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This paper takes, as its starting point, Preston and Haines’ observation in Fatal Years that social class was the most important influence on infant and child mortality in England and Wales in the early twentieth century. A subsequent study suggested that this could in part be due to the spatial distribution of the different classes across different types of place, and that some of the mortality differences by social class might actually reflect the contextual effects of healthy and unhealthy places. Although this line of argument has received a considerable amount of attention in health geography literature, it has rarely been examined for a specific historic period, and then only within particular urban areas. In this paper, we apply multi-level models to a complete count individual-level dataset of the 1911 census of England and Wales, comparing influences on infant and child mortality at the level of the individual couple and for two spatial levels. We find that although most variation in infant and child mortality operates at the individual level, there is also important variation at the two spatial levels and part of the mortality differences between social classes is better explained by the areas in which people lived rather than by their social class. A consideration of independent variables at all three levels suggests that different spatial scales capture different sorts of influences on early age mortality.
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SPECIAL ISSUE ARTICLE
Fatal Places? Contextual Effects on Infant and
Child Mortality in Early Twentieth Century
England and Wales
Alice Reid1, Eilidh Garrett2, Hannaliis Jaadla1, Kevin Schürer1and Sarah Rafferty1
1University of Cambridge, UK and 2University of Edinburgh, Scotland, UK
Corresponding author: Alice Reid; Email: amr1001@cam.ac.uk
Abstract
This paper takes, as its starting point, Preston and Hainesobservation in Fatal Years that
social class was the most important influence on infant and child mortality in England and
Wales in the early twentieth century. A subsequent study suggested that this could in part
be due to the spatial distribution of the different classes across different types of place, and
that some of the mortality differences by social class might actually reflect the contextual
effects of healthy and unhealthy places. Although this line of argument has received a
considerable amount of attention in health geography literature, it has rarely been
examined for a specific historic period, and then only within particular urban areas. In this
paper, we apply multi-level models to a complete count individual-level dataset of the 1911
census of England and Wales, comparing influences on infant and child mortality at the
level of the individual couple and for two spatial levels. We find that although most
variation in infant and child mortality operates at the individual level, there is also
important variation at the two spatial levels and part of the mortality differences between
social classes is better explained by the areas in which people lived rather than by their
social class. A consideration of independent variables at all three levels suggests that
different spatial scales capture different sorts of influences on early age mortality.
Keywords: infant mortality; child mortality; social class; contextual effects; compositional effects; multi-level
modelling; census; England and Wales; historical demography
Introduction
Fatal Years, published in 1991 by Samuel Preston and Michael Haines, rapidly
became a classic text on the influences affecting early age mortality during the
demographic transition (Preston and Haines 1991). It was the first systematic study
of nineteenth century early age mortality in the USA and the first large scale study of
historic early age mortality using individual-level data that enabled the authors to
compare different influences on child survival. The volume therefore quickly
established itself as an important point of reference for studies in historic infant and
child mortality. As one of the first studies to make use of census data (in this case the
© The Author(s), 2023. Published by Cambridge University Press on behalf of the Social Science History Association. This
is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the
original article is properly cited.
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1900 census of the USA) at an individual level, large-scale format, the authors were
able to demonstrate the potential of the census as a source for this type of
investigation, paving the way for a plethora of other studies. Furthermore, the book
illustrated the applicability of indirect estimation techniques (originally developed
by William Brass for use in lower- and middle-income countries lacking reliable
vital registration) to historical data, thus broadening the range of methodological
tools available for the study of historic mortality. Preston and Haines applied the
principles of indirect estimation, designed for aggregate data, to generate an
individual-level measure of mortality: the Mortality Index (MI). This allowed
multivariable regression to be used with the individual data, permitting a finer
analysis of the influence of a range of factors thought to have impacted upon infant
mortality and establishing an approach that has been used in a variety of subsequent
analyses (Connor 2017; Dribe et al. 2020; Garrett et al. 2001; Reid 1997).
Although Preston and Hainess main focus was the American experience, they
also provided a comparative element by undertaking a separate analysis of data from
the 1911 census of England and Wales. This allowed them to argue that while race
was perhaps the most powerful factor influencing infant and child mortality in the
USA, in England and Wales that role was taken by social class, defined by fathers
occupation. Their analysis of England and Wales was, however, limited to the
aggregate tables published in the census reports (HMSO 1917,1923): they could not
directly compare influences nor explore as wide a range of factors as they were able
to for the USA.
Several years after the publication of Fatal Years, some of the authors of the
current paper obtained specially negotiated early access to the individual-level data
for the 1911 census for a set of 53 relatively homogenous areas spread across
thirteen locales (Garrett et al. 2001). They took this opportunity to reassess Preston
and Hainess results for England and Wales (Garrett et al. 2001; Reid 1997).
Although their data set was comparatively small, it confirmed the strong social class
differentials in early age mortality that Preston and Haines had shown in their work
with the published tables. However, there were equally strong mortality differentials
between agricultural, white collar, industrial, and other urban types of place. Both
cross-classifications and multivariable regressions of the individual data demon-
strated that the type of place was, apparently, a stronger influence on mortality than
social class, at least in the communities covered by the data. All social classes
enjoyed low mortality in agricultural places, while the opposite was broadly true in
industrial areas. We concluded that the overall social class result was produced by
the differential sorting of the various classes into different types of place, with the
implication that the higher classes did not have lower mortality because they knew
more or could purchase better food or medical services, but because they could
afford to live in areas with better infrastructure and a more salutogenic
environment. Another way of putting this is to say that agricultural places did
not have better health because they were inhabited mainly by better-off people, but
because of characteristics of the places themselves: it was not the composition of
places in terms of their inhabitants, but aspects of the context, that affected health.
These composition-versus-context and class-versus-place debates have received
considerable attention in the health geography literature, aided by the increasing
availability of large individual-level datasets and the development of powerful
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multi-level regression techniques (Owen et al. 2016; Smyth 2008). The literature has
drawn attention to the complexity of operationalizing an analysis of contextual
effects on health, and in particular to the fact that influences on health are not
simply divided into those that operate at an individual level versus those that
operate at a geographical or community level, but that different contextual
influences may operate at different spatial scales. Although historical demographers
have adopted multi-level modelling, it has more often been used simply to control
for unobserved variation at the place level, rather than to explicitly examine such
variation and address the composition versus context debate. This is perhaps a
missed opportunity as multilevel modelling not only offers a better opportunity to
compare the influences of class and place but, in a historic setting with a paucity of
independent variables capturing particular influences on mortality, it affords the
possibility of identifying different sorts of influences by detecting variation at
particular scales.
In this paper, therefore, we take advantage of the full count data of the 1911
census of England and Wales now available through the Integrated Census
Microdata (I-CeM) project (Schürer and Higgs 2014) to re-examine the extent and
nature of contextual effects on infant and child mortality. We find that although
most variation in infant and child mortality operates at the individual level, there is
also important variation at a place level and part of the mortality differences
between social classes is better explained by the areas in which people lived rather
than by their social class. We also conclude that different spatial scales capture
different sorts of influences on early age mortality.
In the next section, we outline some of the previous research on infant mortality
differentials and introduce the class-versus-place and composition-versus-context
debates. Section 3 describes our data and the methods we use. Section 4 presents and
discusses our results, Section 5 recognizes the limitations of our research, and
Section 6 offers some tentative conclusions.
Previous research
Using the published results from the 1911 census of England and Wales, Preston
and Haines (1991) identified social class as the most important factor influencing
infant and child mortality in England and Wales (see also Haines 1995). The
existence of this gradient in the late nineteenth and early twentieth century had
already been established using both this source and subsequent Registrar Generals
official reports (HMSO 1923; Morris and Heady 1955; Pamuk 1985; Watterson
1988; Woods et al. 1988,1989).
Link and Phelans fundamental cause theory argues that social class gradients in
health have always been present, working through different pathways and
mechanisms in different eras (Clouston et al. 2016; Link and Phelan 1995).
According to this theory, the better-off in society will always be able to use their
resources or knowledge to avoid hazards or exposure, to improve resistance to
disease by purchasing higher quality and quantity of food, or to better aid recovery
from illness through superior access to curative healthcare. The precise mechanisms
through which the rich gain an advantage will vary according to the disease context
and scientific knowledge at the time.
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While the conclusion that social class differences have always been present is not
well supported by the evidence (Antonovsky 1967; Reid 2021; Woods and Williams
1995), the focus on mechanisms and routes to better or worse health in different
groups has chimed with the way that social differences in infant and child health
have been conceptualized and investigated by (historical) demographers. Mosley
and Chen (1984) expressed these mechanisms as proximate determinants: the
intermediary factors through which the more distal (societal) causes have to operate.
They divided the proximate determinants into various categories: maternal (age at
birth, parity, birth interval), environmental (household crowding, water or food
contamination), nutrient deficiency (in the child or mother), injury, and personal
control of illness.
In historic Europe, social class could affect several of the proximate determinants.
Firstly, fertility decline tended to start among the better off, so lower fertility could
affect mortality through the maternal route, although the evidence for this is slim
(Fernihough and McGovern 2014). Secondly, poorer people tended to live in
households that were more crowded (Cage and Foster 2002) and had deficient
ventilation (Barker and Osmond 1987), increasing the potential for transmission of
infectious and respiratory diseases. Such housing may also have lacked reliable
access to clean water (Jaadla and Puur 2016) or efficient sewage disposal (Morgan
2002), increasing the possibility of diarrheal disease through directly infected water
or cross-contamination. Poor maternal nutrition has been linked to lower birth
weight and survival chances (da Silva Lopes et al. 2017) and less well-off women
may have been unable to afford a diet that was both sufficient and nutritious. Child
diets may also have been suboptimal both in terms of quality and quantity,
particularly for children who were artificially fed (Fildes 1998).1Finally, it has been
suggested that the children of better-off parents may have had better survival
because such couples could afford superior health care, either at delivery or while
their children were growing up, or because their education rendered them more
likely to seek healthcare and better able to communicate with health professionals
(Pamuk 1985).
These mechanisms all focus on individual or household attributes, but another
layer of influences operates at a community level (Williams and Galley 1995),
examples of which could include environmental pollution (Hanlon 2022; Morgan
2002), social cohesion, and social capital (Szreter and Woolcock 2004). Many of the
individual and household influences related to social class could also wholly or
partly reflect resources and structures operating at a community or local level rather
than the individual or household level. For example, a house cannot be connected to
a mains water supply unless a water main has been laid in the area. Health service
usage may similarly reflect local provision as much as individual constraints on
access. These structural or local influences are not primarily dependent on
individual social class or resources, although class may be instrumental in sorting
people into more or less salubrious environments (Reid 1997).
1Breastfeeding itself was strongly linked to better infant survival, but this was associated with an inverse
class gradient in early twentieth century Britain, with lower class women more likely to breastfeed their
infants (Reid 2017).
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When examining differences in mortality or health between different places or
types of place, it can be difficult to identify whether poor survival is because a place
is disproportionately inhabited by less well-off people, whose health is affected by
their own characteristics or resources, or whether there is an additional and
independent effect of place. The class-versus-placedebate asks whether the social
class differences in mortality visible in aggregate figures are the consequence of
individual socioeconomic characteristics or due to elements of the neighborhood in
which people live. The closely related, but rather broader, composition-versus-
contextdebate recognizes that most variation in mortality operates at an individual
level, and asks whether, once this is controlled, there is any additional place-level
variation.
These debates have been hard to resolve because much of the data, particularly
for historic health and mortality, have only been available at an aggregate level (Reid
2021). Aggregate data was used not only for all the early analyses of early age
mortality by social class mentioned above, but also for many geographical analyses
of early age mortality. There is a long tradition of investigating geographical
differences in health in Britain, arguably starting with Farrshealthy districts:
certain rural areas that had far lower mortality than contemporaneous towns and
cities (Farr 1859).
Nineteenth century miasmatic and contagion theories of disease causation
supported the notion that places could be injurious to health, and contemporary
British observers focused on the deleterious effects of urban disamenities on the
survival chances of young children (Gregory 2008). In the same tradition, Lee (1991)
used county-level data to argue that employment structure, particularly the presence
of mining, and housing density were key to explaining the geographical patterns in
infant mortality in England and Wales. Using registration districts (RDs), Gregory
(2008) demonstrated not only an urbanrural contrast in mortality, but argued for a
coreperiphery pattern, with places more distant from London having higher
mortality and slower improvements in life expectancy. He argued that more
attention should be paid to rural districts, particularly in terms of their early
mortality decline, and this theme was taken up by Atkinson et al. (2017) who
examined influences on the spatial pattern and declines among rural RDs. Using
more detailed registration sub-district (RSD)-level data for the whole of England
and Wales, Jaadla and Reid (2017) examined the factors affecting child mortality
(ages 14) patterns over and above those affecting infant mortality. They concluded
that aspects of the local disease environments, including overcrowding which
governed transmission of air-borne diseases and urban disamenities such as poor
sanitation, were more important than measures of human capital such as numbers
of health service workers or teachers.
Spatial analyses of historical early age mortality outside of England and Wales
have also focused on describing subnational patterns (Edvinsson et al. 2001;
Ramiro-Fari˜nas and Sanz-Gimeno 2000; Thorvaldsen 2002; van den Boomen and
Ekamper 2015), with a more limited amount of cross-country analysis (Edvinsson
et al. 2008; Klüsener et al. 2014). The ecological nature of many of these studies,
however, means they cannot address the place-versus-class and composition-
versus-context questions.
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The availability of individual-level data offers an avenue for disentangling place
and class using regression techniques that include both individual socioeconomic
status (SES) as well as indicators of the broader environment that would ideally
capture aspects of the environment thought to influence the health outcome being
measured. There are various methodological and conceptual issues that need to be
considered, however. Firstly, if the multilevel structure associated with variables
measured at different levels is not properly accounted for, standard errors for area-
level variables can be under-estimated, leading to spuriously small confidence
intervals: this can be easily dealt with through the use of multilevel models.
Secondly, spatially structured data also pose methodological issues as values of a
particular variable for neighboring areas may not be independent, for example if
people in one area use services and interact with people in a contiguous area or
further afield. Failure to take these sorts of dependencies into account can lead to
mis-specification of models (Manley et al. 2006; Xu et al. 2014). This can be
overcome by the use of spatial models, but these depend on the availability of
boundary data and are highly resource intensive, limiting their application.
Related to these geographical effects, there is an issue regarding the most
appropriate geographical unit to choose for analysis. The modifiable areal unit
problem is very pertinent here. This recognizes that the results for variables
calculated for geographical units are highly dependent on where boundaries are
drawn (the aggregation or zonation issue) and the scale at which the units are
created or the underlying data aggregated (the scale issue) (Openshaw 1983).
Choosing units of an inappropriate size for the analysis, or which may be a suitable
size but do not represent the areas in which the spatial effects operate, can therefore
obscure results. This can pose a problem as the administrative boundaries for which
data are available may not coincide with the areas for which influences on health
operate, or with peoples own conception of their neighborhood.
A strand of research in health geography has investigated the importance of these
effects and, in relation to the aggregation issue, there is some evidence that units of
similar population size but covering different areas could make a big difference to
results (Flowerdew et al. 2008). Stafford et al. (2008), however, also comparing areas
of similar size, concluded that administrative boundaries were reasonably good
approximations of the areas in which health effects operate. The scale issue has
attracted more investigation with respect to health outcomes. Although Duncan
et al. (1993) suggested that the size of the geographical unit used does not make
much difference to health outcomes, a larger body of work has indicated that scale is
important, and that smaller units are generally better at capturing health effects
(Boyle and Willms 1999; Flowerdew et al. 2008; Manley et al. 2006; Oliver and
Hayes 2007). Haynes et al. (2007) also found that the neighborhoods that have
meaning for residents are smaller than the administrative districts that are often
used for research into health outcomes.
Many of these studies also demonstrated that the size of the appropriate unit
varied according to the independent variable considered (Boyle and Willms 1999;
Flowerdew et al. 2008; Xu et al. 2014). For example, automobile traffic affects air
pollution within a radius of about 220 m (Wang et al. 2021), which suggests that
small geographic units would best capture any health effects of traffic concentration.
In contrast, the effect of variations in health service provision might be best captured
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by health service delivery areas, which tend to be quite large. Tarkiainen et al. (2010)
examined spatial variation in adult mortality in Helsinki and found that there was
independent variation at both the sub-district and district levels (see also Meijer
et al. 2012). Xu et al. (2014) found that egocentrically defined neighborhoods were
better at capturing spatial variation in historic infant mortality than even small
administrative units were, however such an approach requires precise geo-coding of
individuals which is difficult to achieve on a very large scale.
Review articles have concluded that the spatial context has a relatively small,
although usually significant, effect on health outcomes (Arcaya et al. 2016; Diez
Roux and Mair 2010; Macintyre et al. 2002; Meijer et al. 2012; Pickett and Pearl
2001). However, spatial effects tend to be stronger for outcomes related to physical
health compared to those related to subjective measures of well-being (Boyle and
Willms 1999), and a systematic review found that place effects are often stronger for
younger ages (Meijer et al. 2012). Xu et al. (2014) argued that child health outcomes
may be susceptible to very local effects because children have a more limited daily
activity space around their home.
Some of the relatively few studies that explicitly examine spatial influences,
independent of individual-level influences, on historic health outcomes, have failed to
take proper account of the multi-level structure of the data (Heady et al. 1955;Reid
1997). Others have concentrated on specific urban case studies that, although allowing
valuable insights into intra-urban variation, do not allow wider aspects of rural versus
urban areas to be examined, nor the different ways that intra-urban effects operate in
different cities (Connor 2017; Thornton and Olson 2011;Xuetal.2014). This paper
investigates the independent roles of context and composition, and class and place, for
the whole of England and Wales, using multi-level models. The considerations
outlined above suggest that although individual influences are still expected to
account for the most variation in mortality outcomes, place-level effects may well be
important, particularly for the risk of death during infancy and early childhood.
Ideally, we would use independent variables that accurately measure each of the
proximate determinants of mortality at the levels on which they are thought to
operate: at an individual level these might include education, income, aspects of
housing quality and sanitation, and at a contextual level community cohesion, area-
level water, sanitation and health provision, pollution indicators, and so on. For
many historic settings these are simply not available. It is common to use other
indicators as proxy variables, but these cannot usually identify particular proximate
determinants of mortality. The observation made above, that different variables
operate at different spatial levels, means that variation at different spatial levels can
provide clues about the contextual influences on mortality outcomes.
Data and methods
Our dataset is derived from the individual-level data for the 36 million people
enumerated in the 1911 census of England and Wales.2We use a mortality outcome
2This work is based on I-CeM, a standardized, integrated data set of most of the censuses of Great
Britain for the period 18511911; see K. Schürer and E. Higgs, Integrated Census Microdata (I-CeM);
18511911 [computer file]. Colchester, Essex: UK Data Archive [distributor], April 2014. SN: 7481,
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based on responses to the special questions asked of married women in relation to
their current marriage: about duration of marriage, children ever born and children
who had died before the census was taken. Following previous work, the dependent
variable in our analysis is the Mortality Index (MI) calculated for each individual
woman (Connor 2017; Dribe et al. 2020; Preston and Haines 1991; Reid 1997;
Garrett et al. 2001). This measures the ratio between the actual number of child
deaths experienced by a woman and the expected number, where the latter is based
on her fertility and marital duration: a value of one indicates that a woman had lost
exactly the number of children expected given the number of children she had
borne, how long she had been married, and overall mortality levels.3We use women
married for less than 15 years with their husband present in the household and with
valid data, who had given birth to at least one child.4Our measures of mortality are
therefore essentially couple-level measures, although in our regressions we weight
them by the number of children ever born so that they represent the risk to an
individual child. Our regressions include a mortality reference date, which is an
estimate of the date to which the mortality estimate refers, but this is still a family-
level estimate (Dribe et al. 2020).5
It is important to realize that we do not have data on the sex, birth order, or age at
death of children who died, and these important sources of variation at the child
level cannot be controlled for. Our measure of mortality relates to children from
birth up to the age of 15, but is heavily weighted towards infancy and early
childhood. This is partly because mortality is highest soon after birth, but it is also a
consequence of the fact that we use information from women married for up to 15
years. Only women married for 15 years could have had a child who died at age 15,
but women of all marital durations could have had a child who died in infancy. Our
measure is therefore neither a pure measure of mortality during infancy nor at any
https://doi.org/10.5255/UKDA-SN-7481-1. The creation of the I-CeM database funded by the UK
Economic and Social Research Council (ESRC), grant RES-062231629. The version of the I-CeM data
used here was enhanced by K. Schürer, H. Jaadla, A. Reid, and E. Garrett as part of the ESRC-funded
An Atlas of Victorian Fertility Declineproject (ES/L015463/1).
3Our calculations use the England and Wales lifetable for 1911 as a standard. See Garrett et al. (2001:
459-65) for more details on the calculation of the Mortality Index.
4We excluded married women whose husband could not be identified as being present in the household
partly because we are interested in social class and need the occupation of a womans husband to identify
this, and partly to exclude women who had separated from their husband or who incorrectly reported
themselves to be married. We also excluded the answers of widowed and unmarried men and women who
answered these questions, people with missing marital duration, implausible age at marriage, or inconsistent
data about children born, surviving and died. Where answers were mistakenly written against a married
man instead of his co-resident wife we transferred them to the latter. Of women married for less than 15
years, half of one percent were excluded due to invalid data, and a further 3.5% because they could not be
linked to a husband in their household. Infant mortality rates calculated using indirect estimation
techniques and the numbers of children born and died in I-CeM are very similar to those calculated using
the numbers published in the official census report (HMSO 1923), indicating that the I-CeM data are of high
quality.
5The children of women married for 04 years will have been younger at death, on average, than the
children of women married for 1015 years. The UN Manual X (1983) provides an equation and multipliers,
to be used with indicators of children ever born, to allow the calculation of the average date to which the
mortality estimates for each marital duration group of women apply. Here, these dates are calculated for
groups of women and applied to the individual women in each group.
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specific age within childhood, but we refer to it as infant and child mortality,or
sometimes child mortalityfor brevity. We also do not know the age of the wife at
the birth of any children not in the household, although in our regression models we
control for both her age at census and that of her husband.
Our multivariable regressions include a suite of independent variables measured
at the individual level, and also variables measured at two area levels. The rest of this
section describes the variables and areas used, firstly at the individual level and then
at the area level.
Table 1provides summary statistics for the individual-level variables. We are
particularly interested in the status or social class of couples and the effect that has on
the survival of their children. We use the social class classification designed for analysis
of the 1911 census and related vital statistics, which has five hierarchical levels and three
separate occupational groups (Szreter 1996). The five levels range from professional and
managerial occupations in social class 1 (high class) to unskilled manual occupations in
social class 5 (low class), and the three separate occupations are textile workers, miners,
and agricultural laborers. These three occupational groups were separated out by the
Registrar General because of their atypical fertility and infant mortality experience.
Many occupations within both the textile and mining industries were skilled, but infant
mortality among these occupations was higher than among other skilled workers. In
contrast agricultural laborers were considered unskilled, but their infant mortality was
much lower than that for unskilled laborers working in other sectors. The mortality
social class gradient in the five hierarchical levels is therefore much stronger when these
groups are separated out than it would be if they were merged with their skill levels.
Nevertheless, we use the scheme for comparability with previous research (Garrett et al.
2001;PrestonandHaines1991;Reid1997). Social class is based on husbands
occupation because the majority of women gave up work on marriage. We also identify
those without a social class, which could indicate that the husband was unemployed, did
not work because he could afford not to, or that his occupation was un-classifiable. This
category is hard to interpret but numerically very small.
In order to tease out how social class affected mortality we would ideally use data on
individual or household characteristics that could represent the mechanisms through
which class affected survival. At a most proximate level these could include access to
health services, water sources, and toilet facilities. Unfortunately, the census does not
provide such information, nor does it provide indicators of education, income or wealth
which would provide clues as to these mechanisms. We are limited to the information
available the number of servants, the size of house, birthplace, womens work status
and position in the household to represent differences in class.
Many middle-class families employed live-in servants and we use couples with
none, one, two, and three or more live-in servants to distinguish levels of income
among the upper- and middle-classes. The 1911 census recorded the number of
rooms in each house (excluding kitchen and bathroom), and we treat this as a
categorical variable, distinguishing those living in fewer than three rooms, those
living in 35 rooms and those with at least six rooms in their household.6House size
could reflect income, but may additionally represent facilities or relative crowding.
6Those reporting over 30 rooms are placed in the missing category together with other non-numeric
answers as these are likely to be errors in recording or transcription.
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Table 1. Summary statistics for individual-level variables
Variable Category Mean Percentage MI
Wifes age 32.00
Husbands age 34.23
Children ever born 2.68
Mortality reference year 1907.40
Social class Social class 1* 9.89 0.671
Social class 2 15.29 0.844
Social class 3 24.24 0.949
Social class 4 17.32 0.986
Social class 5 16.15 1.200
Textile workers 3.19 1.180
Miners 9.26 1.270
Agricultural laborers 3.62 0.790
Missing social class 1.04 0.998
Wifes birth place Great Britain* 94.91 1.000
Ireland 1.00 1.060
Eastern Europe 0.52 0.803
Rest of Europe 0.45 0.844
Other 0.51 0.781
Missing 2.62 1.070
Husbands birth place Great Britain* 94.87 1.000
Ireland 1.18 1.140
Eastern Europe 0.61 0.818
Rest of Europe 0.71 0.919
Other 0.50 0.884
Missing 2.13 1.040
Wifes work status Wife does not work* 91.99 0.969
Wife works at home 1.95 1.170
Wife works outside home 6.06 1.470
Servants No live-in servants* 92.60 1.030
1 servant 5.04 0.660
2 servants 1.32 0.534
3+servants 1.04 0.449
Wifes household position Wife of head* 98.04 0.997
Not wife of head 1.96 1.180
(Continued)
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We chose not to calculate a measure of persons per house because this has a negative
relationship with child mortality, which we interpret as inverse causality: families
with few surviving children tended to have less crowded houses at the time the
crowding is measured (Garrett et al. 2001: 1369). This serves as a reminder that this
analysis combines cross-sectional independent variables with an outcome that took
place up to 15 years before the census, and a familys circumstances may well have
changed between the time a child was at risk of death and the census (Reid
et al. 2016).
Wifes work is another variable that may have been particularly subject to change
across the lives of her children. In this era, most women stopped working when they
married or had children, and we interpret a working wife as a sign of poverty, but it
is also possible that child mortality (through low effective parity) enabled a woman
to return to work (Garrett and Reid 1994). The only women who can be identified as
working are those who recorded a paid occupation at the time of the census, and
instead of trying to distinguish different occupations, we have divided women into
those with no recorded occupation (does not work), those who returned an
occupation other than housework or home duties and said that they carried out
that occupation at home, and those who worked outside the home.7We anticipate
that those who were able to take paid work that they could do at home were better
able to combine work with childcare.
We include indicators of husbandsand wivescountries of birth, distinguishing
those born in Ireland, Eastern Europe, and the rest of Europe. Migrants are often
selected for better health or social status, leading to a healthy migrant effect, and we
expect most migrants to have lower mortality. We singled out those from Ireland
and those from Eastern Europe as two interesting and potentially atypical groups.
Migrants from Ireland tended to be low-skilled and poor and therefore might have
had higher mortality. Many migrants from Eastern Europe in this period were
ethnic Jews fleeing persecution and they were therefore less likely to be positively
selected for health than other migrant groups. Nevertheless, we anticipate that they
would have enjoyed a mortality advantage conferred by the high standards of
hygiene that Jewish communities attached to food preparation (Derosas 2003;
Table 1. (Continued )
Variable Category Mean Percentage MI
Housing <3 rooms 27.29 1.440
3-5 rooms* 16.73 1.020
6+rooms 55.53 0.722
Missing 0.45 1.000
Source: I-CeM dataset.
Notes: Couples married for less than 15 years, with valid information on fertility variables (n=2,295,993). MI is the
weighted mean for women in the category (weighted by number of children born (n=6,155,142)).
Categories used as reference categories in regression analysis are identified by *.
7Unfortunately, the census does not distinguish between part-time and full-time paid work.
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Marks 1994) and their receptiveness to modern medical ideas (Riswick et al. 2022).
Other work has suggested that Jewish enclaves in European cities had low mortality
because their relatively closed communities minimized the transfer of infectious
diseases from outside the community (van Poppel et al. 2002).8
Our final individual-level variable is the wifes household position. Here, we
distinguish wives whose husband is not the head of the household, as such couples
may have been disadvantaged by not being able to afford a household of their own.
Our data is multilevel: individual couples are nested within two levels of
geographical area, which are used to examine variation in our multilevel models. We
also assess the extent to which area-level variation can be explained by indicators,
measured at the area levels, that are used as proxies for influences on health. We first
describe British census geography and the areas we use, before explaining the area-
level indicators.
When working with English and Welsh census data we are constrained by time,
effort, and the availability of boundary data to use the geographical units that were
used for census data collection. In this period, the English and Welsh census
geography consisted of a number of nested units. At the highest level, the countries
were divided into regions, with each region containing a variable number of
administrative counties. Each county was divided into RDs: there were 634 of these
across England and Wales in 1911. Each RD contained between 1 and 14, but
typically three to six, RSDs. In 1911, the populations of RSDs ranged from 300 to
over 150,000 inhabitants. The mean population per RSD was around 18,000
individuals, but the distribution was highly skewed, and the modal figure was just
30004000. There were 2009 RSDs in England and Wales in 1911, and each RSD
was itself subdivided into up to 40 enumeration districts (EDs). There were around
35,000 EDs in total, with a mean population of 650, but this disguised a bi-modal
distribution: rural EDs held a mean of 300 people, while urban EDs held 1400
inhabitants on average.9
RSDs are the smallest unit for which the Registrar General published infant
mortality statistics and there have been several useful analyses of infant mortality at
this scale (Williams 1992; Mooney 1994a, 1994b; Sneddon 2006). As already noted,
RSDs could be very large and were therefore internally heterogeneous. Some cities
were covered entirely or mainly by a single RSD. Even RSDs with fairly small
populations could contain a number of diverse areas. Many towns were
amalgamated with areas of surrounding countryside, while larger towns and
smaller cities could be divided into two or more sections, each of which could be
combined with a different part of the surrounding rural area. It is partly due to the
internal heterogeneity of RSDs that we have chosen here to explore EDs as well as
RSDs: the internal diversity of RSDs means that they are less likely than EDs to
capture health effects that operate at relatively small scales. We examine both EDs
8It has also been suggested that Jewish minorities still in Russia had low-infant mortality because they
were relatively well educated (Glavatskaya 2018).
9Another geographic unit identified in the census is parish. We opted not to use parish in this analysis
because although many parishes are small units, they were much more variable in size than EDs, with more
very small (less than 50 people) and more very large (more than 6,000 people). Particularly populous
parishes, usually urban areas which had grown rapidly over the 19th century, were coterminous with RSDs
and are therefore likely to be less good at differentiating local neighborhoods.
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and RSDs because the literature presented above suggested that different influences
on health could operate at different scales and capturing variation at these two levels
allows us to test for the different sorts of influence even where we lack specific data.
We therefore calculate the same area-level variables at both scales to allow us to
judge at which scale they operate. Table 2shows summary statistics for these
variables at both ED and RSD levels.10
In the absence of detailed information about contextual influences on health such
as sanitation, pollution, health services, and social cohesion, our area-level variables
take the form of the percentages of: working men in particular social groups; people
born in Eastern Europe; people born in Ireland; households with servants; in large
houses; and in small houses. Once individual-level social class, housing, and
birthplace are controlled for, these variables can represent the additional contextual
effects of living in particular types of area, as discussed below. We would have liked
to include population density, but although this is available for RSDs, EDs have not
been geographically mapped and therefore their areas are not known. However, in
the light of the facts that more densely populated RSDs tend also to have larger
populations, and (as already noted) urban EDs also tend to have larger populations,
the log of population was used as a proxy for population density. In regressions,
these area-level variables were standardized so that the coefficients represent the
increase in the MI associated with an increase of one standard deviation in the
Table 2. Summary statistics for area level variables
Variable
ED (n=34,130) RSD (n=2,009)
mean st dev mean st dev
Social class 1 (% of men 1564 years) 8.71 9.68 7.34 5.16
Social class 5 (% of men 1564 years) 13.73 10.59 13.15 6.19
Textile workers (% of men 1564 years) 2.55 8.16 2.69 7.93
Miners (% of men 1564 years) 5.69 16.00 6.71 15.45
Agricultural laborers (% of men 1564 years) 10.72 15.63 13.23 13.29
Households with 1 servant (% of households) 3.92 6.21 5.98 3.21
Households with 2 servants (% of households) 1.28 2.68 1.63 1.52
Households with 3+servants (% of households) 1.00 2.65 1.17 1.64
Households with <3 rooms (% of households) 9.28 12.94 8.30 9.49
Households with 6+rooms (% of households) 30.07 20.02 31.95 12.30
Born in Eastern Europe (% of population) 0.17 1.99 0.08 1.17
Born in Ireland (% of population) 0.61 1.78 0.39 1.12
Population (number) 1,056 614 17,954 20,074
Notes: The average number of valid cases per ED is 67 women and 180 children, and 1143 women and 3064 children per
RSD. Source: I-CeM dataset.
10We combine EDs with fewer than 100 people with the preceding or following ED in enumeration order,
ensuring that both were within the same RSD.
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independent variable. In our final models, we also include a dummy variable for
London.
In order to examine the effect of measuring variables at different spatial scales, we
perform three sets of OLS regressions: two-level (household- and ED-level)
regressions with random intercepts for EDs and area-level variables calculated at the
ED level; two-level (household- and RSD-level) regressions with random intercepts
for RSDs and area-level variables calculated at the RSD level; and three-level
(household-, ED-, and RSD-level) regressions with random intercepts for both ED
and RSD levels, and area-level variables calculated for both.11
Each set of regressions consists of 8 models:
Model 0: Null model
Model 1: Control variables (husbandsand wivesages, parity, mortality
reference date)
Model 2: Control variables and husbands social class
Model 3: Control variables and area-level occupational percentages
Model 4: Control variables, husbands social class and area-level occupational
percentages
Model 5: Control variables, husbands social class, and other individual-level
variables
Model 6: As model 5, with area-level variables (except log of population)
Model 7: As model 6, with log of population and dummy variable for London
These models are summarized in Tables A1,A2, and A3 in the online
supplementary material. Table A4 provides the unadjusted coefficients from
models that include each variable (or group of related variables) controlling only for
wifes and husbands age, parity, and mortality reference date. As robustness checks,
we ran versions of models 2 and 5 with fixed effects for EDs and RSDs respectively
(Table A5), but these are not discussed in the text as they are very similar. We also
ran versions of key models for women married for less than 10 years that are neither
shown nor discussed but confirmed our results.
Descriptive results: class and place
We start by presenting some illustrative analyses. Figure 1shows the MI calculated
for the eight social classes aggregated across England and Wales, and for places
classified into eight different types.12 The upper panel indicates a clear mortality
gradient within the five hierarchical social classes: the children of men belonging to
Class 1 professional and managerial occupations had the lowest mortality, while
the children of those in Class 5 unskilled laborers had the highest. The three
singled-out occupational groups also showed distinctive mortality experiences.
11Previous analyses using the MI as an outcome variable compared ordinary least squares (OLS), tobit,
and probit regressions, which all gave very similar results, leading authors to focus on OLS for ease of
interpretation (Garrett et al. 2001: 468-70; Trussell and Preston 1982).
12See www.PopulationsPast.org and Reid et al. (2020) for details of this categorization.
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The children of textile workers and miners had mortality levels just below and just
above those of Class 5, respectively; perhaps higher than would be predicted from
the skill level of those occupations. In contrast, the children of agricultural laborers
had a very low risk of death; again not as predicted by the skill or status level of those
workers.
The lower panel of Figure 1shows that there was also a strong gradient between
different types of place. Agricultural areas and suburbs with relatively high
proportions of professional workers had low child mortality, while places
specializing in mining or the textile industries, as well as other urban industrial
areas, held far higher risks for infants and young children. Miners were, of course,
disproportionately likely to live in mining areas, textile workers in textile areas,
agricultural laborers in agricultural areas, and Class 1 in professional areas. Other
urban and transport areas had high proportions of unskilled laborers. These results
cannot identify whether the place pattern was produced by the concentration of
people of particular social classes into different types of place (composition) or
whether place exerted an independent effect on mortality (context).
Figure 2starts to disentangle the mortality effects of class and place by presenting
the MI for different class and place combinations. Exactly the same data points are
shown in the left- and right-hand panels, but are arranged in a different order: the
left-hand panel groups the social class results within each type of place while the
right-hand panel allows easier comparison of each class across different types of
place. The class gradient within each type of place is clear from the left-hand panel,
but the fact that it does not entirely explain the differences in mortality by type of
place suggests that geographical differences in mortality are likely to be the result of
contextual as well as compositional effects. Within each social class, the differences
in mortality according to residential location were at least as big as the differences
Figure 1. Mortality Index by social class (above) and type of place (below). Source: I-CeM dataset. Notes:
horizontal lines show 95% confidence intervals.
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between the classes within places. The multilevel regressions allow these contextual
effects, and the scale at which they operate, to be explored in more detail.
Contextual results and the effect of scale
One way to examine the effect of scale is to consider the intraclass coefficients
(ICCs), also known as variance partition coefficients, from the multilevel models
(Castelli et al. 2013; Dribe et al. 2017; Monsalves et al. 2020). These show the
proportions of unexplained variation that operate at each level and enable the
detection and quantification of contextual effects.13 Comparisons of the ICCs for the
ED and RSD versions of each two-level model and for the three-level model can
shed light on the scale at which those effects operate. ICCs for the different models
are shown in Figure 3, where each pair of connected dots shows the ICC values for
Figure 2. Mortality Index for social classes within (left) and across (right) types of place. Source: I-CeM
dataset.Notes: horizontal lines show 95% confidence intervals.
13The formula for the ICC is V
a
/(V
a
+V
i
) where V
a
is the variance between areas and V
i
is the variance
between individuals within areas (Merlo et al. 2018).
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RSD (light grey) and ED (dark grey) levels. The upper pair of dots for each model
shows the values for the two-level models while the lower pair shows the values for
the three-level models.
The ICC values shown in Figure 3are low, indicating that the vast majority of the
overall variation in mortality risk cannot be explained by area-level or contextual
factors (at least not variation at the ED or RSD level). However, it is important to
remember that models of mortality at an individual level are rarely able to explain
much variation. Some variation will be attributable to known but unmeasured
influences here these will include sex, birth order, breast-feeding, season of birth,
genetic factors, and the timing of epidemics in relation to the age of child. There is,
however, also a strong element of chance affecting which children die young, and
this will never be picked up in models, even with much more extensive data.14 This
means we do not expect contextual factors to explain high percentages of variation:
we are more interested whether any contextual effects remain after controlling for
individual-level effects, and the scale at which contextual effects operate.
The ICC values for the null model (model 0) effectively identify the extent to
which there is variation between different units, and this shows that there was nearly
twice as much variation in mortality among EDs than among RSDs. This confirms
our hypothesis that smaller areas (EDs) are better at picking up contextual or area-
level effects on early age mortality in the early twentieth century. Nevertheless, the
three-level model, which controls for variation at both ED and RSD levels, shows
that there is independent variation at both these levels: some influences on mortality
act at a broader geographic scale.
The ICCs are reduced by the addition of individual-level variables (indicated by
the difference between model 0 and models 1, 2, and 5) suggesting that a small
amount of the mortality effect of places is produced by the concentration of people
with particular mortality risks in different areas, in other words by the composition
Figure 3. ICC values for different multilevel models. Source: I-CeM dataset.
14As Daniel Scott Smith wrote, Quantitative analysis in history is more relevant and interesting when the
differences between groups are small and the variance that is explained is low than when the opposite situation
occurs. Large differences and a high R2quite often involve relationships that are obvious(Smith 1984:144).
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of those places. Understandably, it is the addition of independent variables
capturing specific characteristics of geographic areas that has the largest effect on
ICCs (captured by the difference between models 0, 1, 2, and 5 that only contain
individual-level independent variables, and models 3, 4, 6, and 7), and we discuss
what these might represent below.
The composition versus context issue can also be examined by comparing the
effects of social class measured at the individual level and at area levels on early age
mortality. This is illustrated in Figure 4which shows the coefficients for individual-
level social class and coefficients for the area-level percentages in classes 1, 5, and the
three special classes. The crude models (light grey dots) show either the individual-
or the area-level variables only (from models 2 and 3 in Tables A1 and A2), and the
adjusted models (dark grey) include both (from model 5 in Tables A1 and A2).15
Figure 4. The effects of social class measured at individual level (Social class 1 to Social class missing)
and area level (% Social class 1 to % Agricultural laborers), comparing area level variation when this is
controlled, and effects are measured, at ED (upper panel) and RSD (lower panel) levels. Note: Crude
coefficients include wifes and husbands ages, parity, mortality reference date and EITHER social class at
individual level OR social class at area level. Adjusted coefficients include ages, parity, mortality reference
date, and social class measured at both individual and area levels: i.e. these are the coefficients from
multilevel models 2, 3, and 4 in Tables A1 and A2. Horizontal lines show 95% confidence intervals.
15These models also include controls for husbands and wifes age, parity, and the mortality reference
date, but no other independent variables at either individual or area scales.
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The crude results show a strong (although a little uneven) gradient in social classes
15, and also an effect of social class measured at the area level. If the area-level
effects were simply the result of the fact that social classes with higher mortality risks
tended to live in certain sorts of place, we would expect the magnitude of the area-
level coefficients to reduce when individual social class was controlled. If,
alternatively, some of the apparent effect of individual social class was due to the
residential sorting of people into places with higher or lower risks so that everyone
in an area, whatever their class, faced the risks associated with that area we would
expect the magnitude of the coefficients for individual-level social class to reduce.
This latter phenomenon is apparent when area effects are measured at the ED level:
individual social class differences are reduced much more than those for area levels,
confirming results using a small subset of this dataset (Reid 1997). In contrast, when
the unit of measurement for contextual variables is RSDs, controlling for the
percentages in social class groups has virtually no impact on the individual-level
social class coefficients, suggesting that any contextual effects that operate at an RSD
level are not closely linked to the occupational composition of the area. In other
words, the scale at which area characteristics are measured is linked to the way that
local environments affect health risks.
It is notable, although unsurprising, that the coefficients for the three special
occupational groups (textile workers, miners, and agricultural laborers) change
most on the addition of area-level indicators. This is largely because such workers
tended to live in places with large occupational concentrations. It is possible that
textile workers and miners incurred a mortality penalty due to high levels of air
pollution, poor waste disposal, or insalubrious housing, although some mining,
textile and railway companies built housing of a relatively high standard for their
workers, so the pathways are not immediately obvious and would merit further
investigation at a local level. In contrast, the coefficient for agricultural laborers
increases in the adjusted model, indicating that much of their mortality advantage
was not because of their own characteristics, but because they tended to live in
benign rural environments where there were fewer local environmental hazards. In
general, rural housing was more basic than that in urban areas, but this was
probably compensated for by low population density which limited the transmission
of both airborne and waterborne diseases. When the contextual effects of places
where agricultural laborers lived are controlled (i.e. compared to others in such
areas), the child mortality of agricultural laborers was more akin to that of other
manual workers.
Pathways from class and place to mortality: the effect of other variables
So far we have only considered the effect of social class measured at the individual
and area levels, but more specific variables can indicate pathways through which
class or context affect the risks of child mortality. The coefficients for these
additional variables, taken from the final three-level multilevel model (model 7 in
Table A3) are shown in Figure 5, together with unadjusted (crude) coefficients from
three-level multilevel models (Table A4). Generally, the results are as expected: the
individual-level categorical indicators tended to be associated with larger effects
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than the contextual variables, and individual-level social class is likely to have
affected child mortality through many of these variables. For example, people of
higher socioeconomic status lived in large houses and had live-in servants, and these
statistically explain some of their mortality advantage. It is possible that these
variables reflected aspects of housing and living circumstances that directly affected
child health, but they could alternatively simply be capturing unmeasured aspects of
resources, status, or knowledge better than social class based on husbands
occupation. Working wives had higher child loss, with a particular disadvantage for
those whose work took them outside the home.16
Results relating to the nativity of a childs parents are particularly interesting. As
expected, most migrant categories (with the exception of those born outside Europe)
tended to have better child survival than natives, and mothers nativity was generally
Figure 5. Influences on child mortality: unadjusted and final coefficients (I-CeM, 3-level multilevel
models). Notes: Crude coefficients include wifes and husbands ages, and parity, and mortality reference
date (from Table A4). Adjusted coefficients include all variables (model 7 of Table A3). Horizontal lines
show 95% confidence intervals.
16As already noted, it is possible there is some reverse causality here.
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more important than that of the father. The advantage for children with parents
from Eastern Europe was particularly large. These migrants tended to live in close
proximity to each other, and at the ED level a higher percentage of Eastern
European migrants indicates lower mortality when other variables are not
controlled. The fact that this effect disappeared in the fully adjusted model
indicates that this was a purely compositional effect: the mortality advantage of
migrants from Eastern Europe was not strongly linked to their residential
concentration or to neighborhood environments, but are more likely to have been
the result of their behavioral and cultural practices, which did not spread to their
locally-born neighbors. In contrast, there does appear to be a contextual effect
associated with Irish-born couples: EDs with higher percentages of Irish-born
tended to have higher mortality. Despite this, and the probable lower skill levels of
the Irish-born, they also demonstrated a healthy migranteffect for their children.
Marks has attributed low infant mortality among both Irish and Jewish mothers in
London to ethnically specific community organizations (Marks 1990).
This paper is particularly interested in the contextual influences on child
mortality. It is notable that some of the area-level variables have a larger effect at the
RSD level (percentage in Class 1, percentage in textiles, both variables measuring
housing size, and population size), while others have a greater effect at the ED level
(percentages in Class 5, miners, agricultural laborers, and those born in Ireland). It
is not obvious how to interpret these differences, but it is possible that the RSD-level
variables, once other variables are controlled, are measuring broad characteristics of
the housing stock and amenities that may operate at a town rather than
neighborhood level. It is notable here that at the RSD level high percentages of both
very small and large houses are associated with lower mortality, and that high
percentages of households with one servant are associated with higher mortality.
More research is needed on the geography of housing provision and amenities to
understand this.
A high percentage of textile workers in an RSD (but not an ED) is associated with
higher child mortality, whereas the percentage of miners indicates high mortality in
an ED (but not an RSD). This could reflect the size of the built-up areas housing
these workers: mining was often concentrated in villages but whole towns were
dominated by the textile industry. These variables, then, might be picking up
facilities or hazards relating to whole textile or mining areas: avenues worth
exploring might be industrial pollution or municipal water supply. In contrast, high
percentages of men working as unskilled laborers (Class 5) are associated with high
mortality in EDs, but (controlling for this and individual-level class) with lower
mortality in RSDs. Within larger areas it seems that there were pockets of local
disadvantage that could be related to uneven implementation of municipal facilities,
which may have come last to the poorest communities within an area.
The log of population, used as a proxy for population density, had a larger effect
when measured for RSDs than when measured for EDs. Although this might reflect
genuine differences in the scale at which population density operated, it is also
possible that population is a less good proxy for population density at ED level than
at RSD level. London was not associated with higher mortality in the full model,
demonstrating that the other variables did a good job of explaining urban
disadvantage.
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Limitations
Historical analysis is constrained by the form and content of data gathered many
years in the past, and this introduces a number of limitations to our study. The
information on early age mortality from the census is derived from the reports by
married women of the numbers of children they had given birth to and had lost to
mortality. We therefore have no information about the characteristics of the
deceased children, such as their sex, birth order, or cause of death, and we cannot
distinguish child- from family-level frailty although this has been shown to be
important (Bengtsson and Dribe 2010). This also means we have no precise
information on how old the children were when they died, and we are therefore
unable to identify which influences act on mortality at different stages of infancy
and childhood, although as explained in the methods section, our measure of
mortality is strongly weighted towards infancy.
There is also an issue related to the fact that census data are cross-sectional, but
the information on child survival is retrospective. This means that the children of
women who had moved into an area shortly before the 1911 census may have lived
for the bulk of their lives elsewhere, and therefore the area characteristics attributed
to them may be inappropriate an issue which might particularly impact migrants,
urban dwellers and the lower classes (OCampo 2003; Reid et al. 2016). This issue
could be overcome by limiting the analysis to married couples who had been present
in an area throughout their childbearing lives, but the information needed to
determine that is not available. It would be shortsighted to exclude migrants,
however, as both short- and long-distance movements affect the population
composition of areas of origin and destination. Our paper considers only
international migration, but future research could investigate ways in which the
characteristics of areas acted as pull and push factors, driving migration which, in
turn, further shaped these areas and the health outcomes of people living
within them.
Further limitations arising from the census are that information on child survival
is only given for married women, and that we have only used married women who
could be linked programmatically to their husband in the same household.
Illegitimate children are thus automatically excluded, as are those with at least one
parent dead or living elsewhere, all of whom are likely to have been more vulnerable
to mortality, and arguably more sensitive to their local circumstances. Nevertheless,
there is currently no other systematic individual-level mortality data available for
research on this period in England and Wales, so the data used here make a very
important contribution to our knowledge of the influences on mortality in this era
of British history.
Other limitations of our study relate to spatial issues. Firstly, without precise
geocoding of individuals we were unable to define egocentric neighborhoods which
might be best able to capture spatial effects (Xu et al. 2014). Secondly, any spatial
autocorrelation between neighboring areas in our data might have the effect of over-
estimating the significance levels of spatial effects (Manley et al. 2006; Xu et al.
2014). Although previous work on the data set used here established that spatial
autocorrelation at the RSD level was not significant (Jaadla and Reid 2017), Xu et al.
(2014) found that spatial autocorrelation mainly operates at a very small scale. We
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were not able to investigate this as it was beyond the scope of our project to map the
boundaries of all EDs in England and Wales.
The fact that we wanted to investigate the whole of England and Wales at a small
spatial level means that we were very limited in the independent variables available
to us. While sources such as Medical Officer of Health reports can provide quite
detailed information about a variety of local conditions (including water supply,
sanitation, street cleaning and waste disposal, paving, and local nuisances) these
were not produced in a standard format, and their survival is not uniform. More
systematic data reflecting the installation of municipal services such as water supply
and sewage systems is usually available only for large spatial units such as cities. In
addition, the indicators available, such as the value of loans, often do not translate
directly into facilities and are rarely available for units that match up with mortality
statistics (Harris and Helgertz 2019). We were therefore constrained to use
measures that we could calculate from the census itself. Despite the difficulties in
working out which proximate determinants, or mechanisms of mortality causation,
these represent, the multi-level structure has provided a useful overview and allowed
the identification of some fruitful avenues for future research. In particular, further
investigation to determine which contextual factors are important for infant and
child mortality would benefit from detailed local studies as these would allow a
wider variety of influences to be considered at fine spatial scales.
Conclusions
Our paper has undertaken a series of multi-level models of infant and child
mortality in England and Wales in the early twentieth century. We have at least
partially confirmed the findings of an earlier paper (Reid 1997) that argued that part
of the infant mortality advantage for the higher social classes in this period could be
attributed not to superior knowledge, attitudes, or the ability to purchase better
health care or food, but to the opportunity to live in a more salubrious environment
with fewer environmental hazards and better local amenities. In other words, places
with high mortality did not have high mortality simply because they contained a lot
of people whose individual characteristics put them at risk, but also because of
various contextual aspects of those places. In common with other investigations into
the effects of composition and context, we found that although there was a clear
effect of geographic context on mortality, most variation in mortality risks operated
at the individual level, but was not purely attributable to social class.
Our comparison of the contextual effects at different spatial levels indicated that
these operated not only at the ED, or neighborhood, level but also at the larger RSD
level, and that these were often mediated by different variables. Housing stock and
population (density) affected mortality when measured across large, but not small
areas, whereas relatively large proportions of unskilled laborers were associated with
higher mortality only at the smaller spatial scale. More research is needed into how
to interpret these indicators, but investigation of facilities such as water supply,
sanitation, paving, street cleaning, and waste disposal and how these were
implemented variously across districts within particular areas may be helpful.
Fatal Places? Contextual Effects on Child Mortality 23
https://doi.org/10.1017/ssh.2023.5 Published online by Cambridge University Press
The fact that the percentages of various social and occupation groups were still
significant as contextual indicators, particularly at the smallest local scale, even after
these were controlled at an individual level, indicates that the composition of a
community might itself influence the context. This could operate through aspects such
as local cohesion or community influence over local decisions and spending. These can
impact the availability and standard of facilities and resources which in turn will
influence rents and house prices which can be an important mechanism for sorting, or
selecting, people into areas, making context and composition both conceptually and
practically deeply entwined. In this period, as the wealthy moved out to healthy
residential areas with higher rents, the poor were left in the areas with more
environmental hazards and poorer facilities. Better-off people were able to lobby for the
early introduction of new amenities, which therefore came later to the areas where the
poor lived. Less good facilities kept rents comparatively low, making such areas more
attractive to those with smaller budgets, thereby sortingthe poor into such areas.
The reality of the social class influences on early age mortality in the early
twentieth century in England and Wales is far more complicated than is suggested
by Preston and Hainess(1991) pronouncement that, in terms of the determinants
of mortality differentials, social class represented in Britain what race represented in
the USA. At a basic level, the use of the Registrar Generals eight category
classification, which separated out three large and anomalous occupational groups,
artificially amplifies the gradient in the five hierarchical classes. This paper has also
demonstrated that one of the main ways that class affected mortality was through
the sorting of different people into areas that exposed them to different risks, but
these areas were themselves molded by class.
Supplementary material. The supplementary material for this article can be found at https://doi.org/10.
1017/ssh.2023.5.
Acknowledgements. We are grateful to funding received from the Economic and Social Research Council,
UK (projects An Atlas of Victorian Fertility DeclineES/L015463/1 and Britains first demographic
transition: an integrated geographyES/S016805/1 both with PI Alice Reid, Cambridge Group for the
History of Population and Social Structure).
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Kevin Schurer is a Principal Research Associate at the Cambridge Group for the History of Population and
Social Structure, University of Cambridge. He is a Fellow of the Academy of Social Sciences and before
returning to Cambridge worked as Director of the UK Data Archive (University of Essex) and as Pro-Vice-
Chancellor for Research (University of Leicester). He has published widely on a range of topics within
historical demography, migration and household structure and is currently preparing a digital version of the
1921 census returns of England and Wales for future research.
Alice Reid is Professor of Demography at the Department of Geography, University of Cambridge, UK, and
Co-Director of the Cambridge Group for the History of Population and Social Structure. She specialises in
the demography of the British Isles in the 19th and early 20th centuries and has recently published on
maternal mortality (Annales de Demographie Historique, Social Science and Medicine), infant and child
mortality (Historical Life Course Studies, Demographic Research), fertility (Population Studies,
Demography) and the value of a long-term perspective in demography (Population Studies).
Hannaliis Jaadla is a senior research associate at the Cambridge Group for the History of Population and
Social Structure, University of Cambridge and a research fellow at the Estonian Institute for Population
Studies, Tallinn University. Her research broadly addresses the dynamics and mechanisms of demographic
change in historical context and has recently published on social inequalities in health and mortality (The
Economic History Review and Population Studies) and on fertility transition (Population Studies,
Demography, Spatial Demography).
Eilidh Garrett is currently a Senior Researcher at the Scottish Centre for Administrative Data Research
(SCADR), University of Edinburgh. Her research interests lie in the 19th and 20th century historical
demography of the British Isles. She has extensive experience of working with civil registers, large-scale,
individual-level census data and published statistics. Her recent publications include co-authored articles in
Populations Studies, International Journal for Population Data Science, and Historical Life Course Studies.
Sarah Rafferty is a Senior Research Officer at the UK Office for National Statistics, currently working on
health and pandemics. Additionally, Sarah recently defended her PhD thesis on infant and child mortality in
London (18961910) at the Cambridge Group for the History of Population and Social Structure, University
of Cambridge. She has published on historical demography and infectious diseases in Local Population
Studies, the Journal of Historical Geography and Social Science and Medicine.
Cite this article: Reid, Alice, Eilidh Garrett, Hannaliis Jaadla, Kevin Schürer, and Sarah Rafferty (2023)
Fatal Places? Contextual Effects on Infant and Child Mortality in Early Twentieth Century England and
Wales,Social Science History. doi:10.1017/ssh.2023.5
28 Social Science History
https://doi.org/10.1017/ssh.2023.5 Published online by Cambridge University Press
... There was a substantial amount of difference in the probability of surviving at birth, and this variation is dependent on the geographical location during which a kid was born. According to Wei et al.'s research from 2020, the region with the greatest infant mortality rate was Sub-Saharan Africa, which had a rate of 27 (25)(26)(27)(28)(29)(30)(31)(32) deaths per 1000 live births. Central and southern Asia came in a close second with a rate of 23 (21)(22)(23)(24)(25) deaths per 1000 live births. ...
... Congenital defects, deformities, and chromosomal abnormalities were the leading causes of mortality for children and infants ranging from 28 days to 15 years old in 2021. "Coronavirus (COVID-19)" was the root cause of 32 baby and child fatalities in 2021, ranging in age from 28 days to 15 years (Reid et al., 2023). ...
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This study draws on London's long experience with air pollution in order to improve our understanding of the overall effects of pollution exposure and how and why these effects evolve as locations develop. I compare uniquely detailed new mortality data covering 1866-1965 to the timing of London's famous fog events, which trapped emissions in the city. I show that air pollution was a major contributor to mortality in London over this period and that it interacted strongly with specific infectious diseases. As a consequence of this interaction, reductions in the infectious disease burden substantially altered the health costs of pollution.
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Although pollutant sources are often assumed to be spatially uniform, traffic in real cities may vary significantly in space. Consequently the local air quality within a street may not be determined solely by the traffic volume of the street. Using building-resolving large-eddy simulation, the relationship between traffic volume and air quality is investigated in the context of two idealised problems: (i) the influence of pollutants emitted from a main road on the surrounding side streets and (ii) the pedestrianisation of a central thoroughfare. It is shown that the spatial variation of traffic volume is of crucial importance within a near-field region defined by a radius of homogenisation (RAD). Furthermore, the actual impact depends strongly on the wind direction. Hence the benefits of pedestrianisation may be limited: for example, after removing 100% of the traffic along a street in a central business district, the annual-averaged local concentration decreases by ∼30% when the urban background is neglected. The impact may be significantly lower when the background concentration is considered. This work is relevant to the formulation of effective traffic control policy and the improved understanding of spatially inhomogeneous pollutant sources.
Book
This volume is an important study in demographic history. It draws on the individual returns from the 1891, 1901 and 1911 censuses of England and Wales, to which Garrett, Reid, Schürer and Szreter were permitted access ahead of scheduled release dates. Using the responses of the inhabitants of thirteen communities to the special questions included in the 1911 'fertility' census, they consider the interactions between the social, economic and physical environments in which people lived and their family-building experience and behaviour. Techniques and approaches based in demography, history and geography enable the authors to re-examine the declines in infant mortality and marital fertility which occurred at the turn of the twentieth century. Comparisons are drawn within and between white-collar, agricultural and industrial communities, and the analyses, conducted at both local and national level, lead to conclusions which challenge both contemporary and current orthodoxies.
Book
This book offers an original interpretation of the history of falling fertilities in Britain between 1860 and 1940. It integrates the approaches of the social sciences and of demographic, feminist, and labour history with intellectual, social, and political history. It exposes the conceptual and statistical inadequacies of the orthodox picture of a national, unitary class-differential fertility decline, and presents an entirely new analysis of the famous 1911 fertility census of England and Wales. Surprising and important findings emerge concerning the principal methods of birth control: births were spaced from early on in marriage; and sexual abstinence by married couples was a far more significant practice than previously imagined. The author presents a new general approach to the study of fertility change, raising central issues concerning the relationship between history and social science.