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Mortality from the influenza pandemic of 1918-19 in Indonesia
Abstract and Figures
The influenza pandemic of 1918-19 was the single most lethal short-term epidemic of the twentieth century. For Indonesia, the world's fourth most populous country, the most widely used estimate of mortality from that pandemic is 1.5 million. We estimated mortality from the influenza pandemic in Java and Madura, home to the majority of Indonesia's population, using panel data methods and data from multiple quinquennial population counts and two decennial censuses. The new estimates suggest that, for Java alone, population loss was in the range of 4.26-4.37 million, or more than twice the established estimate for mortality for all of Indonesia. We conclude that the standing estimates of mortality from influenza in Java and Indonesia need to be revised upward significantly. We also present new findings on geographic patterns of population loss across Java, and pre-pandemic and post-pandemic population growth rates.
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... He however, did not formally include the flu pandemic as another possible determinant of the spatial variation of income inequality. We included this in this study through employing a unique dataset compiled from Chandra (2013) and de Zwart (2022). Given the small number of observations, however, we limit our analysis to simple correlations and use the results for illustrative purposes only. ...
... This exercise has policy relevance for several reasons. First, the estimated population loss caused by the past pandemic for Java alone is in the range of 4.26-4.37 million (Chandra 2013, van der Eng 2020. At the residency level, the rates of population loss range from 1.10% to 23.71%. ...
... The second source of data was de Zwart (2022) There were 17 regions on the map (Figure 1), including the principalities of Jogjakarta and Surakarta which were excluded from the dataset since these two principalities were governed by different administrative systems. As principalities, they were not classified as government land (Brata et al. 2013) and as such different mechanisms were adopted in the data collection (Chandra 2013). Since the inequality data for the residency of Rembang was also unavailable for 1928, we then extracted data for only 14 residencies in Java. ...
Covid-19 pandemic has renewed the debate over economic inequality as well as the relative importance of policies for saving lives vs. protecting livelihoods during times of crisis. This paper therefore offers some insights from economic history through investigating the relationship between the Spanish Flu pandemic and income distribution at the residency level in late colonial Java, Indonesia’s most populous province. In addition, we examine recent inequality trends in Java during COVID-19. Our econometric analysis shows that population fatality during pandemic is negatively associated with economic inequality across 14 residencies. This in turn improved income distribution across residencies in the post-pandemic period in late colonial Java. We also find some evidence that estate land for commercial plantation moderated the re-distributive role of the pandemic. Based on the results, we further discuss the key lessons learned from the Spanish flu for contemporary times, proposing possible causes of increasing inequality due to the COVID-19 pandemic and the importance of protecting citizens in productive age groups, especially those on low incomes. Referring to more recent spatial and temporal trends, we conjecture on the existence of an inequality trap in Java. Although this did not follow the historical pattern evident in late colonial Java, COVID-19 may have at least a scarring effect on residency-level inequality in Java.
... The influenza pandemic of 1918-19 was the most devastating pandemic in the 20th century [1][2][3][4][5]. The pandemic spread across the world in three consecutive waves: March 1918, September-November 1918, and early 1919 [6]. ...
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.
... To overcome the uncertainty of the actual spread and size of COVID-19 in developing countries, many non-governmental agencies/individuals conducted various methods to gather data on the spread of the pandemic as an alternative to government announced figures. Most of these methods expect that the actual numbers are much larger than those announced by their governments (Barro et al., 2021;Chandra, 2013;Resosudarmo & Irhamni, 2021). This paper aims to assess these data gathering and estimation methods conducted by non-governmental agencies/individuals to be able to quickly assess the spread and the magnitude of the pandemic, with Indonesia as the case study. ...
This paper presents a comparison of the Covid-19 infections between select pairs of border counties in neighboring states. These adjacent border county groups (regions) are where the policies of the Non-Pharmaceutical Interventions (NPI) such as Lockdown/Stay-at-Home differ. These analyses represent a form of a natural experiment and using a Difference-in-Differences (Diff-in Diff) model test the effectiveness of NPI in mitigating COVID-19 infections between border county regions in neighboring states. The border counties are in the states on the Iowa and Illinois border, the Dakotas (North and South) and Minnesota border and the Arkansas and Mississippi border. In each case the policies on each side of the border differ and the border is clearly designated by a river separation. Based on the Diff-in-Diff model output, state policies appear to make a significant difference between some of these specific adjacent border regions, at least early in the pandemic (March-August 2020). This report is an outcome of a short (quick hit) Schar Foundation Grant focused on the first wave of the Covid pandemic in the US. (Feb. 2020-August 2020). Professor Kingsley E. Haynes was the Director and the Principal Investigator with Rajendra Kulkarni as the lead researcher and Abu Siddique and Meng-Ho Li as the primary research assistants with significant research and report writing responsibilities.
... The influenza pandemic of 1918-19 was the most devastating pandemic in the 20th century (Chandra, 2013;Curson & McCracken, 2006;Patterson & Pyle, 1991;Tomkins, 1992;Trilla, Trilla, & Daer, 2008). The pandemic spread across the world in three consecutive waves: March 1918, September-November 1918, and early 1919(Taubenberger & Morens, 2006. ...
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.
... Karena lama pendidikan yang ditempuh oleh perempuan berpengaruh terhadap partisipasi angkatan kerja (Mubarokah, 2017 Jauh sebelum Covid-19, kota-kota di Indonesia juga pernah menjadi episentrum penyebaran pandemi. Kota-kota besar di Pulau Jawa, seperti Surabaya dan Semarang, mengalami penurunan populasi lebih dari 10% sebagai konsekuensi penularan Flu Spanyol pada tahun 1918-1919 (Chandra, 2013). Bahkan, kota-kota besar di luar Pulau Jawa, seperti Makassar dan Banjarmasin, juga tidak luput dari serangan Flu Spanyol, dengan penurunan populasi yang masih belum diketahui (Wibowo, 2009). ...
... We conclude that the approximation of deaths from influenza in Java and Indonesia need to be revised upwards significantly. We also present new findings on population geographic patterns across Java and its pre-pandemic and post-pandemic population growth rates [6]. ...
... Data tersebut kemudian diperbarui oleh riset Siddhart Chandra setelah melakukan kalkulasi ulang serta perbandingan dengan sumber-sumber lain, yang membuatnya sampai pada simpulan bahwa terdapat 4 juta lebih jiwa yang menjadi korban. Jumlah tersebut juga dapat ditinjau pada hasil riset Widjojo Nitisastro tentang depopulasi di wilayah tersebut pada kurun 1917(Chandra 2013. Itu baru di Jawa-Madura, sementara di daerah lain Hindia Belanda, Sulawesi misalnya, Flu Spanyol tercatat telah menimbulkan dampak serius di wilayah Toraja, Sulawesi Selatan (Wibowo et al. 2009), termasuk pula wilayah Sulawesi Tengah yang menjadi objek riset ini. ...
This study aims not only to explain the handling efforts, but also to analyze the experiences and collective memories of the people of Central Sulawesi when they faced influenza pandemic in 1918—1920. A century ago, before the Covid-19 pandemic broke out in 2020, an influenza pandemic had plagued the people of Central Sulawesi. Unfortunately, there was very little literature describing these cases. Hence, historical method is used together with certain sources, such as books, journal articles, newspapers, archives, and interviews related with the issue. The results then show that the influenza pandemic in Central Sulawesi began in late 1918 and lasted until the early 1920s. The colonial government at that time tended to be late in dealing with this pandemic. As a result, treatment efforts at the local level were mostly driven by missionaries and colonial officers who had been equipped with the knowledge of modern medicine for tropical diseases. In addition, certain tactics were carried out by several local communities including migration, self-quarantine, and prevention efforts by implementing a hygiene lifestyle and providing food barns to prevent famine during a pandemic. In conclusion, efforts to deal with the influenza pandemic in Central Sulawesi were very limited due to the lack of health facilities and the number of medical personnel, so it was not sufficient to overcome the disease and its spread. However, people's experiences with influenza at that time were stored quite well in their collective memory. Therefore, some of these experiences related to local knowledge can be used as lessons to deal with the current Covid-19 pandemic.
Objectives
The interaction between genomics, genetic and environmental factors have been implicated in non-syndromic orofacial cleft development. In the current study, we investigated the contributions of rare and novel genetic variants in known cleft genes using whole exome sequencing (WES) data of Indonesians with non-syndromic orofacial clefts.
Design
WES was conducted on 6 individuals. Variants in their exons were called and annotated. These variants were filtered for novelty and rarity using MAF of 0 and 1%.
Setting
Hospital in Indonesia.
Patients/Participants
Indonesians with non-syndromic orofacial clefts.
Interventions
Deleterious variants were prioritized. Pathogenic amino acid changes effect on protein structure and function were identified using HOPE.
Main Outcome Measure(s)
Rare and novel variants in known cleft genes were filtered and their deleteriousness were predicted using polyphen, SIFT and CADD.
Results
We identified rare (MAF <1%) deleterious variants in 4 craniofacial genes namely MMACHC (rs371937044, MAF = 0.00011). SOS1 (rs190222208, MAF = 0.00045), TULP4 (rs199583035, MAF = 0.067), and MTHFD1L (rs143492706, MAF = 0.0044). MMACHC has a mouse knockout model with facial cleft and failure of palatal fusion. The individual with variant in MMACHC presented with nsCPO.
Conclusions
Our study provides additional evidence for the role of TULP4, SOS1, MTHFD1L and MMACHC genes in nsOFC development. This is the first time MMACHC is implicated in nsOFC development in humans.
During the COVID-19 pandemic, governments in developing countries tended to underestimate the actual spread of the pandemic due to limited number of tests. To provide an alternative to the government announced COVID-19 figures, many non-governmental agencies/individuals used various methods to gather data on the spread of the pandemic. One of the methods applied is phone survey approach of directly asking respondents whether they are infected and how the sickness impacts their livelihood. This paper focuses on evaluating the reliability and usefulness of this approach by carefully conducting a phone survey on our own. The findings suggest that by developing a careful algorithm, a phone survey might be able to provide a better estimate than those announced by the government in developing countries where capabilities of conducting COVID-19 tests are limited. Nevertheless, our findings also reveal that the reliability of a phone survey could be low when the response rate is too low.
Estimates of worldwide mortality from the influenza pandemic of 1918-1919 vary widely, from 15 million to 100 million. In terms of loss of life, India was the focal point of this profound demographic event. In this article, we calculate mortality from the influenza pandemic in India using panel data models and data from the Census of India. The new estimates suggest that for the districts included in the sample, mortality was at most 13.88 million, compared with 17.21 million when calculated using the assumptions of Davis (1951). We conclude that Davis' influential estimate of mortality from influenza in British India is overstated by at least 24%. Future analyses of the effects of the pandemic on demographic change in India and worldwide will need to account for this significant downward revision.
Although pregnancy is a recognized risk factor for severe influenza infection, the effect of influenza on miscarriages and births remains unclear. We examined the relationship between influenza and birth rates during the 1918 pandemic in the United States, Denmark, Sweden, and Norway.
We compiled monthly birth rates from 1911 through 1930 in 3 Scandinavian countries and the United States, identified periods of unusually low or high birth rates, and quantified births as "missing" or "in excess" of the normal expectation. Using monthly influenza data, we correlated the timing of peak pandemic exposure and depressions in birth rates, and identified pregnancy stages at risk of influenza-related miscarriage.
Birth rates declined in all study populations in spring 1919 by a mean of 2.2 births per 1000 persons, representing a 5%-15% drop below baseline levels (P < .05). The 1919 natality depression reached its trough 6.1-6.8 months after the autumn pandemic peak, suggesting that missing births were attributable to excess first trimester miscarriages in ∼1 in 10 women who were pregnant during the peak of the pandemic. Pandemic-related mortality was insufficient to explain observed patterns.
The observed birth depressions were consistent with pandemic influenza causing first trimester miscarriages in ∼1 in 10 pregnant women. Causality is suggested by temporal synchrony across geographical areas.
Between August 1918 and March 1919 a flu pandemic spread across the globe and in just under a year 40 million people had died from the virus worldwide. This is the first book to provide a total history and seriously analyze the British experiences during that time. The book provides the most up-to-date tally of the pandemic's impact, including the vast mortality, as well as questioning the apparent origins of the pandemic. A 'total' history, this book ranges from the spread of the 1918-1919 pandemic, to the basic biology of influenza, and how epidemics and pandemics are possible, to consider the demographic, social, economic and political impacts of such a massive pandemic, including the cultural dimensions of naming, blame, metaphors, memory, the media, art and literature. An inter-disciplinary study, it stretches from history and geography through to medicine in order to convey the full magnitude of the first global medical 'disaster' of the twentieth century, and looks ahead to possible pandemics of the future. Niall Johnson brings an impressive scholarly eye on this fascinating and highly relevant topic making this essential reading for historians and those with an interest in British and medical history.
The author continues to examine the causes of the rapid growth of population that occurred in India following World War I. In this second part of a two-part article he looks at the causes of the mortality decline that occurred in the interwar period particularly why mortality declined so rapidly without significant evidence of material advances among the population as a whole. The causes of the decline are identified as a reduction in mortality from diseases such as the plague and the expansion of a general biological immunization in the population rather than as the development of successful social policies.
Abstract In demographic literature Java occupies a special position. It is the island where in the nineteenth century a 'population explosion' occurred. In other developing countries this took place in the twentieth century. Following the official figures Java had a population size of about 4.5 million in 1815 (Raffles's Census) and 28.5 million in 1900. The result is an extraordinary rate of growth of 2.2% per year. In this paper it is argued that it is impossible to correct the data by adjusting them. A more promising method is to study the factors which are responsible for the demographic situation, i.e. economic conditions, the so-called pax neerlandica and the health situation in the period 1800-1850. This period has been specially studied, because it is crucial for the calculation of population size which is normally based on the 1815 period. It is suggested that Java cannot really claim to be an exceptional case in the period 1800-1850. This means that the growth rate - in line with the estimates of Carr Saunders and Sauvy -has to be estimated (greater accuracy is not possible) as between 0.5% and 1.0); per annum. On the basis of estimates and calculations, the population size of Java may have been somewhere between 8 and 10 millions around 1800, the latter estimate being the more realistic figure. The view that there was exceptionally rapid population growth in Java in the nineteenth century is to an important degree the product of a Europe-centred approach to the history of Java.