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Age and sex incidence of influenza and pneumonia morbidity and mortality in the epidemic of 1928-29 with comparative data for the epidemic of 1918-19
... For polio, typhoid, tuberculosis and measles ( Fig. 1a-d, Supplementary Figures S1-4) some datasets suggest that the case fatality rate is already higher by age 10-14 years than in younger children 9,[11][12][13]21,29 . A rise in severity by age 15-19 or 15-24 years is seen clearly in large datasets for case fatality rates for smallpox 35 , HIV 44 , Spanish influenza 47,48 , pertussis 13 , and Salmonella 55 , for hospitalisation rates for chickenpox 40 , and in smaller datasets for hospitalisation for infectious mononucleosis 43 , and case fatality rate for Yellow fever 63 Supplementary Figures S5-12). ...
... For most of the remaining infections for which information was available, the severity rises with age from 30 years (seasonal influenza 48 Three infections show different patterns, with greater severity in children than in young adults. The case fatality rate from diphtheria drops steeply with age in childhood, and continues to fall during adolescence, so the lowest case fatality rate is in the 20 s and 30 s, before rising from age 40 years (Fig. 4f, Supplementary Figure S30 ...
... and influenza47,48 ensured mild cases and all ages are included. Direct estimates of fatalities in well-characterized outbreaks of Yellow fever62,63 , meningococcal meningitis70 , and viral encephalitides73,126 avoid any strain differences or notification differences by age. ...
The COVID-19 pandemic has ignited interest in age-specific manifestations of infection but surprisingly little is known about relative severity of infectious disease between the extremes of age. In a systematic analysis we identified 142 datasets with information on severity of disease by age for 32 different infectious diseases, 19 viral and 13 bacterial. For almost all infections, school-age children have the least severe disease, and severity starts to rise long before old age. Indeed, for many infections even young adults have more severe disease than children, and dengue was the only infection that was most severe in school-age children. Together with data on vaccine response in children and young adults, the findings suggest peak immune function is reached around 5–14 years of age. Relative immune senescence may begin much earlier than assumed, before accelerating in older age groups. This has major implications for understanding resilience to infection, optimal vaccine scheduling, and appropriate health protection policies across the life course.
... Further, although black people more often were placed in noncombatant battalions and more often lived in tent colonies fully segregated from white barracks, both black and white soldiers were at the time assumed to live under relatively controlled conditions and under the same military nutrition, clothing, training, and discipline [7]. One major weakness of military data is that they only include males 18-45 years, while other male age groups and females are not considered [7][8][9][10][11][12][13]. Military data also include the physically and mentally fittest of males 18-45 years [8,10,14,15]. Those who were not in Class I, the classification that made them eligible for immediate induction, were categorized in four other classes. ...
... Survey data exist for 18 cities and some rural areas in the United States [10,14,15,[17][18][19]. All households were interviewed by trained staff. ...
... Second, the US data only cover the 1918 fall wave, and not waves in the spring and summer of 1918 or waves in 1919. Third, because United States participated in the First World War (WWI), the survey (and routine notification data, see next paragraph) for young adult civilian males in the United States might have been biased because 4 million males were drafted for military service, leaving possibly more frail males behind among the civilians [10,14,15]. However, the mortality rates of both males and females in urban and rural areas of one of the canvassed areas, ...
During epidemics, the poorest part of the population usually suffers the most. Alfred Crosby noted that the norm changed during the 1918 influenza pandemic in the US: The black population (which were expected to have higher influenza morbidity and mortality) had lower morbidity and mortality than the white population during the autumn of 1918. Crosby's explanation for this was that black people were more exposed to a mild spring/summer wave of influenza earlier that same year. In this paper, we review the literature from the pandemic of 1918 to better understand the crossover in the role of race on mortality. The literature has used insurance, military, survey, and routine notification data. Results show that the black population had lower morbidity, and during September, October, and November, lower mortality but higher case fatality than the white population. The results also show that the black population had lower influenza morbidity prior to 1918. The reasons for lower morbidity among the black population both at baseline and during the herald and later waves in 1918 remain unclear. Results may imply that black people had a lower risk of developing the disease given exposure, but when they did get sick, they had a higher risk of dying.
... In pandemic influenza, the usual severity pattern-often described as a "U"-shaped curve, with morbidity and mortality concentrated at the extremes of age-takes on different forms, with younger people typically suffering disproportionately severe disease compared to seasonal epidemic influenza (Figure 2). Although overall annual death rates were higher in every age group in the pandemic year of 1918, compared to the non-pandemic year of 1914, the excess death rate was proportionally far larger in young and middle-aged people than in the elderly [93]. Similarly, in non-pandemic years, the elderly die of influenza at much higher rates than any other age group [94], but in the second wave of the 2009 pandemic (August to October, 2009), that trend was reversed, and the elderly had the lowest influenza-attributable death rate of any age group [95]. ...
... Vaccines 2018, 6, x 7 of 14 a "U"-shaped curve, with morbidity and mortality concentrated at the extremes of age-takes on different forms, with younger people typically suffering disproportionately severe disease compared to seasonal epidemic influenza (Figure 2). Although overall annual death rates were higher in every age group in the pandemic year of 1918, compared to the non-pandemic year of 1914, the excess death rate was proportionally far larger in young and middle-aged people than in the elderly [93]. Similarly, in non-pandemic years, the elderly die of influenza at much higher rates than any other age group [94], but in the second wave of the 2009 pandemic (August to October, 2009), that trend was reversed, and the elderly had the lowest influenza-attributable death rate of any age group [95]. ...
... Similarly, in non-pandemic years, the elderly die of influenza at much higher rates than any other age group [94], but in the second wave of the 2009 pandemic (August to October, 2009), that trend was reversed, and the elderly had the lowest influenza-attributable death rate of any age group [95]. [93]. Data from 2001-2009 are graphed against the right y-axis as the age-adjusted death rate (AADR), calculated as follows: Influenza-attributed crude death rate per 100,000 population (CDR) = the number of influenza deaths in each age group ÷ the population in that age group × 100,000; AADR = CDR × the proportion of the entire population within that age group. ...
For centuries, the development of vaccines to prevent infectious disease was an empirical process. From smallpox variolation in Song dynasty China, through the polysaccharide capsule vaccines developed in the 1970s, vaccines were made either from the pathogen itself, treated in some way to render it attenuated or non-infectious, or from a closely related non-pathogenic strain. In recent decades, new scientific knowledge and technologies have enabled rational vaccine design in a way that was unimaginable before. However, vaccines optimal against some infectious diseases, influenza among them, have remained elusive. This review will highlight the challenges that influenza viruses pose for rational vaccine design. In particular, it will consider the clinically beneficial endpoints, beyond complete sterilizing immunity, that have been achieved with vaccines against other infectious diseases, as well as the barriers to achieving similar success against influenza.
... One of the most important factors related to both morbidity and mortality, which are observable for both the samples and the populations, is the age distributions; the sample age-distributions did not deviate from the age distributions in the population of the 3 canvassed areas [15,19]. The overall sample mortality rate was equal to the overall population mortality rate in Bergen [19], whereas the overall sample mortality rate was slightly lower than the population mortality rate in Maryland [20,21]. ...
... The demonstration of higher morbidity in smaller towns and rural areas of Maryland during the pandemic fall wave, compared with Baltimore, is in line with previous assumptions that people living in less densely populated areas had been less exposed to influenza during the first wave than had those living in cities, leaving them more susceptible to ILI during the second wave of the disease [10,19,22]. Even though a summer wave of influenza is not mentioned in previous reports on the surveys conducted in the United States [15,20,21], the survey data from Bergen, together with the recognition of higher morbidity in smaller towns compared with more urban areas in Maryland, gives evidence supporting that a pandemic wave before the autumn of 1918 may actually have hit the city of Baltimore as well. This assumption is also is supported by several others showing that a first wave of influenza was present in the northeastern United States and the maritime region of Canada in April to August 1918 [3-5, 8, 23]. ...
... The author of the report from the Bergen survey believed an explanation for this marked gender difference could be that young male workers were more likely to be exposed to influenza during the first pandemic wave than women, who were the mostly home-based, leading to men being better protected during following waves [19]. On the other hand, authors of reports from the US surveys, claim that there were no marked difference in the ILI rates of the 2 sexes in this age group during the fall wave, and they argue that a higher ILI rate in females compared with males (as seen in Figure 1A) were due to the fact that the homemakers remember their own cases best [20,21]. However, this argument does not explain the higher male-to-female ILIrates at age 20-29 years in the Bergen during the summer. ...
Background. Reanalysis of influenza survey data from 1918 to 1919 was done to obtain new insights into the geographic and host factors responsible for the various waves.
Methods. We analyzed the age- and sex-specific influenza morbidity, fatality, and mortality for the city of Baltimore and smaller towns and rural areas of Maryland and the city of Bergen (Norway), using survey data. The Maryland surveys captured the 1918 fall wave, whereas the Bergen survey captured 3 waves during 1918–1919.
Results. Morbidity in rural areas of Maryland was higher than in the city of Baltimore during the fall of 1918, that was almost equal to that in Bergen during the summer of 1918. In Bergen, the morbidity in the fall was only half of that in the summer, With more females than males just above the age of 20 falling ill, as seen in both regions of Maryland. In contrast, more males than females fell ill during the summer wave in Bergen. Individuals <40 years had the highest morbidity, whereas school-aged children had the lowest fatality and mortality.
Conclusion. A previously unrecognized pandemic summer wave may have hit the 2 regions of Maryland in 1918.
... In table 2 the outstanding result appears that even when the kerosene portion of the mixture is as little as one-fifth, the lethal effect on Aedes aegypti is the same as when much larger proportions of kerosene base are used. Thus, in experiment 10, a miLxture of 4 parts of the carbon-tetrachloride extract and 1 part of the kerosene-base extract killed 100 percent of Aedes aegypti in 5 minutes when sprayed in amounts of 5 cc per 1,000 cubic feet. ...
... Respiratory.- Figure 2 shows the various respiratory diseases. The influenza and grippe curve is the usual one that has been characteristic of those diseases in the several minor epidemics since 1918, as found by special surveys and in the Hagerstown study (2,12). ...
... Tonsillitis, tonsillectomy, and other diseases of the pharynx show high rates among children, particularly in the school ages, with a tendency to decline as age increases. Pneumonia exhibits the characteristic curve, with high rates among the young and old, like the pneumonia of nonepidemic years and of recent respiratory epidemics, but unlike that of the 1918 epidemic, which October 11, 1935 1414 RESPI RATORY was high among young adults (2,12). The only other respiratory disease that shows any marked rise in the older ages is asthma. ...
The increase of international travel by air has brought forth new problems in controlling the spread of quarantinable diseases. With specific reference to yellow fever, the destruction of infected Aedes aegypti on airplanes while in flight presents itself as one means of restricting the possible spread of this disease. In order to accomplish this end, however, it is necessary to have an agent which will kill these mosquitoes without hazard to human occupants of the air-planes. Pyrethrum in a kerosene base answers this purpose in cer-tain respects; it is comparatively innocuous to human beings while at the same time lethal to Aedes in low concentrations. It has the disadvantage of being flammable. Recent observations I at the New Orleans quarantine station showed that only 2 to 4 grams per 1,000 cubic feet of a pyrethrum concentrate in kerosene (2 grams of pyrethrins per 100 cc) were lethal to Aedes when used as a fine spray. Since that time addi-tional observations have been made at this station with pyrethrum in various and modified bases or vehicles with the object of develop-ing a nonflammable mixture.
... While the reasons for the unusual age patterns of the 1918 pandemic are difficult to elucidate from epidemiological data alone, the steep rise in the risk of mortality among individuals aged 10-20 years during the influenza pandemic is worth noting, especially as it consistent in data from New Zealand, Canada, and the United States [1,2,4]. Historical morbidity surveys indicate that clinical attack rates were similar in these age cohorts [5], suggesting that the severity of influenza-related infection increased sharply between ages 10 and 20 years, likely because of a heightened risk of pneumonia caused by common bacterial respiratory pathogens (especially pneumococci, streptococci, and staphylococci) [5][6][7]. People aged 10-20 years had not lived through the 1889 pandemic, although they could have been infected by descendants of the 1889 influenza virus that persisted during 1892-1918. ...
... While the reasons for the unusual age patterns of the 1918 pandemic are difficult to elucidate from epidemiological data alone, the steep rise in the risk of mortality among individuals aged 10-20 years during the influenza pandemic is worth noting, especially as it consistent in data from New Zealand, Canada, and the United States [1,2,4]. Historical morbidity surveys indicate that clinical attack rates were similar in these age cohorts [5], suggesting that the severity of influenza-related infection increased sharply between ages 10 and 20 years, likely because of a heightened risk of pneumonia caused by common bacterial respiratory pathogens (especially pneumococci, streptococci, and staphylococci) [5][6][7]. People aged 10-20 years had not lived through the 1889 pandemic, although they could have been infected by descendants of the 1889 influenza virus that persisted during 1892-1918. ...
... Although the reasons for the atypical mortality risk profile of the 1918-1919 pandemic may remain elusive, it is important to pursue efforts to analyze archival mortality records from a variety of locations, building upon the work by Wilson et al and others [1,2,4,5,8,9]. A systematic epidemiological description of the pandemic in a variety of globally sampled populations may provide unique insights into the host and geographic factors responsible for the unusual severity of disease associated with the 1918 pandemic virus. ...
... The age profile of the 1918-1919 pandemic mortality risk in Kentucky is in agreement with previous studies relying on broader age groups in populations from the Americas, Europe, and Asia [4,9,10,[12][13][14]. While many hypotheses have been put forward to explain the patterns of increased risk in young adults and the (relatively) decreased risk among older individuals in this pandemic, none have been conclusive. ...
... Most 1918-1919 pandemic deaths resulted from secondary bacterial pneumonias [6,43]. Mortality among 1918-1919 influenza patients was most strongly associated with an increased incidence of pneumonia caused by common bacterial pneumopathogens (especially pneumococci, streptococci, and staphylococci), rather than with an increased severity of pneumonia, especially among individuals aged 20-40 years [14,38]. In the Kentucky data, the rise in mortality risk between ages 9 and 26 years was steeper than the decrease between ages 26 and 50 years. ...
... It is interesting to contrast this profile with historical observations on the agespecific illness and case-fatality rates. Fall 1918 surveys indicate that influenza illness rates remained relatively constant between ages 10 and 30 years, at 31%-37%, and dropped sharply at older ages [14]. The steep rise in the excess mortality risk among Kentucky teenagers and young adults is most consistent with a rise in the severity of influenza-related infections due to the increased risk of pneumonia, in agreement with these surveys [14]. ...
Background:
The reasons for the unusual age-specific mortality patterns of the 1918-1919 influenza pandemic remain unknown. Here we characterize pandemic-related mortality by single year of age in a unique statewide Kentucky data set and explore breakpoints in the age curves.
Methods:
Individual death certificates from Kentucky during 1911-1919 were abstracted by medically trained personnel. Pandemic-associated excess mortality rates were calculated by subtracting observed rates during pandemic months from rates in previous years, separately for each single year of age and by sex.
Results:
The age profile of excess mortality risk in fall 1918 was characterized by a maximum among infants, a minimum at ages 9-10 years, a maximum at ages 24-26 years, and a second minimum at ages 56-59 years. The excess mortality risk in young adults had been greatly attenuated by winter 1919. The age breakpoints of mortality risk did not differ between males and females.
Conclusions:
The observed mortality breakpoints in male and female cohorts born during 1859-1862, 1892-1894, and 1908-1909 did not coincide with known dates of historical pandemics. The atypical age mortality patterns of the 1918-1919 pandemic cannot be explained by military crowding, war-related factors, or prior immunity alone and likely result from a combination of unknown factors.
... The 1918 Spanish Flu was most devastating pandemic in modern times and caused an estimated 50 million deaths and infected a third of the global population 30 . A unique and defining characteristic of the 1918 pandemic was its unusually high mortality in the young adult age group which transformed the traditional 'U-shaped' mortality by age curve into a 'W-shaped' curve 31,32 . The virus itself was highly virulent but the high mortality is thought to have been primarily driven by the unusually high incidence of secondary bacterial pneumonia which, in the pre-antibiotic era, could often lead to death 30,33,34 . ...
... In the unvaccinated population ( Figure 5-2b) there was no evidence of an association between age and detectable antibody titre (Chi 2 p=0.09). However, protective antibody titres were less common among participants aged 45-64 years (24%, 95% CI [21][22][23][24][25][26][27][28]) than people aged 16-44 years (32%, 95% CI [28][29][30][31][32][33][34][35][36]) (Wald test p=0.008). The percentage of vaccinated participants ( ...
Background: Influenza causes substantial morbidity and mortality. Novel strains from animals can infect humans, but such transmission is poorly understood. Serosurveillance estimates levels of influenza population immunity and infection but obtaining representative sera is challenging. Health-related quality of life (HRQoL) and absenteeism inform cost-effectiveness models of influenza interventions but these parameters are poorly understood. The National Pandemic Flu Service (NPFS) aimed to treat community cases. Little is known about the scheme’s coverage or effectiveness. / Objectives: 1) Investigate whether occupational exposure to pigs increases risk of seasonal, pandemic and zoonotic influenza infection. 2) Describe population-level patterns of influenza infection and immunity in England during 2012/13. 3) Quantify work and school absences and HRQoL from community influenza illnesses. 4) Evaluate the success of the NPFS and propose algorithm changes to improve antiviral targeting. / Methods: Flu Watch is a prospective community cohort of influenza and included recruitment of pig workers during the 2009 pandemic. The Pandemic Immunity and Population Spread study (PIPS) is a novel, population-level, cross-sectional, pandemic serosurveillance system utilizing the Health Survey for England. / Results: Pig workers had increased odds of seropositivity to seasonal, pandemic, and zoonotic influenza compared to the general population. A(H1N1)pdm09 and A(H3N2) infected 40% and 25% of the population in 2012/13. HRQoL loss and absenteeism is low for individual community-level influenza cases. NPFS consultation was low and the case definition specificity was 51%. / Conclusions: Influenza spreads readily from pigs to pig workers, posing risks for novel virus emergence and pandemics. Representative, population-level serology show that, before COVID-19, a large proportion of the population was infected each winter. Most community influenza cases take little time off work and school and this has implications for transmission. The coverage and impact of NPFS was low. Community-based surveys are needed to inform the control of seasonal and pandemic respiratory infections.
... To that end, we summarize the burden of disease as reflected by illnesses, hospitalizations and mortality associated with the 2009-This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 10 influenza A (H1N1) pandemic, compared to the subsequent influenza seasons 2010 to 2019, as compiled by the Centers for Disease Control. Our goal is to identify specific at-risk populations, for whom public health resources should be marshaled appropriately and equitably. ...
... Second, the disease burden of the 2009-10 influenza A pandemic was strikingly unlike that observed in the subsequent influenza seasons 2010 to 2019, in the United States: In particular, there was substantial negative excess influenza mortality among the elderly in 2009-10 compared to the subsequent decade. This is altogether surprising, especially since there was considerable mortality among the elderly during the 1918 pandemic, also attributed to the H1N1 strain [10]. The CDC methodology for assessing influenza burden in the United States is well-established [2]; nevertheless, the assumptions leading to the 2009-10 estimates relating to elderly mortality [5] ought to be scrutinized more closely. ...
Background:
Annual influenza outbreaks constitute a major public health concern, both in the United States and worldwide. Comparisons of the health burdens of outbreaks might lead to the identification of specific at-risk populations, for whom public health resources should be marshaled appropriately and equitably.
Methods:
We examined the disease burden of the 2009-10 influenza A (H1N1) pandemic relating to illnesses, medical visits, hospitalizations, and mortality, compared to influenza seasons 2010 to 2019, in the United States, as compiled by the Centers for Disease Control.
Results:
With regard to seasonal influenza, rates of illnesses and medical visits were highest in infants aged 0-4 years, followed by adults aged 50-64 years. Rates of hospitalizations and deaths evinced a starkly different pattern, both dominated by elderly adults aged 65 and over. Youths aged 0 to 17 years were especially adversely affected by the H1N1 pandemic relative to hospitalizations and mortality compared to seasonal influenza; but curiously the opposite pattern was observed in elderly adults (aged 65 and older).
Conclusions:
Determination of a baseline influenza mortality profile in the United States over the 2010-19 decade is not straightforward. The disease burden of the 2009-10 influenza A pandemic among the elderly was strikingly unlike that observed in the subsequent influenza seasons 2010 to 2019: the past did not predict the future.
... As stated above, the Spanish influenza pandemic was in three consecutive waves: spring 1918, autumn 1918, and winter 1918-19, responsible for killing approximately 50 million people worldwide with abnormally severe clinical manifestations among the healthy and young adults [5,9,16,17]. Moreover, lack of cellular immunity as well as the pre-existing virus-specific and/or cross-reactive antibodies, individual genetic makeup like the single-nucleotide polymorphisms (SNPs), co-infections with bacterial pathogens, and with the measles and malaria causing agents; and finally the malnutrition or obesity has been found to contribute to the high mortality due to the 1918 H1N1 virus infection [9]. ...
... Moreover, lack of cellular immunity as well as the pre-existing virus-specific and/or cross-reactive antibodies, individual genetic makeup like the single-nucleotide polymorphisms (SNPs), co-infections with bacterial pathogens, and with the measles and malaria causing agents; and finally the malnutrition or obesity has been found to contribute to the high mortality due to the 1918 H1N1 virus infection [9]. Though the 1918 influenza pandemic is known as the ''Spanish Flue''; in real the 1918 influenza virus didn't originate in Spain; rather it originated in the Midwest of the United States of America and then France, from where the viral transmission spread throughout Europe and within the rest of the world [9,17,18]. So, the geographic origins of the 1918 virus remained obscure, and eventually the viral vector (animal reservoir) became controversial. Being a segmented virus, influenza virus was capable of undergoing the reassortment process which is known to occur when two influenza virus strains co-infect the same cell, thereby triggering the evolution of a novel ''reassortant'' virus consisting of an unusual assemblage of genes [9]. ...
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is currently causing the respiratory illness termed as the coronavirus disease 2019 or the COVID-19 pandemic. Indeed, the significant increase in deaths in the current days due to influenza around the world started in 1889 is a continued public health threat because of its intermittent style of pandemic outbreaks. An array of research on the influenza viruses has been conducted especially pointing on (1) the development of the anti-viral drugs and the design of probable vaccines on trial basis, (2) the biochemical and genetic aspects underlying the viral pathogenicity, (3) the viral epidemiology, and on (4) the protective immunity against the influenza viruses. Current review briefly discussed the epidemic/ pandemic history of influenza and correlated with the current epidemiology, the possible preventive measures that may be taken by the public health professionals as well as to increase the protective awareness among the general people. The viral reassortments during the initiation of pandemics have also been focused based on the previous literatures.
... However, the spread of novel infectious diseases is not equally distributed by individual's or region's socioeconomic characteristics (4,5) . Early research on Spanish flu in 1918, which is the worst pandemic the world has ever experienced, argued that the poor are affected more severely than the rich both in terms of morbidity and mortality (6)(7)(8) . ...
... Many Second, if the spread of COVID-19 is analyzed as dynamic rather than a static phenomenon, patterns of associations between municipality-level characteristics and the spread of COVID-19 transform across time. Especially, we found that the negative association between 7 By "bring," see also the left panel of Figure 3. The average income has a positive and statistically significant (at 5% level) association in the hurdle component of the hurdle model (i.e., increases the non-zero confirmation of COVID-19 cases). ...
This study provides preliminary evidence regarding associations between socioeconomic inequalities and variations in the number of COVID-19 confirmed cases across 923 municipalities in Catalonia, Spain, as of the 14th of May, 2020. We consider three types of inequalities at municipality-level: 1) economic development, i.e., unemployment rate, average income, immigrants proportion, and the prevalence of small residence; 2) health vulnerability, i.e., crude death rate and the proportion of elderly (aged 65 +) population; and 3) information communication, i.e., the proportion of people with tertiary education. In addition to the static analysis with the total sum of COVID-19 cases, the dynamic analysis with daily moving weekly sum of cases is conducted. The result draws a rather complex picture of relationships between contextual socioeconomic inequalities and the spread of COVID-19. Many indicators of economic inequalities imply the opposite relationship as intuitively suggested: economically disadvantaged municipalities tend to have less cases of confirmed infection than economically advantaged counterparts. The implications from health inequality indicators show mixed patterns: crude death rate is positively associated, but elderly population is negatively associated, with the number of confirmed cases. The indicator of information inequality shows a consistent tendency, i.e., municipalities with more university educated have less confirmed cases, but this tendency transforms across time: the negative association is particularly strong during the first month of Spanish “state of alarm” measure (mid-March to mid-April). Our evidence suggests the need for more careful consideration regarding the association between socioeconomic inequalities and the regional progression of COVID-19 pandemic.
... Some health professionals attempted to capture data demonstrating the effects of the pandemic. Immediately following the fall disease wave, the US Public Health Service surveyed 146,203 persons from households in twelve localities across the US in an effort to describe age and sex characteristics of the affected (Collins, 1931). In a thorough accounting of the pandemic among U.S. military personnel, the medical officer assured readers that he was presenting only known facts in regard to the disease without any regard for censorship, as he anticipated his paper would not receive publicity until the necessity for military or political censorship ceased to exist (MacNeal, 1919). ...
... The case fatality proportion however, was at least ten times higher than in other pandemics. In 1931, Collins analyzed the 1918 U.S. Public Health survey results and concluded the case fatality proportion was 1.7% (Collins, 1931). Since that time, others have reported case fatality proportions as high as 2.5% (Taubenberger et al., 2006). ...
One hundred years have passed since the 1918 influenza pandemic caused substantial illness globally, with an estimated 50 million deaths. A number of factors, including World War I, contributed to the spread of the pandemic virus, which often caused high symptomatic attack rates and severe illness. Major achievements over the last 100 years have been made in influenza prevention, diagnosis, and treatment; however, the potential for a severe pandemic to emerge remains unchanged. We provide a review of the historical context and clinical aspects of illness due to the influenza A(H1N1) virus as it emerged and spread in 1918, with a focus on the experience in the United States. Understanding the significant social disruption and burden of illness from the 1918 pandemic can help us imagine the possible impacts of a high severity pandemic if it were to emerge now.
... They found that age, but not occupation and housing, was an important factor. However, other contemporary scientists found socioeconomic differences in death rates during the pandemic (5,10,11). In an analysis of 2 socially contrasting parishes in Oslo, Norway, the working classes and those living in small apartments had the highest influenza-related death rate (12). ...
... For unmarried women and women whose husbands had no occupational title, we used women's own occupations. From the Historical International Social Class Scheme, we constructed 5 aggregated classes: white collar (Historical International Social Class Scheme 1-5), skilled manual (6, 7), low-skilled manual (9, 10), unskilled manual (11,12), and farmers (8). Frequent occupations among the different classes included, for the white-collar class, proprietors, bookkeepers, and teachers. ...
There is no consensus in the literature about the role of socioeconomic factors on influenza mortality during the 1918 pandemic. While some scholars have found that social factors were important, others have not. In this study, we analyzed differences in excess mortality by social class in Sweden during the 1918 pandemic. We analyzed individual-level mortality of the entire population aged 30-59, by combining information from death records with census data on occupation. Social class was measured by an occupation-based class scheme. Excess mortality during the pandemic was measured as mortality relative to the same month the year before. Social class differences in mortality were modeled using a complementary log-log model, adjusting for potential confounding at the family, the residential (urban/rural) and the county levels. Our findings indicated notable class differences in excess mortality but no perfect class gradient. Class differences were somewhat larger for men than for women.
... even though the odds of dying from Influenza complicated by pneumonia, once it occurred, were only modestly elevated (OR, 1.77; 95% CI, 1.68-1.88). These data support the notion, widely cited in the medical literature of the 1918 era [13], that high pandemic influenza mortality rates during fall 1918 resulted primarily from increased frequency of postinfluenza bacterial pneumonia rather than increased lethality of such pneumonias, and further that the case fatality of postinfluenza pneumonia in 1918-1919 was not remarkably different from general pneumonia casefatality rates observed in the years before and after the pandemic [13,14]. ...
... even though the odds of dying from Influenza complicated by pneumonia, once it occurred, were only modestly elevated (OR, 1.77; 95% CI, 1.68-1.88). These data support the notion, widely cited in the medical literature of the 1918 era [13], that high pandemic influenza mortality rates during fall 1918 resulted primarily from increased frequency of postinfluenza bacterial pneumonia rather than increased lethality of such pneumonias, and further that the case fatality of postinfluenza pneumonia in 1918-1919 was not remarkably different from general pneumonia casefatality rates observed in the years before and after the pandemic [13,14]. ...
Background. Surveillance for respiratory diseases in domestic National Army and National Guard training camps began after the United States’ entry into World War I, 17 months before the “Spanish influenza” pandemic appeared.
Methods. Morbidity, mortality, and case-fatality data from 605 625 admissions and 18 258 deaths recorded for 7 diagnostic categories of respiratory diseases, including influenza and pneumonia, were examined over prepandemic and pandemic periods.
Results. High pandemic influenza mortality was primarily due to increased incidence of, but not increased severity of, secondary bacterial pneumonias.
Conclusions. Two prepandemic incidence peaks of probable influenza, in December 1917–January 1918 and in March–April 1918, differed markedly from the September–October 1918 pandemic onset peak in their clinical-epidemiologic features, and they may have been caused by seasonal or endemic viruses. Nevertheless, rising proportions of very low incidence postinfluenza bronchopneumonia (diagnosed at the time as influenza and bronchopneumonia) in early 1918 could have reflected circulation of the pandemic virus 5 months before it emerged in pandemic form. In this study, we discuss the possibility of detecting pandemic viruses before they emerge, by surveillance of special populations.
... The third wave hit the population in the following winter, and by spring the virus had run its course. The latter two waves caused half of the deaths, especially among the 20-40-year-olds (Collins, 1931;Hoffman, 2011). It was one of the most devastating pandemics in human history affecting 500 million people with estimated 50 million deaths by the end of 1920 (Murray et al., 2006). ...
... The pandemic spread across the world in three consecutive waves: March 1918, September-November 1918, and early 1919 [6]. It has been estimated that the pandemic infected onethird of the world's population and killed an estimated 50-100 million people worldwide [6,7] with a relatively high mortality rate among young adults [8,9]. These estimated figures may not be accurate as the recorded number of infections and deaths may be an understatement due to "misdiagnosis, non-registration, missing records, and non-medical certification" [10]. ...
The influenza pandemic of 1918-19 was the most devastating pandemic of the 20th century. It killed an estimated 50–100 million people worldwide. In late 1918, when the severity of the disease was apparent, the Australian Quarantine Service was established. Vessels returning from overseas and inter-state were intercepted, and people were examined for signs of illness and quarantined. Some of these vessels carried the infection throughout their voyage and cases were prevalent by the time the ship arrived at a Quarantine Station. We study four outbreaks that took place on board the Medic, Boonah, Devon, and Manuka in late 1918. These ships had returned from overseas and some of them were carrying troops that served in the First World War. By analysing these outbreaks under a stochastic Bayesian hierarchical modeling framework, we estimate the transmission rates among crew and passengers aboard these ships. Furthermore, we ask whether the removal of infectious, convalescent, and healthy individuals after arriving at a Quarantine Station in Australia was an effective public health response.
... The pandemic spread across the world in three consecutive waves: March 1918, September-November 1918, and early 1919(Taubenberger & Morens, 2006. It has been estimated that the pandemic infected one-third of the world's population and killed an estimated 50-100 million people worldwide (Short, Kedzierska, & van de Sandt, 2018;Taubenberger & Morens, 2006) with a relatively high mortality rate among young adults (Collins, 1931;Langford, 2002). These estimated figures may not be accurate as the recorded number of infections and deaths may be an understatement due to "misdiagnosis, non-registration, missing records, and non-medical certification" (N. ...
The influenza pandemic of 1918-19 was the most devastating pandemic of the 20th century. It killed an estimated 50–100 million people worldwide. In late 1918, when the severity of the disease was apparent, the Australian Quarantine Service was established. Vessels returning from overseas and inter-state were intercepted, and people were examined for signs of illness and quarantined. Some of these vessels carried the infection throughout their voyage and cases were prevalent by the time the ship arrived at a Quarantine Station. We study four outbreaks that took place on board the Medic, Boonah, Devon , and Manuka in late 1918. These ships had returned from overseas and some of them were carrying troops that served in the First World War. By analysing these outbreaks under a stochastic Bayesian hierarchical modeling framework, we estimate the transmission rates among crew and passengers aboard these ships. Furthermore, we ask whether the removal of infectious, convalescent, and healthy individuals after arriving at a Quarantine Station in Australia was an effective public health response.
Author Summary
The influenza pandemic of 1918-19 was one of the deadliest pandemics in history. In Australia, when it was apparent that the virus was severe, a quarantine service was established to intercept and quarantine ships that returned from overseas and travelled interstate. In this study, we look at the records of outbreaks on board the Medic, Boonah, Devon , and Manuka . Some of the ships carried surviving troops from the First World War, and infections were prevalent when they arrived at a quarantine station. Infectious, convalescent, and healthy individuals on board were moved to the quarantine station for treatment or isolation. We model the outbreaks on the four ships using stochastic epidemic models and estimate the model parameters within a hierarchical framework. Furthermore, we investigate whether the removal of individuals with various disease states was an effective intervention measure from a public health perspective.
... These results were largely consistent across the six countries, and were in line with previous evidence indicating that the majority of COVID-19 fatalities were among the elderly, while the majority of Spanish flu fatalities were among very young children and young adults (see e.g. Collins, 1931;Simonsen et al., 1998;Taubenberger and Morens, 2006). Thus, the age structure of fatalities was a key difference between the two pandemics. ...
The COVID-19 pandemic that reached Europe in 2020 has often been compared to the Spanish flu pandemic of 1918. In this article, we compare the two pandemics in terms of their respective impacts on the loss of life expectancy at birth in six European countries (France, Italy, the Netherlands, Spain, Sweden, Switzerland) by estimating life expectancy in 2020 using Eurostat data. We found that the loss of life expectancy at birth was up to 20 times larger between 1917 and 1918 than between 2019 and 2020. A decomposition of these losses clearly shows that in all six countries, the main contributors were older age groups in 2020 and younger age groups in 1918. These observations are consistent with evidence indicating that most COVID-19 fatalities were among the elderly, while a majority of Spanish flu fatalities were among the young.
... Nevertheless, we would be remiss were we to overlook the intriguing differences in the age vs mortality curves for the two pandemics. The W-shaped mortality rates during the 1918-19 pandemic, with peaks in infants, young adults, and seniors, are unusual and provocative, especially with regard to the high mortality rates experienced by young adults aged 25 to 40: this has engendered considerable speculation and commentary [6,15,16,17,18,19,20,21] Of comparable interest are the hockey-stick shaped mortality rates by age with COVID-19, and in particular the pronounced diminution of mortality among infants compared to the U-shaped mortality curves generally observed in typical influenza seasons. ...
Background
Examination of the mortality patterns in the United States among racial, ethnic, and age groups attributed to the 1918-19 influenza pandemic revealed stark disparities, causes for which could have been addressed and rectified this past century. However, these disparities have been amplified during the current COVID-19 pandemic.
We have ignored the lessons of the past, and were destined to repeat its failings.
Objectives
Compare and contrast mortality patterns by age, race, and ethnicity attributable to the 1918-19 influenza pandemic in the United States with corresponding patterns during the COVID-19 pandemic.
Methods
This is a retrospective study, establishing mortality rates according to age, race and ethnicity attributable to the 1918-19 influenza pandemic in the United States and to the current COVID-19 pandemic, using mortality data published by the U.S. Public Health Service and the Centers for Disease Control and Prevention. Negative binomial regression models were used to establish rate ratios, that is, ratios of mortality rates across the various racial/ethnic groups, and associated 95% confidence intervals.
Results
Mortality patterns by age differ significantly between the 1918-19 influenza pandemic and the COVID-19 pandemic: with infant and young adult (25 to 40 years old) mortality substantially higher in the former. Disparities in mortality between racial and ethnic groups are amplified in the COVID-19 pandemic compared to the 1918-19 experience.
Conclusions
As we evaluate our nation’s response to COVID-19 and design public policy to prepare better for coming pandemics, we cannot ignore the stark disparities in mortality rates experienced by different racial and ethnic groups. This will require a sustained resolve by society and government to delineate and remedy the causative factors, through science devoid of political interpretation and exploitation.
... CFR= 1% indicates that 1/100 people diagnosed with a disease die (99 recover), but unresolved cases (not dead nor recovered) may yield an underestimation. For comparison, the CFR during the 1918 Influenza pandemic or Spanish flu was 1.7 to 2.5% (Collins, 1931;Dolan, 2020), with an attack rate of 28% (Frost, 1919) and R0 of 1.8 (Biggerstaff ea., 2014). 2 See the John Hopkins University Map for current estimates or WHO interactive map and WHO updates. Reported data was derived from Cohen ea. ...
Novel coronavirus (SARS-CoV-2) disease (COVID-19) spread from Wuhan in China (2019) around the globe over 2020 and became a salient public health emergency and psychosocial shock event that influenced the lives of almost all humans on Earth. This study synthesizes the pandemic processes over timescape 2020-24, from the >663 million humans who were infected over 2020-21 when >21.3 million died due to Covid and secondary excess mortality, and >70% of all humans were subjected to various societal restrictions and lockdowns, such as ~4.5 billion humans over may 2020 alone. The societal lockdowns and largest global economic peturbation in a century (-3% world GDP, large fluctuations in inflation and rent and energy prices), which coincided with a collective drop in life expectancy and mental health and well-being, especially more anxiety, depression, suicides, and domestic violence against women and children were recorded. Coronavirus exploited social contact and transport, urbanization, liberalism, anxiety, and societal lockdowns.
Humans showed marked emotional, cognitive, physical, social, and societal responses over 2020-24, and the collective focused on virus mutations, vaccines, breakthrough infections, long-Covid, and more stress, loneliness, polarization, demonstrations, riots, violence, and conspiracy theories, with more authoritarianism and government coups around the globe (e.g., US Insurrection, 2021). When Russia invaded Ukraine (≥22w8) war returned to Europe, and globally more refugees were observed than since the second world war (WW2). Over 2020-24 we witnessed major individual, sociocultural, geopolitical, and technological changes, from the rise of TikTok and mainstream Artifical Intelligence (ChatGTPo4) to #BLM (2020) and second wave of #MeToo (2021-22), which affected the collective psyche and discourse, and coronavirus and overt nuclear threat coincided with an unprecedented series of natural disasters and record heatwaves that confirmed humans had created a #ClimateCrisis of planetary proportions. Over 2023-24 the planetary climate ventured into uncharted territory driven by the human industrial revolution (>1830), and projections of 3-6 degrees above pre-industrial temperatures within decades were an existential threat. Most humans described the pandemic years 2020-22 as the hardest and most stressful years they had experienced in their lifetime, which affected a generation of young humans. Over 2023 the collective return to “normalcy” was hampered by Ukrainian and Israelian wars, cost-of-living crisis, and climate disasters that made humans realize the world had changed into an era of conflict. The pandemic timescape 2020-24 was an unique period in time to learn about human psychology and collective preparedness (a natural experiment), as engagement with natural hazards is a master task of civilization. Psychological theories could be examined across contexts and in diverse samples, who were often exposed to comparable collective stressors of personal significance. Such perturbations can help to study human stress dynamics and complex social systems, beyond what can be learned from more stable periods. The collective changes and interconnected consequential events over 2020-24 include a global rise in radical right politics (EU/US) as a conservative contra-revolution and rise in military spending and government control and xenophobia and a lapse of reason and in freedom of speech that made this timescape a mini "Axial-period" that shapes the decades ahead.
... The two years are often considered as years of significant antigenic drifts (Beveridge 1991;Collins 1931;Patterson 1986). Although the status of the first is debated (Hill et al. 2017), there is potential for long-term "imprinted cohort effects" for individuals born during those two years, as described above for more recent cohorts. ...
After decades of improvement, life expectancy momentarily declined during 2014-15 in several high-income countries, with subsequent reversals in some cases. The main sources of this stagnation have been increases in mortality from influenza and drug overdoses, mainly for the baby-boomer generation. This trend is unexpected because it has long been assumed that extrinsic mortality, which is due to causes originating outside the body – in opposition to intrinsic mortality from degenerative diseases at old ages –, plays a negligible role in life expectancy changes. For this reason, the temporal patterns of extrinsic mortality have received little attention in demographic research. Period crises such as influenza epidemics and the opioid crisis are considered the main determinants of variations of extrinsic mortality. However, despite recent evidence suggesting that cohort effects have an important role in modulating extrinsic mortality, little is known about this relationship.
The main objective of this dissertation is to help fill this gap by examining cohort influences on extrinsic mortality change, with a particular emphasis on influenza and behavioral causes. More specifically, we aim (1) to quantify cohort differences in mortality from influenza and the influence of early life exposures to the virus on subsequent influenza mortality; (2) to analyze the baby boomers’ disadvantage in mortality in Canada and the United States, while identifying the contributions of behavioral causes to this disadvantage; and (3) to develop a methodological tool that can be used to both conduct visual analysis of the temporal dynamics of nonlinear Age-Period-Cohort (APC) effects, and compare these dynamics across various phenomena or populations.
To achieve these goals, we use micro-level mortality data from vital statistics in Canada and the United States. We also employ death and fertility rates from various countries to generalize the visual analysis of nonlinear effects to other demographic phenomena. The analyses were conducted by applying Serfling models for the estimation of influenza mortality, demographic measures for the decomposition of cause-specific mortality changes, smoothing techniques for the identification of trends, and statistical and visual approaches on the Lexis configuration for the analysis of APC effects.
... This brings us to the contrasts identified for the cohorts born around 1900 and 1928 (see Table 2). The two years are often considered as years of significant antigenic drifts (Beveridge 1991;Collins 1931;Patterson 1986). Although the status of the first is debated (Hill et al. 2017), there is potential for long-term imprinted cohort effects for individuals born during those two years, as described in this study for more recent cohorts. ...
This study examines the roles of age, period, and cohort in influenza mortality trends over the years 1959–2016 in the United States. First, we use Lexis surfaces based on Serfling models to highlight influenza mortality patterns as well as to identify lingering effects of early-life exposure to specific influenza virus subtypes (e.g., H1N1, H3N2). Second, we use age-period-cohort (APC) methods to explore APC linear trends and identify changes in the slope of these trends (contrasts). Our analyses reveal a series of breakpoints where the magnitude and direction of birth cohort trends significantly change, mostly corresponding to years in which important antigenic drifts or shifts took place (i.e., 1947, 1957, 1968, and 1978). Whereas child, youth, and adult influenza mortality appear to be influenced by a combination of cohort- and period-specific factors, reflecting the interaction between the antigenic experience of the population and the evolution of the influenza virus itself, mortality patterns of the elderly appear to be molded by broader cohort factors. The latter would reflect the processes of physiological capital improvement in successive birth cohorts through secular changes in early-life conditions. Antigenic imprinting, cohort morbidity phenotype, and other mechanisms that can generate the observed cohort effects, including the baby boom, are discussed.
... A sharper perspective emerges when 1918 agespecific influenza morbidity rates (21) are used to adjust the W-shaped mortality curve (Figure 3, panels, A, B, and C [35,37]). Persons <35 years of age in 1918 had a disproportionately high influenza incidence (Figure 3, panel A). But even after adjusting agespecific deaths by age-specific clinical attack rates ( Figure 3, panel B), a W-shaped curve with a casefatality peak in young adults remains and is significantly different from U-shaped age-specific case-fatality curves typically seen in other influenza years, e.g., 1928-1929 ( Figure 3, panel C). ...
La pandemia de influenza Española de 1918-1919, causó alrededor de 50 millones de muertes alrededor del mundo y permanece como una ominosa advertencia para la salud pública. Muchas preguntas sobre su origen, sus características epidemiológicas poco comunes y las bases de su patogenia permanecen sin respuesta. Por lo tanto, las implicaciones en salud pública de esta pandemia, aún nos hacen dudar de cómo vamos a enfrentarnos con la temida emergencia de una pandemia causada por el H5N1 o por otros virus. Sin embargo, nueva información acerca del virus de 1918 está emergiendo, como la secuenciación completa del genoma del virus en tejidos de autopsia archivados. Pero el genoma viral solo, es poco factible que proporcione respuestas a algunas preguntas críticas. Entender la pandemia de 1918 y sus implicaciones para futuras pandemias requiere de una cuidadosa experimentación y un profundo análisis histórico.
... In 1918 a mysterious and deadly disease spread around the world in three consecutive waves (spring 1918, autumn 1918, and winter 1918-19). This pandemic infected over one third of the world's population and killed an estimated 50 million people (Johnson and Mueller, 2002;Murray et al., 2006), with unusually severe clinical manifestations in previously healthy young adults (Collins, 1931;Hoffman, 2011). In 1918, the etiological agent that caused this disease was unknown (Hildreth, 1991). ...
2018 marks the 100-year anniversary of the 1918 influenza pandemic, which killed ~50 million people worldwide. The severity of this pandemic resulted from a complex interplay between viral, host, and societal factors. Here, we review the viral, genetic and immune factors that contributed to the severity of the 1918 pandemic and discuss the implications for modern pandemic preparedness. We address unresolved questions of why the 1918 influenza H1N1 virus was more virulent than other influenza pandemics and why some people survived the 1918 pandemic and others succumbed to the infection. While current studies suggest that viral factors such as haemagglutinin and polymerase gene segments most likely contributed to a potent, dysregulated pro-inflammatory cytokine storm in victims of the pandemic, a shift in case-fatality for the 1918 pandemic toward young adults was most likely associated with the host's immune status. Lack of pre-existing virus-specific and/or cross-reactive antibodies and cellular immunity in children and young adults likely contributed to the high attack rate and rapid spread of the 1918 H1N1 virus. In contrast, lower mortality rate in in the older (>30 years) adult population points toward the beneficial effects of pre-existing cross-reactive immunity. In addition to the role of humoral and cellular immunity, there is a growing body of evidence to suggest that individual genetic differences, especially involving single-nucleotide polymorphisms (SNPs), contribute to differences in the severity of influenza virus infections. Co-infections with bacterial pathogens, and possibly measles and malaria, co-morbidities, malnutrition or obesity are also known to affect the severity of influenza disease, and likely influenced 1918 H1N1 disease severity and outcomes. Additionally, we also discuss the new challenges, such as changing population demographics, antibiotic resistance and climate change, which we will face in the context of any future influenza virus pandemic. In the last decade there has been a dramatic increase in the number of severe influenza virus strains entering the human population from animal reservoirs (including highly pathogenic H7N9 and H5N1 viruses). An understanding of past influenza virus pandemics and the lessons that we have learnt from them has therefore never been more pertinent.
... The pandemic was caused by the H1N1 virus. Unlike the seasonal flu, which is typically caused by slight variations in pre-existing strains, the vast majority of individuals lacked immunity to the virus. 1 Approximately 30 percent of the U.S. population contracted the H1N1 virus in 1918-1919, and fatality rates among those who contracted the virus exceeded 2.5 percent, which is far higher than the typical mortality of 0.1 percent (Collins 1930). The Spanish Flu was also characterized by an unusual "W" age distribution of mortality (see Online Appendix Figure A.1). ...
The 1918 Influenza Pandemic killed millions worldwide and hundreds of thousands in the United States. This article studies the impact of air pollution on pandemic mortality. The analysis combines a panel dataset on infant and all-age mortality with a novel measure of air pollution based on the burning of coal in a large sample of U.S. cities. We estimate that air pollution contributed significantly to pandemic mortality. Cities that used more coal experienced tens of thousands of excess deaths in 1918 relative to cities that used less coal with similar pre-pandemic socioeconomic conditions and baseline health. Factors related to poverty, public health, and the timing of onset also affected pandemic mortality. The findings support recent medical evidence on the link between air pollution and influenza infection, and suggest that poor air quality was an important cause of mortality during the pandemic.
... 22 Sugerimos que la ausencia de la causa del CHIK como causa de la muerte puede ser debido a la difi cultad de diagnóstico en esas localidades. El concepto de exceso de muertes ha sido utilizado por muchos años para describir la mortalidad asociada a la gripe y los desastres naturales (huracanes, terremotos, olas calientes y frías) (26,27). Creemos que este concepto debe ser usado para evaluar el impacto del Chikungunya en la salud de la población. ...
Epidemiologia El término Chikungunya en la lengua original africana macaba (lengua del grupo Banto) signifi ca "aquello que vierte", esto se debe al hecho de que el cuadro clínico de quien tiene esta enfermedad es dolor articular intenso y fi ebre aguda. 1,2 La fi ebre asociada a la artralgia incapacitante es la marca registrada de la enfermedad. El virus Chikungunya (CHIKV) fue aislado por primera vez en 1952, en la región de la actual Tanzania, durante una epidemia que inicialmente había sido atribuida al dengue. El CHIKV es un virus ARN de la familia Togaviridae y el género Alphavirus y por lo tanto no relacionado genéticamente con el virus del dengue, sino con los virus Mayaro, otro virus emergente en las Américas. Es clasifi cado como arbovirus (del inglés ARthropod-BOrne VIRUS, virus transmitido por artrópodos), en las Américas es predominantemente transmitido por Aedes aegypti, aunque también puede ser transmitido por el Aedes albopictus. Hay tres linajes conocidos, siendo dos originarios de África (ECSA, East / Central / South African Genotype y WA, West Africa) y una de Asia (linaje Asiático). 3 Hasta 2004 el Chikungunya era poco conocido y responsable sólo por pequeños brotes en localidades en Asia y África, con pacientes muy sintomáticos pero pocos casos graves y muertes. Las epidemias de Chikungunya llegan a alcanzar el 38% al 63% de la población de las localidades afectadas. A partir de 2004, el linaje ECSA inició una gran expansión geográfi ca que afectó por el África continental y luego hacia las islas del Índico, Pacífi co y Asia continental. En la gran epidemia ocurrida en la Isla Reunión en 2006, ocurrieron los primeros casos graves y muertes bien documentadas por el Chikungunya. Hubo una gran epidemia en la India, con cerca de 1,4 millones de casos en 2010 y brotes en Francia. 3 En diciembre de 2013, el virus Chikungunya del linaje asiático llegó a las Américas, con notifi cación de los primeros casos en las islas del Caribe de San Martín, Martinica y Guadalupe. 4 A parir de allí se extendió rápidamente por el Caribe causando epidemias en varios países de la región como República Dominicana, Haití, Jamaica y Colombia entre otros. En octubre de 2014 fue documentada la transmisión autóctona linaje ECSA del Chikungunya en Brasil, desde entonces él ya se esparció por todos los estados. En el año 2016 hubo algunas epidemias signifi cativas en el noreste del país, en capitales como Salvador, Recife, Natal y Fortaleza. 5 Actualmente los dos linajes ocurren en las Américas de manera independiente el linaje ECSA en Brasil y el linaje Asiático en el resto de las Américas. 6 Se conocen los ciclos silvestres y urbanos, en las Américas está confi rmado hasta el momento sólo el ciclo urbano, en que los humanos transmiten a los humanos por el vector Aedes aegypti. En Asia y África también hay el ciclo silvestre con la participación de primates no humanos, pero hay predominio del ciclo urbano desde los años 1980-1990. La perspectiva en los próximos años es que este virus se vuelva endémico en las Américas causando epidemias esporádicas. Este comportamiento se debe al hecho de que sólo hay un serotipo, a diferencia del dengue, que presenta cuatro serotipos distintos que causan recirculación de virus y nuevas epidemias con mayor frecuencia. Desde el punto de vista de control, el desafío es el control del vector, pues aún no hay vacunas y no hay antivirales que tengan efi ciencia comprobada. Aspectos clínicos El virus Chikungunya es introducido por la piel, migra a los ganglios linfáticos regionales, entra en la circulación sanguínea y se difunde a todos los tejidos. Al infi ltrarse en las articulaciones promueve la artritis, con edema y
... Others, however, argue that, as a result of postepidemic survey studies of influenza, reproductively aged women were more likely to adopt the sick role behavior and were susceptible to recall biases. Consequently, they were overrepresented as cases during the epidemic (Collins 1931), and, furthermore, the gender bias occurs with other illnesses in the contemporary context (Marcus and Seeman 1981). This logic does not hold up against illness data collected at the time of epidemics or upon diagnosis; such is the case with notification records. ...
This study examines the morbidity experience of the island populations of Malta and Gozo during the 1918–1919 influenza pandemic. The epidemic pattern for the two islands showed considerable diversity that was manifested in Gozo’s lack of a herald wave and significantly higher morbidity rates during wave 2 (September–November 1918), followed by a distinctly muted pattern of morbidity in wave 3 relative to Malta. Common features or noncontributory factors that reduced the morbidity disparity across the two islands included poverty, female gendered roles and children aged to 10–14 as introducers of sickness to the household, mass gatherings as effective means of disease transmission, and tuberculosis as a syndemic potential. A rarely available resource, the register of notifiable diseases in Gozo, enabled our research to gain a far deeper appreciation of intrapopulation variation at the empirical level, which in turn allowed for a better understanding of how elements of isolation, exposure history, and rurality could play important roles shaping the epidemic experience.
... A sharper perspective emerges when 1918 age-specific influenza morbidity rates (21) are used to adjust the Wshaped mortality curve (Figure 3, panels, A, B, and C [35,37]). Persons <35 years of age in 1918 had a disproportionately high influenza incidence (Figure 3, panel A). ...
... The influenza-censuses carried out in USA and Norway 1918-1919 give a reliable picture of the age-sex pattern of morbidity (see Vaughan 1921;Hanssen 1923;Collins 1931;Sydenstricker 1931;Britten 1932). These studies clearly show an unusually high incidence under 30 years and a rather rapidly falling incidence above age 30 (Figure 2), a definitive contrast to the w-curve of mortality ( Figure 1). ...
... The 2009 A/H1N1 influenza pandemic illustrated well the challenges involved in rapid severity assessment. It was clear from early in the outbreak (3) that the severity of the epidemic was substantially less than that of the 1918 A/H1N1 pandemic (for which the sCFR was approximately 0.02 (4)), yet it proved more difficult to determine whether severity was intermediate (sCFR ≈ 10 −3 ) or mild (sCFR ≈ 10 −4 ) (5)(6)(7)(8)(9)(10)(11)(12)(13). Robust, precise estimates establishing that the pandemic had mild severity (sCFR = 4.5 × 10 −4 , 95% credible interval: 2 × 10 −4 , 9 × 10 −4 ) were published by July 2009 (9). ...
In the management of emerging infectious disease epidemics, precise and accurate estimation of severity indices, such as the
probability of death after developing symptoms—the symptomatic case fatality ratio (sCFR)—is essential. Estimation of the
sCFR may require merging data gathered through different surveillance systems and surveys. Since different surveillance strategies
provide different levels of precision and accuracy, there is need for a theory to help investigators select the strategy that
maximizes these properties. Here, we study the precision of sCFR estimators that combine data from several levels of the severity
pyramid. We derive a formula for the standard error, which helps us find the estimator with the best precision given fixed
resources. We further propose rules of thumb for guiding the choice of strategy: For example, should surveillance of a particular
severity level be started? Which level should be preferred? We derive a formula for the optimal allocation of resources between
chosen surveillance levels and provide a simple approximation that can be used in thinking more heuristically about planning
surveillance. We illustrate these concepts with numerical examples corresponding to 3 influenza pandemic scenarios. Finally,
we review the equally important issue of accuracy.
... Between July 1, 2009 and January 19, 2010, approximately 6.5 million people were infected, 13 000 patients were hospitalized and 656 persons died in Turkey due to 2009 pandemic H1N1 infection (5). Nearly half of the influenzarelated deaths during the 1918-1919 pandemic occurred among young (20-40 years of age) and previously healthy adults for reasons that have never been explained adequately (6). The data concerning clinical course and appropriate treatment of severe influenza are insufficient for immunodeficient patients. ...
Background
The appropriate treatment of pandemic H1N1 influenza which was first identified in April 2009 in Mexico is insufficient especially for immunocompromised patients. We aimed to evaluate the features and prognostic factors of the children with H1N1, especially immunocompromised ones, and whether if intravenous immunoglobulin G (IVIG) replacement could aid for a better outcome.Methods
Twenty one hospitalized children with laboratory-confirmed H1N1 were evaluated retrospectively. Data were extracted from files and electronic medical records.ResultsThe median age was 37 (1-216) months; 62% of them were under 5 year of age and 71.4% had one or more underlying disorders. Main symptoms were high fever, cough, fatigue and vomitting. Lower respiratory tract manifestations were seen in 66.6% of children. Mortality rate was 4.7%. The patient who died had the lowest lymphocyte (100/mm3), thrombocyte (21000/mm3) and highest blood urea nitrogen (87mg/dl) levels. Fifty eight percent of evaluated patients had one of the primary immunodeficiency disorders. Surprisingly, none of the 6 patients with primary immunodeficiency who are on regular IVIG replacement needed intensive care unit and died. Although median durations of cough, fever and hospitalization were lower, they did not change statistically according to get IVIG replacement regularly (P=0.47, 0.97, 0.09, respectively).Conclusion
Our study is important while it is the first one which shows the course of primary immunodeficient children with H1N1 infection who were on regular IVIG replacement. A trial of high dose IVIG may be a useful adjunctive therapy in severe H1N1 influenza, particularly in the immunocompromised patients.
... In New York City, excess mortality rates in people aged <45 years exceeded those in people aged >45 years and young adults between the ages of 25 and 44 years suffered a ~50-fold higher mortality risk during the 1918-1920 pandemic than in typical epidemic seasons [4]. Further, historical studies in USA, UK and Denmark suggest that those aged >65 years suffered lower mortality rates during the 1918-1920 pandemic than would otherwise have been expected during a typical influenza epidemic season [4,5,[9][10][11][12]. ...
Historical studies of influenza pandemics can provide insight into transmission and mortality patterns, and may aid in planning for a future pandemic. Here, we analyse historical vital statistics and quantify the age-specific mortality patterns associated with the 1918-1920 influenza pandemic in Japan, USA, and UK. All three countries showed highly elevated mortality risk in young adults relative to surrounding non-pandemic years. By contrast, the risk of death was low in the very young and very old. In Japan, the overall mortality impact was not limited to winter 1918-1919, and continued during winter 1919-1920. Mortality impact varied as much as threefold across the 47 Japanese prefectures, and differences in baseline mortality, population demographics, and density explained a small fraction of these variations. Our study highlights important geographical variations in timing and mortality impact of historical pandemics, in particular between the Eastern and Western hemispheres. In a future pandemic, vaccination in one region could save lives even months after the emergence of a pandemic virus in another region.
... Sources: Collins (1931), Chin et al. (1960), Davis et al. (1970), and Shrestha et al. (2011). ...
Catastrophic mortality events are characterized by a sudden and concentrated increase in mortality and as such present a major risk to life insurers. Such events include pandemics, war, natural disasters, terrorist attacks, and industrial, transport, and other accidents. Of these, pandemics arising from influenza are considered the most significant threat to the life insurance industry due to their capacity to cause a major increase in claims. We review the features and mortality implications of an influenza pandemic for life insurers, and describe a range of other risks that are likely to emerge as well.
The pandemic of 1918–20-commonly known as the Spanish flu-infected over a quarter of the world's population and killed over fifty million people. It is by far the greatest humanitarian disaster caused by an infectious disease in modern history. Epidemiologists and health scientists often draw on this experience to set the plausible upper bound (the 'worst case scenario') on future pandemic mortality. The purpose of this study is to piece together and analyse the scattered multi-disciplinary literature on the pandemic in order to place debates on the evolving course of the current COVID-19 crisis in historical perspective. The analysis focuses on the changing characteristics of pathogens and disease over time, the institutional factors that shaped the global spread, the demographic and socio-economic consequences, and pharmaceutical and non-pharmaceutical responses to the pandemic. This title is also available as Open Access on Cambridge Core.
Biological anthropologists are ideally suited for the study of pandemics given their strengths in human biology, health, culture, and behavior, yet pandemics have historically not been a major focus of research. The COVID-19 pandemic has reinforced the need to understand pandemic causes and unequal consequences at multiple levels. Insights from past pandemics can strengthen the knowledge base and inform the study of current and future pandemics through an anthropological lens. In this paper, we discuss the distinctive social and epidemiological features of pandemics, as well as the ways in which biological anthropologists have previously studied infectious diseases, epidemics, and pandemics. We then review interdisciplinary research on three pandemics–1918 influenza, 2009 influenza, and COVID-19–focusing on persistent social inequalities in morbidity and mortality related to sex and gender; race, ethnicity, and Indigeneity; and pre-existing health and disability. Following this review of the current state of pandemic research on these topics, we conclude with a discussion of ways biological anthropologists can contribute to this field moving forward. Biological anthropologists can add rich historical and cross-cultural depth to the study of pandemics, provide insights into the biosocial complexities of pandemics using the theory of syndemics, investigate the social and health impacts of stress and stigma, and address important methodological and ethical issues. As COVID-19 is unlikely to be the last global pandemic, stronger involvement of biological anthropology in pandemic studies and public health policy and research is vital.
The influenza pandemic continues to threaten public health due to its high morbidity and mortality rates, despite some successes in antiviral research. Natural drugs are important alternative therapies in the treatment of and recovery from influenza and have been the subjects of intense investigation during the last few decades. Many reports have shown that the development of novel bioactive chemicals extracted from natural drugs has significant advantages. Oseltamivir is a successful case of an anti‐influenza drug synthesized using two natural products, quinic acid, and shikimic acid, as starting materials. In China, traditional Chinese medicine (TCM) plays an important role in the treatment of influenza. TCM herbal extracts and prescriptions or their isolated bioactive constituents have shown significant therapeutic and preventive effects against influenza. For example, the roots of Isatis indigotica (Banlangen) fight viral infection by targeting both the virus and the host and have significantly different effects than those of synthetic chemicals. Lianhuaqingwen capsule exerts its anti‐influenza activity by regulating the immune response to interfere with both viral and host reactions and might well be an alternative therapeutic option to treat influenza virus infection. This paper reviews the chemical ingredients, crude extracts, and TCM prescriptions with anti‐influenza activity reported during the period of 2010–September 2019. We hope that this comprehensive review will not only fuel research on anti‐influenza active natural products and TCM research but also provide a promising alternative candidate for further anti‐influenza drug development.
Background: Microcephaly represents the main
congenital malformation in the Dominican Republic. Since
2016, 483 malformations have been reported, of which
64% correspond to microcephaly. National surveillance
of microcephaly was introduced in the context of the
Zika epidemic during 2016. An analysis is carried out
with the objective of describing its magnitude according
to the clinical-epidemiological characteristics during
2016-2017. Methods: A cross-sectional descriptive study
of the demographic, clinical and laboratory data available
in the national surveillance database was conducted.
Case defi nition: Live newborn with cephalic perimeter
less than two standard deviations at 24 hours postpartum,
according to standardized references according to
gestational age and sex. Proportions, measures of central
tendency and dispersion were calculated from the cases
notifi ed by the health centers. Results: 310 microcephaly
were reported with an average of 3.4 ± 2.7 per week,
showing a progressive increase from week 30 of 2016
and a decrease in week 2 of 2017, evidencing a behavior
propagated probably at the expense of pregnancies <24
weeks of gestation suspects of Zika. Th e Metropolitan
Region has a rate of 31 / 10,000 live births. Th e female /
male microcephaly ratio was 1: 0.7. 13% (41) presented
clinical complications, characterized by respiratory
distress 88% (36/41), liver failure 7% (3) and other 5%
(2). 63% (95/150) of the samples were positive for Zika.
Conclusions: Th e data analyzed indicate a tendency to decrease cases of microcephaly that coincides with the
post-epidemic period of the Zika virus. Th e female sex was
the most aff ected and the clinical complications were few.
Th e teratogenic capacity of Zika implies the coexistence of
other malformations, therefore, it is necessary to evaluate
the attributes of the surveillance to recommend specifi c
actions and improve the system.
Influenza A viruses are negative strand RNA viruses. Their segmented genome consists of eight RNA segments coding for ten proteins. Two glycosylated proteins, hemagglutinin (HA) and neuraminidase (NA), are involved in viral attachment to cells. Two nonstructural proteins, NS1 and NS2 (also called NEP), are involved in regulating numerous aspects of the viral life cycle. These four proteins will be discussed in more detail below. Three viral proteins, PA, PB1 and PB2, are responsible for viral replication, while the nucleoprotein, NP, is the nucleocapsid structural protein. Finally, two membrane proteins, Ml and M2, appear to be involved in nuclear export and pH maintenance, among other activities (Lamb and Krug, 1996).
Die Krankheiten der Myxovirusgruppe umfassen solche des Menschen und der Tiere. Zum Teil sind es reine Menschen- oder Tierkrankheiten, zum Teil Tierkrankheiten, die auch auf den Menschen übertragbar sind (Zoo-Anthroponosen). Bei den Menschenkrankheiten handelt es sich vorwiegend um respiratorische Syndrome.
Two years after the First World War ended there was a surge in European birth rates, including in Norway that had been a neutral country. This paper tests the hypothesis that it was in fact the Spanish influenza that caused the Norwegian baby boom rather than the close of the war. The paper uses multivariate regression analysis, while previous studies have been univariate and largely descriptive. By using regional monthly data, the independent effect of the Spanish influenza morbidity on fertility over the years 1918-1920, net of the effect of mortality, is estimated. The fact that Norway was neutral was important in counter-balancing the influence of the war on fertility and nuptiality. Furthermore, the Norwegian data utilized in the analysis are of superior quality in a European context in that registration of population data, including vital statistics, continued normally in Norway undisturbed by the war.
Description of process used to evaluate measures of influenza transmissibility and severity characterized historically in the literature. Parts A and B review measures that could be used to characterize novel influenza viruses and pandemics and include a detailed discussion of their strengths and limitations. Part C outlines several data quality issues that should be considered in the inclusion of data in the assessment framework. Finally, Part D provides additional detail on the data abstracted from the literature on past pandemics and selected seasons that were used to scale examples provided in the manuscript.
Pandemic influenza, by definition, affects the overwhelming majority of countries and population subgroups in the world in a very short period of time. The impact of pandemics is not merely a matter of the biology of the particular virus in individuals. Pandemics are a social phenomenon affected by prevailing social circumstances, e.g., war, economic conditions, crowding, and food supply. In turn, pandemics affect social organization and events, e.g., governance and famine. Much of the study of pandemic influenza has been in industrialized countries in temperate zones; the occurrence of excess morbidity and mortality, and the strain on health care and other services in these countries are well known. A conference in 1998 brought together an increasingly large body of historical research about the pandemic of “Spanish influenza” in 1918–1919. It included interesting contributions about the impact of the pandemic in areas such as sub-Saharan Africa, India (where mortality is estimated at 17 million, or about half the world total), and the Pacific Islands. There are important lessons for contemporary society from the impact of the pandemic of 1918–1919 and other pandemics. One can make a compelling case for pandemic preparedness, including developing and executing strategies both to prevent and to ameliorate pandemic spread.
In the fall and winter of 1918–1919, an influenza pandemic of unprecedented virulence swept the globe leaving 40 million or more dead in its wake. The virus responsible for this catastrophe was not isolated at the time, however, it has recently become possible to study the genetic features of the 1918 ‘Spanish’ influenza virus using frozen and fixed autopsy tissue. Gene sequences of the 1918 virus can be used to frame hypotheses about the origin of the 1918 virus, and to look for clues to its virulence. The study of the 1918 virus is not just one of historical curiosity. An understanding of the genetic make-up of the most virulent influenza strain in history may facilitate prediction and prevention of future pandemics.
The classic risk factors for developing coronary heart disease (CHD) explain less than 50% of the decrease in mortality observed since 1950. The transition currently under way, from the degenerative to the infectious-inflammatory paradigm, requires a new causal interpretation of temporal trends. The following is an ecological study based on data from the United States showing, in men and women, an association between the age distribution of mortality from influenza and pneumonia (I&P) during the 1918-1919 influenza pandemic in the 10-49-year age bracket and the distribution of CHD mortality from 1920 to 1985 in survivors from the corresponding birth cohorts. It further shows a significant negative correlation (r = -0.68, p = 0.042) between excess mortality from I&P accumulated in epidemics from 1931 to 1940 (used as indicator for persistent circulation of H1N1 virus combined with vulnerability to infection) and the order of the beginning in the decline in CHD mortality in nine geographic divisions in the United States. In light of current biological knowledge, the data suggest that the 1918 influenza pandemic (and subsequent epidemics up to 1957) might have played a determinant role in the epidemic of CHD mortality registered in the 20th century.
Background. The reasons for the unusual age-specific mortality patterns of the 1918-19influenza pandemic remain unknown. Here we characterize pandemic-related mortality by single year of age in a unique statewide Kentucky datasetand explore breakpoints in the age curves.Methods. Individual death certificates from Kentucky were abstracted by medically-trained personnelfor 1911-1919. Pandemic excess mortality rates were calculated by subtracting observed rates during pandemic months from rates in previous years, separately for each single-year of age group and gender.Results. The age profile of excess mortality risk infall 1918 was characterizedby a maximum in infants, a minimum at ages9-10 yrs, a maximum at ages 24-26 yrs, and a second minimumat ages 56-59 yrs. The excess mortality risk in young adults had been greatly attenuated by winter 1919. The age breakpoints of mortality risk did not differ between males and females.Conclusions. The observed mortality breakpoints in male and female cohorts born in 1859-62, 1892-94, and 1908-9 did not coincide with known dates of historical pandemics. The atypical age mortality patterns of the 1918-19 pandemic cannot be explained by military crowding, war-related factors, or prior immunity alone, and likely result from a combination of unknown factors.
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