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Effect of the interaction between high altitude and socioeconomic factors on birth weight in a large sample from South America.

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AMERICAN JOURNAL OF PHYSICAL ANTHROPOLOGY 129:305–310 (2006)
Effect of the Interaction Between High Altitude and
Socioeconomic Factors on Birth Weight in a Large
Sample From South America
Jorge S. López Camelo,1,2* Hebe Campaña,1 Rita Santos,1 and Fernando A. Poletta1,2
1
Latin-American Collaborative Study of Congenital Malformations (ECLAMC), Instituto Multidisciplinario
de Biologia Celular (IMBICE), 1900 La Plata, Argentina
2
Latin-American Collaborative Study of Congenital Malformations (ECLAMC), Dirección de Investigación,
Centro de Educación Médica e Investigaciones Clı́nica (CEMIC), 1431 Buenos Aires, Argentina
KEY WORDS
altitude; birth weight; interaction risk; socioeconomic level
ABSTRACT
Several studies of South American populations showed that altitude is associated with low birth
weight and intrauterine growth retardation. Although
some of them analyzed the altitude-birth weight association, only a few assessed the effect of the interaction
between altitude and socioeconomic factors on birth
weight. The purpose of this research is to evaluate such
effects on birth weight, after adjustment for confounding
factors. This observational descriptive study includes a
sample of 37,022 live-born infants without congenital
anomalies, weighing 500 g, from 75 South American
maternity hospitals, during 1982–1999. Of the total sample, 1,187 infants were born in two South American
cities located at more than 2,000-m altitude: La Paz,
According to a report by UNICEF (1994), 12% of live-born
infants have low birth weight (LBW, less than 2,500 g), and
one third suffer from some type of brain damage. Furthermore, epidemiological studies revealed that low birth weight
significantly contributes to perinatal mortality (Morrison
and Olsen, 1985; Wilcox, 1993). Ten percent of LBW infants
are stillborn, while the stillbirth rate among infants weighing more than 2,500 g is 1:154. Social factors such as maternal malnutrition (Haas et al., 1980; Beall, 1981a; DelgadoRodrı́guez et al., 1998; Dutra et al., 1993) or biological factors such as low maternal age (Fraser et al., 1995; Dutra
et al., 1993) are known to decrease birth weight.
Several studies on South American populations showed
that altitude is associated with low birth weight and
reduced growth rate during childhood. Based on a review
of the literature from the last 35 years, at least 70 publications reported data on the weight of infants born at high
altitude (Moore, 2003). When compared with neonates
born at sea level, those from higher altitudes (>2,000 m)
showed a 2–3-fold increase in LBW rate, mainly related to
a higher incidence of intrauterine growth retardation
(IUGR) (Haas et al., 1977; Beall, 1981a; Yip, 1987; Dutra
et al., 1993; Jensen and Moore, 1997; Mortola et al., 2000;
Keyes et al., 2003).
However, demographic differences between lowland and
highland populations, such as racial admixture, income,
and availability of social security, among others, interfere
when correlating altitude and birth weight. Hypertension
during pregnancy or preeclampsia (Arias and Tomich,
1982; Keyes et al., 2003), primiparity (Sanjose and
Roman, 1991), low maternal age (Fraser et al., 1995) and
social level (Wilcox et al., 1995), lack of prenatal care
C 2005
V
WILEY-LISS, INC.
Bolivia (N ¼ 974 at 3,600 m) and Bogota, Colombia (N ¼
274 at 2,600 m). Among the seven risk factors analyzed,
altitude was the main predictor of birth weight (except
for gestational age). After adjustment for the other risk
factors, birth weight at cities located above 2,000 m
showed a decrease of approximately 200 g. When comparing highest and lowest socioeconomic levels, birth
weight also showed differences for levels of altitude analyzed (lowland, <2,000 m; middle land, 2,600 m; and
highland, 3,600 m). Interaction between both factors
showed no effect. High altitude seems to act independent of socioeconomic status in explaining birth weight
reduction. Am J Phys Anthropol 129:305–310, 2006.
C 2005
V
Wiley-Liss, Inc.
(Kramer, 1987), low weight gain during pregnancy
(Abrams et al., 1989), and maternal smoking (Wilcox,
1993), are well-known predictors of LBW that could interact with high altitude.
Few studies (Jensen and Moore, 1997; Mortola et al.,
2000; Giussani et al, 2001) reported that socioeconomic
factors might relatively contribute to LBW in comparison
with altitude effects. However, limited access to specialized medical care and poverty are significant factors
inducing LBW and mortality rates. Therefore, it would be
interesting to quantify this contribution and to evaluate
the possible effect of the interaction between altitude and
economic status on birth weight.
The aims of this work are: 1) to assess the effect of altitude on birth weight, adjusting for other risk factors, and
2) to evaluate the joint effect of altitude and socioeconomic
factors on birth weight.
Grant sponsor: Agencia Nacional de Promoción Cientı́fica y Tecnológica; Grant sponsor: Consejo Nacional de Investigaciones Cientı́ficas y Técnicas; Grant sponsor: Comisión de Investigaciones Cientı́ficas de la Provincia de Buenos Aires.
*Correspondence to: Dr. J. López-Camelo, Instituto Multidisciplinario de Biologia Celular (IMBICE), Casilla de Correo 403, CP 1900
La Plata, Argentina. E-mail: jslc@satlink.com
Received 16 June 2004; accepted 17 December 2004.
DOI 10.1002/ajpa.20274
Published online 1 December 2005 in Wiley InterScience
(www.interscience.wiley.com).
306
J.S. LÓPEZ CAMELO ET AL.
MATERIALS AND METHODS
Sample definition and size
This observational descriptive study was performed
with material obtained by the Estudio Colaborativo Latinoamericano de Malformaciones Congénitas (ECLAMC)
(Castilla and López-Camelo, 1990) network involving 75
maternity hospitals from eight South American countries:
Argentina (32), Bolivia (2), Brazil (16), Chile (9), Colombia
(3), Peru (1), Uruguay (6), and Venezuela (6). During
1982–1999, 1,444,646 consecutive live- and stillborn infants weighing 500 g were examined for congenital
malformations. For every malformed infant, the next liveborn nonmalformed baby of the same sex and from the
same hospital was selected as control. Of these controls,
37,022 were included in the study; 1,187 were born in two
South American cities located above 2,000 m altitude: La
Paz, Bolivia (3,600 m, N ¼ 913) and Bogota, Colombia
(2,600 m, N ¼ 274).
Birth weight was registered for each infant with a gestational age of 24–43 weeks and weighing 500 g. Gestational age was calculated by last menstrual date. The hospital of birth was used as proxy for maternal residence
during pregnancy. According to these data, three altitude
levels were defined: lowland (below 2,000 m), middle land
(2,600 m), and highland (3,600 m).
The following risk factors were analyzed: gestational
age (date of last menstrual period, in days), sex of newborn, maternal age, parity, and Native or Black ethnic
origin. The ECLAMC operational manual includes eight
ethnic categories: Latin European, non-Latin European
(Anglo-Saxon), Jewish, Arabian, Native, Black, and Oriental. The newborn ethnic definition depends on phenotypic characteristics, grandparents’ place of birth, and
other features considered relevant by the pediatrician
after questioning the mother. Native ethnicity is defined
as the group with highest probability of Amerindian
and Latin European admixture (ancestors born in Latin
America).
A newborn can have up to eight types of ethnic ancestry.
In this work, the Native category includes only Native
ethnic ancestry, excluding any other ethnic admixture;
the Black category includes Black phenotype of the baby,
regardless of the ethnic background of ancestors.
Socioeconomic level: definition and score
Four variables were considered when assigning socioeconomic level (SEL): maternal and paternal education
(eight levels), paternal occupation (eight levels), and
health system of the hospital taking care of the mother
and her baby. The eight levels considered for maternal
and paternal education were: 1, illiterate; 2, less than 7
years of education; 3, 7 years of education; 4, less than 12
years of education; 5, 12 years of education; 6, less than
18 years of education; and 9, 18 years of education. Paternal occupational levels were: 1, housework; 2, unqualified
worker; 3, independent worker; 4, boss, chief, or owner; 5,
unemployed; 6, qualified worker; 7, clerk; and 8, professional or executive. Hospital health systems considered were
either public or private.
The eight levels of maternal and paternal education
were combined into three levels with the following scores:
low (score ¼ 2, less than 5 years of education), medium
(score ¼ 1, from 6–8 years of education), and high (score
¼ 0, more than 10 years of education). Paternal occupational levels were also combined into three levels, with
the following scores: low (score ¼ 2, including unemployed, unqualified worker, and independent worker),
medium (score ¼ 1, including boss, chief, or owner), and
high (score ¼ 0, including qualified worker, clerk, and
professional or executive). According to the health system
of the hospital, score 1 was assigned to the public system,
and score 0 to a private system. Adding partial scores, a
final score was determined for each case. Socioeconomic
level scores ranged from 0 (high) to 7 (low).
Statistics
In order to compare variables among the three altitude
levels, a one-way ANOVA test (for continuous variables)
and chi-square test (for categorical variables) were
applied. A multiple lineal regression analysis was performed to assess the independent effects of altitude and
socioeconomic level on birth weight, adjusted for other
confounding factors. The dependent variable was birth
weight (in grams); independent variables were altitude
(three altitude levels, and two dummy variables), socioeconomic level (seven levels), Native ancestry (yes/no),
Black ancestry (yes/no), sex of newborn (male/female),
maternal age (in years), and gravidity (number of pregnancies). In order to assess the joint effect of altitude and
ethnic origin and socioeconomic factors on birth weight,
four multiplicative terms (highland * SEL, middle land *
SEL, Native ancestry * SEL, and Black ancestry * SEL)
were included in the regression analysis.
RESULTS
Altitude as a risk factor for low birth weight
Table 1a shows the distribution of birth weight, gestational age, maternal age, parity, socioeconomic level, sex,
and ethnic origin for the three altitude levels. Significant
differences were detected for maternal age, parity, socioeconomic level, and ethnic origin in the three altitude levels (Table 1a,b). These differences were observed among
the three levels of altitude for maternal age and socioeconomic level, and between lowland and the other two altitude categories for birth weight and parity (Table 1b).
Nonsignificant differences were observed for gestational
age and sex.
Birth weight distribution of highland and middle land
populations shifted to the left when compared with the
lowland population. Birth weight mean for the lowland
population (3,221.9 6 556.0 g) was approximately 200 g
lower than for highlanders (3,068.4 6 494.1 g) and 100 g
lower than for the middle land. No differences were observed among lowland (273.8 6 18.5 days), middle land
(275.1 6 18.4 days), and highland (273.6 6 19.3 days) gestational age means. After adjustment for confounding risk
factors, birth weight mean differences among lowland, middle land, and highland infants were similar to nonadjusted
birth weight mean differences.
Effect of the variables on birth weight
Table 2 shows regression coefficients and levels of significance of each risk factor on birth weight. After adjusting for the remaining risk factors, gestational age was the
main birth weight predictor, followed by altitude, sex, parity, maternal age, socioeconomic level, and Native ancestry. All these variables showed a significant regression
coefficient. Black ancestry did not show any effect on birth
weight.
307
ALTITUDE, SOCIOECONOMIC FACTORS, AND BIRTH WEIGHT
TABLE 1a. Distribution of risk factors for birthweight in lowland, middle-land, and highland populations
Risk factor
Lowland
(N ¼ 35835)
Means 6 SD
Middle land
(N ¼ 274)
Means 6 SD
Highland
(N ¼ 913)
Means 6 SD
Birth weight
Gestational age
Maternal age
Parity
SEL
Sex (male)
Native ancestry
Black ancestry
3221.8 6 556.0
273.8 6 18.5
25.5 6 6.4
2.8 6 2.0
3.7 6 1.9
51.3%
70.8%
16.2%
3133.7 6 511.4
275.1 6 18.4
29.8 6 5.4
2.3 6 1.4
0.8 6 1.0
51.7%
95.2%
4.4%
3068.4 6 494.1
273.6 6 19.4
24.1 6 5.9
2.2 6 1.7
4.0 6 1.5
51.6%
98.5%
0.1%
Test
P
F
F
F
F
v2
v2
v2
¼
¼
¼
¼
¼
¼
¼
¼
37.27
0.74
85.48
46.50
335.9
0.38
411.5
201.2
P
<0.001***
0.476, NS
<0.001***
<0.001***
<0.001***
0.825, NS
<0.001***
<0.001***
TABLE 1b. A posteriori multiple comparison test (Bonferroni test)1
Risk factor
Individual comparison
Birth weight
1)
2)
3)
1)
2)
3)
1)
2)
3)
1)
2)
3)
Maternal age
Parity
SEL
Lowland vs.
Lowland vs.
Middle land
Lowland vs.
Lowland vs.
Middle land
Lowland vs.
Lowland vs.
Middle land
Lowland vs.
Lowland vs.
Middle land
middle land
highland
vs. highland
middle land
highland
vs. highland
middle land
highland
vs. highland
middle land
highland
vs. highland
DM
P
88.1
153.4
65.2
4.33
1.39
5.73
0.47
0.60
0.14
2.91
0.33
3.25
0.026*
<0.001***
0.262, NS
<0.001***
<0.001***
<0.001***
<0.001***
<0.001***
0.985, NS
<0.001***
<0.001***
<0.001***
1
DM; difference of magnitude between means.
*, P < 0.05; **, P < 0.01; ***, P < 0.001.
TABLE 2. Multiple lineal regression on birth weight
for risk factors
Risk factor
b1
t2
P3
Altitude 2,600 m
Altitude 3,600 m
Socioeconomic level
Native ancestry
Black ancestry
Male sex
Maternal age
Parity
Gestational age
SEL - altitude 2600 m
interaction
SEL - altitude 3600 m
interaction
SEL - Black ancestry
interaction
SEL - Native ancestry
interaction
163.2
187.8
11.9
61.6
34.4
123.9
3.5
20.0
12.4
34.6
4.3
3.9
3.3
4.4
1.7
23.9
6.7
12.0
88.9
1.2
<0.001***
<0.001***
0.001***
<0.001***
0.099, NS
<0.001,***
<0.001***
<0.001***
<0.001***
0.216, NS
12.2
1.1
0.270, NS
3.5
0.6
0.496, NS
11.8
2.9
0.003**
1
b, estimated regression coefficient.
t, Student’s t-test.
3
NS, not significant.
*, P < 0.05; **, P < 0.01; ***, P < 0.001.
2
The only statistically significant interaction observed
was Native ancestry-socioeconomic level.
Effect of interaction between altitude and
socioeconomic level on birth weight
The regression coefficient for altitude-socioeconomic
level interaction on birth weight was not significant (Table
2). Birth weight means were adjusted for all covariates
used in the regression model. Adjusted birth weight mean,
standard deviation, and sample size of each SEL for alti-
tude levels analyzed are shown in Table 3. Adjusted birth
weight mean differences among the three levels of altitude
were similar for each socioeconomic level. The parallel
lines of birth weight means by socioeconomic levels shown
in Figure 1 confirm the lack of interaction effect.
DISCUSSION
Maternal age, parity, SEL, and Native ancestry showed
statistically significant differences among populations
for the altitude levels analyzed. Some of these differences, such as for Native ancestry, were expected. The
others could mainly be explained by sample size differences among groups, as well as by the fact that data were
obtained through a hospital-based registry.
Altitude’s effect on birth weight
In agreement with Zamudio et al. (1993) and DelgadoRodrı́guez et al. (1998), when compared with the lowland
population, the birth weight distribution of highlanders
showed a shift to the left. Highland birth weight means
were approximately 200 g lower than those of the lowland
population, before and after adjustment for other variables.
Nonsignificant differences in gestational age were observed
among lowland, middle land, and highland populations.
We considered three altitude levels in order to assess the
effect of lowland (above 2,000 m), middle land (2,600 m),
and highland (3,600 m) on birth weight. Mortola et al.
(2000) proposed that at high altitude, birth weight decreases after reaching a critical barometric pressure (BP).
Those authors showed that a threshold for a hypoxic effect
exists at an altitude of approximately 2,000 m, corresponding to a BP of 590 mm.
Except for the obvious relationship between birth
weight and gestational age, altitude was the main predic-
308
J.S. LÓPEZ CAMELO ET AL.
TABLE 3. Altitude-socioeconomic level interaction1
Socioeconomic level
Altitude
0
1
2
3
4
5
6
7
Lowland >2,000 m
M
3,304.0
SD
182.8
n
3,018.0
3,272.1
221.6
2,516.0
3,256.5
229.5
3,471.0
3,238.4
237.8
5,565.0
3,216.8
243.0
8,030.0
3,192.9
251.5
6,800.0
3,175.3
274.9
4,134.0
3,154.3
280.7
2,300.0
Middle land 2,600 m
M
3,142.8
SD
242.1
n
150.0
3,150.8
221.8
61.0
3,147.6
170.7
38.0
3,018.4
221.3
20.0
3,302.3
225.0
3.0
2,568.6
597.2
2.0
Highland 3,600 m
M
SD
n
3,140.3
240.7
64.0
3,087.2
271.0
84.0
3,110.0
225.1
123.0
3,070.3
230.1
303.0
3,043.3
262.6
189.0
3,015.7
301.6
105.0
3,033.9
264.5
45.0
Birth weight means were adjusted for all covariates used in regression model.1 M, means; SD, standard deviation; n, sample size.
in the causal pathway. Although the association is wellknown, this observation is relevant for validating the socioeconomic level indicators used in the present study.
Effect of interaction between altitude and
socioeconomic level on birth weight
Fig. 1. Adjusted birth weight means by socioeconomic level
in lowland (<2,000 m), middle land (2,600 m), and highland
(3,600 m) populations. Due to small sample size of SELs 3–5 for
middle land, they were included in SEL 2.
tor of birth weight variations: its effect was even more significant than that of sex or any other risk factor known for
low birth weight. The effect of altitude on birth weight
was reported by several authors (Haas et al., 1977; Beall,
1981a; Mortola et al., 2000; Keyes et al., 2003; Moore,
2003), and results suggest that hypoxia is by far the most
important parameter responsible for low birth weight at
high altitude (Ballew and Haas, 1986; Yip et al., 1988;
Delgado-Rodrı́guez et al., 1998). In agreement with others
(Yip, 1987; Mortola et al., 2000; Giussani et al., 2001), no
gestational age differences were observed, suggesting that
altitude only leads to IUGR and not to prematurity.
Reduced fetal O2 supply (maternal, placental, or fetal
causes) was associated with IUGR. Although the mechanisms by which hypoxia retards fetal growth are unclear,
recent studies were carried out regarding two possible
contributing factors: an increased incidence of preeclampsia (Keyes et al., 2003), and reduced maternal glucose concentrations (Krampl et al., 2001).
Socioeconomic level’s effect on birth weight
A significant association was observed between low SEL
and reduced birth weight at any of the altitude levels
studied. Low SEL is obviously associated with low birth
weight, and factors such as maternal nutrition (Frisancho
et al., 1977; Haas et al., 1980; Beall, 1981b) and maternal
smoking (Wilcox, 1993), among others, could be involved
Although birth weight seems to depend on the availability of both oxygen and glucose (Krampl et al., 2001; Keyes
et al., 2003), the extent to which altitude affects birth
weight, independent of other risk factors, is still
unknown.
Maternal plasma glucose fasting that is lower at high
altitude than at sea level in the presence of similar insulin
secretion (Krampl et al., 2001) may partly explain lower
birth weight at high altitude. Since transplacental transfer of glucose is directly proportional to maternal blood
glucose (Economides and Nicolaides, 1989), decreased
maternal nutrition could also affect the availability of glucose for the fetus.
At this point, it would be interesting to determine
whether altitude and decreased maternal nutrition affect
birth weight through the same biological mechanism or if
they act through different mechanisms. A possible interaction effect would support the hypothesis that the actions
unchained by both factors converge in a purely biological
mechanism (e.g., low availability of glucose for the fetus).
Haas et al. (1980) suggested that fetal growth retardation at high altitude correlates with the duration of highaltitude residence, independent of maternal nutrition.
Jensen and Moore (1997) analyzed the effect of the interaction between altitude and 17 risk factors on birth
weight. Only two of these factors (nulliparity and premature rupture of membranes) were associated with reduced
birth weight at high altitude, but the significance of their
effect was low. Those authors concluded that high altitude
acts independently of other risk factors in reducing birth
weight. A study carried out in a relatively large population of North American children of low socioeconomic status (Yip et al., 1988) showed a dose-response relationship
between reduction of height/age and weight/height scores
at increasing altitude. Those authors proposed that two
components seem to contribute to growth restriction when
related to altitude: an indirect effect mediated by birth
weight reducing the effect of altitude, and a direct effect of
altitude, independent of birth weight. In a study performed at lowland and highland hospitals on patients with
low and high incomes from two Bolivian cities, Giussani
ALTITUDE, SOCIOECONOMIC FACTORS, AND BIRTH WEIGHT
et al. (2001) did not find evidence of an altitude-socioeconomic level interaction. Those authors suggested that
regardless of maternal economic status, high altitude is
associated with low weight and body shape alteration at
birth.
Our results from a large sample of South American populations agree with these observations. No evidence for
altitude and socioeconomic level interaction was observed,
which suggests that both factors could be acting independently of birth weight.
Strengths
This large South American sample (37,122 births) covers a wide range of altitude levels, from sea level up to the
highest urban settlements in the world, such as La Paz,
Bolivia (3,600 m, 913 births) and Bogota, Colombia (2,600
m, 274 births).
Low maternal/paternal education, low paternal occupational level, and health system of the hospital were used
as proxies for low socioeconomic level, and the indicator
applied was able to detect birth weight differences among
socioeconomic levels, independent of altitude. Therefore,
socioeconomic level indicators showed acute sensibility for
the indirect measurement of maternal economic and
nutrition status.
Limitations
Although highland and lowland populations differed in
several demographic characteristics, socioeconomic levels
as well as the other variables included in this surrogate
were registered in a similar way.
According to the ECLAMC operational manual, ancestry refers to the ethnic admixture in the child’s ancestors
and not to the child’s race. This concept applies to Latin
American populations in which the wide variety of racial
admixtures makes it difficult to establish a newborn’s
race. However, newborn ancestry can be determined with
reasonable reliability. South American racial admixture is
di-hybrid, basically formed by Natives and Caucasians in
countries such as Argentina, Chile, Bolivia, and Peru, and
tri-hybrid, formed by Natives, Caucasians, and Blacks as
in Uruguay, Brazil, Ecuador, Colombia, and Venezuela
(Carlson, 1937; Parra et al., 1998; Vieira et al., 2002).
Selection bias may occur if migration exists between
highland and lowland populations. Using data from the
Maryland Birth Defects Reporting and Information System, Khoury et al. (1988) evaluated the potential bias
introduced by migration. Twenty percent of 295 cases with
one or more of the 12 birth defects analyzed reported residence changes during gestation, and 7.5% had migrated
from their original county. When place of residence at
moment of birth of the child is taken as a proxy for residence at conception, when the putative exposure might
have occurred, the classification error increases with the
mobility rate and frequency of exposure. Thus, this error
tends to minimize the association expected. Although not
evaluated in the present study, migration values between
lowland and highland are probably lower than in Khoury
et al. (1988), and therefore a low bias is expected. Healthcare, diet, or maternal smoking were not evaluated.
Although weight for gestational age was not measured
directly, nonsignificant differences in gestational age were
observed between lowland and highland populations.
Therefore, differences in birth weight cannot be attributed
to differences in gestational age, and we can conclude that
309
LBW at high altitude was the result of IUGR and not prematurity.
CONCLUSIONS
This study, performed on a large sample from South
American populations, is in agreement with results published in literature, in the sense that high altitude seems
to act independently of socioeconomic status in explaining
birth weight reduction.
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