According to a recent Statista survey around 64 percent of those aged 18 to 30 years caught a cold 1 to 3 times in the past year. The common cold is a viral infection that affects the nose and throat and usually lasts from 1 week to 10 days. Common symptoms can include a runny nose, sore throat, cough, congestion, and sneezing.

Treatment

There is no cure for the common cold and antibiotics are not effective, so treatment focuses on relieving symptoms. Over-the-counter (OTC) pain relievers, decongestants and cough syrups are common treatments as well as home remedies such as drinking plenty of fluids and resting. According to a survey from Statista, the most common remedies for the common cold include, sleeping, eating soup, drinking tea, and staying home.

Prevention

The common cold is extremely contagious but commonsense measures can be taken to reduce one’s risk of contracting it. Measures to prevent the cold include regularly washing hands, using disinfectant, avoiding those who are sick, and generally eating well, exercising, and getting enough sleep. Even though the common cold is contagious and burdensome, around 40 percent of adults in the U.S. reported they went to work when they were sick within the last year.

The mortality rate from influenza in the United States is by far highest among those aged 65 years and older. During the 2023-2024 flu season, the mortality rate from influenza for this age group was around 32.1 per 100,000 population. The burden of influenza The impact of influenza in the U.S. varies from season to season, but in the 2023-2024 flu season, there were an estimated 40 million cases. These cases resulted in around 470,000 hospitalizations. Although most people recover from influenza without requiring medical treatment, the disease can be deadly for young children, the elderly, and those with weakened immune systems or chronic illnesses. During the 2023-2024 flu season, around 28,000 people in the U.S. lost their lives due to influenza. Impact of vaccinations The most effective way to prevent influenza is to receive an annual vaccination at the beginning of flu season. Flu vaccines are safe and can greatly reduce the burden of the disease. During the 2022-2023 flu season, vaccinations prevented around 2,479 deaths among those aged 65 years and older. Although flu vaccines are usually cheap and easily accessible, every year a large share of the population in the U.S. still does not get vaccinated. For example, during the 2022-2023 flu season, only about 35 percent of those aged 18 to 49 years received a flu vaccination.

The graph illustrates the number of flu-related deaths in the United States for each flu season from 2010-2011 to 2023-2024*. The x-axis represents the flu seasons, labeled from '10-11 to '23*-24*, while the y-axis shows the annual number of flu deaths. Throughout this period, flu deaths vary significantly, ranging from a low of 4,900 in the 2021-2022* season to a high of 51,000 in both the 2014-2015 and 2017-2018 seasons. Other notable figures include 36,000 deaths in 2010-2011, 42,000 in 2012-2013, and a recent increase to 28,000 in the 2023*-2024* season. The data exhibits considerable fluctuations with no consistent upward or downward trend, highlighting the variability in flu mortality rates over the years. This information is presented in a line graph format, effectively showcasing the yearly changes and peaks in flu-related deaths across the United States.

*Data for the 2021-2022 and 2022-2023 seasons are estimated.

These data represent the predicted (modeled) prevalence of adults (Age 18+) who received a Flu Vaccine (flu shot or a vaccine sprayed in the nose) within the past 12 months for each census tract in Colorado. The length and intensity of each annual flu season varies from year to year, and there can be large variability between age groups in terms of who is receiving the annual flu vaccine.The estimate for each census tract represents an average that was derived from multiple years of Colorado Behavioral Risk Factor Surveillance System data (2014-2017).CDPHE used a model-based approach to measure the relationship between age, race, gender, poverty, education, location and health conditions or risk behavior indicators and applied this relationship to predict the number of persons' who have the health conditions or risk behavior for each census tract in Colorado. We then applied these probabilities, based on demographic stratification, to the 2013-2017 American Community Survey population estimates and determined the percentage of adults with the health conditions or risk behavior for each census tract in Colorado.The estimates are based on statistical models and are not direct survey estimates. Using the best available data, CDPHE was able to model census tract estimates based on demographic data and background knowledge about the distribution of specific health conditions and risk behaviors.The estimates are displayed in both the map and data table using point estimate values for each census tract and displayed using a Quintile range. The high and low value for each color on the map is calculated based on dividing the total number of census tracts in Colorado (1249) into five groups based on the total range of estimates for all Colorado census tracts. Each Quintile range represents roughly 20% of the census tracts in Colorado. No estimates are provided for census tracts with a known population of less than 50. These census tracts are displayed in the map as "No Est, Pop < 50."No estimates are provided for 7 census tracts with a known population of less than 50 or for the 2 census tracts that exclusively contain a federal correctional institution as 100% of their population. These 9 census tracts are displayed in the map as "No Estimate."

Abstract copyright UK Data Service and data collection copyright owner.

To determine the prevalence of respiratory symptoms, past history of respiratory diseases, lung function and evidence of middle ear disease in school-children coming from different social and environmental backgrounds, in particular from areas with widely differing levels of air pollution.
Main Topics:

Attitudinal/Behavioural Questions
Whether child has had earache/running ear/sore throat in last 12 months and how many times. Whether child has had whooping cough/measles/asthma/pneumonia/bronchitis/other chest trouble - if so, at what age, whether any complications (whooping cough and measles), number of times (pneumonia, bronchitis, other chest trouble), whether still has asthma. Whether had pneumonia/bronchitis/chest trouble in 1966 (which months), ever admitted to hospital as an in-patient (names of illnesses), had surgery to tonsils or adenoids (age), had eczema (age)/ sinusitus (age and number of times). Whether child has a blocked or runny nose on most days, snores at night, has bedroom heated all night in winter, shares a bedroom with a person with bronchitis or chest trouble. Whether other family members have bronchitis or chest trouble. Whether child coughs/brings up phlegm first thing in the morning/during the day/at night in winter, and for how many months. Whether chest ever sounds wheezy and how often, whether colds usually go to the chest.
Background Variables
Child's sex, date and place of birth, whether has lived in one place for more than 5 years, father's occupation, number of siblings, main source of household heating, number of rooms in house, number of occupants.
Medical Examination
Sex, race, date of birth, school attended, date and time of examination, standing height, weight. Information on condition of child's ears, nose and throat. Measurement of peak expiratory flow rate. School absences in previous term.
Created Variables
Sex, area, age in years, mean peak expiratory flow rate, age in months, social class, crowding index, attendance rate, whether child has had bronchitis/pneumonia, whether family is bronchitic, whether child coughs during day, obesity index, condition of ears.

The burden of influenza in the United States can vary from year to year depending on which viruses are circulating, how many people receive an influenza vaccination, and how effective the vaccination is in that particular year. During the 2023-2024 flu season, around 28,000 people lost their lives to the disease. Although most people recover from influenza without needing medical care, the disease can be deadly among young children, the elderly, and those with weakened immune systems or chronic illnesses. Deaths due to influenza Even though most people recover from influenza without medical care, influenza and pneumonia can be deadly, especially for older people and those with certain preexisting conditions. Influenza is a common cause of pneumonia and although most cases of influenza do not develop into pneumonia, those that do are often more severe and more deadly. Deaths due to influenza are most common among the elderly, with a mortality rate of around 32 per 100,000 population during the 2023-2024 flu season. In comparison, the mortality rate for those aged 50 to 64 years was 9.1 per 100,000 population. Flu vaccinations The most effective way to prevent influenza is to receive an annual influenza vaccination. These vaccines have proven to be safe and are usually cheap and easily accessible. Nevertheless, every year a large share of the population in the United States still fails to get vaccinated against influenza. For example, in the 2022-2023 flu season, only 35 percent of those aged 18 to 49 years received a flu vaccination. Unsurprisingly, children and the elderly are the most likely to get vaccinated. It is estimated that during the 2022-2023 flu season, vaccinations prevented over 929 thousand influenza cases among children aged 6 months to 4 years.

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.

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.

Flu vaccine uptake (%) in children aged 2 to 3 years old, 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 70% vaccine uptake in those aged 2 to 3 years old. Prior to this, the national vaccine uptake ambition was 75% 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-uptakeCaveatsThis collection has received approval from the Data Coordination Board (DCB).Data is final and represents a percentage of all GP practices in England responding to the final survey. Where a total for England is quoted (e.g., a sum of the number of patients registered and number vaccinated), this is taken from the GP practice sample and is therefore not an extrapolated figure.For definitions of clinical at-risk groups for those aged 6 months to under 65 years, see the annual flu letter published at Annual Flu Programme.The age under 65 clinical at-risk group data includes pregnant women with other risk factors but excludes otherwise 'healthy' pregnant women and carers.All figures are derived from data as extracted from records on GP systems or as submitted by GP practices, Area Teams, and CCGs.Data source: ImmForm website: registered patient GP practice data, Influenza Immunisation Vaccine Uptake Monitoring Programme, OHID.

Collection of socio-economic and meteorological indicators as well as travel patterns and cases of H1N1 during the swine flu pandemic in Sweden in 2009. Comprise the supplementary information for the paper titled "Socioeconomic and environmental patterns behind H1N1 spreading in Sweden" by András Bóta, Martin Holmberg, Lauren Gardner and Martin Rosvall, Sci Rep 11, 22512 (2021). https://doi.org/10.1038/s41598-021-01857-4 Identifying the critical socio-economic, travel and climate factors related to influenza spreading is critical to the prediction and mitigation of epidemics. In the paper we study the 2009 A(H1N1) outbreak in the municipalities of Sweden, following it for six years between 2009 and 2015. Our goal is to discover the relationship between the above indicators and the timing of the epidemic onset of the disease. We also identify the municipalities playing a key role in the outbreak as well as the most critical travel routes of the country.

Publication available at: https://doi.org/10.1038/s41598-021-01857-4

Municipality codes for the municipalities of Sweden can be found here: https://www.scb.se/en/finding-statistics/regional-statistics/regional-divisions/counties-and-municipalities/counties-and-municipalities-in-numerical-order/

Data available according to Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license

Model inputs 1. giim_kommun_graph.csv Set of frequent travel routes between the municipalities of Sweden. The graph was constructed from "Trafikanalys, 2016. Resvanor. (accessed 26.8.19). Available from: http://www.trafa.se/RVU-Sverige/." using the methodology described in the paper. Date of construction: 2018-12-01 Format: csv Structure: edge list in (kommun1;kommun2) format with rows indicating a directed link between two municipalities. Municipalities are denoted according to their official municipal code

1. giim_casecounts.xlsx Number of new H1N1 cases in the municipalities of Sweden between 2009 and 2015. Our data set consists of all laboratory-verified cases of A(H1N1)pdm09 between May 2009 and December 2015, extracted from the SmiNet register of notifiable diseases, held by the Public Health Agency of Sweden. Due to confidentiality reasons, cases are anonymized, and addresses are aggregated at the DeSo level together with the date of diagnosis, age, and gender. We obtained ethical approval for the data acquisition. Date of construction: 2018-12-01 Format: xlsx Structure: Each tab represents a single flu season from the 2009/2010 season to the 2014/2015 season. Each tab is a matrix with rows indicating municipalities according to their official municipal code, and columns indicating epidemic weeks. Values of the matrices indicate the number of new laboratory-verified cases of A(H1N1)pdm09

2. giim_kommun_indicators.csv Socioeconomic and meteorological indicators are assigned to the municipalities of Sweden according to the methodology described in the paper. Indicators included are: a, mean temperature in degree Celsius, b, absolute humidity in grams per cubic metre, c, population size as the number of people living in each municipality, d, population density as the number of people per sq. km of land area, e, median income per household in thousand SEK, f, fraction of people on social aid (as a percentage), g, average number of children younger than 18 years per household. Meteorological data was obtained from the European Climate Assessment Dataset "Klein Tank A, Wijngaard J, Können G, Böhm R, Demarée G, Gocheva A, et al. Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment. International Journal of Climatology: A Journal of the Royal Meteorological Society. 2002;22(12):1441–1453." Data from the dataset was converted to the municipality level according to the methodology described in the paper. Variables are mean temperature and relative humidity converted to absolute humidity for all municipalities of Sweden. Socioeconomic data was collected from Statistics Sweden between 2018 Ocotber and 2019 February. Available from: https://www.scb.se/en/. Variables are: The average household income as an economic indicator. The average number of children younger than 18 years per household to indicate family size. The fraction of people receiving social aid to represent poverty in a municipality. Population size and population density as the number of people per sq. km of land area. Date of construction: 2018-02-01 Format: csv Structure: Each row corresponds to a municipality denoted according to their official municipal code. Columns indicate socioeconomic and meteorological indicators as marked by the header row.

Model outputs 1. giim_export_risk.csv Exportation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate exportation risk values (should not be interpreted as probabilities).

1. giim_import_risk.csv Importation risk values for all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Table with rows denoting Swedish municipalities according to their official municipal code, columns denoting epidemic weeks. Values indicate importation risk values (should not be interpreted as probabilities).

2. giim_transmission_prob.csv Transmission probabilities between all municipalities from week 37 to week 50 in the fall of 2009 computed using the methodology described in the paper. Date of construction: 2020-12-01 Format: csv Structure: Edge list with multiple edge weights. Rows indicate a directed link between the two municipalities (kommun1;kommun2) in the beginning of the row. The rest of the values in each row denote the corresponding transmission probabilities for each epidemic week computed according to the methodology described in the paper.

Market Overview:

The global seasonal Influenza Vaccine market is expected to grow from USD 7.5 billion in 2017 to USD 11.8 billion by 2030, at a CAGR of 5.1% from 2022 to 2030. The growth in the market can be attributed to the increasing incidence of seasonal influenza, raising awareness about the benefits of vaccination, and technological advancements in vaccine production. Based on type, the global seasonal influenza vaccine sales market is segmented into trivalent influenza vaccine and quadrivalent influenza vaccine.

Product Definition:

A vaccine is a biological preparation that improves immunity to a particular disease. Seasonal Influenza Vaccine Sales usually refers to the sale of vaccines used to prevent seasonal influenza, also known as the flu. The importance of seasonal influenza vaccine sales is that they help protect people from getting sick during the flu season.

Trivalent Influenza Vaccine:

The trivalent influenza vaccine is a type of vaccine that provides protection against three strains of the flu; there are two types of vaccines available on the market, one trivalent and the other quadrivalent. Trivalent Influenza Vaccines have been available for many years whereas quadrivalent ones are relatively new. The difference between them is only in the number of viruses included in their composition, both provide a similar level of protection against the seasonal flu virus.

Quadrivalent Influenza Vaccine:

The quadrivalent influenza vaccine is a type of influenza vaccine that protects against four different strains of the flu. It is approved for use in people over 65 years of age and individuals with chronic conditions, such as cardiovascular disease, diabetes mellitus, renal disease, or immunosuppression. The seasonal flu vaccination has been recommended by the CDC since 2010 to reduce hospitalizations by 30% and deaths by 50%.

Application Insights:

Based on application, the global seasonal influenza vaccine market is segmented into children, adults, and the elderly. The adult segment held around 60% of the overall revenue share in 2015. This can be attributed to growing awareness about health hazards associated with circulating viruses and increasing healthcare spending in developed countries like the U.S., Germany, etc. The growing geriatric population across the globe is expected to boost demand for vaccines in upcoming years which would drive growth further during the forecast period. The elderly segment held another 40% of the overall revenue share in 2015 due to the growing incidence of diseases related to old age such as Alzheimer's disease, Parkinson disease, etc., especially in high-income countries like Japan and Italy respectively owing to which these two segments are expected generate significant demand for seasonal influenza vaccine over next eight years (through 2030).

Regional Analysis:

North America dominated the global market in terms of revenue share in 2020 with a value of USD X billion and is expected to grow at a lucrative rate over the forecast period. The presence of high-income countries such as the U.S., favorable government initiatives, and increasing awareness about seasonal vaccines are some factors attributing to its largest market share throughout the forecast period. Asia Pacific is anticipated to be one of the fastest growing regional markets from 2022 to 2030 owing to the rising demand for Flu Vaccine across this region due to the increasing number of susceptible people along with rapid economic development in emerging economies including China and India which are among top 10 countries where most people get infected each year (as per WHO). These two countries alone account for more than 50% population who get infected every year thus creating huge potential for seasonal influenza vaccine sales within the Asia Pacific region over the next eight years (as per GSK).

Growth Factors:

• Increasing awareness about the benefits of the seasonal influenza vaccine among people.
• Government initiatives and campaigns to promote vaccination against seasonal influenza.
• Growing demand for preventive healthcare services.
• Rising incidence of seasonal influenza outbreaks.
• Technological advancements in the manufacturing of seasonal influenza vaccines.

Report Scope

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Report Attributes	Report Details

In the United States, the highest rate of hospitalizations due to influenza are among those aged 65 years and older. During the 2023-2024 flu season, the rate of hospitalizations due to influenza among this age group was about 401 per 100,000 population, compared to a rate of around 47 per 100,000 for those aged 5 to 17 years. Influenza is a common viral infection that usually does not require medical treatment. However, for the very young, the old, and those with certain pre-existing conditions, influenza can be serious and even deadly. The burden of influenza in the United States The impact of influenza in the United States varies from year to year depending on the strain that is most prevalent during that season and the immunity in the population. Preliminary estimates show that around 28,000 people died from influenza during the 2023-2024 flu season. However, during the 2017-2018 flu season, an estimated 52,000 people lost their lives to influenza. The importance of flu vaccines The best way to avoid catching the flu and to reduce the virus’s overall burden on society is by receiving an annual flu vaccination. The CDC currently recommends that everyone over 6 months of age should get a flu vaccination every year, preferably by the end of October. The flu vaccine is safe, efficient, and reduces the number of illnesses, hospitalizations, and deaths caused by the virus. For example, during the 2022-2023 flu season, it was estimated that vaccinations averted almost 65 thousand influenza-related hospitalizations. However, despite the proven benefits and wide availability of flu vaccinations, a large percentage of people in the United States fail to receive a vaccination every year. During the 2022-2023 flu season, only about 35 percent of those aged 18 to 49 years were vaccinated against influenza, compared to 70 percent of those aged 65 years and older.

Number and percentage of persons who reported having their last influenza immunization in the past 12 months, by age group and sex.

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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In the United States, influenza vaccination rates differ greatly by age. For example, during the 2022-2023 flu season, around 70 percent of those aged 65 years and older received an influenza vaccination, compared to just 35 percent of those aged 18 to 49 years. The CDC recommends that everyone six months and older in the United States should get vaccinated against influenza every year, with a few exceptions. Although influenza is mild for most people it can lead to hospitalization and even death, especially among the young, the old, and those with certain preexisting conditions.

The impact of flu vaccinations Flu vaccinations are safe and effective, preventing thousands of illnesses, medical visits, and deaths every year. However, the effectiveness of flu vaccines varies each year depending on what flu viruses are circulating that season and the age and health status of the person receiving the vaccination. During the 2022-2023 flu season it was estimated that influenza vaccination prevented almost 31 thousand hospitalizations among those aged 65 years and older. In addition, flu vaccinations prevented 2,479 deaths among those aged 65 years and older as well as 63 deaths among children six months to four years.

The burden of influenza The impact of influenza is different from season to season. However, during the 2022-2023 flu season there were around 31 million cases of influenza in the United States. Furthermore, there were around 21,000 deaths due to influenza, an increase from the previous year but significantly fewer than in 2017-2018 when influenza contributed to 51,000 deaths. Most of these deaths are among the elderly. In 2022-2023 the death rate due to influenza among those aged 65 years and older was around 26.6 per 100,000 population. In comparison, those aged 18 to 49 years had an influenza death rate of just .7 per 100,000 population.

Influenza and pneumonia caused around 12.3 deaths in the U.S. per 100,000 population in 2019. Influenza and pneumonia are among the leading causes of death in the United States, accounting for around 1.6 percent of all deaths in 2020. Influenza, or the flu, is a viral infection that is highly contagious and especially common in the winter season. Influenza is a common cause of pneumonia, although most cases of the flu do not develop into pneumonia. Pneumonia is an infection or inflammation of the lungs and is particularly deadly among young children and the elderly.

Influenza cases

Influenza is very common in the United States, with an estimated 35 million cases reported in 2019-2020. Common symptoms of the flu include cough, fever, runny or stuffy nose, sore throat and headache. Symptoms can be mild but can also be severe enough to require medical attention. In 2019-2020, there were around 16 million influenza-related medical visits in the United States.

Prevention

To prevent contracting the flu people can take everyday precautions such as regularly washing their hands and avoiding those who are sick, but the best way to prevent the flu is by receiving the flu vaccination every year. Receiving a flu vaccination is especially important for young children and the elderly as they are most susceptible to flu complications and associated death. In 2021, around 75 percent of those aged 65 years and older received a flu vaccine, while only 38 percent of those aged 18 to 49 years had done so.

Influenza, also called the flu, is one of the most infectious diseases worldwide. Its symptoms range from mild to severe, and include sore throat, cough, runny nose, fever, headache, and muscle pain, but can also cause severe illness and death among high-risk populations such as the elderly and children. During the 2023-2024 flu season, there were 40 million cases of influenza in the United States. Influenza deaths Although influenza does not require medical attention for most people, it can be deadly, and causes thousands of deaths every year. The impact of influenza varies from year to year. The number of influenza deaths during the 2023-2024 flu season was 27,965. The vast majority of deaths attributed to influenza during the 2023-2024 flu season occurred among those aged 65 years and older. Vaccination An annual influenza vaccination remains the most effective way of preventing influenza. During the 2022-2023 flu season, influenza vaccinations prevented an estimated 2,479 deaths among U.S. adults aged 65 years and older. Although, flu vaccinations are accessible and cheap, a large share of the United States population still fails to get vaccinated every year. In 2022-2023, only 35 percent of those aged 18 to 49 years received a flu vaccination, much lower compared to children and the elderly.

The child mortality rate in the United States, for children under the age of five, was 462.9 deaths per thousand births in 1800. This means that for every thousand babies born in 1800, over 46 percent did not make it to their fifth birthday. Over the course of the next 220 years, this number has dropped drastically, and the rate has dropped to its lowest point ever in 2020 where it is just seven deaths per thousand births. Although the child mortality rate has decreased greatly over this 220 year period, there were two occasions where it increased; in the 1870s, as a result of the fourth cholera pandemic, smallpox outbreaks, and yellow fever, and in the late 1910s, due to the Spanish Flu pandemic.