Investigating survivorship bias: the case of the 1918 flu pandemic

Floris, Joël; Kaiser, Laurent; Mayr, Harald; Staub, Kaspar; Woitek, Ulrich

doi:10.1080/13504851.2021.1971614

Cited by 5 publications

(11 citation statements)

References 21 publications

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“…This is consistent with the evidence from demographic studies on the population level for the USA, Scandinavian countries or Japan [ 20 – 25 ]. In the United States, it was found that increasing mortality rates from ILI in the population were associated with an increase in neonatal and infant mortality in the late 20 th century [ 49 ]. Depending on the trimester of exposure, various adverse pregnancy outcomes were also highlighted.…”

Section: Discussionmentioning

confidence: 99%

“…A possible increase in miscarriage risk due to exposure to influenza during the first trimester would have gone unnoticed because it was underreported. Thus, we do not have information on unobserved fetal loss early in pregnancy [ 49 ].…”

Section: Discussionmentioning

confidence: 99%

“…In the dataset covering the years 1914–1922, the following variables are given: insurance status (yes or no) and marital status of the mother (married or not married). These variables can be considered a rough proxy for socioeconomic background in the absence of occupation or educational level [ 49 ]. Quantitative information on maternal height and information on the fathers’ occupation were not available for the entire series.…”

Section: Methodsmentioning

confidence: 99%

“…In Switzerland, birth registers of maternity hospitals in Basel and Bern have proven to be very useful sources of data. Recent studies have thus examined changes in neonatal health during the World War 1 in Basel or selection effects for the same period in Bern [ 2 , 49 , 50 ]. Switzerland is interesting from an international perspective because it was neutral during World War I but the nutritional status of its population was nevertheless affected, although to a lower extent than among neighboring countries [ 51 ].…”

Section: Introductionmentioning

confidence: 99%

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Health of neonates born in the maternity hospital in Bern, Switzerland, 1880–1900 and 1914–1922

Salvisberg,

Le Vu,

Floris

et al. 2023

PLoS ONE

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The identification of factors impeding normal fetal development and growth is crucial for improving neonatal health. Historical studies are relevant because they show which parameters have influenced neonatal health in the past in order to better understand the present. We studied temporal changes of neonatal health outcomes (birth weight, gestational age, stillbirth rate) and the influence of different cofactors in two time periods. Moreover, we investigated particularly neonatal health in the wake of the 1918/19 influenza pandemic. Data were transcribed from the Bern Maternity Hospital and consists of two time periods: A) The years 1880, 1885, 1890, 1895 and 1900 (N = 1530, births’ coverage 20%); B) The years 1914–1922 (N = 6924, births’ coverage 40–50%). Linear regression models were used to estimate the effect of birth year on birth weight, and logistic regression models to estimate the effect of birth year and of the exposure to the pandemic on premature birth, stillborn and low birth weight (LBW). Mean birth weight increased only minimally between the two datasets; whereas, in the years 1914–1922, the preterm birth and stillbirth rates were markedly reduced compared with the years 1880–1900. Sex, parity, gestational age and maternal age were significantly associated with birth weight in both time periods. The probability of LBW was significantly increased in 1918 (OR 1.49 (95% CI 1.00–2.23)) and in 1919 (OR 1.55 (95% CI 1.02–2.36)) compared to 1914. Mothers who were heavily exposed to the influenza pandemic during pregnancy had a higher risk of stillbirth (OR 2.27 (95% CI 1.32–3.9)). This study demonstrated that factors influencing neonatal health are multifactorial but similar in both time periods. Moreover, the exposure to the 1918/19 pandemic was less associated with LBW and more associated with an increased risk of stillbirth. If this trend is confirmed by further studies, it could indicate some consistency across pandemics, as similar patterns have recently been shown for COVID-19.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Health of neonates born in the maternity hospital in Bern, Switzerland, 1880–1900 and 1914–1922

Salvisberg,

Le Vu,

Floris

et al. 2023

PLoS ONE

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“…Another example of studying the survivorship bias is paper [5], in which the positive effect of a suppressed immune system on protection against the most severe consequences of infection was investigated. At the same time, it should be emphasized that the work does not provide a procedure for eliminating the error of the one who survived on one side.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Eliminating "systematic survivorship bias" in the attitude of specialists to the significance of investment attractive features of examined objects

Reva,

Kamyshyn,

Borsuk

et al. 2023

EEJET

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Methods for detecting and eliminating false measurements are well known in the theory of metrology. However, the relevant methodology is not adapted to the needs of the qualitative assessment of the impact of the human factor on expert decision-making. The "systematic survivorship bias" refers to the following: when involving experts in conducting examinations, they usually focus on that part of them, where statistically probable agreed opinions are observed based on the results of these examinations. Other experts are considered "marginal", their opinions are discarded and not taken into account, which defines the "systematic survivorship bias", also called the "paradox of information availability". Although the specified "marginality" may be a consequence of the unique experience of conducting examinations or, for example, the use of modern technologies by a specific specialist, little known to the general public. It should be noted that manipulation of statistical data with an orientation only on "successful" cases could be really dangerous, for example, in studies of the human factor in complex ergastic active and organizational management systems, in particular aviation. The rationale and implementation of the algorithm for detecting and eliminating the "systematic survivorship bias" have been given in this paper. m=90 specialists who are usually involved in various examinations by UkrINTEI took part in the research. The actual elimination of the "systematic survivorship bias" occurs after the implementation of a certain number of iterations of the algorithm given in the current work. As a result of iterations of the above-mentioned algorithm, it was established that four subgroups can be distinguished from the initial sample with the number of m=90, with the following numbers: mC=30 people, mH=12 people, mM=11 people, mT=6 people. For the specified subgroups, the consistency of group opinions satisfies the entire spectrum of hypothesis testing criteria established in this paper

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Baby Fever

Belciug

Iliescu²

2022

Intelligent Systems Reference Library

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Investigating survivorship bias: the case of the 1918 flu pandemic

Cited by 5 publications

References 21 publications

Health of neonates born in the maternity hospital in Bern, Switzerland, 1880–1900 and 1914–1922

Health of neonates born in the maternity hospital in Bern, Switzerland, 1880–1900 and 1914–1922

Eliminating "systematic survivorship bias" in the attitude of specialists to the significance of investment attractive features of examined objects

Baby Fever

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