Paper writing

RESEARCH ARTICLE

Association between malnutrition and anemia

in under-five children and women of

reproductive age: Evidence from Bangladesh

Demographic and Health Survey 2011

M. Shafiqur RahmanID*, Muntaha Mushfiquee, Mohammad Shahed Masud, Tamanna Howlader

Institute of Statistical Research and Training, University of Dhaka, Dhaka, Bangladesh

* [email protected]

Abstract

Background

Bangladesh is one of the most anemia prone countries in South Asia. Children of age under

five years and women of reproductive age are particularly vulnerable in this region. Although

several studies have investigated the risk factors of anemia, only few have explored its

association with malnutrition, despite its high prevalence in the same group. The objective

of this paper is to investigate the association of malnutrition with anemia by conducting sep-

arate analyses for under-five children and women of reproductive age using data from the

nationally representative 2011 Bangladesh Demographic and Health Survey.

Methods

Two binary outcome variables are considered separately: presence of anemia in children

under five years of age (Hb<11.0 g/dl) and presence of anemia in women of childbearing age (Hb<12.0 g/dl). The exposures of interest corresponding to these two outcomes are stunting (low height-for-age) and low BMI (<18.5 kg/m2), respectively. Preliminary analysis involves estimating the association between exposure and outcome while controlling for a

single confounder by computing adjusted odds ratios (adjOR) using the Cochran-Mantel-

Haenszel approach in stratified analysis. Later, associations between the exposures and

outcomes are estimated separately for under-five children and women of reproductive age

by fitting multivariable regression models that adjust simultaneously for several

confounders.

Results

The prevalence of anemia is found to be higher among both the stunted children and

women with low BMI compared to their healthy counterparts (Children: 56% vs 48%;

women: 50% vs 43%). Furthermore, stunted children and women with low BMI have signifi-

cantly increased odds of developing anemia, as reflected by the adjusted ORs of 1.76 (95%

CI:1.10–2.83) and 1.81 (95% CI: 1.11–3.48), respectively. The association of stunting with

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OPEN ACCESS

Citation: Rahman MS, Mushfiquee M, Masud MS,

Howlader T (2019) Association between

malnutrition and anemia in under-five children and

women of reproductive age: Evidence from

Bangladesh Demographic and Health Survey 2011.

PLoS ONE 14(7): e0219170. https://doi.org/

10.1371/journal.pone.0219170

Editor: Seth Adu-Afarwuah, University of Ghana,

GHANA

Received: August 27, 2017

Accepted: June 18, 2019

Published: July 3, 2019

Copyright: © 2019 Rahman et al. This is an open access article distributed under the terms of the

Creative Commons Attribution License, which

permits unrestricted use, distribution, and

reproduction in any medium, provided the original

author and source are credited.

Data Availability Statement: All data are freely

available upon request from the DHS website

http://dhsprogram.com/data/.

Funding: The author(s) received no specific

funding for this work.

Competing interests: The authors have declared

that no competing interests exist.

anemia in children was modified by their age and socio-economic condition, where risk of

being anemic decreases with increasing age but with a lower rate for stunted children from

richest family. In addition, stunted children of anemic mothers are at greater risk of being

anemic compared to non-stunted children of anemic or non-anemic mothers. Again the

association between BMI and anemia in women is modified by the level of education, with

risk of anemia being lowest among women with low BMI and higher education.

Conclusion

Evidence–based policies targeting the vulnerable groups are required to combat anemia

and nutritional deficiencies simultaneously under the same program.

Introduction

Anemia, which is characterized by low level of hemoglobin in the blood, is one of the major

public health hazards affecting people in both developed and developing countries [1–3]. Ane-

mia may occur at all stages of life, however, young children and women in the childbearing age

are the most vulnerable [4, 5]. When anemia occurs in children, it could affect their cognitive

performance and physical growth [6]. In women, anemia could adversely affect their capacity

to work and may lead to poor pregnancy outcomes [7]. According to the World Health Orga-

nization (WHO), globally about 38% of women of reproductive age and 43% of children under

five years of age were affected by anemia in 2011 [2, 3]. Anemia is more prevalent in develop-

ing countries [4, 8] contributing to about one million deaths each year world-wide. Three-

quarters of these deaths occur in Africa and South-East Asia [2, 3, 9, 10]. Bangladesh has been

reported as one of the most anemia prone countries in South Asia [11–13]. According to the

National Nutrition Project (NNP), the prevalence of anemia among children of ages 6–59

months was estimated to be 47% in 2004 and 68% in 2013 [14, 15]. The National Micronutri-

ent Survey 2011–12 estimated an anemia prevalence of 33% among the children of the age

group 6–59 months and 26% among the non-pregnant and non-lactating women [14].

Another study reported that childhood anemia decreased with increasing age, with a preva-

lence of 64% among children of ages 6–23 months and 42% among children of ages 24–59

months [15, 16]. Such high prevalence reported by multiple studies indicates that anemia is

a major public health threat in Bangladesh. Although Bangladesh has made remarkable prog-

ress in health and social development achieving most of the Millenium Development Goals

(MDGs) in the last decade [17], it is still struggling to tackle the burden of some diseases

including anemia.

Several studies [1, 18–21] have been conducted to identify the factors associated with ane-

mia among the vulnerable groups of the population. Most of these studies reported iron defi-

ciency as the primary cause of anemia in developing countries along with other associated

causes including malaria, parasitic infection, nutritional deficiencies, and haemoglobinpathies

(a genetic condition). However, recent evidence suggests that iron deficiency may not be the

primary cause of anemia in Bangladesh [22, 23]. The reason is that iron is abundant in ground-

water and majority of the country’s population relies on groundwater for drinking purposes

[24]. Thus consumption of iron occurs through consumption of groundwater. Furthermore,

there are studies that have found a link between the amount of iron intake through groundwa-

ter and the level of iron in the body [23, 25]. Thus, iron deficiency does not appear to be the

Association of malnutrition with anemia in children and women

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most important risk factor of anemia, which explains why most of the iron-supplementation

intervention programmes are ineffective in reducing the burden of anemia in Bangladesh [26].

Given the low prevalence of iron deficiency but high prevalence of anemia among women

and children in Bangladesh, some recent studies in similar [27, 28] or different socio-economic

settings [29–31] were conducted to examine whether socio-demographic factors are associated

with anemia. Most of these studies identified household socioeconomic status, food insecurity,

and geographical location as risk factors. Although some of these studies identified stunting

and low body-mass-index (BMI) as one of the risk factors of anemia in under-five children

and women of reproductive age, respectively, limited studies have explicitly explored the asso-

ciation of these malnutrition indicators with anemia, despite their high prevalence in both

women and children. According to recent evidence from the national surveys and studies [32,

33], about 36% of under-five children are stunted (low height-for-age), 33% are underweight

(low weight-for-age) and 14% are wasted (low weight- for-height). Similarly 19% of women of

reproductive age are malnourished (low BMI against the standard level 18.5). Thus, women of

reproductive age and under-five children in Bangladesh are also vulnerable to malnutrition

[32, 34]. High prevalence of anemia and malnutrition in both populations hints at a possible

link between these two conditions. Thus, an intensive investigation is required to determine

how and to what extent the nutritional statuses of women and children are associated with

their anemia levels and whether the association is modified by other risk factors. Although

some studies have [35, 36] tried to identify common factors associated with anemia and

growth (measured by stunting, underweight and wasting in children and low BMI in women)

through separate analysis of anemia and growth data, such an analysis may not be useful in

identifying whether there is any association between growth and anemia. Against this back-

drop, the current paper examines the association between anemia and stunting in under-five

children and the association between anemia and BMI in childbearing women using nationally

representative data extracted from the Bangladesh Demographic and Health Survey 2011.

Findings of the study will be useful in providing new insights that may help design effective

policies for reducing the burden of anemia as well as malnutrion in both women and children.

Methods

Data

Data on anemia, stunting and BMI were extracted from the 2011 Bangladesh Demographic

and Health Survey (BDHS) [37] conducted during November 2010- March 2011. BDHS is a

nationally representative health survey conducted every three years since 1993 with collabora-

tive efforts of the National Institute of Population Research and Training (NIPORT), ICF

International (USA), and Mitra and Associates under the demographic and health survey

(DHS) program based on developing countries across the world. It is a cross-sectional study

based on a two-stage stratified cluster sampling design. The entire population was stratified

into 14 strata based on 7 administrative divisions and urban-rural areas in each division. In

the first stage, the primary sampling units (PSUs) consisting of ward in rural area or sub-ward

in urban area were randomly selected from a list of PSUs in each stratum. An equal number of

households were then randomly selected from each PSU in the second stage. The survey col-

lected information on health, nutrition and demographic history for men, women and chil-

dren. In particular, data on anemia were collected from women of reproductive age and

children in the age group 6–59 months belonging to every third household in the selected sam-

pled households by screening the hemoglobin(Hb) levels (g/dl) in their blood samples at the

time of survey. The 2011 BDHS used HemoCue 1

Hb 201 +

rapid testing methodology, which

consists of a battery-operated photometer and a disposible microcuvatte to measure Hb levels

Association of malnutrition with anemia in children and women

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in the blood sample (a drop of capillary blood taken from fingertip). The classification of

women and children as anemic or non-anemic was performed after adjusting their Hb levels

for altitude, and in the case of women, pregnancy status was also adjusted for using Centre for

Disease Control (CDC) formula [38]. Information on stunting and BMI were also collected at

the time of the survey by taking anthropometric measurments such as height, weight and age

of the women and children. For more details on survey methodology and methods for mea-

surement of anemia, see elsewhere [37].

Ethical considerations

The Ethics committee at NIPORT, Mitra and Associates, and ICF international approved a

waiver from ethical approval for this retrospective study. As the de-identified data for this

study came from the secondary sources, this study does not require eithical approval.

Variables

The present study considered two populations, i.e., under-five children (6–59 months) and

women of reproductive age (15–49 years) and therefore separate outcome-exposure pairs for

the two populations. The outcome variables were ‘maternal-anemia’ (yes, no) representing

anemia status in women and ‘child-anemia’ (yes, no) representing anemia status in children.

Children were classified as anemic if their adjusted hemoglobin levels (Hb) were less than the

cuttoff of 11.0 g/dl, and women were classified as anemic if Hb<12.0g/dl. The exposure vari-

ables of interest were BMI in the case of women and stunting in the case of children. BMI is a

quantitative variable that was calculated using height and weight measurements of women

and is a commonly used nutritional status indicator. BMI was convereted to a binary expo-

sure with categories low (BMI<18.5) and high (BMI �18.5). Thus, women were considered

malnourished if their BMI was less than 18.5. Similarly stunting (low height for age), which is

a good nutritional status indicator for children, was also converted to a binary exposure

(stunted vs normal). Children having z-score for the height-for-age index less than two stan-

dard deviations from the median of WHO reference population [37] were considered as

stunted.

In addition to the above exposures, a set of background factors were chosen based on pre-

vious literature to control for potential confounders of the association between the exposure

and outcome [27, 39]. Tables 1 and 2 list the qualitative background risk factors and their

corresponding levels/categories for the outcomes child anemia and maternal anemia, respec-

tively. In Table 1, a child was considered to have access to food if he/she had three square

meals per day regularly. Otherwise, the child had limited access. In Table 2, information on

size at birth was collected retrospectively by asking mothers to recall whether the child’s size

was ‘very small’, ‘smaller than average’, ‘average’, or ‘above average’ at birth. This information

is commonly used as proxy for birth weight in many studies [40, 41] as majority of births in

Bangladesh occur at home where there is usually no facility for taking the baby’s weight. The

binary risk factor ‘size at birth’ was created by classifying a child as ‘small’ if the mother

reported ‘very small’ or ‘smaller than average’. Otherwise, the child’s size was considered nor-

mal. The variable socio-economic status (SES) was determined from wealth index that was

calculated by performing principal component analysis on the assests owned by the house-

holds. The first principal component, which explains maximum variability of the data, gives

the wealth index. The wealth index was then used to categorize the individuals into five equal

groups, where the first quintile refers to the poorest group and the fifth quintile refers to the

richest group [37].

Association of malnutrition with anemia in children and women

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Statistical analysis

First, univariate analysis was performed by calculating descriptive statistics that were used to

summarize the data. Next, bivariate analysis was performed by calculating odds ratios from

2x2 contigency tables for the exposure and outcome variables. However, the true association

between stunting and child-anemia or BMI and maternal-anemia may be confounded by

other risk factors known as confounders that are associated with both the outcome and expo-

sure of interest. To mitigate the effects of confounders, stratified analysis was performed by

constructing 2x2 contigency tables for the exposure and outcome at each level of the con-

founder and computing the stratum specific odds ratios (OR). These odds ratios are then com-

bined using Cochran-Mantel-Haenszel approach [42], which takes the weighted average of the

strafum specific odds ratios to obtain the adjusted odds ratio (adjOR). The stratum specific

analyses are also useful for identifying potential confounders and effect modifiers thereby pro-

viding insights for building a good multivariable regression model. Following stratified analy-

sis, the net effects of malnutrition on child-anemia and maternal-anemia were assessed

separately while controlling for the effects of all confounders simultaneously by fitting multi-

variable logistic models containing both main and interaction effects. Although several

Table 1. Distribution of anemic children by their background characteristics.

Variables Number at risk % of anemic children Variables2 Number at risk % of aanemic children

Stunting Maternal anemia status

Stunted 925 56.5 not anemic 1239 45

Normal 1309 48.5 anemic 1000 61.3

Sex Size at birth

Male 1173 53.1 Normal 1883 52.3

Female 1110 51.1 Small 399 51.6

Age in months Birth order

24-Jun 780 71 First 791 52.2

25–48 1019 44.5 2–3 1020 51.5

48–59 484 38 4+ 472 53.6

Maternal Education Child had fever recently

No education 432 53.5 no 1366 49.6

Primary 744 54.6 Yes 917 56.1

Secondary 947 52.1 Child had diarrhea recently

Higher 160 38.1 no 2154 51.8

SES Yes 129 58.1

Poorest 521 59.5 Received Vitamin A supplement

Poorer 445 58 Yes 531 58

Middle 408 52.4 no 1740 50.3

Richer 431 45.7 Access to food

Richest 408 44 Yes 1830 51.3

Region Limited 453 55.6

Barisal 256 59.4 Area of residence

Chittagong 437 52.2 Urban 686 48.3

Dhaka 370 47.8 rural 1597 53.8

Khulna 254 54.3 Total 2271 52.1

Rajshahi 281 48.4

Rangpur 305 57.7

Sylhet 380 48.4

https://doi.org/10.1371/journal.pone.0219170.t001

Association of malnutrition with anemia in children and women

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confounders were identified during the bivariate analysis, the final model contained the main

and interaction effects of only a few confounders. The other confounders were excluded one

by one based on the p-value (>0.10) of the likelihood ratio-test performed by adding the con-

founder to the model starting with stunting (for the model with child anemia) and BMI (for

model with maternal anemia) in different combinations either as main effects and/or interac-

tion effect. The non-linearity of the continuous covariate was assessed by introducing a qua-

dratic term for the covariate and observing whether it is significant. If the quadratic term was

significant, it was retained along with the linear term to capture the non—linear effect of the

covariate on the outcome. Each estimate from the above analysis including the model fitting

was obtained by employing sampling weight. All statistical analyses were performed using

combination of Stata package ‘svy’, svyset, ‘epitab’, and ‘logit’ in Stata version 14.

Table 2. Distribution of anemic women by their background characteristics.

Variables Number at risk % of anemic women

BMI

BMI<18.5 719 49.5

BMI> = 18.5 1739 43.2

Births in last five years

1 1713 42.6

2–4 754 50.5

Age in years

15–19 307 46.3

20–29 1530 43.5

30+ 628 48.3

Education

No education 486 51.6

Primary 798 47.2

Secondary 1010 41.4

Higher 173 38.2

SES

Poorest 562 51.6

Poorer 494 50.4

Middle 458 47.2

Richer 457 41.6

Richest 496 33.7

Region

Barisal 270 48.5

Chittagong 465 41.7

Dhaka 404 49.7

Khulna 274 35.8

Rajshahi 313 45.7

Rangpur 328 48.8

Sylhet 413 44.8

Area of residence

Urban 747 41.2

Rural 1720 46.7

Access to food

Yes 1977 43.5

Limited 490 51.2

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Results

The analysis was based on a sample of 2283 children of ages between 6–59 months (male

41.4% and female 58.6%) and 2467 women of ages between 15–49 years. The average age of

the children was 33.03 months with standard deviation (SD) of 15.86 months (results not

shown). More than two-fifth of the children (41.4%) were stunted. Fifty one percent of chil-

dren (n = 2234) with ages between 6 to 59 months were estimated to be anemic. The average

age of the women in the sample was 25.91 years with SD of 5.97 years. The percentage of

women of reproductive age having BMI<18.5 was 29.25%. Forty five percent of the women

(n = 2467) were found to be affected by anemia. Table 1 presents the percentage of anemic

children in the different categories of the background risk factors. Results indicate that the

prevalence of anemia is higher among stunted children than among normal children (56.5% vs

48.5%). The prevalence of anemia is higher among women with low BMI (<18.5) than among

women with normal BMI (>18.5) (49.5% vs 43.2%). Furthermore, prevalence of anemia is

found to be higher among stunted children compared to non-stunted children and among

women with low BMI compared to those with normal BMI at each level of the confounder in

stratified analysis (Tables 3 and 4).

When the strength of the association between anemia and stunting in children, or anemia

and BMI in women was quantified by calculating the stratum-specific odds ratio at each level

of the confounder, strong and significant associations were observed in each stratum. Tables 3

and 4 also show the adjOR, which measures the association between anemia and the exposure

variable (stunting or BMI) after adjusting for the confounder and are calculated using the

Cochran-Mantel-Haenszel approach. One observes that the associations remain significant

even after adjusting for the effect of the confounder. For example, after controlling for the

effect of sex, stunted children had 39% greater odds of developing anemia than normal chil-

dren with estimated adjOR of 1.39 (95% CI: 1.18–1.66) (Table 3). Higher odds of being anemic

among stunted children compared to non-stunted children were also observed after control-

ling for each of the other confounders, such as, age, SES etc, separately. Again, women with

low BMI (BMI<18.5) had 29% greater odds of having anemia than women with normal BMI

[adjOR = 1.29 (95% CI: 1.08–1.53)], after controlling for the effect of age (Table 4). Similar

findings were observed when controlling for the other confounders such as age, education,

and SES etc, separately.

Further, the associations between stunting and anemia in children and low BMI and ane-

mia in women were assessed by controlling for several confounders simultaneously in a multi-

variable binary logistic model, where confounders were selected based on p-value (<0.10) of

the likelihood ratio test. In the final model for child anemia, the exposure stunting along with

the covariate child’s age and its quadratic form, and the confounders maternal anemia and

SES, and three—way interaction between stunting, SES and age were found to be significant

(Table 5). The results suggest that stunted children are more likely to develop anemia com-

pared to normal children with the estimated adjOR being 1.76 (95% CI: 1.10–2.83). The signif-

icant interaction effect of stunting with SES and age suggests that the effect of stunting could

be modified by SES and age of the child. Fig 1 describes how stunting interacts with age and

SES. It suggests that the likelihood of developing anemia decreases with increasing age and the

rate of decrease is comparatively high till the age of 36 months. In addition, the risk of develop-

ing anemia decreases with the improvement of household economic condition, especially in

the case of normal children. For stunted children, the decline is less. The differences in risk

between stunted children and normal children at various ages are largest in the richest cate-

gory, while the curves for the risk cross in the poorest category. SES is a strong modifier of the

effect of stunting on anemia. Improvement in SES results in rapid decline in the risk of

Association of malnutrition with anemia in children and women

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Table 3. Association between stunting and anemia in children of age 6–59 months.

Confounder Categories Nutritional Status Number at risk % anemic child OR (95%CI) adj OR (95% CI)

Sex of child

Male stunted 465 58.5 1.47(1.16–1.87) 1.39(1.18–1.66)

normal 679 48.9

Female stunted 465 54.8 1.32(1.04–1.68)

normal 625 47.8

Age of a child 6 to 24 stunted 294 75.2 1.42(1.02–1.97) 1.50(1.26–1.79)

normal 467 68.1

25 to 48 stunted 447 49.2 1.46(1.13–1.87)

normal 553 39.9

48 to 59 stunted 189 45.5 1.74(1.19–2.54)

Size of a child Normal size stunted 732 56.0 1.32(1.09–1.59)

normal 1115 49.2

Below average stunted 198 59.1 1.87(1.25–2.79)

Birth order First stunted 308 56.5 1.35(1.01–1.80) 1.39(1.17–1.65)

normal 463 49.0

2 to 3 stunted 383 54.3 1.24(.96–1.60)

normal 619 48.9

4+ stunted 239 60.7 1.85(1.28–1.67)

normal 222 45.5

Child recently had diarrhea No stunted 873 56.6 1.42(1.19–1.69) 1.39(1.18–1.65)

normal 1234 47.9

Yes stunted 57 57.9 1.03(.51–2.08)

normal 70 57.1

Child had fever recently No stunted 544 54.6 1.44(1.16–1.79) 1.33(1.17–1.64)

normal 795 45.5

Yes stunted 386 59.6 1.32(1.00–1.72)

normal 509 52.6

Currently breast feeding No stunted 306 47.1 1.61(1.20–2.16) 1.37(1.15–1.62)

normal 481 35.6

Yes stunted 624 61.4 1.25(1.01–1.55)

Normal 823 55.9

Mother’s education No education stunted 232 56.5 1.34(.91–1.97) 1.34(1.13–1.59)

normal 189 49.2

Primary stunted 363 55.1 1.07(.79–1.43)

normal 365 53.4

Secondary stunted 304 59.9 1.63(1.23–2.15)

normal 623 47.8

Higher stunted 31 45.2 1.50(.69–3.29)

normal 127 35.4

SES

(Continued)

Association of malnutrition with anemia in children and women

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developing anemia among normal children but not in stunted children. (Fig 2: left). The effect

of stunting also varies across the levels of maternal anemia and SES (Fig 2: right). The stunted

children of anemic mothers are at greatest risk of developing anemia whereas the normal chil-

dren of non-anemic mothers are at the lowest risk of developing anemia. The difference in risk

between these groups increases slightly with increasing SES. Interestingly, non-stunted chil-

dren of anemic mothers have elevated risk of developing anemia compared to non-stunted

children of anemic mothers across all SES. Improvement in SES results in rapid decline in risk

among normal children but not as much among stunted children. Thus, both maternal anemia

and SES are strong effect modifiers of stunting.

Similarly, in the final multivariable model for women anemia, BMI is found to be signifi-

cantly associated with anemia status, with the estimated adjOR of 1.80 (95% CI: 1.10–3.48)

(Table 6). The interaction plot for BMI, age and education suggests that the risk of maternal

anemia increases with increasing age for women with no education or primary education and

Table 3. (Continued )

Confounder Categories Nutritional Status Number at risk % anemic child OR (95%CI) adj OR (95% CI)

Poorest stunted 295 58.3 .91(.63–1.29) 1.26(1.06–1.50)

normal 216 60.7

Poorer stunted 210 60.0 1.22(.84–1.78)

normal 234 55.1

Middle stunted 161 52.8 1.06(.71–1.59)

normal 238 51.3

Richer stunted 149 53.7 1.66(1.11–2.48)

normal 272 41.2

Richest stunted 115 55.7 1.89(1.24–2.90)

normal 344 39.8

Region Barisal stunted 112 62.5 1.34(.80–2.22) 1.40(1.18–1.67)

normal 137 55.5

Chittagong stunted 185 58.4 1.60(1.09–2.36)

normal 238 46.6

Dhaka stunted 158 47.5 1.00(.66–1.52)

normal 205 47.3

Khulna stunted 86 63.9 1.75(1.02–2.99)

normal 165 50.3

Rajshahi stunted 99 55.6 1.60(.98–2.62)

normal 178 43.8

Rangpur stunted 122 63.1 1.48(.92–2.36)

normal 177 53.4

Sylhet stunted 168 51.8 1.33(.89–2.00)

normal 204 44.6

Type of place of residence Urban stunted 247 59.5 2.09(1.52–2.87) 1.38(1.16–1.63)

normal 426 41.3

Rural stunted 683 55.6 1.17(.95–1.43)

normal 878 51.8

Overall stunted 925 56.5 1.38(1.17, 1.63)

normal 1309 48.5

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Table 4. Association between BMI and anemia in women of age 15–49 years.

Confounder categories BMI Numbers’ at risk % of anemic women OR (95%CI) adjOR (95%CI)

Mother’s age

15 to 19 BMI<18.5 153 51.6 1.36(.89–2.07) 1.29(1.08–1.53)

BMI> = 18.5 214 43.9

20 to 29 BMI<18.5 428 48.1 1.25(1.00–1.56)

BMI> = 18.5 1187 42.6

30 to 39 BMI<18.5 144 55.6 1.35(.93–1.98)

BMI> = 18.5 425 48

40 to 49 BMI<18.5 24 50.0 1.19(.45–3.16)

BMI> = 18.5 46 45.7

Food

Access to food BMI<18.5 549 47.2 1.17(.96–1.42) 1.25(1.05–1.48)

BMI> = 18.5 1565 43.3

Limited access to food BMI<18.5 200 59 1.55(1.08–2.21)

BMI> = 18.5 307 48.2

Birth in last 5 year

1 BMI<18.5 472 46.0 1.17(.95–1.44) 1.25(1.06–1.49)

BMI> = 18.5 1339 42.1

2–4 BMI<18.5 277 57.8 1.43(1.06–1.91)

BMI> = 18.5 533 48.9

SES

Poorest BMI<18.5 242 55.4 1.12(.81–1.56) 1.13(.95–1.35)

BMI> = 18.5 343 52.5

Poorer BMI<18.5 183 56.3 1.48(1.03–2.13)

BMI> = 18.5 329 47.1

Middle BMI<18.5 144 48.6 1.09(.75–1.62)

BMI> = 18.5 350 46.3

Richer BMI<18.5 119 45.4 1.20(.79–1.81)

BMI> = 18.5 376 40.9

Richest BMI<18.5 61 24.6 .56(.31–1.03)

BMI> = 18.5 474 36.7

Region

Barisal BMI<18.5 79 50.6 1.06(.63–1.78) 1.26(1.06–1.49)

BMI> = 18.5 193 49.2

Chittagong BMI<18.5 123 48.0 1.38(.92–2.08)

BMI> = 18.5 390 40.0

Dhaka BMI<18.5 125 58.4 1.60(1.05–2.43)

BMI> = 18.5 295 46.9

Khulna BMI<18.5 66 40.9 1.23(.71–2.15)

BMI> = 18.5 228 36.0

Rajshahi BMI<18.5 84 45.2 .98(.60–1.61)

BMI> = 18.5 243 45.7

Rangpur BMI<18.5 113 52.2 1.06(.68–1.65)

BMI> = 18.5 281 42.7

Type of place of residence

Urban BMI<18.5 160 46.8 1.32(.94–1.88) 1.25(1.05–1.48)

BMI> = 18.5 641 39.9

Rural BMI<18.5 589 51.3 1.22(1.00–1.49)

(Continued)

Association of malnutrition with anemia in children and women

PLOS ONE | https://doi.org/10.1371/journal.pone.0219170 July 3, 2019 10 / 18

decreases with age for women with secondary and higher education (Fig 3). In addition, the

differences in risk at various ages between women with low BMI and women with normal

BMI are significantly greater for uneducated women and diminish with increasing levels of

education. Interestingly, for women with higher education, the risk of developing anemia is

less in women with low BMI and the risk decreases with age. Fig 4 shows how SES and educa-

tion modify the effect of BMI in women. The risk of being anemic decreases with increasing

SES and the differences in risk between the two groups (low BMI and normal BMI) remain

almost the same across all SES levels (Fig 4: left). Increasing education leads to significant drop

in the risk of developing anemia among women with low BMI. In contrast, education does not

appear to have any significant effect among women with normal BMI levels (Fig 4: right).

Table 4. (Continued )

Confounder categories BMI Numbers’ at risk % of anemic women OR (95%CI) adjOR (95%CI)

BMI> = 18.5 1231 46.2

Mother’s education

No education BMI<18.5 172 60.5 1.66(1.15–2.42) 1.22(1.03–1.45)

BMI> = 18.5 330 47.9

Primary BMI<18.5 282 52.5 1.24(.93–1.66)

BMI> = 18.5 527 42.1

Secondary BMI<18.5 279 42.7 1.04(.79–1.36)

BMI> = 18.5 842 41.8

Higher BMI<18.5 16 37.5 .95(.34–2.64)

BMI> = 18.5 173 38.7

Overall BMI<18.5 719 49.5 1.29 (1.08, 1.53)

BMI> = 18.5 1739 43.2

https://doi.org/10.1371/journal.pone.0219170.t004

Table 5. Results from multivariable logistic regression analysis of child anemia data.

Variables OR P-value 95% CI

Stunting

normal RC

stunted 1.763 <0.01 1.099 2.829

Child age 0.929 <0.001 0.902 0.956

Child age^2 1.003 <0.01 1.001 1.004

maternal anemia 1.801 <0.001 1.501 2.160

Stunting�SES�child age

normal�poorest RC

normal�poorer 0.995 0.320 0.984 1.005

normal�middle 0.992 0.145 0.982 1.003

normal�richer 0.984 <0.01 0.974 0.994

normal�richest 0.976 <0.001 0.966 0.986

stunted�poorest 0.987 0.085 0.972 1.002

stunted�poorer 0.988 0.129 0.972 1.004

stunted�middle 0.982 <0.05 0.967 0.998

stunted�richer 0.981 <0.05 0.965 0.997

stunted�richest 0.987 0.150 0.970 1.005

Constant 4.347 <0.001 2.824 6.692

https://doi.org/10.1371/journal.pone.0219170.t005

Association of malnutrition with anemia in children and women

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Discussion

This paper has investigated the association between anemia and stunting among children in

age group 6–59 months as well as the association between anemia and BMI among women of

reproductive. In general, the prevalence of anemia was markedly high both among stunted

children and women of low BMI compared to their normal counterparts. However, the higher

prevalence among children suggests that they are more vulnerable to anemia than women. In

both cases, there was a positive association between nutritional deficiency (reflected by stunt-

ing or low BMI) and anemia that was statistically significant even after controlling for the

effects of possible confounders. This study identified important interactions that have interest-

ing interpretations. There was significant interaction between stunting, child’s age and house-

hold socio-economic condition (SES). The risk of child anemia decreased with increasing age,

however, the rate of decline was lower for stunted children. The implication of this finding is

that there is a greater prevalence of anemia among stunted children compared to non-stunted

children. In general, improvements in socio-economic status decreased the risk of being ane-

mic in both groups. Thus, very young children belonging to poor households and experiencing

stunting form a high risk group and should be the focus of interventions. Again, the presence

of maternal anemia significantly increased the risk of child anemia even when there was

improvement in socio-economic condition. The interaction effect of stunting and maternal

Fig 1. Interaction plot describing the effect of interaction between stunting, SES and age on the risk of child anemia.

https://doi.org/10.1371/journal.pone.0219170.g001

Association of malnutrition with anemia in children and women

PLOS ONE | https://doi.org/10.1371/journal.pone.0219170 July 3, 2019 12 / 18

Fig 2. Interaction plots describing the association of stunting interact with SES, and maternal anemia, anemia separately on the risk of child

anemia.

https://doi.org/10.1371/journal.pone.0219170.g002

Table 6. Results from multivariable logistic regression analysis of maternal anemia data.

Variables OR P-value 95% CI

BMI

BMI>18.5

BMI< = 18.5 1.807 <0.05 1.100 3.485

SES

poorest RC

poorer 1.022 0.866 0.798 1.308

middle 0.894 0.395 0.690 1.157

richer 0.731 <0.05 0.560 0.953

richest 0.519 0.000 0.388 0.694

Education�BMI�age

no educ�BMI> = 18.5 1.012 0.162 0.995 1.029

no educ�BMI<18.5 1.009 0.470 0.985 1.033

primary�BMI> = 18.5 1.015 0.111 0.997 1.034

primary�BMI<18.5 0.996 0.784 0.969 1.024

secondary�BMI> = 18.5 1.013 <0.05 1.011 1.034

secondary�BMI<18.5 0.987 0.411 0.958 1.018

higher�BMI> = 18.5 1.018 <0.05 1.012 1.040

higher�BMI<18.5 0.978 0.330 0.935 1.023

Constant 0.670 0.112 0.409 1.098

https://doi.org/10.1371/journal.pone.0219170.t006

Association of malnutrition with anemia in children and women

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anemia status suggests that stunted children of anemic mothers are at greater risk of being ane-

mic. The strong association observed between maternal anemia and child anemia may be

explained by the fact that there are certain factors influencing anemia that are common to

both [43]. For example, both the mother and child could have a common dietary pattern and

access to the same source of iron-rich micronutrient food. In addition, they share the same

environment, have access to the same health facilities and are likely to have similar genetic

traits. On the other hand BMI, which reflects nutritional deficiency among the women of

reproductive age, is found to be significantly associated with maternal anemia. Although the

risk of being anemic decreases with improvements in household economic condition, the dif-

ference in risk between women with low BMI and those with normal BMI remained almost

the same. On the other hand, education strongly modifies the effect of BMI and has a profound

effect on women with low BMI. Higher education lowers the risk of being anemic even if the

BMI is low. These findings are similar to those found in other studies conducted for relevant

population with similar [27, 28, 44] or different settings [29, 31, 43].

The results obtained in the study have some important implications. The strong associa-

tions between stunting and anemia in children, and low BMI and anemia in women, could

be due to several factors. Nutritional deficiency may not be directly associated with anemia,

however, it leads to certain changes in the body that make it susceptible to health hazards that

may cause anemia. One hypothesis is that children and women suffering from nutritional

Fig 3. Interaction plot describing the association of BMI interact with education and age on the risk of maternal anemia.

https://doi.org/10.1371/journal.pone.0219170.g003

Association of malnutrition with anemia in children and women

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deficiency are more likely to have weaker immune systems which make them vulnerable to

various illnesses and health hazards such as parasitic infections or chronic inflammation [45–

46]. Many of these conditions reduce the hemoglobin level in blood leading to increased ane-

mia prevalence. The statement is supported by the evidence given in other studies [47] that

nutritional deficiency causes several health hazards.

Strong associations between stunting and anemia in children as well as BMI and anemia in

women indicate that it is necessary to tackle both nutritional deficiency and anemia simulta-

neously under the same program targeting both mother and children. Improving women’s

education and empowerment might be one way to curb high anemia and malnutrition preva-

lence rates. Another possible tool to tackle anemia and malnutrition is mass-and-social-media

campaign which works by increasing awareness and knowledge about these conditions and

methods of prevention.

Strengths and limitations of the study

This study is based on data from a nationally representative survey, which is regularly con-

ducted by an international expert group, and hence the quality of data is high. In addition,

findings from nationally representative data are more helpful for policy makers to design

appropriate interventions. Furthermore, based on data conditions, sophisticated epidemiologi-

cal and statistical analyses have been performed to meet the main objective of the study.

Fig 4. Interaction plot describing the association of BMI interact with SES and education, separately on the risk of maternal anemia.

https://doi.org/10.1371/journal.pone.0219170.g004

Association of malnutrition with anemia in children and women

PLOS ONE | https://doi.org/10.1371/journal.pone.0219170 July 3, 2019 15 / 18

However, this study has some limitations. These include i) unavailability of data on intake

of iron rich food for all subjects [37], ii) data may suffer from re-call bias of information on

SES, size of the child at birth and evidence of fever or diarrhea in last fifteen days of the survey,

and iii) measurement errors in data on anemia (Hb level).

Acknowledgments

The authors acknowledge NIPORT, Mitra and Associates, and ICF international for providing

the data used in this study.

Author Contributions

Conceptualization: M. Shafiqur Rahman.

Data curation: Muntaha Mushfiquee.

Formal analysis: M. Shafiqur Rahman, Muntaha Mushfiquee.

Methodology: M. Shafiqur Rahman.

Software: M. Shafiqur Rahman.

Supervision: M. Shafiqur Rahman.

Writing – original draft: M. Shafiqur Rahman.

Writing – review & editing: M. Shafiqur Rahman, Mohammad Shahed Masud, Tamanna

Howlader.

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