Objectives: To estimate years of potential life lost (YPLL) and present value of future lost productivity (PVFLP) associated with premature mortality due to HPV-attributable cancers, specifically those targeted by nonavalent HPV (9vHPV) vaccination, in the United States (US) before vaccine use.Methods: YPLL was estimated from the reported number of deaths in 2017 due to HPV-related cancers, the proportion attributable to 9vHPV-targeted types, and age- and sex-specific US life expectancy. PVFLP was estimated as the product of YPLL by age- and sex-specific probability of labor force participation, annual wage, value of non-market labor, and fringe benefits markup factor.Results: An estimated 7,085 HPV-attributable cancer deaths occurred in 2017 accounting for 154,954 YPLL, with 5,450 deaths (77%) and 121,226 YPLL (78%) attributable to 9vHPV-targeted types. The estimated PVFLP was $3.3 billion for cancer deaths attributable to 9vHPV-targeted types (86% from women). The highest productivity burden was associated with cervical cancer in women and anal and oropharyngeal cancers in men.Conclusions: HPV-attributable cancer deaths are associated with a substantial economic burden in the US, much of which could be vaccine preventable.

Seasonal influenza epidemics have a substantial public health and economic burden in the United States (US). On average, over 200,000 people are hospitalized and an estimated 23,000 people die from respiratory and circulatory complications associated with seasonal influenza virus infections each year. Annual direct medical costs and indirect productivity costs across the US have been found to average respectively at $10.4 billion and $16.3 billion. The objective of this study was to estimate the economic impact of severe influenza-induced illness on the US Veterans Affairs population. The five-year study period included 2010 through 2014. Influenza-attributed outcomes were estimated with a statistical regression model using observed emergency department (ED) visits, hospitalizations, and deaths from the Veterans Health Administration of the Department of Veterans Affairs (VA) electronic medical records and respiratory viral surveillance data from the Centers for Disease Control and Prevention (CDC). Data from VA’s Managerial Cost Accounting system were used to estimate the costs of the emergency department and hospital visits. Data from the Bureau of Labor Statistics were used to estimate the costs of lost productivity; data on age at death, life expectancy and economic valuations for a statistical life year were used to estimate the costs of a premature death. An estimated 10,674 (95% CI 8,661–12,687) VA ED visits, 2,538 (95% CI 2,112–2,964) VA hospitalizations, 5,522 (95% CI 4,834–6,210) all-cause deaths, and 3,793 (95% CI 3,375–4,211) underlying respiratory or circulatory deaths (inside and outside VA) among adult Veterans were attributable to influenza each year from 2010 through 2014. The annual value of lost productivity amounted to $27 (95% CI $24–31) million and the annual costs for ED visits were $6.2 (95% CI $5.1–7.4) million. Ninety-six percent of VA hospitalizations resulted in either death or a discharge to home, with annual costs totaling $36 (95% CI $30–43) million. The remaining 4% of hospitalizations were followed by extended care at rehabilitation and skilled nursing facilities with annual costs totaling $5.5 (95% CI $4.4–6.8) million. The annual monetary value of quality-adjusted life years (QALYs) lost amounted to $1.1 (95% CI $1.0–1.2) billion. In total, the estimated annual economic burden was $1.2 (95% CI $1.0–1.3) billion, indicating the substantial burden of seasonal influenza epidemics on the US Veterans Affairs population. Premature death was found to be the largest driver of these costs, followed by hospitalization.

This indicator includes the estimated lifetime wages lost due to premature opioid-related mortality. This value is the sum of the product of the number of years of life lost due to premature opioid-related mortality and the average quarterly wages for county of residence—as reported by the Bureau of Labor Statistics—adjusted for inflation and then discounted.

BackgroundSeasonal influenza causes considerable morbidity and mortality across all age groups, and influenza vaccination was recommended in 2010 for all persons aged 6 months and above. We estimated the averted costs due to influenza vaccination, taking into account the seasonal economic burden of the disease.MethodsWe used recently published values for averted outcomes due to influenza vaccination for influenza seasons 2005-06, 2006-07, 2007-08, and 2008-09, and age cohorts 6 months-4 years, 5-19 years, 20-64 years, and 65 years and above. Costs were calculated according to a payer and societal perspective (in 2009 US$), and took into account medical costs and productivity losses.ResultsWhen taking into account direct medical costs (payer perspective), influenza vaccination was cost saving only for the older age group (65≥) in seasons 2005-06 and 2007-08. Using the same perspective, influenza vaccination resulted in total costs of $US 1.7 billion (95%CI: $US 0.3–4.0 billion) in 2006-07 and $US 1.8 billion (95%CI: $US 0.1–4.1 billion) in 2008-09. When taking into account a societal perspective (and including the averted lost earnings due to premature death) averted deaths in the older age group influenced the results, resulting in cost savings for all ages combined in season 07-08.DiscussionInfluenza vaccination was cost saving in the older age group (65≥) when taking into account productivity losses and, in some seasons, when taking into account medical costs only. Averted costs vary significantly per season; however, in seasons where the averted burden of deaths is high in the older age group, averted productivity losses due to premature death tilt overall seasonal results towards savings. Indirect vaccination effects and the possibility of diminished case severity due to influenza vaccination were not considered, thus the averted burden due to influenza vaccine may be even greater than reported.

This statistic describes the percent distribution of disability-adjusted life-years in the United Kingdom among persons aged under ** as of 2012, by condition and disease. Cardiovascular diseases contributed to **** of all DALYs among those under 70 years of age. People with mental health problems have been found to have higher rates of physical illnesses than the general population. Mental health can cost up to ** billion pounds per year and is listed as one of the most common reasons to claim disability benefits in Britain.

Disability-adjusted life-years

Mental health disorders and cancers are among the highest in terms of disability-adjusted life-years (DALYs) in the United Kingdom as of 2012 leading to **** DALYs and **** DALYs, respectively. DALYs are calculated by combining the years of life lost due to premature mortality and the years lost due to disability caused by the condition. In high-income countries, chronic diseases contribute to high DALY values. For example, cardiovascular diseases are among the leading causes of death worldwide at **** million deaths in 2012. Chronic diseases can create indirect costs that can be a major hindrance in low-income families. Reduced income from loss of productivity, forgoing earnings from those that must care for the patient, and potential lost opportunity in young family members who leave school to care for the ill or to help household economy are indirect costs that chronic diseases can incur.

Neuropsychiatric conditions account for almost ** percent of the global disease burden. However, this value is suspected to be much higher due to the complex relationships between physical and mental illness. It is also quite common for those with mental health disorders to be experiencing more than one disorder. People living in Alabama and California have some of the highest levels of poor mental health in the country, at **** percent and **** percent of the population reporting this condition, respectively, as of 2012. In the United States, *** percent of individuals between 55 and 64 years of age have reported experiencing serious psychological distress.

It is widely acknowledged that there are costs involved with fatal injury to workers. These costs cross numerous boundaries, and generally address the overall costs to victims and the affected groups, and to society as a whole. This represents a cause for concern to employers, worker groups, policy makers, medical personnel, economists and others interested in workplace safety and health. This broad-reaching burden can include social costs, organizational costs, familial and interpersonal group costs, as well as personal costs such as suffering and loss of companionship. The data in the accompanying tables focus on monetary costs of fatal occupational injury which largely consist of foregone wages, but also include the direct costs of medical care and the indirect costs of household production and certain ancillary measures.

These data represent a continuation of prior research by the National Institute for Occupational Safety and Health (NIOSH) that attempted to delimit the economic consequences of workplace injury for earlier years. Interested parties should be aware that these data serve as a supplemental update to prior NIOSH publications which described the magnitude and circumstances of occupational injury deaths for earlier years 1,2.

The current data build on this research, and the findings are compelling. Over the period studied, 2003-2010, the costs from these 42,380 premature deaths exceeded $44 billion, an amount greater than the reportable gross domestic product for some States. These findings inform the national will to reduce this severe toll on our nation’s workers, institutions, communities, and the nation itself. Researchers and concerned parties within the occupational and public health professions, academia, organizations focusing on workplace safety, labor unions and the business community have all proven to be willing and avid users of this data, and have used this research to continue their efforts, in concert with continuing NIOSH research efforts, to reduce the great toll that injury imposes on our workers, workplaces, and Nation.

China and India saw the largest number of air pollution-related deaths worldwide in 2021, with more than *********** recorded in each. Together, the world's two most populous countries accounted for approximately ** percent of global deaths from diseases linked to air pollution that year. Health effects of air pollution There are a number of health impacts linked to air pollution. These range from milder symptoms like sore throats and irritated eyes, to more serious effects that increase the risk of premature mortality, including strokes, heart disease, and lung cancer. Where is air pollution highest? In 2024, the world's most polluted countries based on PM2.5 concentrations were Chad, Bangladesh, and Pakistan, with average levels in each country more than ** times above World Health Organization (WHO) recommended guidelines. Although India ranked fifth that year, it was still home to ** of the ** most polluted cities in the world in 2024.

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

This statistic shows the 20 countries* with the highest infant mortality rate in 2024. An estimated 101.3 infants per 1,000 live births died in the first year of life in Afghanistan in 2024. Infant and child mortality Infant mortality usually refers to the death of children younger than one year. Child mortality, which is often used synonymously with infant mortality, is the death of children younger than five. Among the main causes are pneumonia, diarrhea – which causes dehydration – and infections in newborns, with malnutrition also posing a severe problem. As can be seen above, most countries with a high infant mortality rate are developing countries or emerging countries, most of which are located in Africa. Good health care and hygiene are crucial in reducing child mortality; among the countries with the lowest infant mortality rate are exclusively developed countries, whose inhabitants usually have access to clean water and comprehensive health care. Access to vaccinations, antibiotics and a balanced nutrition also help reducing child mortality in these regions. In some countries, infants are killed if they turn out to be of a certain gender. India, for example, is known as a country where a lot of girls are aborted or killed right after birth, as they are considered to be too expensive for poorer families, who traditionally have to pay a costly dowry on the girl’s wedding day. Interestingly, the global mortality rate among boys is higher than that for girls, which could be due to the fact that more male infants are actually born than female ones. Other theories include a stronger immune system in girls, or more premature births among boys.

Lynching in the United States is estimated to have claimed over 4.7 thousand lives between 1882 and 1968, and just under 3.5 thousand of these victims were black. Today, lynching is more commonly associated with racial oppression, particularly in the south, however, in early years, victims were more commonly white (specifically Mexican), and lynchings were more frequent in western territories and along the southern border. It was only after Reconstruction's end where the lynching of black people became more prevalent, and was arguably the most violent tool of oppression used by white supremacists. Nationwide, the share of the population who was black fluctuated between 10 and 13 percent in the years shown here, however the share of lynching victims who were black was almost 73 percent. North-south divide Of the 4.7 thousand victims of lynching between 1882 and 1968, over 3.5 thousand of these were killed in former-Confederate states. Of the fourteen states where the highest number of lynching victims were killed, eleven were former-Confederate states, and all saw the deaths of at least one hundred people due to lynching. Mississippi was the state where most people were lynched in these years, with an estimated 581 victims, 93 percent of whom were black. Georgia saw the second most lynchings, with 531 in total, and the share of black victims was also 93 percent. Compared to the nationwide average of 73 percent, the share of black victims in former-Confederate states was 86 percent. Texas was the only former-Confederate state where this share (71 percent) was below the national average, due to the large number of Mexicans who were lynched there. Outside of the south Of the non-Confederate state with the highest number of lynching victims, most either bordered the former-Confederate states, or were to the west. Generally speaking, the share of white victims in these states was often higher than in the south, meaning that the majority took place in the earlier years represented here; something often attributed to the lack of an established judiciary system in rural regions, and the demand for a speedy resolution. However, there are many reports of black people being lynched in the former border states in the early-20th century, as they made their way northward during the Great Migration. Between 1882 and 1968, lynchings were rare in the Northeast, although Connecticut, Massachusetts, New Hampshire and Rhode Island were the only states** without any recorded lynchings in these years.

In 1870, the average person born in the Philippines could expect to live to just under the age of 31 years old. This figure would remain unchanged until the early 1900s, when life expectancy would fall to just over 25 years in the Philippine-American War of 1899-1902, as disruptions in food supply and healthcare would result in the loss of several hundred thousand Filipinos to famine and disease. This drop would be accompanied by another drop in the 1920s as the Spanish Flu would ravage the country. However, life expectancy would quickly recover and begin to rise under the United States military administration of the island, as investment by the American government would result in significant expansion in access to nutrition and healthcare. As a result, life expectancy would rise to over 41 years by 1940.

Life expectancy in the Philippines would decline once more in the 1940s, however, in the 1941 invasion and subsequent occupation of the island nation by the Empire of Japan in the Second World War, in which famine and causalities of war would result in the death of an estimated 500,000 Filipinos. Despite significant destruction in the Second World War, and an ending to the bulk of American investment in the country following its independence from the U.S. in 1946, life expectancy in the Philippines would quickly rise in the post-war years as the country would modernize; almost doubling in the two decades between 1945 and 1965 alone. It then plateaued throughout the 1970s and 1980s, during the authoritarian regime of Ferdinand Marcos, before the People Power Revolution in 1986 returned democracy to the country, and living standards began to improve once more. Life expectancy has also increased since this time, and in 2020, it is estimated that the average person born in the Philippines can expect to live to just over the age of 71 years old.

The United States military has a long history of ethnic minorities serving in its ranks, with black Americans having served as far back as the Revolutionary War. The Vietnam War took place during a period of changing race relations in the United States, with the Civil Rights Movement reaching its peak in the mid-1960s, and this too was reflected in the military. The Vietnam War was the first major conflict in which black and white troops were not formally segregated, however, discrimination did still occur with black soldiers reporting being subject to overt racism, being unjustly punished, and having fewer promotion opportunities than their white counterparts.

In the early phases of the war, black casualty rates were much higher than for other races and ethnicities, with some reports showing that black soldiers accounted for 25 percent of the casualties recorded in 1965. This declined substantially as the war progressed, however, the proportion of black service personnel among those fallen during the war was still disproportionately high, as black personnel comprised only 11 percent of the military during this era. A smaller number of other ethnic minorities were killed during the war, comprising two percent of the total.