Spanish Influenza in Madrid

Spanish Inﬂuenza in Madrid: Contextualizing timing and strength diﬀerences in spatial and social variations

Laura Cilek, Diego Ramiro Fari˜nas, Beatriz Echeverri Davila ∗

1 Background

1.1 1918-20 Influenza Pandemic Waves in Madrid No different from the rest of the world, Madrid experienced several waves of the pandemic influenza in 1918 and the following years that ultimately killed between 50-100 million people across the globe [1]. Echeverri presents a comprehensive background on the events, their timing, and outcomes of the Spanish influenza pandemic in Spain [2]. Analyses reveal four unique waves in the city beginning in May of 1918 and followed by additional waves in the fall of 1918, winter of 1919, and winter of 1919-1920 [2]. While other other cities such as New York and Copenhagen also reported spring and summer waves [3, 4], the herald wave in Madrid is clearly defined and much stronger than the following two outbreaks. The city’s moderate fall and winter waves of 1918-1919 were followed by an additional wave of influenza in December 1919 to January 1920, which produced the highest peak of mortality during the period. Madrid, in the middle of a rapid period of growth as the country of Spain continued to urbanize, had a considerable degree of both individual and geographic social and economic variation [5]; this may have played a significant role in the spread of influenza and spatial mortality differences [6]. This period of growth contributed to significant differences in social and economic conditions of districts and neighborhoods within the city [7]. In this paper, we focus our analysis on two of the ten city districts, Inclusa and Centro and their respective neighborhoods (barrios). Located in the South of the city, Inclusa had particularly high mortality. It was home to the city’s Foundling Hospital, the main institution devoted to the care of the population under 1 year of age [5] and was also home to many migrants, as well as a generally poorer population than the rest of the city. The district of Centro serves as a contrast to Inclusa; Centro had average mortality rates relative to the rest of the city, far lower than those found in Inclusa. While the Centro district, located in the middle of the city, was also undergoing rapid urban and industrial transformations during this time, the population tended to be less floating and temporal than in Inclusa [7].

1.2 Differences Across Time and Space Considerable research has examined how spatial differences and temporal patterns affect influenza mortality, yet the high world-wide mortality of the 1918 Spanish influenza pandemic [1] indicates climactic patterns did not play a major role in regional death outcomes. The presence and severity of each successive wave differed by location, creating a debate regarding transmission mechanisms and the role acquired immunity in consecutive breakouts may have played in the tempering of each successive wave [8, 4, 9]. However, these analyses examine larger populations, focusing on entire cities. While some efforts have been made to look at the compositional effects of local districts or census tracts on overall pandemic mortality (see section 1.3) little attention has been paid to how social and demographic characteristics interact with spatial differences over several successive influenza waves. For example, Mathews et al note that a virus will spread more slowly in a population with some immunity (i.e. the reproduction number Reffective in a partially-immune population will be lower than R0), assuming the virus has not yet mutated and evolved to be significantly different from the initial strain [10]. Two standard measurements are used to understand the effect of an infectious outbreak; the Reproduction Number (R0), can be interpreted as the number of additional cases a single case will cause [11]. In an epidemic period, the Reproduction number will be greater than one, meaning that the number of infected individuals increases as time progresses. In the context of this particular analysis, the total length of pandemic wave is important, as it describes the heightened risk of others to contract the virus, allowing it to continue to spread.

∗[email protected] Centro de Ciencias Humanas y Sociales (CCHS) Consejo Superior de Investigaciones Cientiﬁcas (CSIC) Grupo de Estudios de Poblaci`on y Sociedad, Universidad Complutense de Madrid.

1 1.3 Influenza Mortality in the Social Context The extent to which a socioeconomic gradient contributed to mortality in the 1918 pandemic remains an ongoing debates. Clear evidence points toward a social gradient in the transmission and strength of seasonal influenza outbreaks and preventative vaccination campaigns; several papers identify these patterns [12, 13] in the US and across the world, particularly in the most affected populations such as minorities and those above 65. However, many social scientists believe the viral strain present in the 1918-920 pandemic events was so virulent that aside from affecting all age groups, the airborne natures of the disease outweighed any other potential of any other social variables to create class mortality differentials [14, 15, 16]. Mamelund provides several examples of these studies in his own paper, such as in a Great Britain Ministry of Health survey about fatality and social status in 1920 [17, 15]. More recent research using newly digitized data sources have, in fact, found a social gradient. Much of these analyses also consider the role of geographic differences, even within the same urban environment, could have effected mortality due to underlying differences in one’s neighborhood socioeconomic environment. For example, Grantz et al use poisson regression to find that during the strong fall wave in Chicago (the end of September to November 1918), influenza and related mortality was higher in census tracts with higher illiteracy [18]. Mamelund used individual and household level data to find that both neighborhood of residence and apartment size, as a proxy for household wealth, had effects on influenza survival in Kristiana during 1918 [17]. Given that there is no significant geo-spatial change within the city, the transmission of the influenza should affect each district equally, yet our current understanding of social inequalities suggests mortality differences between the two districts should exist[18, 12, 19]. Thus, in the context of both Madrid and the strength and timing of each successive wave from 1918-1920, we aim to disentangle how, beyond the demographic characteristics of a district, social and economic differences may have played a role in influenza- related mortality.

2 Data

To complete the analysis of establishing wave and timing strength, we use death records from the Civil Register records on deaths in Madrid from 1917-1922. During this time, the register holds 103,500 records, and the Inclusa and Centro districts contain 12,452 and 5,286 records, respectively; in addition to death date, each record contains demographic and socio-economic information and place of death. This unique, individual-level data allows for analyses to demonstrate the flow and characteristics of each wave across districts, as well as the changes in death by age group and specific cause within and between each location. The 1920 listing of inhabitants for the city of Madrid contains individual level demographic and social information for each household in Madrid, including age, sex, marital status, as well as literacy and writing variables and employment information as at December 31, 1920. Currently, these individual records have been typed for the districts of Inclusa (n = 68, 707) and Centro (n = 47, 448) [20]. These districts were chosen for both differences in the manifestation of the influenza epidemic and for the underlying social and demographic variation between them, as noted in the Background section. Any additional information regarding the population composition of Madrid comes from the citys yearly population books, which provide a yearly summary of the population and demographic events in each part of the city. With the totality of this data, we aim to use the individual level death records and listing of inhabitants to discern how some of the neighborhood (barrio) and district level demographic and social differences contributed to the variations in timing and strength of each wave.

3 Methods

Our ongoing analysis mathematically estimated the starting, peak, and ending dates of each wave for each district in Madrid. The use of secondary sources and visual analyses to determine dates of each wave can lead to an observation bias and subsequent incorrect estimations. Thus, we employed the Segmented Regression technique to ﬁnd each epidemic phase in the four waves for the city and each of the ten districts [21, 22, 23]. Using the log number of deaths as the response variable considering each phase to be a log-linear component of a piecewise regression, Muggeos linearization technique allows us to estimate break points in the data,

2 thereby identifying likely dates and associated standard errors of the beginning, peak, and end of each wave [21, 22]. Finally, we estimate the Reproduction Number of each wave for each district using the ﬁrst and r∗t second breakpoint, such that R0 = e , which is equal to the slope of the increase in deaths during the ascending period of the epidemic. Given the nature of our listing of inhabitants data, which only provides the individual data at a single point in time, there are limitations to the types of analyses we can employ. One possibility is to use a negative binomial regression approach to estimate the predicted mortality in each of the barrios within out districts, using a multi-level approach. Another possibility to to use our individuals records to create a logistic regression determining the odds of an individual, given certain characteristics, fell prey to the disease. In this case, the negative binomial approach would provide particular strengthen to the data, which tends to be quite overdispersed due to the variation in mortality between barrios.Ultimately, the structure of our data may limit the inclusion of some of the aaves and/or individuals in this type of analysis.

4 Current and Forthcoming Results Table 1: Variations in Wave Timing and Strength, 1918-1920 District Start Date Peak Date End Date Length (days) R0 Spring 1918 Madrid 21 May 31 May 15 June 25 2.97 Inclusa 04 May 23 May 28 May 24 1.20 Centro 21 May 27 May 09 June 19 3.46 Fall 1918 Madrid 24 Sept 26 Oct 19 Nov 56 1.81 Inclusa 13 Aug 31 Aug 08 Dec 118 0.62 Centro 29 Sept 22 Oct 06 Nov 39 2.37 Winter 1918-19 Madrid 25 Dec 11 Jan 26 Feb 44 1.33 Inclusa 07 Dec 11 Dec 31 jan 55 0.61 Centro 11 Dec 17 Dec 31 Dec 20 0.48 Winter 1919-20 Madrid 25 Dec 11 Jan 18 Jan 39 2.38 Inclusa 10 Dec 03 Jan 08 Jan 29 2.87 Centro 04 Dec 02 Jan 0.40

In the four successive waves (Table 1), the city of Madrid experienced three epidemic waves of influenza, each with lower intensity than the preceding on. The spring herald wave occurs throughout the month of May and early June, beginning in the districts of Inclusa, Latina, and Hospital, then moving towards other parts of the city. As seen in other fall 1918 waves throughout the world, the city-wide ascending phase lasts much longer than the spring, though overall, R0 is lower and does not reach epidemic levels in all districts. Considering all districts in Madrid, the winter wave is much shorter and lower in intensity (R0) than both the Spring and Summer. The weakness of this wave is further evident at the district level, where most districts (including both Inclusa and Centro) do not reach epidemic levels. The next year, Madrid experienced another large fourth wave of influenza. The strength of the wave is high across nearly all districts (see previous footnote). Ongoing analyses of our mortality records reveals influenza spread through each of Madrid’s 10 districts with varying length and virulence. Using a segmented regression technique [23] to estimate the of the start, peak, and end date of each wave have been found for each district within the city; results for the entire city, Inclusa, and Centro may be found in table 1. This approach allows for the calculation of R based on the estimations for the start and peak dates of each wave. In the first two waves of 1918, the epidemic begins in Inclusa before spreading to the rest of the city. The overall mortality impact in the fall in Inclusa is quite small; in fact, the Reproduction Number does not indicate the presence of pandemic activity. Meanwhile, Centro has the highest R of any particular district in the fall, through the virulence does not appear as high as in the spring. While the Rs for both Inclusa and Centro indicate an absence of pandemic activity. Perhaps most interesting, the final, extremely virulent winter wave of 1919-1920 cannot be estimated for Centro form our segmented regression approach; however, Inclusa has a high R, further indicating variations in the way

3 Figure 1: Estimated piecewise segmented model based on ﬁtted poisson regression for each wave using all deaths in the city.

Figure 2: All-Cause estimated mortality baseline and actual weekly mortality rates for districts of forthcoming analysis

these viral strains affected district. Additional analyses as suggested in Section 3 will examine to what extent demographic and social factors of the individuals in the district played a role in the mortality differences. Initial estimations of wave strength and timing in the districts of Madrid reveal considerable variability, but the causes of these differences remain unknown. Given the ongoing debate regarding social stratification and mortality outcomes during the epidemic events of 1918-1920, our analysis aims to provide further information and contribute to contemporary knowledge. While seasonal influenza outbreaks have become the norm, understanding the ways social and economic differences affect mortality outcomes in particularly large pandemic events can provide information to public health policy makers and implementors, to minimize the potential mortality impact another large outbreak may have on contemporary societies. While the data identifies and confirms district-level variation in the timing and intensity of influenza waves in Madrid, the districts each have a unique demographic and socio-economic structure (age, income, etc.). Continuing work on this paper will identify these unique facets and standardize the results in a way that separates to what extent the age structure and social composition of the district affected the intensity of each wave in each district.

4 References

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