Between the beginning of January 2020 and June 14, 2023, of the 1,134,641 deaths caused by COVID-19 in the United States, around 307,169 had occurred among those aged 85 years and older. This statistic shows the number of coronavirus disease 2019 (COVID-19) deaths in the U.S. from January 2020 to June 2023, by age.

As of April 26, 2023, around 27 percent of total COVID-19 deaths in the United States have been among adults 85 years and older, despite this age group only accounting for two percent of the U.S. population. This statistic depicts the distribution of total COVID-19 deaths in the United States as of April 26, 2023, by age group.

Provisional deaths registration data for single year of age and average age of death (median and mean) of persons whose death involved coronavirus (COVID-19), England and Wales. Includes deaths due to COVID-19 and breakdowns by sex.

Data for CDC’s COVID Data Tracker site on Rates of COVID-19 Cases and Deaths by Vaccination Status. Click 'More' for important dataset description and footnotes

Dataset and data visualization details: These data were posted on October 21, 2022, archived on November 18, 2022, and revised on February 22, 2023. These data reflect cases among persons with a positive specimen collection date through September 24, 2022, and deaths among persons with a positive specimen collection date through September 3, 2022.

Vaccination status: A person vaccinated with a primary series had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after verifiably completing the primary series of an FDA-authorized or approved COVID-19 vaccine. An unvaccinated person had SARS-CoV-2 RNA or antigen detected on a respiratory specimen and has not been verified to have received COVID-19 vaccine. Excluded were partially vaccinated people who received at least one FDA-authorized vaccine dose but did not complete a primary series ≥14 days before collection of a specimen where SARS-CoV-2 RNA or antigen was detected. Additional or booster dose: A person vaccinated with a primary series and an additional or booster dose had SARS-CoV-2 RNA or antigen detected on a respiratory specimen collected ≥14 days after receipt of an additional or booster dose of any COVID-19 vaccine on or after August 13, 2021. For people ages 18 years and older, data are graphed starting the week including September 24, 2021, when a COVID-19 booster dose was first recommended by CDC for adults 65+ years old and people in certain populations and high risk occupational and institutional settings. For people ages 12-17 years, data are graphed starting the week of December 26, 2021, 2 weeks after the first recommendation for a booster dose for adolescents ages 16-17 years. For people ages 5-11 years, data are included starting the week of June 5, 2022, 2 weeks after the first recommendation for a booster dose for children aged 5-11 years. For people ages 50 years and older, data on second booster doses are graphed starting the week including March 29, 2022, when the recommendation was made for second boosters. Vertical lines represent dates when changes occurred in U.S. policy for COVID-19 vaccination (details provided above). Reporting is by primary series vaccine type rather than additional or booster dose vaccine type. The booster dose vaccine type may be different than the primary series vaccine type. ** Because data on the immune status of cases and associated deaths are unavailable, an additional dose in an immunocompromised person cannot be distinguished from a booster dose. This is a relevant consideration because vaccines can be less effective in this group. Deaths: A COVID-19–associated death occurred in a person with a documented COVID-19 diagnosis who died; health department staff reviewed to make a determination using vital records, public health investigation, or other data sources. Rates of COVID-19 deaths by vaccination status are reported based on when the patient was tested for COVID-19, not the date they died. Deaths usually occur up to 30 days after COVID-19 diagnosis. Participating jurisdictions: Currently, these 31 health departments that regularly link their case surveillance to immunization information system data are included in these incidence rate estimates: Alabama, Arizona, Arkansas, California, Colorado, Connecticut, District of Columbia, Florida, Georgia, Idaho, Indiana, Kansas, Kentucky, Louisiana, Massachusetts, Michigan, Minnesota, Nebraska, New Jersey, New Mexico, New York, New York City (New York), North Carolina, Philadelphia (Pennsylvania), Rhode Island, South Dakota, Tennessee, Texas, Utah, Washington, and West Virginia; 30 jurisdictions also report deaths among vaccinated and unvaccinated people. These jurisdictions represent 72% of the total U.S. population and all ten of the Health and Human Services Regions. Data on cases among people who received additional or booster doses were reported from 31 jurisdictions; 30 jurisdictions also reported data on deaths among people who received one or more additional or booster dose; 28 jurisdictions reported cases among people who received two or more additional or booster doses; and 26 jurisdictions reported deaths among people who received two or more additional or booster doses. This list will be updated as more jurisdictions participate. Incidence rate estimates: Weekly age-specific incidence rates by vaccination status were calculated as the number of cases or deaths divided by the number of people vaccinated with a primary series, overall or with/without a booster dose (cumulative) or unvaccinated (obtained by subtracting the cumulative number of people vaccinated with a primary series and partially vaccinated people from the 2019 U.S. intercensal population estimates) and multiplied by 100,000. Overall incidence rates were age-standardized using the 2000 U.S. Census standard population. To estimate population counts for ages 6 months through 1 year, half of the single-year population counts for ages 0 through 1 year were used. All rates are plotted by positive specimen collection date to reflect when incident infections occurred. For the primary series analysis, age-standardized rates include ages 12 years and older from April 4, 2021 through December 4, 2021, ages 5 years and older from December 5, 2021 through July 30, 2022 and ages 6 months and older from July 31, 2022 onwards. For the booster dose analysis, age-standardized rates include ages 18 years and older from September 19, 2021 through December 25, 2021, ages 12 years and older from December 26, 2021, and ages 5 years and older from June 5, 2022 onwards. Small numbers could contribute to less precision when calculating death rates among some groups. Continuity correction: A continuity correction has been applied to the denominators by capping the percent population coverage at 95%. To do this, we assumed that at least 5% of each age group would always be unvaccinated in each jurisdiction. Adding this correction ensures that there is always a reasonable denominator for the unvaccinated population that would prevent incidence and death rates from growing unrealistically large due to potential overestimates of vaccination coverage. Incidence rate ratios (IRRs): IRRs for the past one month were calculated by dividing the average weekly incidence rates among unvaccinated people by that among people vaccinated with a primary series either overall or with a booster dose. Publications: Scobie HM, Johnson AG, Suthar AB, et al. Monitoring Incidence of COVID-19 Cases, Hospitalizations, and Deaths, by Vaccination Status — 13 U.S. Jurisdictions, April 4–July 17, 2021. MMWR Morb Mortal Wkly Rep 2021;70:1284–1290. Johnson AG, Amin AB, Ali AR, et al. COVID-19 Incidence and Death Rates Among Unvaccinated and Fully Vaccinated Adults with and Without Booster Doses During Periods of Delta and Omicron Variant Emergence — 25 U.S. Jurisdictions, April 4–December 25, 2021. MMWR Morb Mortal Wkly Rep 2022;71:132–138. Johnson AG, Linde L, Ali AR, et al. COVID-19 Incidence and Mortality Among Unvaccinated and Vaccinated Persons Aged ≥12 Years by Receipt of Bivalent Booster Doses and Time Since Vaccination — 24 U.S. Jurisdictions, October 3, 2021–December 24, 2022. MMWR Morb Mortal Wkly Rep 2023;72:145–152. Johnson AG, Linde L, Payne AB, et al. Notes from the Field: Comparison of COVID-19 Mortality Rates Among Adults Aged ≥65 Years Who Were Unvaccinated and Those Who Received a Bivalent Booster Dose Within the Preceding 6 Months — 20 U.S. Jurisdictions, September 18, 2022–April 1, 2023. MMWR Morb Mortal Wkly Rep 2023;72:667–669.

As of May 2, 2023, of 34,206 COVID-19 cases deceased in Canada, around 4,058 were aged 60 to 69 years. This statistic shows the number of COVID-19 deaths in Canada as of May 2, 2023, by age.

Background and objectiveEstimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.MethodsWe used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.FindingsThis study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20–24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30–34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75–79 year group. For children 0–4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20–24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors’ assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.ConclusionsOur findings confirm the universality of certain COVID-19 risk factors—such as gender and age—while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

Background and objectiveEstimating the contribution of risk factors of mortality due to COVID-19 is particularly important in settings with low vaccination coverage and limited public health and clinical resources. Very few studies of risk factors of COVID-19 mortality used high-quality data at an individual level from low- and middle-income countries (LMICs). We examined the contribution of demographic, socioeconomic and clinical risk factors of COVID-19 mortality in Bangladesh, a lower middle-income country in South Asia.MethodsWe used data from 290,488 lab-confirmed COVID-19 patients who participated in a telehealth service in Bangladesh between May 2020 and June 2021, linked with COVID-19 death data from a national database to study the risk factors associated with mortality. Multivariable logistic regression models were used to estimate the association between risk factors and mortality. We used classification and regression trees to identify the risk factors that are the most important for clinical decision-making.FindingsThis study is one of the largest prospective cohort studies of COVID-19 mortality in a LMIC, covering 36% of all lab-confirmed COVID-19 cases in the country during the study period. We found that being male, being very young or elderly, having low socioeconomic status, chronic kidney and liver disease, and being infected during the latter pandemic period were significantly associated with a higher risk of mortality from COVID-19. Males had 1.15 times higher odds (95% Confidence Interval, CI: 1.09, 1.22) of death compared to females. Compared to the reference age group (20–24 years olds), the odds ratio of mortality increased monotonically with age, ranging from an odds ratio of 1.35 (95% CI: 1.05, 1.73) for ages 30–34 to an odds ratio of 21.6 (95% CI: 17.08, 27.38) for ages 75–79 year group. For children 0–4 years old the odds of mortality were 3.93 (95% CI: 2.74, 5.64) times higher than 20–24 years olds. Other significant predictors were severe symptoms of COVID-19 such as breathing difficulty, fever, and diarrhea. Patients who were assessed by a physician as having a severe episode of COVID-19 based on the telehealth interview had 12.43 (95% CI: 11.04, 13.99) times higher odds of mortality compared to those assessed to have a mild episode. The finding that the telehealth doctors’ assessment of disease severity was highly predictive of subsequent COVID-19 mortality, underscores the feasibility and value of the telehealth services.ConclusionsOur findings confirm the universality of certain COVID-19 risk factors—such as gender and age—while highlighting other risk factors that appear to be more (or less) relevant in the context of Bangladesh. These findings on the demographic, socioeconomic, and clinical risk factors for COVID-19 mortality can help guide public health and clinical decision-making. Harnessing the benefits of the telehealth system and optimizing care for those most at risk of mortality, particularly in the context of a LMIC, are the key takeaways from this study.

Background: In clinical trials, several SARS-CoV-2 vaccines were shown to reduce risk of severe COVID-19 illness. Local, population-level, real-world evidence of vaccine effectiveness is accumulating. We assessed vaccine effectiveness for community-dwelling New York City (NYC) residents using a quasi-experimental, regression discontinuity design, leveraging a period (January 12-March 9, 2021) when ≥ 65-year-olds were vaccine-eligible but younger persons, excluding essential workers, were not. Methods: We constructed segmented, negative binomial regression models of age-specific COVID-19 hospitalization rates among 45-84-year-old NYC residents during a post-vaccination program implementation period (February 21-April 17, 2021), with a discontinuity at age 65 years. The relationship between age and hospitalization rates in an unvaccinated population was incorporated using a pre-implementation period (December 20, 2020-February 13, 2021). We calculated the rate ratio (RR) and 95% confidence interval (CI) for the interaction between implementation period (pre or post) and age-based eligibility (45-64 or 65-84 years). Analyses were stratified by race/ethnicity and borough of residence. Similar analyses were conducted for COVID-19 deaths. Results: Hospitalization rates among 65-84-year-olds decreased from pre- to post-implementation periods (RR 0.85, 95% CI: 0.74-0.97), controlling for trends among 45-64-year-olds. Accordingly, an estimated 721 (95% CI: 126-1,241) hospitalizations were averted. Residents just above the eligibility threshold (65-66-year-olds) had lower hospitalization rates than those below (63-64-year-olds). Racial/ethnic groups and boroughs with higher vaccine coverage generally experienced greater reductions in RR point estimates. Uncertainty was greater for the decrease in COVID-19 death rates (RR 0.85, 95% CI: 0.66-1.10). Conclusion: The vaccination program in NYC reduced COVID-19 hospitalizations among the initially age-eligible ≥ 65-year-old population by approximately 15% in the first eight weeks. The real-world evidence of vaccine effectiveness makes it more imperative to improve vaccine access and uptake to reduce inequities in COVID-19 outcomes.

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.

In the United States in 2021, the death rate was highest among those aged 85 and over, with about 17,190.5 men and 14,914.5 women per 100,000 of the population passing away. For all ages, the death rate was at 1,118.2 per 100,000 of the population for males, and 970.8 per 100,000 of the population for women. The death rate Death rates generally are counted as the number of deaths per 1,000 or 100,000 of the population and include both deaths of natural and unnatural causes. The death rate in the United States had pretty much held steady since 1990 until it started to increase over the last decade, with the highest death rates recorded in recent years. While the birth rate in the United States has been decreasing, it is still currently higher than the death rate. Causes of death There are a myriad number of causes of death in the United States, but the most recent data shows the top three leading causes of death to be heart disease, cancers, and accidents. Heart disease was also the leading cause of death worldwide.

In Grasselli et al. [21], which we use to estimate death risks, individuals up to 39 years old are grouped into two groups corresponding to 0–19 and 20–39. No deaths were registered in the youngest group: rates for the 20–29 and 30–39 were obtained from the second group. The Infection Fatality Rate is obtained by appropriately combining the other five columns.

Abstract INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 has infected more than 9,834,513 Brazilians up to February 2021. Knowledge of risk factors of coronavirus disease among Brazilians remains scarce, especially in the adult population. This study verified the risk factors for intensive care unit admission and mortality for coronavirus disease among 20-59-year-old Brazilians. METHODS: A Brazilian database on respiratory illness was analyzed on October 9, 2020, to gather data on age, sex, ethnicity, education, housing area, and comorbidities (cardiovascular disease, diabetes, and obesity). Multivariate logistic regression analysis was performed to identify the risk factors for coronavirus disease. RESULTS: Overall, 1,048,575 persons were tested for coronavirus disease; among them, 43,662 were admitted to the intensive care unit, and 34,704 patients died. Male sex (odds ratio=1.235 and 1.193), obesity (odds ratio=1.941 and 1.889), living in rural areas (odds ratio=0.855 and 1.337), and peri-urban areas (odds ratio=1.253 and 1.577) were predictors of intensive care unit admission and mortality, respectively. Cardiovascular disease (odds ratio=1.552) was a risk factor for intensive care unit admission. Indigenous people had reduced chances (odds ratio=0.724) for intensive care unit admission, and black, mixed, East Asian, and indigenous ethnicity (odds ratio=1.756, 1.564, 1.679, and 1.613, respectively) were risk factors for mortality. CONCLUSIONS: Risk factors for intensive care unit admission and mortality among adult Brazilians were higher in men, obese individuals, and non-urban areas. Obesity was the strongest risk factor for intensive care unit admission and mortality.

Socioeconomic disparities play an important role in the development of severe clinical outcomes including deaths from COVID-19. However, the current scientific evidence in regard the association between measures of poverty and COVID-19 mortality in hospitalized patients is scant. The objective of this study was to investigate whether there is an association between the Colombian Multidimensional Poverty Index (CMPI) and mortality from COVID-19 in hospitalized patients in Colombia from May 1, 2020 to August 15, 2021. This was an ecological study using individual data on hospitalized patients from the National Institute of Health of Colombia (INS), and municipal level data from the High-Cost Account and the National Administrative Department of Statistics. The main outcome variable was mortality due to COVID-19. The main exposure variable was the CMPI that ranges from 0 to 100% and was categorized into five levels: (i) level I (0%−20%), (ii) level II (20%−40%), (iii) level III (40%−60%), (iv) level IV (60%−80%); and (v) level V (80%−100%). The higher the level, the higher the level of multidimensional poverty. A Bayesian multilevel logistic regression model was applied to estimate Odds Ratio (OR) and their corresponding 95% credible intervals (CI). In addition, a subgroup analysis was performed according to the epidemiological COVID-19 waves using the same model. The odds for dying from COVID-19 was 1.46 (95% CI 1.4–1.53) for level II, 1.41 (95% CI 1.33–1.49) for level III and 1.70 (95% CI 1.54–1.89) for level IV hospitalized COVID-19 patients compared with the least poor patients (CMPI level I). In addition, age and male sex also increased mortality in COVID-19 hospitalized patients. Patients between 26 and 50 years-of-age had 4.17-fold increased odds (95% CI 4.07–4.3) of death compared with younger than 26-years-old patients. The corresponding for 51–75 years-old patients and those above the age of 75 years were 9.17 (95% CI 8.93–9.41) and 17.1 (95% CI 16.63–17.56), respectively. Finally, the odds of death from COVID-19 in hospitalized patients gradually decreased as the pandemic evolved. In conclusion, socioeconomic disparities were a major risk factor for mortality in patients hospitalized for COVID-19 in Colombia.

Seroprevalence and infection fatality rate (IFR) estimates in New York City for five age classes, using confirmed COVID-19 deaths (excluding probable deaths).

The spread of coronavirus (COVID-19) in Italy has hit every age group uniformly and claimed over 190 thousand lives since it entered the country. As the chart shows, however, mortality rate appeared to be much higher for the elderly patient. In fact, for people between 80 and 89 years of age, the fatality rate was 6.1 percent. For patients older than 90 years, this figure increased to 12.1 percent. On the other hand, the death rate for individuals under 60 years of age was well below 0.5 percent. Overall, the mortality rate of coronavirus in Italy was 0.7 percent.

Italy's death toll was one of the most tragic in the world. In the last months, however, the country started to see the end of this terrible situation: as of May 2023, roughly 84.7 percent of the total Italian population was fully vaccinated.

Since the first case was detected at the end of January in Italy, coronavirus has been spreading fast. As of May, 2023, the authorities reported over 25.8 million cases in the country. The area mostly hit by the virus is the North, in particular the region of Lombardy.

For a global overview visit Statista's webpage exclusively dedicated to coronavirus, its development, and its impact.

As of May 2, 2023, Canadians aged 20 to 29 years accounted for 17 percent of COVID-19 cases in Canada, the largest share of all age groups.

Which groups of people are at higher risk? The number of new coronavirus cases in Canada had dropped dramatically in the beginning of 2021, suggesting the country had regained control of the second wave of infections, before spiking again around April and then dropping again in the summer months. A new surge began in December 2021, with the number of new daily cases skyrocketing, reaching never-before-seen levels. While there are much more cases among younger people, the number of COVID-19 deaths are particularly high for older people, especially those with pre-existing medical conditions. During these challenging times, it is important to protect older people living alone and those in care facilities. Groups should not be discriminated against because of age, and all communities need to be supported.

As of January 11, 2023, the highest number of deaths due to the coronavirus in Sweden was among individuals aged 80 to 90 years old. In this age group there were 9,124 deaths as a result of the virus. The overall Swedish death toll was 22,645 as of January 11, 2023.

The first case of coronavirus (COVID-19) in Sweden was confirmed on February 4, 2020. The number of cases has since risen to over 2.68 million, as of January 2023. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Inspiration

Which countries have the most social contacts in the world? In particular, do countries with more social contacts among the elderly report more deaths caused by a pandemic caused by a respiratory virus?

Context

With the emergence of the COVID-19 pandemic, reports have shown that the elderly are at a higher risk of dying than any other age groups. 8 out of 10 deaths reported in the U.S. have been in adults 65 years old and older. Countries have also began to enforce 2km social distancing to contain the pandemic.

To this end, I wanted to explore the relationship between social contacts among the elderly and its relationship with the number of COVID-19 deaths across countries.

Content

This dataset includes a subset of the projected social contact matrices in 152 countries from surveys Prem et al. 2020. It was based on the POLYMOD study where information on social contacts was obtained using cross-sectional surveys in Belgium (BE), Germany (DE), Finland (FI), Great Britain (GB), Italy (IT), Luxembourg (LU), The Netherlands (NL), and Poland (PL) between May 2005 and September 2006.

This dataset includes contact rates from study participants ages 65+ for all countries from all sources of contact (work, home, school and others).

I used this R code to extract this data:

load('../input/contacts.Rdata') # https://github.com/kieshaprem/covid19-agestructureSEIR-wuhan-social-distancing/blob/master/data/contacts.Rdata
View(contacts)
contacts[["ALB"]][["home"]]
contacts[["ITA"]][["all"]]
rowSums(contacts[["ALB"]][["all"]])
out1 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[16,]; out <- rbind(out, data.frame(x)) }
out2 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[15,]; out <- rbind(out, data.frame(x)) }
out3 = data.frame(); for (n in names(contacts)) { x = (contacts[[n]][["all"]])[14,]; out <- rbind(out, data.frame(x)) }
m1 = data.frame(t(matrix(unlist(out1), nrow=16)))
m2 = data.frame(t(matrix(unlist(out2), nrow=16)))
m3 = data.frame(t(matrix(unlist(out3), nrow=16)))
rownames(m1) = names(contacts)
colnames(m1) = c("00_04", "05_09", "10_14", "15_19", "20_24", "25_29", "30_34", "35_39", "40_44", "45_49", "50_54", "55_59", "60_64", "65_69", "70_74", "75_79")
rownames(m2) = rownames(m1)
rownames(m3) = rownames(m1)
colnames(m2) = colnames(m1)
colnames(m3) = colnames(m1)
write.csv(zapsmall(m1),"contacts_75_79.csv", row.names = TRUE)
write.csv(zapsmall(m2),"contacts_70_74.csv", row.names = TRUE)
write.csv(zapsmall(m3),"contacts_65_69.csv", row.names = TRUE)

Rows names correspond to the 3 letter country ISO code, e.g. ITA represents Italy. Column names are the age groups of the individuals contacted in 5 year intervals from 0 to 80 years old. Cell values are the projected mean social contact rate.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1139998%2Ffa3ddc065ea46009e345f24ab0d905d2%2Fcontact_distribution.png?generation=1588258740223812&alt=media" alt="">

Acknowledgements

Thanks goes to Dr. Kiesha Prem for her correspondence and her team for publishing their work on social contact matrices.

References

• The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study
• Projecting social contact matrices in 152 countries using contact surveys and demographic data
• Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases (POLYMOD study)

Related resources

• My starter notebook
• http://www.socialcontactdata.org/
• https://www.kaggle.com/tsubasatwi/close-contact-status-of-corona-in-japan
• Facebook Data for Good Mobility Dashboard

As of 2023, the third leading cause of death among teenagers aged 15 to 19 years in the United States was intentional self-harm or suicide, contributing to around 17 percent of deaths among this age group. The leading cause of death at that time was unintentional injuries, contributing to around 38.6 percent of deaths, while 20.7 percent of all deaths in this age group were due to assault or homicide. Cancer and heart disease, the overall leading causes of death in the United States, are also among the leading causes of death among U.S. teenagers. Adolescent suicide in the United States In 2021, around 22 percent of students in grades 9 to 12 reported that they had seriously considered attempting suicide in the past year. Female students were around twice as likely to report seriously considering suicide compared to male students. In 2023, New Mexico had the highest rate of suicides among U.S. teenagers, with around 28 deaths per 100,000 teenagers, followed by Idaho with a rate of 22.5 per 100,000. The states with the lowest death rates among adolescents are New Jersey and New York. Mental health treatment Suicidal thoughts are a clear symptom of mental health issues. Mental health issues are not rare among children and adolescents, and treatment for such issues has become increasingly accepted and accessible. In 2021, around 15 percent of boys and girls aged 5 to 17 years had received some form of mental health treatment in the past year. At that time, around 35 percent of youths aged 12 to 17 years in the United States who were receiving specialty mental health services were doing so because they had thought about killing themselves or had already tried to kill themselves.

As of February 17, 2022, there had been approximately 139.5 thousand deaths due to COVID-19 recorded in England. When broken down by age, almost 37 percent of these deaths occurred in the age group 80 to 89 years, while a further fifth of deaths were recorded among over 90 year olds. For further information about the COVID-19 pandemic, please visit our dedicated Facts and Figures page.