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Birth weight and environmental heat load A between-population analysis.

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Birth Weight and Environmental Heat Load:
A Between-Population Analysis
Jonathan C.K. Wells1* and Tim J. Cole2
MRC Childhood Nutrition Research Centre, Institute of Child Health, London WC1N 1EH, UK
Centre for Paediatric Epidemiology and Biostatistics, Institute of Child Health, London WC1N 1EH, UK
temperature; thermoregulation; fetal growth
Low birth weight, a major cause of infant
morbidity and mortality, is caused by different factors in
Western and developing-country populations. In addition
to differing in terms of ethnicity, maternal size, maternal
nutritional status, and disease load, developing-country
and Western populations are also characterized by different environmental heat loads. Thermodynamic theory
predicts that heat stress is mitigated by reduced size of
both mother and offspring, and therefore generates the
hypothesis that reduced birth weight may be an adaptation to environmental heat load. The aim of this study was
to test the hypothesis that environmental heat load is
associated with between-population variability in birth
weight. Data on birth weight and thermal environment
were obtained from the literature for 140 populations.
Low birth weight is a major cause of infant morbidity and mortality, both in developed and developing countries, and is a major contributing factor to
the approximately 9 million infant deaths that occur
worldwide annually (World Health Organization
(WHO), 1992). The incidence of low birth weight,
classified by the WHO (1992) as weight less than
2,500 g, is the product of incidences of preterm birth
and intrauterine growth retardation. The proportion
of low birth weights attributable to these factors
differs markedly between the developing and developed worlds, with most low birth weights in developing countries arising from intrauterine growth retardation.
A number of factors are associated with low birth
weight in developing countries, including ethnicity,
low energy intake, low pre-pregnancy weight, short
stature, malaria, and general morbidity (Kramer,
1987; WHO, 1992). This epidemiology differs in several ways from that of developed countries, where
preterm birth represents a significant proportion of
low birth weights, and cigarette smoking is an important additional risk factor. The incidence of low
birth weight is highest in Asia, where it exceeds 20%
of all births, but it is also high in Africa (15%) and
Latin America (10%) (WHO, 1992). In southern
Asia, approximately 1 in 3 liveborn infants is underweight (WHO, 1992), whereas in Southeast Asia and
Africa, the rate of low birth weights is closer to 1 in
Further data on several physical, social, and biological
confounders were also collated. After adjusting for confounding factors, of which only maternal height and per
capita gross domestic product were statistically significant, heat stress showed a significant inverse relationship
with birth weight in 108 populations, accounting for an
additional 9.6% of the between-population variance.
Though based on data collected from more than one
source, these results are consistent with the hypothesis
that heat stress is inversely associated with birth weight,
as previously reported for within-population studies. Further studies are needed to establish to what extent heat
stress is a determinant of low birth weight in developing
countries. Am J Phys Anthropol 119:276 –282, 2002.
2002 Wiley-Liss, Inc.
6. It has proved difficult to explain this regional
disparity purely in terms of the factors listed above.
Following earlier observations of an association
between climate and birth weight (Roberts, 1978),
thermal stress has been proposed as an additional
potential cause of intrauterine growth retardation
in developing countries (Wells, 2002). Heat stress is
assumed to have played an important role in determining size and shape throughout hominid evolution (Ruff, 1994), due to the fundamental relationship between mammalian physiology and the
thermal environment.
Thermal stress is the product of internal heat
production, capacity for heat loss to the environment, and environmental heat load. Core body temperature must be regulated within narrow limits to
avoid significant morbidity or mortality, and various
aspects of human morphology and physiology are
attributable to thermoregulatory requirements.
*Correspondence to: Jonathan C.K. Wells, MRC Childhood Nutrition Research Centre, Institute of Child Health, 30 Guilford St.,
London WC1N 1EH, UK. E-mail:
Received 30 August 2001; accepted 22 February 2002.
DOI 10.1002/ajpa.10137
Published online in Wiley InterScience (www.interscience.wiley.
Classical rules describing a relationship between
body size and climate were presented in the 19th
century (Bergman, 1847; Allen, 1877), and inspired
subsequent interest in the role of heat stress in
natural selection of body size and physique (Roberts,
1953, 1978; Newman, 1953; Schreider, 1964; Crognier, 1981; Froment and Hiernaux, 1984). Most authors focused on adults, due to the large quantity of
data available, and reported that heat stress was
positively related to the ratio of surface area to
mass, or proxy indices of this ratio. For example,
humans conform both to the rule of Bergman (1847)
in showing a negative association between body
mass and mean annual temperature, and also to the
rule of Allen (1877) in having relatively shorter limb
lengths in colder climates (Roberts, 1953).
Recently, such relationships were reanalyzed in
order to assess the effect of secular trends in body
size. The slopes of regressions between indices of
size and temperature are reported to be more modest in contemporary populations (Katzmarzyk and
Leonard, 1998) compared to those studied a half
century previously (Roberts, 1953). However, this
finding does not negate the role of climate in selecting physique. Ruff (1994) reported that body breadth
varies much less than stature, such that the effect of
increasing stature is to increase linearity. Thus, although changes in body weight may alter the surface
area to mass ratio, adaptive physique may remain
preserved in other dimensions that are less sensitive
to nutritional influences. Other factors which may
confound the temperature/size relationship include
altitude (Stinson, 1990), while the strength of associations may also differ between the sexes (Roberts,
1978; Stinson, 1990).
In addition to basic physical aspects of size, several other aspects of human physiology contribute to
the mitigation of heat stress. These include differential sweat gland distribution (Millington and
Wilkinson, 1983), relative blood flow (Hanna and
Brown, 1983), and variation in the level of heat
production at rest (Roberts, 1978; Hayter and
Henry, 1994). Behavior may also influence both heat
stress and heat tolerance. Thus the human body
may be considered a thermodynamic system, with
populations demonstrating adaptation of both physical and physiological properties of the system in
relation to the thermal environment.
The majority of work on human form and heat
stress has been conducted on adults, with infants
and children discussed only rarely (Roberts, 1978;
Hanna and Brown, 1983). Pregnancy represents a
particularly interesting aspect of this general relationship, due to the fact that mother and offspring
are characterized by genetic conflict (Trivers, 1974).
Both mother and offspring are subject to heat stress,
and generate heat in relation to their size and body
composition. “Mitigation of heat stress” may be considered a resource subject to competition, similar to
the conflict over nutritional resources (Haig, 1993)
and indeed influencing its outcome.
In hot environments, the physiological changes
that accompany pregnancy are predicted to exacerbate maternal heat stress for several reasons. First,
fat deposition increases the specific heat of the body,
such that the increase in core body temperature due
to a given thermal stress is higher in women with
greater adiposity (Falk, 1998). Second, the change in
body weight and shape decreases the ratio of surface
area to body mass, thereby reducing the ability of
the body to lose heat to the environment by sweating
(Wells, 2002). Third, the combination of weight gain
and fetal growth increases basal heat production,
although less so in populations inhabiting the tropics than those in temperate climates (Prentice et al.,
1989). Fourth, the interaction between increased
body mass and physical activity may further exacerbate heat production (Wells, 2002). Finally, the
fetus itself also contributes to maternal heat stress
through its body composition and metabolic rate
(Wells, 2002).
For these theoretical reasons, heat stress has been
proposed as a possible factor associated with intrauterine growth retardation in hot environments
(Wells, 2002). There are two ways in which heat
stress might affect birth weight. Chronic heat stress
during pregnancy might exert marked effects on
fetal growth, and might be a major determinant of
intrauterine growth retardation in individuals. Alternatively, heat stress might exert small effects on
individual infants, but nevertheless cause a slight
shift downwards in the spectrum of birth weight,
thus increasing the prevalence of intrauterine
growth retardation in the population as a whole.
The aim of the present paper is to consider
whether the hypothesis that environmental heat
load and birth weight are associated is supported by
data from a wide range of human populations.
Data on birth weight were obtained from the
WHO global database (WHO, 1992). This database
summarizes data from the medical literature on
birth weight, the prevalence of intrauterine growth
retardation and preterm birth, sample size, and year
of study. The heterogeneous nature of the literature
means that not all data are equivalent. For example,
some reports refer to total births, whether live or
stillborn, or single or multiple births. Other reports
variously list all single births whether live or stillborn, all live births whether single or multiple, or all
live singleton births. For a significant proportion of
the data, no such information is available.
For the present study, the database was initially
constructed by selecting the optimum data for each
population. The first criterion was that populationspecific data on birth weight and thermal environment must be available. Secondly, the population
must comprise at least 200 individuals. Thirdly,
data were selected in the order of preference of 1)
live singleton births, 2) all live births, 3) single
births whether live or stillborn, 4) no data available,
and 5) total births. Where more than one data set
was available for a given population, preference was
given firstly to larger sample sizes and secondly to
more recent reports.
Data on thermal environments were obtained
from the Hutchinson World Weather Guide (Pearce
and Smith, 1998), which lists mean temperature
and humidity for various locations in the world’s
countries. Heat stress is a combination of environmental temperature and the water content of the
air, which influences the ability to lose heat by
sweating. Humidity is extremely important in estimating heat stress: at higher temperatures, when
the environment is hotter than the body, alternative
paths of heat loss such as convection and radiation
cease to operate, and sweating is the only possible
means of losing heat (Dubois, 1937).
Many environments are characterized by seasonality, with heat stress peaking during hot and humid seasons and decreasing during cooler, drier periods. For between-population comparisons, it is
therefore necessary to derive an index representing
average annual thermal load. Two approaches were
Firstly, monthly values for heat stress were calculated using the data on average maximum daily
temperature and afternoon humidity. Temperatures
were corrected for humidity, using the heat index
published by the US Meteorological Bureau (Schneider, 1996). These monthly values were then averaged to give a mean humidity-adjusted temperature
per year. The second approach involved a simpler
calculation (Pearce and Smith, 1998). For each location, the weather guide provides a monthly rating of
discomfort arising from the combined stress of temperature and humidity. The categories were none,
moderate, medium, high, extreme, and dangerous. A
schematic diagram, showing how data on temperature and humidity were assigned their scale value,
is given in Figure 1. The categories were given a
numeric score of 0 –5, and a mean monthly score was
To compare the two indices of thermal stress, 10
locations selected randomly from the guide were
assessed by both approaches. The two indices were
very highly correlated (r ⫽ 0.97), indicating that the
simpler method is a valid index of heat load. This
simpler method was therefore used in the present
study, and was termed “heat index.”
Data on latitude and altitude were likewise obtained from the same weather guide (Pearce and
Smith, 1998). All latitude data were expressed as
positive values, and thus represented distance from
the equator, irrespective of hemisphere. Data on
maternal height were taken from the tables published by James and Schofield (1985).
Data on socioeconomic status (per capita gross
domestic product), average daily energy intake, and
infant mortality were obtained from the World Bank
(1990). This year was selected, as the birth weight
data were collected prior to 1992. Data on gross
Fig. 1. Schematic diagram indicating how monthly data on
temperature and humidity were converted into a simple index of
thermal stress. Monthly values were then combined to provide a
mean annual index, termed “heat index.”
domestic product represented a 3-year average, designed to smooth out fluctuations in prices and exchange rates in each country. It should be borne in
mind that the calculated values, in US dollars, do
not represent local purchasing power. However,
published data on purchasing power parity were not
available. Per capita dietary energy intakes were
calculated from data on food supplies from the Food
and Agriculture Organization, divided by the size of
the population. Data on infant mortality, calculated
per 1,000 liveborn births up to age 1 year, were used
as a general index of environmental disease load. All
data for confounding factors necessarily refer to the
total population, either national or regional, and in
the present analysis it is assumed that they were
representative of the sample of mothers providing
the birth weight data.
All data were transformed where necessary to
achieve normality. Birth weight, gross domestic
product, and altitude were natural log-transformed,
latitude and mortality were transformed to square
roots, and heat index and maternal height were
unadjusted. In addition, the log-transformed variables were multiplied by 100 to give regression coefficients in percentage units (Cole, 2000).
Comparable data on birth weight and temperature were available for a total of 140 populations.
The median sample size was 5,558 infants, with only
20 samples comprising ⬍1,000 births. However, due
to the effects of a small number of very large samples, the average sample size was 97,237. The numbers of populations per birth weight data category
TABLE 1. Mean birth weight (g) by global region
No. countries
Central and South America
Western industrialized1
Europe, North America, Australia, and New Zealand.
TABLE 2. Description of independent variables for 108
populations used in final model
Standard deviation
Altitude (m)
Latitude (degrees)1
Mortality index2
Energy intake (kcal/day)
Gross domestic product
Maternal height (cm)
Heat index (units)3
Degrees north or south from equator.
Deaths per 1,000 live births to 1 year of age.
See text for calculation of index.
Fig. 2. Relationship between average annual heat index and
average birth weight in 140 human populations, using birth
weight data from WHO (1992) and heat index values calculated
from data in Pearce and Smith (1998).
were as follows: total births, 14; no description available, 35; single births, 13; live births, 49; and live
single births, 29.
Mean birth weight by global region is given in
Table 1. Birth weight in Asian and African populations was slightly lower than that in Central and
South American populations, but this difference did
not reach significance. All three non-Western regions had highly significantly lower mean birth
weight than the Western group of populations (P ⬍
0.0001). A further 10 populations were excluded
from Table 1 due to their not being readily included
in one of these four groups. Descriptive data for the
independent variables are presented in Table 2.
The relationship between heat index and population mean birth weight for 140 populations is shown
in Figure 2. There was a significant correlation of
⫺0.59 (p ⬍ 0.001) between variables. Correlations
between all variables considered in the analysis are
given in Table 3.
With one exception (altitude and birth weight),
every variable was significantly correlated with every other variable, indicating the complexity of associations between geographic factors, size, socioeconomic factors, and indices of health. Heat index was
negatively related to maternal size, latitude, gross
domestic product, and energy intake, and positively
related to infant mortality. There was a strong inverse correlation between energy intake and infant
Stepwise regression analysis was conducted to obtain the best model for predicting birth weight, using the 108 populations with available data as appropriate. The initial regression model included
gross domestic product, energy intake, infant mortality, and maternal height. Of these, only gross
domestic product and maternal height were significant. Latitude and altitude were then added, with
only latitude being significant. Finally, heat index
was added, at which point latitude became insignificant. Heat index was more significant than the
other two predictors, and its addition to the model
increased R2, the proportion of variance explained,
from 46.1% to 55.7%. The initial, intermediate, and
final regression models for the 108 populations with
appropriate data are given in Table 4. The regression statistics indicate that a 1-cm increase in maternal height and a 1-unit decrease in heat index are
associated with 0.6% and 2.7% increases, respectively, in birth weight.
The same analysis was repeated with the sample
restricted to the 65 populations in which birth
weight data referred to live births only. The findings
were similar to those for the whole sample, except
that gross domestic product was no longer significant. The addition of heat index to the model increased R2 from 36.9% to 50.2% (see Table 5).
Mean birth weight is highly variable between
human populations, despite the strong relationship between birth weight and subsequent health.
The range in the present study was from 2,429 g in
Bangladesh to 3,632 g in South Korea. There is
little evidence for genetic factors influencing birth
weight (Clausson et al., 2000), although within a
single population, small but significant differences
between ethnic groups were found after adjusting
for maternal anthropometric and socioeconomic
variables (Shiono et al., 1986). Environmental factors are thought to be responsible for the vast
majority of between-population variabilities in
birth weight (Kramer, 1987; WHO, 1992), and the
purpose of the present study was to investigate
whether environmental heat load, previously not
considered in the context of health, might be a
further factor.
In the present study, data were gathered from
several different sources. The tabulation of global
data on birth weight by WHO (1992) represents an
TABLE 3. Correlation coefficients for relationships between variables included in analysis1
Heat index
Energy intake
Infant mortality
Maternal height
Birth weight
Heat index
Energy intake
Infant mortality
All correlations above 0.16 significant, p ⬍ 0.05.
TABLE 4. Stepwise regression of birth weight (%) in 108 populations
Regression coefficient
Standard error
1.2 * 10⫺3
4.8 * 10⫺3
9.17 * 10⫺3
Initial Model
GDP (%)
Energy intake
Infant mortality
Maternal height
R2 ⫽ 47.0%
Intermediate model
GDP (%)
Maternal height
R2 ⫽ 48.0%
Final model
GDP (%)
Maternal height (cm)
Heat index
R2 ⫽ 55.7%.
GDP, gross domestic product per capita.
TABLE 5. Regression of birth weight (%) in 65 populations where birth weight data refer to live births only
Regression coefficient
Standard error
Maternal height (cm)
Heat index
R2 ⫽ 50.2%
important database for research, but fully comparable data for the world’s nations have never been
published. The data utilized were therefore heterogeneous, referring variously to all births, live births,
single births, or single live births. Similarly, birth
weight data could only be matched against other
data relevant to their general population, rather
than the sample of mothers giving birth. For some
smaller populations, data on maternal height, gross
domestic product, energy intake, and infant mortality were unavailable. Despite these shortcomings, a
relatively large sample of data points was available
for analysis, and the results were similar whether
all birth weight data were included, or just those
referring to live births.
Data on both birth weight and heat stress were
reduced to an annual average value, and thus ignored seasonal variation in climate. If births are
evenly spaced throughout the year, the use of such
averaged data will introduce minimal bias into the
model. However, if birth frequency follows a seasonal pattern, then the averaged values will be biased. For example, data from Gambia suggest that
births are seasonally regulated, with fewer conceptions occurring in the hungry season, and hence
fewer births occur in the harvest season (Prentice
and Cole, 1994). Similar birth rate seasonality was
reported in Lesotho (Johnston, 1993). In practice,
however, the bias due to seasonality of birth frequency is likely to be small.
Maternal size, pre-pregnancy nutritional status,
and pregnancy weight gain are well-established determinants of birth weight. Energy intake during
pregnancy might be assumed to be particularly important, and one possible explanation as to why
energy intake was not significant in our model is
that our intake data are simply too approximate,
being estimated for all adults rather than specifi-
cally for mothers during pregnancy. However, average per capita intakes are still likely to rank populations with reasonable accuracy, and our results
are in agreement with previous research reporting
that maternal intake during pregnancy had little
impact on birth weight except in famine conditions
(Susser, 1991). Likewise, intervention studies have
failed to increase birth weight substantially through
nutritional supplementation during the third trimester (Ceesay et al., 1997). Maternal height exerted a strong effect on birth weight, as is wellestablished (Kramer, 1987), but data on maternal
nutritional status and pregnancy weight gain could
not be incorporated in the present study. Maternal
height alone accounted for a third of the betweenpopulation variance in birth weight in our analysis,
but is itself inversely related to heat stress (r ⫽
⫺0.48; p ⬍ 0.001).
The contribution of gross domestic product to the
model was significant, but that of infant mortality
index, used as a proxy for maternal disease load
during pregnancy, was not. Together, gross domestic
product and maternal height explained 46.1% of the
between-population variance in birth weight. The
addition of latitude increased this only to 47.6%, and
it no longer contributed significantly to the model
once heat index was added. After taking into account gross domestic product and maternal height,
heat index explained an additional 9.6% of the between-population variance in mean birth weight,
with the effect highly significant.
Almost half the between-population variance in
birth weight was not explained by the predictors in
our model. Several factors closely associated with
low birth weight in developing countries could not be
included in the analysis, including ethnic group,
maternal nutritional status, pregnancy weight gain,
and malaria status. The heat index used in the analysis might act as a proxy for one or more of these
variables. However, each of these missing factors is
predicted to be influenced by heat stress. For example, theoretical models predict that greater heat load
should favor lower body fatness and reduced pregnancy weight gain (Wells, 2002). Malaria may increase heat production through fever. Ethnic differences in body size have also been linked to
environmental temperature (Roberts, 1953; Newman, 1953; Schreider, 1964; Hiernaux, 1974; Crognier, 1981; Froment and Hiernaux, 1984), although
both sexual selection and adaptation to marginal
resources are plausible alternative explanations for
such variation in size. Our analysis indicates that
both maternal size and, independently of that factor,
infant birth weight are inversely associated with
thermal stress.
Although necessarily preliminary in nature, our
analysis fails to refute the hypothesis that heat
stress is a significant determinant of population
variability in birth weight. Consistent with theoretical models, increased heat stress is associated with
reduced birth weight. The issue now requires further investigation using more comprehensive data
sets, in particular to determine if the relationship is
causal. If supported, the hypothesis could help explain the paradox of particularly low birth rates in
the humid environments of Asia.
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