Age-Specific Excess Mortality Patterns During the 1918-1920 Influenza Pandemic in Madrid, Spain

What can we learn from historical pandemics? A systematic review of the literature

What are the insights from historical pandemics for policymaking today? We carry out a systematic review of the literature on the impact of pandemics that occurred since the Industrial Revolution and prior to Covid-19. Our literature searches were conducted between June 2020 and September 2023, with the final review encompassing 169 research papers selected for their relevance to understanding either the demographic or economic impact of pandemics. We include literature from across disciplines to maximise our knowledge base, finding many relevant articles in journals which would not normally be on the radar of social scientists. Our review identifies two gaps in the literature: (1) the need to study pandemics and their effects more collectively rather than looking at them in isolation; and (2) the need for more study of pandemics besides 1918 Spanish Influenza, especially milder pandemic episodes. These gaps are a consequence of academics working in silos, failing to draw on the skills and knowledge offered by other disciplines. Synthesising existing knowledge on pandemics in one place provides a basis upon which to identify the lessons in preparing for future catastrophic disease events.

Socioeconomic mortality differences during the Great Influenza in Spain

Despite being one of the deadliest viruses in history, there is limited information on the socioeconomic factors that affected mortality rates during the Great Influenza Pandemic. In this study, we use occupation-province level data to investigate the relationship between influenza excess mortality rates and occupation-related status in Spain. We obtain three main results. Firstly, individuals in low-income occupations experienced the highest excess mortality, pointing to a notable income gradient. Secondly, professions that involved more social interaction were associated with a higher excess of mortality, regardless of income. Finally, we observe a substantial rural mortality penalty, even after controlling for income-related occupational groups. Based on this evidence, it seems that the high number of deaths was caused by not self-isolating. Some individuals did not quarantine themselves because they could not afford to miss work. In rural areas, home confinement was likely more limited because their inhabitants did not have immediate access to information about the pandemic or fully understand its impact due to their limited experience handling influenza outbreaks.

Age-specific mortality and the role of living remotely: The 1918-20 influenza pandemic in Kautokeino and Karasjok, Norway

The 1918-20 pandemic influenza killed 50-100 million people worldwide, but mortality varied by ethnicity and geography. In Norway, areas dominated by Sámi experienced 3-5 times higher mortality than the country's average. We here use data from burial registers and censuses to calculate all-cause excess mortality by age and wave in two remote Sámi areas of Norway 1918-20. We hypothesise that geographic isolation, less prior exposure to seasonal influenza, and thus less immunity led to higher Indigenous mortality and a different age distribution of mortality (higher mortality for all) than was typical for this pandemic in non-isolated majority populations (higher young adult mortality & sparing of the elderly). Our results show that in the fall of 1918 (Karasjok), winter of 1919 (Kautokeino), and winter of 1920 (Karasjok), young adults had the highest excess mortality, followed by also high excess mortality among the elderly and children. Children did not exhibit excess mortality in the second wave in Karasjok in 1920. It was not the young adults alone who produced the excess mortality in Kautokeino and Karasjok. We conclude that geographic isolation caused higher mortality among the elderly in the first and second waves, and among children in the first wave.

Associations between rurality and regional differences in sociodemographic factors and the 1918-20 influenza and 2020-21 COVID-19 pandemics in Missouri counties: An ecological study

This study compares pandemic experiences of Missouri's 115 counties based on rurality and sociodemographic characteristics during the 1918-20 influenza and 2020-21 COVID-19 pandemics. The state's counties and overall population distribution have remained relatively stable over the last century, which enables identification of long-lasting pandemic attributes. Sociodemographic data available at the county level for both time periods were taken from U.S. census data and used to create clusters of similar counties. Counties were also grouped by rural status (RSU), including fully (100%) rural, semirural (1-49% living in urban areas), and urban (>50% of the population living in urban areas). Deaths from 1918 through 1920 were collated from the Missouri Digital Heritage database and COVID-19 cases and deaths were downloaded from the Missouri COVID-19 dashboard. Results from sociodemographic analyses indicate that, during both time periods, average farm value, proportion White, and literacy were the most important determinants of sociodemographic clusters. Furthermore, the Urban/Central and Southeastern regions experienced higher mortality during both pandemics than did the North and South. Analyses comparing county groups by rurality indicated that throughout the 1918-20 influenza pandemic, urban counties had the highest and rural had the lowest mortality rates. Early in the 2020-21 COVID-19 pandemic, urban counties saw the most extensive epidemic spread and highest mortality, but as the epidemic progressed, cumulative mortality became highest in semirural counties. Additional results highlight the greater effects both pandemics had on county groups with lower rates of education and a lower proportion of Whites in the population. This was especially true for the far southeastern counties of Missouri ("the Bootheel") during the COVID-19 pandemic. These results indicate that rural-urban and socioeconomic differences in health outcomes are long-standing problems that continue to be of significant importance, even though the overall quality of health care is substantially better in the 21st century.

From pandemic to endemic: Spatial-temporal patterns of influenza-like illness incidence in a Swiss canton, 1918-1924

In pandemics, past and present, there is no textbook definition of when a pandemic is over, and how and when exactly a respiratory virus transitions from pandemic to endemic spread. In this paper we have compared the 1918/19 influenza pandemic and the subsequent spread of seasonal flu until 1924. We analysed 14,125 reports of newly stated 32,198 influenza-like illnesses from the Swiss canton of Bern. We analysed the temporal and spatial spread at the level of 497 municipalities, 9 regions, and the entire canton. We calculated incidence rates per 1000 inhabitants of newly registered cases per calendar week. Further, we illustrated the incidences of each municipality for each wave (first wave in summer 1918, second wave in fall/winter 1918/19, the strong later wave in early 1920, as well as the two seasonal waves in 1922 and 1924) on a choropleth map. We performed a spatial hotspot analysis to identify spatial clusters in each wave, using the Gi* statistic. Furthermore, we applied a robust negative binomial regression to estimate the association between selected explanatory variables and incidence on the ecological level. We show that the pandemic transitioned to endemic spread in several waves (including another strong wave in February 1920) with lower incidence and rather local spread until 1924 at least. At the municipality and regional levels, there were different patterns of spread both between pandemic and seasonal waves. In the first pandemic wave in summer 1918 the probability of higher incidence was increased in municipalities with a higher proportion of factories (OR 2.60, 95%CI 1.42-4.96), as well as in municipalities that had access to a railway station (OR 1.50, 95%CI 1.16-1.96). In contrast, the strong fall/winter wave 1918 was very widespread throughout the canton. In general, municipalities at higher altitude showed lower incidence. Our study adds to the sparse literature on incidence in the 1918/19 pandemic and subsequent years. Before Covid-19, the last pandemic that occurred in several waves and then became endemic was the 1918-19 pandemic. Such scenarios from the past can inform pandemic planning and preparedness in future outbreaks.

Historically High Excess Mortality During the COVID-19 Pandemic in Switzerland, Sweden, and Spain

BACKGROUND

Excess mortality quantifies the overall mortality impact of a pandemic. Mortality data have been accessible for many countries in recent decades, but few continuous data have been available for longer periods.

OBJECTIVE

To assess the historical dimension of the COVID-19 pandemic in 2020 for 3 countries with reliable death count data over an uninterrupted span of more than 100 years.

DESIGN

Observational study.

SETTING

Switzerland, Sweden, and Spain, which were militarily neutral and not involved in combat during either world war and have not been affected by significant changes in their territory since the end of the 19th century.

PARTICIPANTS

Complete populations of these 3 countries.

MEASUREMENTS

Continuous series of recorded deaths (from all causes) by month from the earliest available year (1877 for Switzerland, 1851 for Sweden, and 1908 for Spain) were jointly modeled with annual age group-specific death and total population counts using negative binomial and multinomial models, which accounted for temporal trends and seasonal variability of prepandemic years. The aim was to estimate the expected number of deaths in a pandemic year for a nonpandemic scenario and the difference in observed and expected deaths aggregated over the year.

RESULTS

In 2020, the number of excess deaths recorded per 100 000 persons was 100 (95% credible interval [CrI], 60 to 135) for Switzerland, 75 (CrI, 40 to 105) for Sweden, and 155 (CrI, 110 to 195) for Spain. In 1918, excess mortality was 6 to 7 times higher. In all 3 countries, the peaks of monthly excess mortality in 2020 were greater than most monthly excess mortality since 1918, including many peaks due to seasonal influenza and heat waves during that period.

LIMITATION

Historical vital statistics might be affected by minor completeness issues before the beginning of the 20th century.

CONCLUSION

In 2020, the COVID-19 pandemic led to the second-largest infection-related mortality disaster in Switzerland, Sweden, and Spain since the beginning of the 20th century.

PRIMARY FUNDING SOURCE

Foundation for Research in Science and the Humanities at the University of Zurich, Swiss National Science Foundation, and National Institute of Allergy and Infectious Diseases.

Individual-level Association of Influenza Infection With Subsequent Pneumonia: A Case-control and Prospective Cohort Study

BACKGROUND

Pneumonia is a leading cause of mortality worldwide. Influenza may result in primary pneumonia or be associated with secondary bacterial pneumonia. While the association with secondary pneumonia has been established ecologically, individual-level evidence remains sparse and the risk period for pneumonia following influenza poorly defined.

METHODS

We conducted a matched case-control study and a prospective cohort study among Nicaraguan children aged 0-14 years from 2011 through 2018. Physicians diagnosed pneumonia cases based on Integrated Management for Childhood Illness guidelines. Cases were matched with up to 4 controls on age (months) and study week. We fit conditional logistic regression models to assess the association between influenza subtype and subsequent pneumonia development, and a Bayesian nonlinear survival model to estimate pneumonia hazard following influenza.

RESULTS

Participants with influenza had greater risk of developing pneumonia in the 30 days following onset compared to those without influenza (matched odds ratio [mOR], 2.7 [95% confidence interval {CI}, 1.9-3.9]). Odds of developing pneumonia were highest for participants following A(H1N1)pdm09 illness (mOR, 3.7 [95% CI, 2.0-6.9]), followed by influenza B and A(H3N2). Participants' odds of pneumonia following influenza were not constant, showing distinct peaks 0-6 days (mOR, 8.3 [95% CI, 4.8-14.5] days) and 14-20 (mOR, 2.5 [95% CI, 1.1-5.5] days) after influenza infection.

CONCLUSIONS

Influenza is a significant driver of both primary and secondary pneumonia among children. The presence of distinct periods of elevated pneumonia risk in the 30 days following influenza supports multiple etiological pathways.

Magnitude, change over time, demographic characteristics and geographic distribution of excess deaths among nursing home residents during the first wave of COVID-19 in France: a nationwide cohort study

BACKGROUND

The objectives were to assess the excess deaths among Nursing Home (NH) residents during the first wave of the COVID-19 pandemic, to determine their part in the total excess deaths and whether there was a mortality displacement.

METHODS

We studied a cohort of 494,753 adults in 6,515 NHs in France exposed to COVID-19 pandemic (from 1 March to 31 May 2020) and compared with the 2014-2019 cohorts using data from the French National Health Data System. The main outcome was death. Excess deaths and standardized mortality ratios (SMRs) were estimated.

RESULT

There were 13,505 excess deaths. Mortality increased by 43% (SMR: 1.43). The mortality excess was higher among males than females (SMR: 1.51 and 1.38) and decreased with increasing age (SMRs in females: 1.61 in the 60-74 age group, 1.58 for 75-84, 1.41 for 85-94 and 1.31 for 95 or over; males: SMRs: 1.59 for 60-74, 1.69 for 75-84, 1.47 for 85-94 and 1.41 for 95 or over). No mortality displacement effect was observed up until 30 August 2020. By extrapolating to all NH residents nationally (N = 570,003), we estimated that they accounted for 51% of the general population excess deaths (N = 15,114 out of 29,563).

CONCLUSION

NH residents accounted for half of the total excess deaths in France during the first wave of the COVID-19 pandemic. The excess death rate was higher among males than females and among younger than older residents.

Tracking Pandemic Severity Using Data on the Age Structure of Mortality: Lessons From the 1918 Influenza Pandemic in Michigan

Objectives. To test whether distortions in the age structure of mortality during the 1918 influenza pandemic in Michigan tracked the severity of the pandemic. Methods. We calculated monthly excess deaths during the period of 1918 to 1920 by using monthly data on all-cause deaths for the period of 1912 to 1920 in Michigan. Next, we measured distortions in the age distribution of deaths by using the Kuiper goodness-of-fit test statistic comparing the monthly distribution of deaths by age in 1918 to 1920 with the baseline distribution for the corresponding month for 1912 to 1917. Results. Monthly distortions in the age distribution of deaths were correlated with excess deaths for the period of 1918 to 1920 in Michigan (r = 0.83; P < .001). Conclusions. Distortions in the age distribution of deaths tracked variations in the severity of the 1918 influenza pandemic. Public Health Implications. It may be possible to track the severity of pandemic activity with age-at-death data by identifying distortions in the age distribution of deaths. Public health authorities should explore the application of this approach to tracking the COVID-19 pandemic in the absence of complete data coverage or accurate cause-of-death data.

Archival Big Data and the Spanish Flu in Copenhagen

Purpose

The Spanish Flu 1918–1920 saw a high degree of excess mortality among young and healthy adults. The purpose of this paper is a further exploration of the hypothesis that high mortality risk during The Spanish Flu in Copenhagen was associated with early life exposure to The Russian Flu 1889–1892.

Design/methodology/approach

Based on 37,000 individual-level death records in a new unique database from The Copenhagen City Archives combined with approximate cohort-specific population totals interpolated from official censuses of population, the author compiles monthly time series on all-cause mortality rates 1916–1922 in Copenhagen by gender and one-year birth cohorts. The author then analyses birth cohort effects on mortality risk during The Spanish Flu using regression analysis.

Findings

The author finds support for hypotheses relating early life exposure to The Russian Flu to mortality risk during The Spanish Flu. Some indications of possible gender heterogeneity during the first wave of The Spanish Flu – not found in previous studies – should be a topic for future research based on data from other countries.

Originality/value

Due to lack of individual-level death records with exact dates of birth and death, previous studies on The Spanish Flu in Denmark and many other countries have relied on data with lower birth cohort resolutions than the one-year birth cohorts used in this study. The analysis in this paper illustrates how archival Big Data can be used to gain new insights in studies on historical pandemics.

Patterns of mortality during pandemic: An example of Spanish flu pandemic of 1918

Now the attention of the whole world is focused on the developing pandemic of the coronavirus infection COVID-19. This article discusses mortality patterns of the deadliest epidemic in the last 120 years - the Spanish flu pandemic of 1918. Statistical sources from Italy and the USA, published shortly after the pandemic, were analyzed. The analysis was carried out for mortality from all causes, since in this case inaccuracies associated with establishing the causes of death are minimized. Despite the fact that the first cases of the Spanish flu appeared in the United States as early as March 1918, this first wave of epidemic practically did not affect the total mortality rate. The main peak of mortality in 1918 occurred in October 1918 both in the USA and Italy, with a gradual decrease in mortality over several months. Analysis of age-specific mortality demonstrates a significant increase in mortality at middle ages (20-50 years) in 1918 compared with 1917. Analysis of mortality trends using the method of latent variables shows a significant increase in the background mortality factor in 1918, which turned out to be higher for Italy than the mortality losses during the Second World War. The Spanish flu pandemic differs from the current coronavirus pandemic, because of significant increase in mortality of middle-aged people, while the COVID-19 pandemic causes a more marked increase in mortality among the elderly. With this, the COVID-19 pandemic is more like the recent flu epidemics than the earlier Spanish flu pandemic.

Anti-influenza hyperimmune intravenous immunoglobulin for adults with influenza A or B infection (FLU-IVIG): a double-blind, randomised, placebo-controlled trial

BACKGROUND

Since the 1918 influenza pandemic, non-randomised studies and small clinical trials have suggested that convalescent plasma or anti-influenza hyperimmune intravenous immunoglobulin (hIVIG) might have clinical benefit for patients with influenza infection, but definitive data do not exist. We aimed to evaluate the safety and efficacy of hIVIG in a randomised controlled trial.

METHODS

This randomised, double-blind, placebo-controlled trial was planned for 45 hospitals in Argentina, Australia, Denmark, Greece, Mexico, Spain, Thailand, UK, and the USA over five influenza seasons from 2013-14 to 2017-18. Adults (≥18 years of age) were admitted for hospital treatment with laboratory-confirmed influenza A or B infection and were randomly assigned (1:1) to receive standard care plus either a single 500-mL infusion of high-titre hIVIG (0·25 g/kg bodyweight, 24·75 g maximum; hIVIG group) or saline placebo (placebo group). Eligible patients had a National Early Warning score of 2 points or greater at the time of screening and their symptoms began no more than 7 days before randomisation. Pregnant and breastfeeding women were excluded, as well as any patients for whom the treatment would present a health risk. Separate randomisation schedules were generated for each participating clinical site using permuted block randomisation. Treatment assignments were obtained using a web-based application by the site pharmacist who then masked the solution for infusion. Patients and investigators were masked to study treatment. The primary endpoint was a six-category ordinal outcome of clinical status at day 7, ranging in severity from death to resumption of normal activities after discharge. The choice of day 7 was based on haemagglutination inhibition titres from a pilot study. It was analysed with a proportional odds model, using all six categories to estimate a common odds ratio (OR). An OR greater than 1 indicated that, for a given category, patients in the hIVIG group were more likely to be in a better category than those in the placebo group. Prespecified primary analyses for safety and efficacy were based on patients who received an infusion and for whom eligibility could be confirmed. This trial is registered with ClinicalTrials.gov, NCT02287467.

FINDINGS

313 patients were enrolled in 34 sites between Dec 11, 2014, and May 28, 2018. We also used data from 16 patients enrolled at seven of the 34 sites during the pilot study between Jan 15, 2014, and April 10, 2014. 168 patients were randomly assigned to the hIVIG group and 161 to the placebo group. 21 patients were excluded (12 from the hIVIG group and 9 from the placebo group) because they did not receive an infusion or their eligibility could not be confirmed. Thus, 308 were included in the primary analysis. hIVIG treatment produced a robust rise in haemagglutination inhibition titres against influenza A and smaller rises in influenza B titres. Based on the proportional odds model, the OR on day 7 was 1·25 (95% CI 0·79-1·97; p=0·33). In subgroup analyses for the primary outcome, the OR in patients with influenza A was 0·94 (0·55-1·59) and was 3·19 (1·21-8·42) for those with influenza B (interaction p=0·023). Through 28 days of follow-up, 47 (30%) of 156 patients in the hIVIG group and in 45 (30%) of 152 patients in the placebo group had the composite safety outcome of death, a serious adverse event, or a grade 3 or 4 adverse event (hazard ratio [HR] 1·06, 95% CI 0·70-1·60; p=0·79). Six (4%) patients in the hIVIG group and five (3%) in the placebo group died, but these deaths were not necessarily related to treatment.

INTERPRETATION

When administered alongside standard care (most commonly oseltamivir), hIVIG was not superior to placebo for adults hospitalised with influenza infection. By contrast with our prespecified subgroup hypothesis that hIVIG would result in more favourable responses in patients with influenza A than B, we found the opposite effect. The clinical benefit of hIVIG for patients with influenza B is supported by antibody affinity analyses, but confirmation is warranted.

FUNDING

NIAID and NIH. Partial support was provided by the Medical Research Council (MRC_UU_12023/23) and the Danish National Research Foundation.

The 1918 Influenza Pandemic: Looking Back, Looking Forward

In commemoration of the centennial of the 1918 influenza pandemic, the American Journal of Epidemiology has convened a collection of 12 articles that further illuminate the epidemiology of that pandemic and consider whether we would be more prepared if an equally deadly influenza virus were to emerge again. In the present commentary, we place these 12 articles in the context of a growing body of work on the archeo-epidemiology of past pandemics, the socioeconomic and geographic drivers of influenza mortality and natality impact, and renewed interest in immune imprinting mechanisms and the development of novel influenza vaccines. We also highlight persisting mysteries in the origins and severity of the 1918 pandemic and the need to preserve rapidly decaying information that may provide treasure troves for future generations.