Influenza and pneumonia caused around 10.9 deaths in the U.S. per 100,000 population in 2023. 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 40 million cases reported in 2023-2024. 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 2023-2024, there were around 18 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 2024, around 70 percent of those aged 65 years and older received a flu vaccine, while only 33 percent of those aged 18 to 49 years had done so.

Over 12 million people in the United States died from all causes between the beginning of January 2020 and August 21, 2023. Over 1.1 million of those deaths were with confirmed or presumed COVID-19.

Vaccine rollout in the United States Finding a safe and effective COVID-19 vaccine was an urgent health priority since the very start of the pandemic. In the United States, the first two vaccines were authorized and recommended for use in December 2020. One has been developed by Massachusetts-based biotech company Moderna, and the number of Moderna COVID-19 vaccines administered in the U.S. was over 250 million. Moderna has also said that its vaccine is effective against the coronavirus variants first identified in the UK and South Africa.

Legacy unique identifier: P00601

In 2020, approximately ** men and ** women per 100,000 population died as a result of pneumonia in England and Wales. In every year in the provided time interval the mortality rate was higher among men, although both genders have experienced a general decline in deaths from pneumonia. Regionally, the North West had the highest mortality rate for both genders.

Pneumonia risk groups

The age groups most at risk from pneumonia is undoubtedly the older age groups. In 2021, in England and Wales, pneumonia was the cause of death for approximately *** thousand over ** year olds, of which *** thousand were women. Furthermore, around *** thousand individuals aged between 80 and 89 years lost their lives due to pneumonia in 2021.

Prevalence of other lung diseases

In England and Wales in 2019, the mortality rate from bronchitis for men was around ** per 100,000 population, while the rate for women was approximately **. The mortality rate for bronchitis was higher than pneumonia, this is caused in part by the large decline in the mortality rate of pneumonia since the year 2000.

Basic Metadata *Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

***API: Asian/Pacific Islander. ***AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook: https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20Codebook_Data%20Guide_Metadata_10.2.19.xlsx

In 2023, there were **** deaths from influenza and pneumonia in Canada per 100,000 population. Influenza, more commonly known as the flu, is a highly contagious viral infection and frequent cause of pneumonia. Pneumonia is a more serious infection of the lungs and is particularly deadly among young children, the elderly, and those with certain chronic conditions. Vaccination There exist vaccines for both influenza and pneumonia, and although effectiveness varies, vaccination remains one of the best ways to prevent these illnesses. Nevertheless, only around ** percent of Canadians received an influenza vaccination in the past year in 2022. The most common reason why Canadian adults received the influenza vaccination was to prevent infection or because they did not want to get sick. Pneumonia hospitalization Every year tens of thousand of people in Canada are hospitalized for pneumonia. In *********, there were over ****** emergency room visits for pneumonia in Canada, a substantial decrease from the numbers recorded from 2010 to 2020. Perhaps unsurprisingly, those aged 65 years and older account for the highest number of emergency room visits for pneumonia. The median length of stay for emergency department visits for pneumonia in Canada has increased in recent years, with the median length of stay around *** minutes in *********.

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Abstract Background: This study analyzed the causes of death in the Korean population in 2020. Methods: Cause-of-death data for 2020 from Statistics Korea were examined based on the Korean Standard Classification of Diseases and Causes of Death, 7th revision and the International Statistical Classification of Diseases and Related Health Problems, 10th revision. Results: In total, 304,948 deaths occurred, reflecting an increase of 9,838 (3.3%) from 2019. The crude death rate (the number of deaths per 100,000 people) was 593.9, corresponding to an increase of 19.0 (3.3%) from 2019. The 10 leading causes of death, in descending order, were malignant neoplasms, heart diseases, pneumonia, cerebrovascular diseases, intentional self-harm, diabetes mellitus, Alzheimer’s disease, liver diseases, hypertensive diseases, and sepsis. Cancer accounted for 27.0% of deaths. Within the category of malignant neoplasms, the top 5 leading organs of involvement were the lung, liver, colon, stomach, and pancreas. Sepsis was included in the 10 leading causes of death for the first time. Mortality due to pneumonia decreased to 43.3 (per 100,000 people) from 45.1 in 2019. The number of deaths due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was 950, of which 54.5% were in people aged 80 or older. Conclusion: These changes reflect the continuing increase in deaths due to diseases of old age, including sepsis. The decrease in deaths due to pneumonia may have been due to protective measures against SARS-CoV-2. With the concomitant decrease in fertility, 2020 became the first year in which Korea’s natural total population decreased.

Analysis of ‘COVID-19 State Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/nightranger77/covid19-state-data on 30 September 2021.

--- Dataset description provided by original source is as follows ---

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

--- Original source retains full ownership of the source dataset ---

Basic Metadata

Note: this is the combination of influenza (flu) and pneumonia combined as they often co-occur together.

*Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population.

**Blank Cells: Rates not calculated for fewer than 5 events. Rates not calculated in cases where zip code is unknown.

*API: Asian/Pacific Islander.

*AIAN: American Indian/Alaska Native.

Prepared by: County of San Diego, Health & Human Services Agency, Public Health Services, Community Health Statistics Unit, 2019.

Code Source: ICD-9CM - AHRQ HCUP CCS v2015. ICD-10CM - AHRQ HCUP CCS v2018. ICD-10 Mortality - California Department of Public Health, Group Cause of Death Codes 2013; NHCS ICD-10 2e-v1 2017.

Data Guide, Dictionary, and Codebook:

https://www.sandiegocounty.gov/content/dam/sdc/hhsa/programs/phs/CHS/Community%20Profiles/Public%20Health%20Services%20CodebookData%20GuideMetadata_10.2.19.xlsx

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.

Background: Patients with chronic obstructive pulmonary disease (COPD) are at a heightened risk of pneumonia. Whether coexisting community-acquired pneumonia (CAP) can predict increased mortality in hospitalized COPD patients is still controversial. Objective: This systematic review and meta-analysis aims to assess the association between CAP and mortality and morbidity in COPD patients hospitalized for acute worsening of respiratory symptoms. Methods: In this review, cohort studies and case-control studies investigating the impact of CAP in hospitalized COPD patients were retrieved from 4 electronic databases from inception until December 2019. Methodological quality of included studies was assessed using Newcastle-Ottawa Quality Assessment Scale. The primary outcome was mortality. The secondary outcomes included length of hospital stay, need for mechanical ventilation, intensive care unit (ICU) admission, length of ICU stay, and readmission rate. The Mantel-Haenszel method and inverse variance method were used to calculate pooled relative risk (RR) and mean difference (MD), respectively. Results: A total of 18 studies were included. The presence of CAP was associated with higher mortality (RR = 1.85; 95% CI: 1.50–2.30; p < 0.00001), longer length of hospital stay (MD = 1.89; 95% CI: 1.19–2.59; p < 0.00001), more need for mechanical ventilation (RR = 1.48; 95% CI: 1.32–1.67; p < 0.00001), and more ICU admissions (RR = 1.58; 95% CI: 1.24–2.03; p = 0.0002) in hospitalized COPD patients. CAP was not associated with longer ICU stay (MD = 5.2; 95% CI: −2.35 to 12.74; p = 0.18) or higher readmission rate (RR = 1.02; 95% CI: 0.96–1.09; p = 0.47). Conclusion: Coexisting CAP may be associated with increased mortality and morbidity in hospitalized COPD patients, so radiological confirmation of CAP should be required and more attention should be paid to these patients.

Pathogen distribution in community-acquired pneumonia (CAP).

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.

Results data for the thesis on estimating the age-, sex-, cause-specific excess mortality during the COVID-19 pandemic in Hong Kong and South Korea.Thesis abstractBackgroundFew studies used a consistent methodology and adjusted for the risk of influenza-like illness (ILI) in historical mortality trends when estimating and comparing the cause-specific excess mortality (EM) during the COVID-19 pandemic. Previous studies demonstrated that excess mortality was widely reported from CVD and among the elderly. This study aims to estimate and compare the overall, age-, sex-, and cause-specific excess mortality during the COVID-19 pandemic in Hong Kong (HK) and South Korea (SK) with consideration of the impact of ILI.MethodsIn this population-based study, we first fitted a generalized additive model to the monthly mortality data from Jan 2010 to Dec 2019 in HK and SK before the COVID-19 pandemic. Then we applied the fitted model to estimate the EM from Jan 2020 to Dec 2022. The month index was modelled with a natural cubic spline. Akaike information criterion (AIC) was used to select the number of knots for the spline and inclusion of covariates such as monthly mean temperature, absolute humidity, ILI consultation rate, and the proxy for flu activity.FindingsFrom 2020 to 2022, the EM in HK was 239.8 (95% CrI: 184.6 to 293.9) per 100,000 population. Excess mortality from respiratory diseases (RD) (ICD-10 code: J00-J99), including COVID-19 deaths coded as J98.8, was 181.3 (95% CrI: 149.9 to 210.4) per 100,000. Except for RD, the majority of the EM in HK was estimated from cardiovascular diseases (CVD) (22.4% of the overall EM), influenza and pneumonia (16.2%), ischemic heart disease (8.9%), ill-defined causes (8.6%) and senility (6.7%). No statistically significant reduced deaths were estimated among other studied causes.From 2020 to 2022, the EM in SK was 204.7 (95% CrI: 161.6 to 247.2) per 100,000 population. Of note, COVID-19 deaths in SK were not included in deaths from RD but were recorded with the codes for emergency use as U07.1 or U07.2. The majority of the EM was estimated from ill-defined causes (32.0% of the overall EM), senility (16.6%), cerebrovascular disease (6.8%) and cardiovascular diseases (6.1%). Statistically significant reduction in mortality with 95 CrI lower than zero was estimated from vascular, other and unspecified dementia (-26.9% of expected deaths), influenza and pneumonia (-20.7%), mental and behavioural disorders (-18.8%) and respiratory diseases (-7.7%).InterpretationExcluding RD in HK which includes COVID-19 deaths, the majority of the EM in HK and SK was from CVD and senility. Mortality from influenza and pneumonia was estimated to have a statistically significant increase in HK but a decrease in SK probability due to different coding practices. HK had a heavier burden of excess mortality in the elderly age group 70-79 years and 80 years or above, while SK had a heavier burden in the age group of 60-69 years. Both HK and SK have a heavier burden of excess mortality from males than females. Better triage systems for identifying high-risk people of the direct or indirect impact of the epidemic are needed to minimize preventable mortality.

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 3 procedures performed (Carotid Endarterectomy, Pancreatic Resection, and Percutaneous Coronary Intervention) in California hospitals. The 2023 IMIs were generated using AHRQ Version 2024, while previous years' IMIs were generated with older versions of AHRQ software (2022 IMIs by Version 2023, 2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

Unadjusted and adjusted parameter estimates of weighted generalized equation estimation to predict inpatient mortality.

Causes of death among the general population based on ICD-10 codes from 2015 to 2019.

ObjectiveThe aim of this study was to further clarify clinical characteristics and predict mortality risk among patients with viral pneumonia.MethodsA total of 528 patients with viral pneumonia at RuiJin hospital in Shanghai from May 2015 to May 2019 were recruited. Multiplex real-time RT-PCR was used to detect respiratory viruses. Demographic information, comorbidities, routine laboratory examinations, immunological indexes, etiological detections, radiological images and treatment were collected on admission.Results76 (14.4%) patients died within 90 days in hospital. A predictive MuLBSTA score was calculated on the basis of a multivariate logistic regression model in order to predict mortality with a weighted score that included multilobular infiltrates (OR = 5.20, 95% CI 1.41–12.52, p = 0.010; 5 points), lymphocyte ≤ 0.8∗109/L (OR = 4.53, 95% CI 2.55–8.05, p < 0.001; 4 points), bacterial coinfection (OR = 3.71, 95% CI 2.11–6.51, p < 0.001; 4 points), acute-smoker (OR = 3.19, 95% CI 1.34–6.26, p = 0.001; 3 points), quit-smoker (OR = 2.18, 95% CI 0.99–4.82, p = 0.054; 2 points), hypertension (OR = 2.39, 95% CI 1.55–4.26, p = 0.003; 2 points) and age ≥60 years (OR = 2.14, 95% CI 1.04–4.39, p = 0.038; 2 points). 12 points was used as a cut-off value for mortality risk stratification. This model showed sensitivity of 0.776, specificity of 0.778 and a better predictive ability than CURB-65 (AUROC = 0.773 vs. 0.717, p < 0.001).ConclusionHere, we designed an easy-to-use clinically predictive tool for assessing 90-day mortality risk of viral pneumonia. It can accurately stratify hospitalized patients with viral pneumonia into relevant risk categories and could provide guidance to make further clinical decisions.

To assess risk factors for mortality in invasive pneumococcal disease (IPD). We conducted a systemic literature search in January 2019. The main outcome measure included death within 30 days after diagnosis of IPD. The study protocol was registered in PROSPERO (CRD42019120189). After reviewing 2514 potentially relevant records, remaining 190 articles were included in the analysis. A total of 228,782 IPD patients were identified and the mortality rate was 17.2% in the included articles. No significant evidence of publication bias was found according to the funnel plot and Egger’s test (t = 1.464, p = 0.145). Male sex, older age, alcohol abuse, previous tuberculosis, meningitis, hospital acquired infections, multilobar infiltrate or effusion, Pitt bacteremia score≥4, Pneumonia Severity Index≥4, clinical conditions requiring intensive care, underlying clinical conditions, disease caused by serotypes 3, 6B, 9 N, 10A, 11A, 16 F, 17 F, 19, 19 F, 22 F, 23A, 23 F, 31 and 35 F, previous antibiotic use, inappropriate initial antibiotic therapy, penicillin resistance, and vancomycin use during the course of treatment were predicators of 30-day mortality. This meta-analysis highlights important risk factors for IPD-related mortality, many of which may be targeted through preventive measures.