a significant heterogeneity among studies (I2 = 27%, P = 0.150). Meta-regression analysis finding indicated that country, latitude, average age, economic level, follow-up period and average daily vitamin D intake did not cause the statistical heterogeneity. The study finding indicates that substitution with vitamin D significantly reduces the risk of influenza infections (RR = 0.78, 95% CI:0.64–0.95). No evidence of publication bias was observed. Omission of any single trial had little impact on the pooled risk estimates.Conclusions: The meta-analysis produced a corroboration that vitamin D supplement has a preventive effect on influenza. Strategies for preventing influenza can be optimized by vitamin D supplementation.

ObjectiveExposure to environmental pollutants is increasingly recognized as a risk factor for the development of psoriasis. Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the air and might induce reactions such as oxidative stress. Nevertheless, it is still unclear if PAHs have any influence on the prevalence of psoriasis over the entire population of the United States. The objective of this study was to assess the association between urine PAHs and psoriasis.MethodsThe research included 3,673 individuals aged 20 years or older who participated in the 2003–2006 and 2009–2012 National Health and Nutrition Examination Surveys (NHANES). We employed logistic regression models to evaluate the relationship between levels of urine PAH metabolites and psoriasis and smoothed curve fitting to illustrate the concentration-response relationship. Additionally, subgroup and interaction analyses were conducted to elucidate these associations. Furthermore, we employed weighted quartile sum (WQS) regressions to examine the distinct effects of individual and mixed urine PAH metabolites on psoriasis. However, it is important to note that the NHANES sample may be subject to selectivity and self-reporting bias, which may influence the data’ generalisability.ResultsWe observed that the highest tertiles of 2-NAP and 2-FLU had a 63% (95% CI 1.02, 2.61) and 83% (95% CI 1.14, 2.96) higher odds of association with psoriasis prevalence, respectively. Meanwhile, tertile 2 and tertile 3 of 3-PHE were also significantly associated with psoriasis, with higher odds of 65% (95% CI 1.01, 2.69) and 14% (95% CI 1.17, 3.00), respectively. The subgroup analyses revealed a significant correlation between urine PAH metabolites and the odds of psoriasis in specific groups, including males, aged 40–60 years, with a BMI > 30, and those with hyperlipidemia. In the WQS model, a positive association was found between the combination of urine PAH metabolites and psoriasis (OR 1.43, 95% CI 1.11, 1.84), with 2-FLU being the most prevalent component across all mixtures (0.297).ConclusionsOur findings indicate a significant association between urine PAH metabolites and the odds of psoriasis prevalence in adults. Among these metabolites, 2-FLU demonstrated the most prominent impact. Controlling PAH exposure, as an important strategy for minimizing exposure to environmental contaminants and lowering the risk of psoriasis, is critical for raising public knowledge about environmental health and preserving public health.

To escape immune recognition in previously infected hosts, viruses evolve genetically in immunologically important regions. The host’s immune system responds by generating new memory cells recognizing the mutated viral strains. Despite recent advances in data collection and analysis, it remains conceptually unclear how epidemiology, immune response, and evolutionary factors interact to produce the observed speed of evolution and the incidence of infection. Here we establish a general and simple relationship between long-term cross-immunity, genetic diversity, speed of evolution, and incidence. We develop an analytic method fusing the standard epidemiological susceptible-infected-recovered approach and the modern virus evolution theory. The model includes the factors of strain selection due to immune memory cells, random genetic drift, and clonal interference effects. We predict that the distribution of recovered individuals in memory serotypes creates a moving fitness landscape for the circulating strains which drives antigenic escape. The fitness slope (effective selection coefficient) is proportional to the reproductive number in the absence of immunity R0 and inversely proportional to the cross-immunity distance a, defined as the genetic distance of a virus strain from a previously infecting strain conferring 50% decrease in infection probability. Analysis predicts that the evolution rate increases linearly with the fitness slope and logarithmically with the genomic mutation rate and the host population size. Fitting our analytic model to data obtained for influenza A H3N2 and H1N1, we predict the annual infection incidence within a previously estimated range, (4-7)%, and the antigenic mutation rate of Ub = (5 − 8) ⋅ 10−4 per transmission event per genome. Our prediction of the cross-immunity distance of a = (14 − 15) aminoacid substitutions agrees with independent data for equine influenza.