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All content in this area was uploaded by Ghosh Saswata on Jul 19, 2017
Content may be subject to copyright.
EXPLORING SOCIOECONOMIC
VULNERABILITY OF ANAEMIA AMONG
WOMEN IN EASTERN INDIAN STATES
SASWATA GHOSH
Institute of Development Studies Kolkata (IDSK), India
Summary. The present study investigates the socioeconomic risk factors of
anaemia among women belonging to eastern Indian states. An attempt has
been made to find out differences in anaemia related to social class and place
of residence, and age and marital status. It was hypothesized that rural
women would have a higher prevalence of anaemia compared with their
urban counterparts, particularly among the poorest social strata, and that
ever-married women would be at elevated risk of anaemia compared with
never-married women, particularly in the adolescent age group. Using data
from National Family Health Survey-3, 2005–6, a nationally representative
cross-sectional survey that provided information on anaemia level among
19,695 women of this region, the present study found that the prevalence of
anaemia was high among all women cutting across social class, location and
other attributes. In all 47.9% were mildly anaemic (10.0–11.9.9 g/dl), 16.1%
were moderately anaemic (7.0–9.9 g/dl) and 1.6% were severely anaemic (<7.0
g/dl). Protective factors include frequent consumption of pulses, milk and
milk products, fruits and fish, educational attainment, mass media exposure
and high socioeconomic status. Urban poor women and adolescent ever-
married women had very high odds of being anaemic. New programme
strategies are needed, particularly those that improve iron storage and
enhance the overall nutritional status of women throughout the life-cycle.
Introduction
Anaemia is one of the important public health problems that persists worldwide,
particularly among women of reproductive age and children of developing countries,
with major consequences for health, survival and economic development. However,
the population group with the greatest number of individuals affected is non-pregnant
women (468.4 million, 95% Confidence Interval (CI): 446.2, 490.6) (World Health
Organization (WHO), 2008). Although the primary cause of anaemia is iron
deficiency, it co-exists with a number of other causes, such as malaria, parasitic
infection, nutritional deficiencies and haemoglobinopathies. According to regional
estimates of WHO (2008) generated for pre-school age children, and pregnant and
J. Biosoc. Sci., (2009) 41, 763–787, Cambridge University Press, 2009
doi:10.1017/S0021932009990149 First published online 17 July 2009
763
non-pregnant women, the greatest number of affected individuals of these groups
belong to South-East Asian countries (315 million, 95% CI: 291, 340) (WHO, 2008).
Severe anaemia among women is an important contributor to maternal mortality and
morbidity, lowered physical activity and lowered productivity (Allen, 1997, 2000;
Ramanakumar, 2004), low birth weight and perinatal and neonatal mortality
(McCormick, 1985; Seshadri, 1997, 1998; Steer, 2000; International Institute for
Population Sciences (IIPS) & ORC Macro, 2000). New analyses have shown that even
mild and moderate anaemia have serious consequences for women and children
(Stolzfus et al., 2003); women with even mild anaemia may experience fatigue and
have reduced work ability (Gillespie, 1998).
The third round of the National Family Health Survey (NFHS) conducted during
2005–6 in India reported a disturbingly high burden of anaemia and undernutrition.
More than 55% of women of reproductive age are anaemic while 36% have a body
mass index (BMI) <18.5 kg/m
2
, indicating a high prevalence of chronic energy
deficiency (IIPS & Macro International, 2007). About 39% of these anaemic women
are classified as mildly anaemic (Hb 10.0–11.9 g/dl), 15% as moderately anaemic
(Hb 7.0–9.9 g/dl) and 1.8% as severely anaemic (Hb <7.0 g/dl). Moreover, the
proportion of anaemic women has increased more than 3 percentage points between
1998–99 and 2005–6 (IIPS & Macro International, 2007).
Various micro-level studies conducted in India reported high prevalence of
anaemia among pregnant, non-pregnant and adolescent girls (Verma et al., 1998;
Rajaratnam et al., 2000; Chakma et al., 2000; Kanani & Poojara, 2000; Malhotra
et al., 2004; Das & Biswas, 2005). However, these studies were basically focused on
determining level of anaemia among population sub-groups. In their study of
socioeconomic determinants of anaemia in Andhra Pradesh, a southern state of India,
Bentley & Griffiths (2003) using NFHS-2 data for 1998–99 found, that place of
residence, standard of living of the households, educational attainment, nutritional
status and dietary habits and health practices are influential factors in determining
anaemia status of women. Studies conducted in other developing and least-developed
countries have shown that the underlying determinants of anaemia include diet (low
in home-iron and bioavailability), infectious and parasitic diseases such as malaria,
hookworm etc., and repeated reproductive cycles that deplete iron stores (Allen, 1997;
Stoltzfus, 1997; Stoltzfus et al., 1997; Gillespie, 1998; Tatala et al., 1998; Hwalla et al.,
2004; Brooker et al., 2008).
Although the high prevalence of anaemia among women in India cuts across states
and regional boundaries, the prevalence is the highest among women belonging to the
eastern Indian states of Assam, West Bengal, Orissa, Jharkhand and Bihar – ranging
from nearly 70% in Assam and Jharkhand to 61% in Orissa, with an average of 65%
(IIPS & Macro International, 2007). Also, percentages of women having moderate
and severe anaemia are also very high in these states.
Against this backdrop, the present research investigates the determinants of
anaemia among women of reproductive age in eastern India, where anaemia
prevalence is particularly high. It is expected to find differences in levels of anaemia
related to social class, rural/urban location, and age and marital status, even after
controlling other potential confounding variables. It is hypothesized that prevalence
of anaemia would be higher among rural women compared with urban women,
764 S. Ghosh
particularly among the lowest social stratum. It is also hypothesized that women who
married at an early age, started cohabiting and were exposed to the risk of
childbearing or experienced childbearing are at elevated odds of being anaemic
compared with their unmarried counterparts.
Demographic, social and economic profile of the study area
The states of Jharkhand, Bihar, West Bengal, Orissa and Assam constitute the eastern
area of India. Approximately 25% of the Indian population resides in these states.
Two of the states, namely West Bengal and Bihar, are very densely populated, while
the remaining three are sparsely populated. Table 1 shows the demographic,
socioeconomic and health indicators of these states, indicating that they are not
among the developed states of India; they are characterized by a large rural
population, high dependence on agriculture in the absence of heavy industry and
widespread poverty with significant proportions living below the poverty line
(National Sample Survey Organization, 2007). It may be noted that the indicators
presented in Table 1 reflect the uniqueness of each state in terms demographic and
socioeconomic characteristics. It seems that of these states, West Bengal is the best in
terms of literacy rate and maternal and child health indicators, except the high
fertility rate among adolescents. Assam is characterized by the least percentage of
population living below the poverty line, the highest percentage of literate women and
relatively low fertility among adolescents but a high rate of infant mortality. On the
other hand, more than two-fifths of the population in Bihar, Jharkhand and Orissa
live below the poverty line. Availability of toilet facility is also substantially lower in
these states compared with West Bengal and Assam. While the maternal health
indicators in Orissa are better than those of Bihar, Jharkhand and Assam, infant
mortality rate is the highest in Orissa compared with all other states in this region.
Nonetheless, except West Bengal to some extent, these states lag behind average India
in many respects, including, in many instances, with regard to the situation of women
of reproductive age.
Methods
Data
Data for this study were drawn from India’s third National Family Health
Survey (NHFS-3), as part of the Demographic and Health Survey (DHS)
programme, coordinated by the International Institute for Population Sciences,
Mumbai, under the aegis of the Government of India. The survey was carried out
during 2005–6. It covered a nationally representative stratified random sample of
124,385 women (ever-married and never-married) in the age group 15–49 years and
74,369 men in the age group 15–54 years, residing in 109,041 households in order
to provide estimates of a wide range of demographic, socioeconomic and nutritional
health indicators. A total of 21,975 eligible women were interviewed in the five
eastern Indian states and of these 20,826 respondents were not pregnant on the
Anaemia among women in eastern Indian states 765
Table 1. Selected demographic, socioeconomic and health indicators of eastern states of India
Demographic and socioeconomic indicators India West Bengal Bihar Jharkhand Assam Orissa
Population, 2001 (‘000s)
a
10,28,610 80,221 82,879 26,909 26,638 36,707
Population density, 2001 (persons/sq km)
a
825 903 881 338 340 236
Sex ratio, 2001 (female/1000 male)
a
933 934 921 941 932 972
% population aged 6+ that is literate, 2001
a
64.6 68.6 47.0 53.6 63.3 63.1
% women aged 15–40 who are literate, 2005–06
b
55.1 58.8 37.0 37.1 63.0 52.2
Child (0–6 years) sex ratio, 2001
a
927 960 942 965 965 953
% residing in a pucca house, 2005–06
b
45.9 39.5 20.4 28.3 19.8 31.9
% households have toilet facility, 2005–06
b
44.6 59.6 25.2 22.6 76.4 19.3
% population living below poverty line, 2004–05
c
27.5 24.7 41.4 40.3 19.7 46.4
Infant mortality rate, 2007
d
55 37 58 48 66 71
Total fertility rate, 2005–06
b
2.7 2.3 4.0 3.3 2.4 2.4
% women who began childbearing between age 15–19, 2005–06
b
16.0 25.3 25.0 27.5 16.4 14.4
Prevalence of any anaemia, 2005–06
b
55.3 63.2 67.4 69.5 69.5 61.2
% mothers who had at least 3 ANC for last birth, 2005–06
b
52.0 62.0 17.0 35.9 39.3 61.8
% skilled attendance at delivery
b
46.6 47.6 29.3 27.8 31.0 44.0
% institutional delivery
b
38.7 42.0 19.9 18.3 22.4 35.6
% mothers who received skilled postnatal care within 2 days of
delivery, 2005–06
b
37.3 40.7 15.9 17.0 13.9 33.3
Sources:
a
Registrar General of India (2001);
b
International Institute for Population Sciences & Macro International (2007);
c
National
Sample Survey Organization (2007);
d
Registrar General of India (2008).
766 S. Ghosh
survey date according to their own reporting and were selected for the present study.
The remaining 1149 pregnant women (5% of the sample) were excluded from the
data set since by definition the level of anaemia is different for them compared with
non-pregnant women. Finally, 19,695 (weighted 183,575) women, constituting 90%
of the total sample for whom information on anaemia was available, were retained
in the analysis.
The main strata used in the sampling procedure were rural and urban areas. The
primary sampling units (PSUs) (villages in the rural areas and census enumeration
blocks in urban areas) were selected with probability proportional to size sampling
and the households were selected from within the PSUs. The non-response was not
different by background characteristics for women and had not caused any bias in the
data.
Measures
Measurement of haemoglobin levels was conducted by using a HemoCue Hb 201+
analyser. The HemoCue analyser has been used extensively for estimating the
haemoglobin concentration in capillary blood in field situations. It has been found to
give accurate results, comparable to estimates from more refined laboratory instru-
ments (Gehring et al., 2002; Medina et al., 2005; Gupta et al., 2007). The analyser
that was used in the NFHS-3 was validated with all tested systems (IIPS & Macro
International, 2007). This system uses a single drop of blood from a finger prick
(after removing first two drops to ensure that the sample is based on fresh capillary
blood), which is drawn into a cuvette and then inserted into a portable, battery-
operated instrument, and within one minute the haemoglobin concentration is
indicated on a digital read-out. Based upon haemoglobin status and following
international references (WHO, 1992), three levels of anaemia were distinguished:
mild anaemia (10.0–11.9 g/dl), moderate anaemia (7.0–9.9 g/dl) and severe anaemia
(less than 7.0 g/dl). Appropriate adjustments to these cut-offpoints were made for
respondents living at altitude above 1000 m (3300 feet) and for respondents who
smoke (Centres for Disease Control and Prevention (CDC), 1998). It may be
mentioned here that about 4% of the total population in Assam live in the hilly areas
(Registrar General of India, 2001). The adjustment of anaemia level for altitude was
made with the following formulae: (1) adjust=0.032alt+0.022alt
2
; and (2)
adjHg=Hgadjust (for adjust > 0), where adjust is the amount of the adjustment, alt
is altitude in feet (convert from metres by multiplying 3.3), adjHg is the adjusted
haemoglobin level, and Hg is the measured haemoglobin level in g/dl. Similarly, an
adjustment was also made for women who smoke as follows: for less than ten
cigarettes smoked per day no adjustment was required; 10–19 cigarettes smoked per
day required an adjustment of 0.3 g/dl in Hb concentration; 20–39 cigarettes
smoked per day required an adjustment of 0.5 g/dl in Hb concentration; 40 or more
cigarettes smoked per day required an adjustment of 0.7 g/dl in Hb concentration;
unknown quantity or non-cigarette smoking required an adjustment of 0.3 g/dl in
Hb concentration (CDC, 1998). In addition, the survey also measured the weights and
the heights of women in order to calculate BMI by dividing weight (kg) by height
squared (m
2
).
Anaemia among women in eastern Indian states 767
Analytical model
To identify the socioeconomic, diet, demographic, health habit and regional
determinants of anaemia status, ordered logit models were used. The primary
outcome variable in the analysis was created from the haemoglobin measurement, as
mentioned earlier. However, only 1.5% of women were classified as being severely
anaemic. For the purpose of regression modelling (more precisely to avoid problems
with zero cell counts while estimating models) severe and moderate groups were
clubbed together and to create a severe/moderate category (a haemoglobin concen-
tration of less than 10.0 g/dl). In the ordered model the response variable was coded
so that women with no anaemia were given 0 value, those with mild anaemia a value
of 1, and those with moderate/severe anaemia 2.
The ordered logit model simultaneously estimates multiple equations by pooling
the categories of response variable although it provides only one set of coefficients for
each predictor variable. The number of equations it estimates would be the number
of categories in the dependent variable minus one. In the present case two equations
were estimated: in the first equation the response variable was categorized as no
anaemia (coded as 0) and mild/moderate/severe anaemia (coded as 1) and in the
second equation the response variable was categorized as no anaemia/mild anaemia
(coded as 0) and moderate/severe anaemia (coded as 1). Thus, there is an implicit
assumption of parallel regression, i.e. the coefficients for the variables in the equations
would not vary significantly if they were estimated separately. To verify the validity
of the aforesaid assumption, the parallel regressions were also estimated accordingly.
It was found that (not reported in the Tables) the coefficients of the predictor
variables did not vary significantly from the ordered logit estimates.
Moreover, multiple linear regressions (MLR) were also estimated with haemo-
globin level as the continuous response variable (not reported in the tables). It was
observed that although the signs and the significance of the coefficients of the
predictor variables remained similar, the intercept was found to be large and values
of the coefficients also differed significantly. It is worth mentioning that employing
MLR in this case is possibly not an appropriate technique since the effect of predictor
variables on the response variable may not be linear, because, for example, after some
level of an explanatory variable, there may not be any rise in the haemoglobin level
since it cannot increase further.
Predictor variables used in the ordered logit models in order to test their
significant association with anaemia are presented in Table 2. The variables primarily
fall into six main categories: place of residence and household wealth; age and marital
status; other socioeconomic and demographic variables; health habits and dietary
variables; BMI; and state of residence. As information on household income or
expenditure is not directly available, the standard of living index (calculated by using
factor analysis by NFHS-3) has been taken as the proxy for household economic
status. The standard of living index consists of the following household and economic
characteristics: type of house, toilet facility, source of lightning, main fuel for cooking,
source of drinking water, use of separate room for cooking, ownership of house,
ownership of agricultural land, ownership of irrigated land, ownership of livestock
and ownership of durable goods. On the basis of the composite score related to these
768 S. Ghosh
characteristics, the household standard of living was divided into five quintiles, viz.,
poorest, poorer, middle, richer and richest. After observing the variations of anaemia
level among women belonging to these households, the variable was re-classified
into three categories: poor, middle (pooling poorer and middle) and rich (combining
richer and richest). Both household wealth and place of residence, and age and
marital status, were combined to form a single categorical variable. For the age and
marital status variable, age was classified into 15–19 years representing adolescent,
20–24 years representing young, and 25–49 years representing older.
Also, caste and religion were pooled together to form a single categorical variable.
The categories were upper (forward) caste Hindu, scheduled caste (SC) Hindu,
scheduled tribe (ST) Hindu, other backward caste (OBC) Hindu, Muslim and other
non-Hindu. The variable ‘mass media exposure’ was created from four variables,
namely, ‘reads newspaper or magazine at least once a week’, ‘listens to the radio at
least once in a week’, ‘watches television at least once a week’ and ‘visits the
cinema/theatre at least one a month’. If a woman was exposed to any one of these,
she was classified as ‘exposed to mass media of any sort’.
Altogether seven multivariate models were estimated. Model 1 included a variable
that is a combination of household wealth and place of residence. This allowed a test
of whether there were differences in the likelihood of being anaemic between economic
groups both within urban and rural areas. In a similar way, Model 2 included a
variable that is a combination of age and marital status to find out differences in the
odds of being anaemic between age groups and marital status. Model 3 introduced the
Table 2. Variables tested for significant association with anaemia in ordered logit
regression Models 1–7
Model Description
Model 1 Place of residence and wealth index: rural poor, rural middle, rural rich, urban
poor, urban middle and urban rich
Model 2 Marital status and age: adolescent never-married, adolescent ever-married, younger
never-married, younger ever-married, older never- married and older ever-married
Model 3 Place of residence and wealth index, and marital status and age
Model 4 Socioeconomic variables: religion and caste (Hindu Upper Caste, Hindu SC, Hindu
ST, Hindu OBC and non-Hindu), education (illiterate, up to middle school and
more than middle school), exposure to mass media (exposed and non-exposed)
Model 5 Health habits and dietary variables: drinking alcohol (never drink, drink often and
drink less often), chew/smoke tobacco (no, yes), frequency of eating milk/curd
(never/occasionally and daily/weekly), frequency of eating pulses
(never/occasionally and daily/weekly), frequency of eating green leafy vegetables
(never/occasionally and daily/weekly), frequency of eating fruits (never/occasionally
and daily/weekly), frequency of eating eggs (never/occasionally and daily/weekly),
frequency of eating fish (never/occasionally and daily/weekly) and frequency of
eating meat/chicken (never/occasionally and daily/weekly)
Model 6 Body mass index (<18.5 kg/m
2
, 18.5–24.9 kg/m
2
, and R25 kg/m
2
)
Model 7 States (West Bengal, Bihar, Assam, Jharkhand and Orissa)
Anaemia among women in eastern Indian states 769
age and marital status variables in addition to household wealth and place of
residence variables. Model 4 included other socioeconomic and demographic variables
in addition to the variables of Model 3. Three additional models were also tested,
adding to the variables already included in the earlier models. Model 5 introduced the
health habits and dietary variables, Model 6 included BMI, and Model 7 incorporated
state of residence.
Estimating the models in this way allows the testing of the significance of the
association of place of residence and household wealth with anaemia status, and age
and marital status with anaemia after controlling for a wide range of other
confounding factors. Moreover, it also allows the identification of factors that
reduced the significance of the variable of interest in each model, hence enabling the
identification of variables that are associated with the place of residence and
household wealth, age and marital status, and haemoglobin status of women.
Data were analysed using Stata Release 9 (Stata Corporation, 2005). To obtain
the basic socioeconomic characteristics of samples, descriptive statistics were
produced for the eastern Indian states after pooling data of the individual states
using the individual state weights. Using sample weight in the analysis allows
correction of disproportionate representation of women from states because of
complex survey design. The results presented in the tables and used for interpretation
are weight-adjusted in the sense that sample weights, which were inversely
proportional to a woman’s probability of selection and dependent on the number of
women in the population at each level of stratification, were taken into account
during analyses. The differences in the anaemia status variable in relation to
household wealth and place of residence, or age and marital status variable, were
examined through bivariate analysis using Pearson’s chi-squared test of significance
at p<0.05. The odd ratios produced by ordered logit regression were used for
interpretation. The model assumes that the effect of any of the predictor vari-
ables should be the same regardless of the choice of category of the response
variable. Only the significant variables with a two tailed p-value <0.05 are reported
in Table 5.
Results
Sample characteristics
Table 3 presents the weight-adjusted sample characteristics of the respondents of
each state of the eastern region of India. In general, rural predominance in the sample
population was observed (78%) with the highest in Orissa (84%) and the lowest in
West Bengal (69%). More than two out of five respondents were found to be
non-literate in this region with the highest in Bihar (more than 60%) and the lowest
in Assam (31%). More than 62% of the respondents of this region belonged to the age
group 25–49 years, while around 80% were ever-married. The proportion of
adolescents was higher in Bihar and Jharkhand compared with the other states.
Although upper (forward) caste Hindu and other backward caste (OBC) Hindu
constituted more than half of the total sample, the proportion of respondents by
religion and caste varied substantially across states. Proportions of respondents
770 S. Ghosh
belonging to the forward caste Hindu as well as to the SCs were highest in West
Bengal (42% and 30% respectively) and lowest in Jharkhand (about 11% on average).
The majority of the ST respondents were mainly from Orissa (20%), Jharkhand (13%)
and Assam (11%). The highest proportions of OBC respondents were from Bihar
(about 53%) followed by Jharkhand (36%), while the proportion was the lowest in
West Bengal (4%). Respondents belonging to the Muslim and other non-Hindu
communities were found to be highest in West Bengal (18%) and Jharkhand (14%),
respectively, while their proportions were lowest in Orissa. Although the proportions
of respondents belonging to the highest and the lowest economic strata were almost
the same (29% and 28% respectively) in the sample, considerable differences may be
observed among and between the states. Jharkhand (47%) and Orissa (36%)
comprised the majority of the respondents belonging to the lowest economic strata
while West Bengal (33%) followed by Assam (31%) had the highest proportion of
respondents from affluent households.
Table 3. Sample characteristics of the respondents in the eastern Indian states,
NFHS-3, 2005–6 (percentages)
Variables West Bengal Bihar Assam Jharkhand Orissa Total
Religion/caste
UC Hindu 41.8 14.5 26.9 11.5 30.7 28.0
SC Hindu 29.6 15.7 16.9 10.9 17.9 20.0
ST Hindu 5.2 0.2 11.2 13.0 20.1 9.5
OBC Hindu 4.4 52.6 26.8 36.3 28.7 26.4
Muslim 17.8 16.8 12.5 14.5 1.1 12.8
Others 0.9 0.1 5.8 14.0 1.5 3.4
Education
Illiterate 36.7 60.9 31.1 58.9 40.6 43.5
Up to middle school 41.6 21.3 31.4 19.6 31.2 31.4
>Middle school 21.8 17.9 37.5 21.5 28.2 25.1
Place of residence
Rural 69.2 83.2 82.9 74.1 84.1 77.7
Urban 30.8 16.8 17.1 25.9 15.9 22.3
Marital status
Never-married 17.4 17.2 26.2 17.8 24.0 20.3
Ever-married 82.6 82.8 73.8 82.3 76.0 79.7
Age
Adolescent 18.4 24.7 18.1 22.1 19.1 20.0
Young 17.6 15.8 18.3 18.5 18.1 17.6
Older 64.0 59.6 63.6 59.4 62.9 62.3
Household standard of living
Poorest 23.0 24.9 15.6 47.3 36.1 27.9
Middle 44.3 46.9 53.6 26.8 39.0 43.0
Richest 32.7 28.3 30.8 25.9 24.9 29.1
Total sample (weighted) 59,331 31,364 31,233 23,283 38,364 183,575
Anaemia among women in eastern Indian states 771
Prevalence of anaemia among different population sub-groups
Tables 4 and 5 present the weighted bivariate results of anaemia status by place
of residence and household wealth variable, and the age and marital status variable.
These are the main variables of interest, and from these the main hypothesis was
constructed, i.e. that the differences in the prevalence of anaemia are related to
economic well-being, rural/urban location, age and marital status. It was found that
around 65% of women were classified as anaemic, about 48% as mildly anaemic,
around 16% as moderately anaemic, and 1.5% as severely anaemic.
The chi-squared statistics displayed in Tables 4 and 5 show the statistically
significant differences in the prevalence of anaemia between groups based upon the
aforesaid variables. Although rich and never-married women have statistically
significant reduced risk of anaemia (p<0.01), the anaemia prevalence was high among
all groups in the eastern Indian states. The prevalence of mild anaemia ranged from
42.5% among the urban poor to 50% among the rural poor. The prevalence of mild
anaemia was also highest among adolescent ever-married (about 51%) and the lowest
among younger never-married (about 45%). A low prevalence of moderate anaemia
was found among both the rural and urban rich (just above 11%) and the highest
prevalence was found among the urban poor (about 29%). Similarly, the prevalence
of moderate anaemia was highest among the younger never-married (11.5%), and
highest among adolescent ever-married (about 18%). In contrast, the prevalence of
severe anaemia was highest among older never-married and the lowest among
younger ever-married women. For severe anaemia the highest and lowest prevalences
were observed among the urban poor, and both among rural and urban rich women
respectively.
Econometric analysis
The odd ratios and 95% CI of the ordered logit regressions are presented in Table
6. Women belonging to urban poor households were observed to have the highest
odds of being mildly, moderately or severely anaemic (OR=1.55, 95% CI=1.40, 1.72)
compared with the urban rich women, even after controlling for a range of
demographic and socioeconomic, health habit and dietary, BMI and state of residence
Table 4. Level of anaemia (in percentages) by wealth and place of residence in the
eastern Indian states, NFHS-3, 2005–6
Level of anaemia
Urban
rich
Urban
middle
Urban
poor
Rural
rich
Rural
middle
Rural
poor Total
Not anaemic 41.7 35.3 26.3 40.2 34.0 28.6 34.6
Mild 45.4 46.2 42.5 47.4 47.9 50.0 47.9
Moderate 11.9 17.0 28.6 11.4 16.5 19.5 16.1
Severe 1.0 1.6 2.6 1.0 1.6 1.8 1.5
Total sample (weighted) 29,572 9817 1618 23,917 69,093 49,558 183,575
2
(p) 347.22 (<0.01)
772 S. Ghosh
Table 5. Level of anaemia (in percentages) by age and marital status in the eastern Indian states, NFHS-3, 2005–6
Level of anaemia
Older
ever-married
Older
never-married
Younger
ever-married
Younger
never-married
Adolescent
ever-married
Adolescent
never-married Total
Not anaemic 34.4 32.8 32.4 41.7 30.0 38.0 34.6
Mild 47.9 46.4 49.1 44.8 50.7 46.6 47.9
Moderate 16.4 17.6 17.3 11.5 18.1 13.9 16.1
Severe 1.4 3.2 1.3 2.1 1.6 1.7 1.5
Total sample (weighted) 110,458 3974 24,058 8310 11,796 24,979 183,575
2
(p) 102.38 (<0.01)
Anaemia among women in eastern Indian states 773
variables in Model 7. Rural poor women were also found to have moderately higher
odds of being anaemic compared with women from urban well-offhouseholds
(OR=1.05, 95% CI=1.01, 1.09). It is interesting to note that women belonging to rural
rich households have significantly lower odds of being anaemic (OR=0.90, 95%
CI=0.87, 0.93) compared with women from urban rich households. In the unadjusted
model, i.e. in Model 1, women belonging to the rural middle class (OR=1.42, 95%
CI=1.38, 1.45), urban middle class (OR=1.38, 95% CI=1.32, 1.44), rural poor
(OR=1.79, 95% CI=1.74, 1.84) and urban poor (OR=2.49, 95% CI=2.26, 2.74) had
significantly higher odds of being anaemic compared with women belonging to urban
affluent households.
From Model 7 it can also be ascertained that younger and adolescent unmarried
women were significantly less likely to be anaemic (OR=0.94, 95% CI=0.91, 0.97 for
adolescent never-married and OR=0.89, 95% CI=0.85, 0.93 for younger never-
married), while younger and adolescent ever-married, and older never-married were
significantly more likely to be anaemic (OR=1.18, 95% CI=1.31, 1.22 for adolescent
ever-married; OR=1.06, 95% CI=1.03, 1.09 for younger ever-married; and OR=1.17,
95% CI=1.10, 1.26 for older never-married) compared with the older ever-married
women after controlling for all other potentially confounding variables. The results
are consistent with the unadjusted models (Models 2 and 3); only the degree of
association varies.
Other socioeconomic factors such as religion and caste membership, maternal
education and exposure to mass media of any sort have a significant influence on
differences in anaemia status. In Model 7, women belonging to the scheduled tribes
and scheduled castes were significantly more likely to be anaemic (OR=1.61, 95%
CI=1.55, 1.67 for scheduled tribe and OR=1.14, 95% CI=1.10, 1.17 for scheduled
caste) than upper caste Hindu women. It may be observed that although Muslim
women were found to be significantly more likely to be anaemic compared with the
same reference category in the unadjusted Models 4, 5 and 6, the association became
insignificant and the direction of association also reversed after inclusion of the state
of residence variable in Model 7.
Maternal education was found to be a protective factor for anaemia. Higher
educated women were significantly less likely to be anaemic compared with
non-literate women (OR=0.91, 95% CI=0.89, 0.93 for women educated up to middle
school and OR=0.81, 95% CI=0.78, 0.83 for women educated more than middle
school). Women who were exposed to any sort of print or audio-visual media were
significantly less likely to be anaemic compared with women who did not have any
media exposure (OR=0.90, 95% CI=0.88, 0.92).
In Model 7 it was observed that the respondents who reported chewing or
smoking tobacco were more likely to be mildly, moderately or severely anaemic than
those who did not use any tobacco-related products (OR=1.22, 95% CI=1.19, 1.26).
Though alcohol consumption is associated with numerous health and social problems,
surprisingly, it was observed that women who drank alcohol, either often or less
often, were significantly less likely to be anaemic than their counterparts who did not
drink alcohol after controlling for all other variables in the models (OR=0.86, 95%
CI=0.81, 0.91 for women who drank less often and OR=0.87, 95% CI=0.82, 0.93 for
women who drank often). Though the exact bio-chemical mechanism about how
774 S. Ghosh
Table 6. Determinants of anaemia status for women in the eastern Indian states from the NFHS, 2005–6 (odd ratios and 95%
CI obtained from ordered logit regression models)
Predictor variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Place of residence and wealth
Urban rich (Ref.) 1.00 1.00 1.00 1.00 1.00 1.00
Urban middle 1.38***
(1.32, 1.44)
1.37***
(1.31, 1.43)
1.09***
(1.04, 1.14)
1.06*
(1.01, 1.11)
0.99
(0.95, 1.04)
0.98
(0.93, 1.02)
Urban poor 2.49***
(2.26, 2.74)
2.43***
(2.21, 2.68)
1.75***
(1.58, 1.94)
1.61***
(1.46, 1.79)
1.48***
(1.33, 1.64)
1.55***
(1.40, 1.72)
Rural rich 1.03*
(1.00, 1.07)
1.03
(0.99, 1.06)
0.97
(0.94, 1.00)
0.97
(0.94, 1.00)
0.93***
(0.90, 0.96)
0.90***
(0.87, 0.93)
Rural middle 1.42***
(1.38, 1.45)
1.40***
(1.36, 1.44)
1.11***
(1.08, 1.15)
1.09***
(1.06, 1.12)
1.00
(0.97, 1.04)
0.97
(0.94, 1.00)
Rural poor 1.79***
(1.74, 1.84)
1.75***
(1.71, 1.80)
1.19***
(1.15, 1.23)
1.11***
(1.07, 1.16)
1.02
(0.98, 1.06)
1.05*
(1.01, 1.09)
Age and marital status
Older ever-married (Ref.) 1.00 1.00 1.00 1.00 1.00 1.00
Older never-married 1.13***
(1.06, 1.20)
1.27***
(1.20, 1.35)
1.31***
(1.23, 1.40)
1.30***
(1.22, 1.38)
1.27***
(1.19, 1.35)
1.17***
(1.10, 1.26)
Younger ever-married 1.08***
(1.05, 1.11)
1.05***
(1.02, 1.08)
1.06***
(1.03, 1.09)
1.08***
(1.05, 1.11)
1.04***
(1.01, 1.07)
1.06***
(1.03, 1.09)
Younger never-married 0.73***
(0.70, 0.76)
0.82***
(0.78, 0.85)
0.92***
(0.88, 0.96)
0.93***
(0.89, 0.97)
0.88***
(0.84, 0.92)
0.89*
(0.85, 0.93)
Adolescent ever-married 1.20***
(1.16, 1.25)
1.12***
(1.08, 1.16)
1.17***
(1.12, 1.21)
1.21***
(1.16, 1.26)
1.17*
(1.13, 1.22)
1.18***
(1.31, 1.22)
Anaemia among women in eastern Indian states 775
Table 6. Continued
Predictor variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Adolescent never-married 0.86***
(0.84, 0.88)
0.87***
(0.86, 0.90)
0.96*
(0.94, 0.99)
0.99
(0.96, 1.02)
0.92***
(0.90, 0.95)
0.94***
(0.91, 0.97)
Religion and caste
UC Hindu (Ref.) 1.00 1.00 1.00 1.00
SC Hindu 1.21***
(1.18, 1.25)
1.19***
(1.16, 1.22)
1.18***
(1.15, 1.21)
1.14***
(1.10, 1.17)
ST Hindu 1.63***
(1.57, 1.69)
1.57***
(1.51, 1.63)
1.56***
(1.51, 1.63)
1.61***
(1.55, 1.67)
OBC Hindu 1.06***
(1.03, 1.09)
1.06***
(1.03, 1.09)
1.05***
(1.02, 1.08)
1.01
(0.98, 1.04)
Muslim/other 1.09***
(1.06, 1.12)
1.07***
(1.03, 1.10)
1.06**
(1.03, 1.09)
0.98
(0.94, 1.00)
Education
Not literate (Ref.) 1.00 1.00 1.00 1.00
Up to middle school 0.88***
(0.86, 0.90)
0.89***
(0.87, 0.92)
0.91***
(0.89, 0.93)
0.91***
(0.89, 0.93)
More than middle school 0.77***
(0.75, 0.79)
0.80***
(0.76, 0.82)
0.82***
(0.80, 0.85)
0.81***
(0.78, 0.83)
Exposure to mass media
Non-exposed (Ref.) 1.00 1.00 1.00 1.00
Exposed 0.85***
(0.84, 0.88)
0.86***
(0.84, 0.88)
0.87***
(0.85, 0.89)
0.90***
(0.88, 0.92)
776 S. Ghosh
Table 6. Continued
Predictor variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Drink alcohol
Never (Ref.) 1.00 1.00 1.00
Not often 0.89***
(0.84, 0.94)
0.89***
(0.84, 0.95)
0.86***
(0.81, 0.91)
Often 0.94*
(0.88, 1.00)
0.94*
(0.88, 1.00)
0.87***
(0.82, 0.93)
Chew/smoke tobacco
No (Ref.) 1.00 1.00 1.00
Yes 1.20***
(1.17, 1.23)
1.18***
(1.15, 1.21)
1.22***
(1.19, 1.26)
Frequency of eating milk/curd
Never/occasionally (Ref.) 1.00 1.00 1.00
Regularly/weekly 0.92***
(0.90, 0.94)
0.92***
(0.90, 0.94)
0.91***
(0.89, 0.93)
Frequency of eating pulses
Never/occasionally (Ref.) 1.00 1.00 1.00
Regularly/weekly 0.83***
(0.80, 0.85)
0.82***
(0.80, 0.85)
0.86***
(0.83, 0.89)
Frequency of eating fruits
Never/occasionally (Ref.) — 1.00 1.00
Regularly/weekly 0.95*
(0.91, 1.00)
0.94***
(0.92, 0.97)
Anaemia among women in eastern Indian states 777
Table 6. Continued
Predictor variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7
Frequency of eating fish
Never/occasionally (Ref.) 1.00 1.00 1.00
Regularly/weekly 0.93***
(0.91, 0.95)
0.94***
(0.91, 0.96)
0.91**
(0.92, 0.96)
Body mass index
<18.5 kg/m
2
1.19***
(1.17, 1.21)
1.19***
(1.17, 1.22)
18.5–24.9 kg/m
2
(Ref.) 1.00 1.00
R25 kg/m
2
0.73***
(0.70, 0.76)
0.74***
(0.71, 0.76)
State
West Bengal (Ref.) 1.00
Bihar 1.06**
(1.02, 1.10)
Assam 1.50***
(1.46, 1.55)
Jharkhand 1.05**
(1.02, 1.09)
Orissa 0.78***
(0.75, 0.80)
Total cases (weighted) 183,505 183,505 183,505 169,038 168,599 168,505 168,505
Ref., reference category.
*p<0.05; **p<0.01; ***p<0.001.
778 S. Ghosh
alcohol consumption reduces the risk of anaemia is unknown, there is plenty of
literature on increased iron status and absorption related to alcohol consumption
(Millman & Kirchhoff, 1996; Hallberg & Hulthen, 2000). This finding also supports
the earlier finding of Bentley & Griffiths (2003) regarding anaemia among women in
Andhra Pradesh, India. Cautious investigation of data revealed that women who
consume alcohol largely belonging to the scheduled tribe community for whom
drinking is a cultural habit. In this context, it may be stated that promoting alcohol
consumption among women is not an appropriate public health intervention in this
setting.
Among dietary variables, women who consumed milk/curd, pulses, or ate fruits or
fish frequently were significantly less likely to be anaemic than those who either did
not consume or consume occasionally these aforesaid food items, even after
controlling for other confounding variables (Model 7). However, it must be
mentioned that frequent consumption of pulses has the strongest influence on reduced
risk of anaemia compared with other food items mentioned above (OR=0.91, 95%
CI=0.89, 0.93 for consumption of milk/curd; OR=0.86, 95% CI=0.83, 0.89 for
consumption of pulses; OR=0.94, 95% CI=0.92, 0.97 for consumption of fruits;
OR=0.91, 95% CI=0.92, 0.96 for consumption of fish).
Respondents with a BMI less than 18.5 kg/m
2
were observed to be significantly
more likely to be anaemic (OR=1.19, 95% CI=1.17, 1.22) that those with a normal
BMI between 18.5 and 24.9 kg/m
2
. In contrast, overweight respondents were found
to be significantly less likely to be anaemic compared with the same reference category
in Model 7 (OR=0.74, 95% CI=0.71, 0.76).
Even after controlling for all other variables, state-specific effects can also be
observed from Model 7. It has been observed that respondents belonging to the state
of Assam were significantly more likely to be mildly, moderately or severely anaemic
than respondents from West Bengal (OR=1.50, 95% CI=1.46, 1.55). In contrast,
women from Orissa were significantly less likely to have anaemia of any kind
compared with women from West Bengal (OR=0.78, 95% CI=0.75, 0.80). In addition,
women belonging to the other two states were found to be moderately more likely to
be anaemic compared with their counterparts in West Bengal (OR=1.06, 95%
CI=1.02, 1.10 for Bihar and OR=1.05, 95% CI=1.02, 1.09 for Jharkhand).
Discussion
The present findings on determinants of prevalence of anaemia among women of the
eastern states of India have to be interpreted in light of the demographic,
socioeconomic and cultural context of India in general and of these states in
particular. Although, in India, there has been an appreciable reduction in extreme
hunger and poverty, a spectacular increase in life expectancy accompanied by a
decline in fertility and mortality rates, particularly infant mortality rates, during the
last three decades or so, improvement in nutritional status of the general population
has been unimpressive (Shetty, 2002; Bentley & Griffiths, 2003). It is worth noting
that though these observations hold for the Indian population in general, it is not the
case for these states in particular as the various indicators shown in Table 1 suggest.
As mentioned earlier, this region is marked by a high prevalence of absolute poverty,
Anaemia among women in eastern Indian states 779
less significant decline in total fertility rates and infant mortality rates, low female
literacy, low age at marriage, less availability of basic amenities and high rates of
non-institutional deliveries etc., and these are reflected in the higher economic, social
and biological vulnerability of women of this region compared with the average
Indian women.
It is worth mentioning that anaemia among women in the eastern region of India
(and also possibly India at large) is all pervasive and cuts across socioeconomic class,
location of residence, age and marital status, and all other attributes and differences
are only relative. For example, the prevalence of anaemia was about 56% among
women belonging to the richest section while it was around 72% among the poorest
women.
Our hypothesis regarding anaemia vulnerability, and economic status and place of
residence, was established partially. It was presumed that, since most of the health
indicators are worse for rural women compared with their urban counterparts, the
proportion of anaemic women would be higher in rural areas compared with urban
areas. However, it was found that although both rural and urban poor women were
at higher risk of being anaemic, the poorest urban women were at considerably
elevated risk of anaemia compared with their rural counterparts. This supports the
earlier findings of Bentley & Griffiths (2003) among women of reproductive age in
Andhra Pradesh on the relationship between anaemia and poverty, and also confirms
the findings of many other studies on the nature of urban poverty during the last two
decades and its magnitude in India, particularly in this region (Deaton & Dreze, 2002;
Sen & Himanshu, 2004a,b; Chandrasekhar & Mukhopadhyay, 2007; Himanshu, 2007;
Madhiwalla, 2007). The results of recent National Sample Surveys (NSSs) also
indicate that the decline in poverty has been lower in urban compared with rural areas
in India (NSSO, 2007). Lack of services such as potable drinking water and safe
sanitation, drainage, solid waste collection and disposal and electricity is an indicator
of urban poverty (Laquian, 2004). These unhygienic conditions make urban poor
women more susceptible to infectious diseases such as malaria, dengue and filaria.
The higher purchasing power of the rich drives up the prices of food and private
health care goods, making them unaffordable for the poor and benefiting the rich
(Dye, 2008). Urban poor women may also have higher risk of poor nutritional status
and anaemia due to their reduced economic power within the household and
uncertain availability of casual wage employment (Banerjee, 1995; Hatekar & Rode,
2003; Bentley & Griffiths, 2003).
The second hypothesis regarding level of anaemia, and age and marital status, has
also partially been supported. Never-married women, whether adolescent or younger,
were significantly less likely to be anaemic compared with older ever-married women,
while ever-married adolescent and younger women were significantly more likely to be
anaemic compared with the same reference category. This supports the earlier finding
that early age at marriage, which is often accompanied by repeated childbearing,
decreases iron storage in women (Allen, 1997; Gillespie, 1998). To understand the
relationship between repeated childbearing and the risk of anaemia, women’s parity
was included in the model initially but dropped later because of its high correlation
with the age and marital status variable. However, it may be noted that in the case
of overall India, at age 15–24, only 32% of ever-married women want no more
780 S. Ghosh
children and more than 75% of them have already had three living children, showing
the extent of repeated childbearing (IIPS & Macro International, 2007). Older
never-married women were also found to be significantly more likely to be anaemic
compared with their older ever-married counterparts. Careful investigation of the data
reveals that the majority of these unmarried women belong to the urban rich section,
and possibly they are more likely frequently to consume fast-food items, which
contain less bioavailable iron and other essential micronutrients on the one hand and
also interfere with bio-absorption. In this connection one could also note explicitly
that a rise in income may not necessarily lead to a corresponding rise in food intake
if assessed in terms of nutritional value.
Social deprivation has been observed to be an important determinant of anaemia
status, even after controlling for all other potentially confounding variables: socially
marginalized sections of the social strata, i.e. scheduled caste and scheduled tribe
women are more likely to be anaemic compared with upper caste Hindu women.
Structural discrimination against these groups takes place in the Indian social system,
not only in the eastern region but also in other parts of India. They have a low
literacy rate, have meager purchasing power, poor access to basic amenities, resources
and entitlements and are often employed as casual labourers (Chatterjee & Sheoran,
2007). It has been found that health care utilization in general, and utilization of
maternal health care in particular, is substantially lower among these groups
compared with upper caste Hindus since they are less likely to afford and access
health services when required (Govindaswamy & Ramesh, 1997; IIPS & ORC Macro,
2000, 2007; Sandhyarani et al., 2007). They are also prone to infectious diseases and
lag behind in the epidemiologic transition, even after controlling for economic status
(Ghosh & Kulkarni, 2005). All these factors potentially have a negative impact on
their nutritional and health outcome resulting in a high incidence of infant and child
mortality, morbidity and anaemia, among other things.
The study also found that Muslim women were insignificantly related to the risk
of anaemia compared with upper caste Hindu women only after controlling for the
state of residence variable, which occurs possibly due to a confounding of state of
residence and minority factors. Some minorities, particularly Muslims, reside in some
states such as Assam in greater proportions than in some other states (for example
in Orissa and Jharkhand). Hence, without controlling for states, the observed
minority effect may involve some state effect, but once state effect is controlled, the
minority effect may become clearer. However, a detailed examination regarding how
socio-cultural factors influence the risk of anaemia at the state level is called for,
because these factors cannot be addressed with the available data and they are beyond
the scope of the present study.
There is plenty of evidence establishing the significant positive influence of
women’s education in enhancing their health status and also that of their children
through various pathways (Caldwell, 1979; Cochrane et al., 1980; United Nations,
1985; Da Vanzo & Habicht, 1986; Cleland & van Ginneken, 1989; Bicego & Boerma,
1993; Bentley & Griffiths, 2003; Ghosh, 2004, 2005). Our present finding also supports
these studies. It is interesting to note that women who were exposed to any print or
audio-visual media were significantly less likely to be anaemic even after controlling
other variables. Although there is hardly any study that shows any direct relationship
Anaemia among women in eastern Indian states 781
between mass media exposure and anaemia status, arguably, exposure to mass media
can play an important role in enhancing knowledge on safe health practices and
behaviour, and eating a proper diet rich in essential nutrients and iron among other
things.
Most of the dietary variables such as daily or weekly eating of pulses, milk and
milk products, fruits and fish are found to be protective covariates against anaemia
even after controlling for other variables. Using data from the National Nutrition
Monitoring Bureau (NNMB) various other studies have observed that predominantly
cereal- and vegetable-based diets are being consumed by women not only in this
region but also in India as a whole (Shetty, 2002). This type of diet provides low
amounts of bioavailable iron (De Maeyer, 1989; Torre et al., 1991) because of the
high content of iron-absorption inhibitors such as phytate and polyphenols. It is
worth noting that production as well as consumption of pulses and legumes, which
are a rich source of iron, has been reduced dramatically during the last twenty years
or so and is a matter of concern, as various other surveys conducted in India had
pointed out (Shetty, 2002). Though India has a prominent share in the global
production of fruits (Kaul, 1998), they are not important in the diet possibly because
they are important as cash crops for sale within the country and also abroad (Shetty,
2002). Further, a significant proportion of the Indian population (in this region as
well) practise vegetarianism and thus do not eat iron-rich fish and other animal
products.
Although thin women (BMI<18.5 kg/m
2
) were more likely to be anaemic
compared with normal or overweight women, careful investigation revealed that
nearly 9% of women with BMI R25 kg/m
2
were moderately or severely anaemic. This
suggests that inadequate iron intake due to improper dietary practices or prevalence
of hookworm, malaria infections, other infections such as reproductive tract
infections, micronutrient deficiency, which interfere with iron metabolism, is wide-
spread even among women who have no apparent resource constraints (Griffiths &
Bentley, 2001; Bentley & Griffiths, 2003). But due to lack of data on these indicators,
including morbidities, it was not possible to measure accurately the role of these
variables in anaemia.
It is worth noting that even after controlling for other confounding variables
state-specific factors also emerged as a significant contributor to the risk of anaemia
in women. Although the prevalence of anaemia among women belonging to this
region was very high, the present study observed that women from the state of Assam
were at significantly higher risk and women of Bihar and Jharkhand had moderately
higher risk of being anaemic compared with the women of West Bengal, while women
of Orissa had a relatively lower risk of being anaemic compared with the same
reference category. It may be observed that the possibility of hookworm infection and
the incidence of malaria among women, which increase the risk of anaemia
substantially, is higher in Assam compared with other states due to the local level
agro-climatic conditions (local climate is mainly humid with heavy rainfall for most
of the year). The susceptibility to infections, especially hookworm infection, rises
substantially since a sizeable proportion (about 41%) of women is engaged in
agriculture-based activities including plantation (WHO, 1991; RGI, 2001). In addition
to ecological conditions, differential levels of socioeconomic development between
782 S. Ghosh
states, and other state-specific factors such as access to and utilization of health care
facilities, successful implementation of various disease control programmes etc. could
also account for emerging anaemia status differences.
What are the policy implications of these findings? It may be mentioned that many
of the protective factors of anaemia would not be changed through rapid interference,
but may be amenable to long-term intervention. The risk of anaemia is minimum for
the women belonging to the rich or middle classes irrespective of location of
residence, unmarried women 20–24 years of age, for women who have completed at
least middle school and have been exposed to any print or audio-visual media, do not
use tobacco, eat pulses, milk/curd, fruits and fish frequently or are overweight.
As it was found that poor urban women have the highest risk of being anaemic,
targeted interventions exclusively focusing on this groups is urgently required. It is
also envisaged that the health situation among the poor is not simple, since for most
of the health indicators rural poor women are not in an advantageous position
compared with urban women (IIPS & Macro International, 2007). Policies and
programmes should address the issue of iron fortification through a life-cycle
approach so that iron deficiency during childhood and pregnancy is reduced at the
outset. Appropriate breast-feeding and timely introduction of weaning foods with
high amounts of bioavailable iron should be promoted. An iron-rich diet should be
made available in childhood nutritional programmes such as the Integrated Child
Development Programme (ICDS). It should be carefully noted that there may,
however, be deleterious effects associated with high concentrations of iron and these
may pose some risk of infection (Hernell & Lönnerdal, 2002).
Programmes for adolescent girls (for instance, Kishori Shakti Yojana, which was
launched in 2000–1 as a part of the ICDS scheme aiming to break the intergenera-
tional life-cycle of nutritional and gender disadvantage, and also to provide a
supportive environment for self-development) must incorporate the provision of
distributing iron tablets and an iron-rich diet in order to fortify iron during the
adolescent period. Besides, vigorous distribution of iron and folic acid (IFA) tablets
during pregnancy should also be strengthened and monitored so that every pregnant
woman consumes an adequate IFA.
Low age at marriage for girls and early childbearing in India is really a worrisome
issue in the Indian context as about 16% of women aged 15–19 have begun
childbearing. Moreover, more than a quarter of these women with no education have
become mothers and almost a third of them have begun childbearing (IIPS & Macro
International, 2007). Thus, age at marriage among girls may be raised by providing
education and vocational training for self-development and also by sensitizing the
community about the importance of women’s education for their own and also for the
well-being of their offspring. It requires a massive campaign from the concerned
agency.
Improving overall nutritional status and access to income will have the greatest
impact in reducing anaemia in India (World Bank, 1993; Bentley & Griffiths, 2003).
It has been observed from the present dataset that a proper intake of those dietary
items such as milk/curd, pulses, fruits and fish, which can reduce the risk of anaemia,
is significantly associated with household economic status. Consumption of these food
items, especially pulses (which is the strongest protective factor among the dietary
Anaemia among women in eastern Indian states 783
variables according to the present findings), could protect against anaemia since these
items have a high iron content. Since these food items are not affordable for the
poorer sections of the society in the era of rising prices, in the short-run, Government
agencies should intervene directly by strengthening the Public Distribution System
(PDS) and ensuring the availability of quality pulses and other food items to this
vulnerable section of society. It must be acknowledged that detailed exploration of the
relationship between dietary pattern and anaemia was not possible due to inadequate
dietary variables available in the NFHS. Additionally, an increased intake of
bioavailable iron should be promoted in the community. Ways to achieve this may
include the consumption of low-cost fresh fruit and vegetables rich in vitamin C and
the use of common household food processing methods such as soaking, germination
etc., which enhance iron absorption and are effective methods in reducing phytate
content in cereals thereby improving the availability of iron (Svanberg & Sandberg,
1988; Svanberg et al., 1993; Tatala et al., 1998).
Multi-pronged strategies and integrated programmes for hookworm eradication,
malaria prophylaxis and micronutrient deficiencies are called for to reduce the burden
of anaemia (Stoltzfus, 1997; Bentley & Griffiths, 2003). It may be noted that about
a 9% prevalence of moderate to severe anaemia among obese women is a matter of
concern and this relationship needs to be explored in great detail. Moreover,
information, education and counselling (IEC) activities concerning protective food
habits against anaemia must be strengthened. Information about anaemia and the
ways of prevention may be aired or displayed in the audio-visual media since in this
study it was found that mass media exposure plays an independent protective factor
for anaemia. Furthermore, health and nutrition education should be introduced
vigorously by local-level institutions in rural and urban areas and made a continuous
activity to help the community adapt to new behaviours and food habits that may
result from these interventions.
The factors identified as significant in predicting socioeconomic vulnerability to
anaemia among women suggest the direction that national health policies and
programmes for the women could take. The proposed interventions may have a
fundamental impact on personal and domestic health and hygiene, food habits and
meal preparation, and the importance of conveying these ideas to women and in turn
reducing the incidence of anaemia through a life-cycle approach in the long run.
Acknowledgments
The author would like to thank the anonymous referees for their critical and
constructive comments. The author would also like to extend his gratitude to
Professor P. M. Kulkarni of Jawaharlal Nehru University, New Delhi, for his
comments while drafting the paper.
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