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Original Article
Prevalence and risk factors of anaemia
among men: A study based on Empowered
Action Group states, India
Pradeep Kumar
1
, Himani Sharma
1
, and Kamalesh Kumar Patel
2
Abstract
Background: Despite various programmes initiated by the Government of India, the nutritional indicators are not
encouraging, as several problems like undernutrition, malnutrition and anaemia – still persist in the country, especially in
the Empowered Action Group (EAG) states. Aim: Because of the dearth of studies regarding anaemia among men in
India, the present study aimed to determine its prevalence in this population in the EAG states and to analyse its geo-
graphical and socio-demographic determinants. Methods: The study utilized nationally representative, cross-sectional
survey data from round 4 of the National Family Health Survey conducted in 2015–16. Bivariate analysis along with binary
logistic regression were performed to assess the predictors of anaemia among men in the EAG states. Results: Around a
quarter of the men in the EAG states suffered from anaemia. A similar high-prevalence pattern was observed across the
EAG states. Wherein, Bihar and Jharkhand had the highest prevalence of anaemia while Uttarakhand showed the lowest.
Age, place of residence, marital status and caste were positively associated with the likelihood of anaemia among men in
the EAG states. Conclusions: Focusing on the EAG states, this study considered the severity of anaemia as a public health
problem among men. Strategies to reduce the burden of anaemia among this population are needed. The government
should formulate programmes targeting anaemia specifically, and improving the nutritional status among men in general in
the EAG states.
Keywords
Nutrition, men, anaemia, EAG, diet, health
Introduction
Anaemia, which refers to low level haemoglobin in the
blood, is often indicative of several health issues among
individuals. Developing and underdeveloped countries
already face challenges regarding undernutrition, of which
anaemia constitutes a substantial part. Anaemia may not
have direct health consequences for individuals but can
have various latent effects. In 2013, approximately 27%of
the world’s population (nearly 1.93 billion people) was
affected by anaemia; developing countries were responsi-
ble for more than 89%of that share (Kassebaum, 2016).
Anaemia in the South Asian countries, particularly India, is
a major public health problem (Jose, 2011). Affecting over
half the population in almost all age groups, anaemia can
have devastating consequences for human health and for
socioeconomic development more widely (Bhatia et al.,
2018). The prevalence of undernourishment has fallen in
many countries; however, globally, the burden remains
high and rates of anaemia have not fallen rapidly enough to
keep pace with current global trends. According to the
Global Nutrition Report 2017, 52 countries had the double
burden of overweight and anaemia, whereas 38 were
affected by a stunting and anaemia double burden (Hawkes
and Fanzo, 2017).
According to the World Health Organization (WHO), iron
deficiency anaemia is considered a public health problem
when the prevalence of low haemoglobin concentration, i.e,
anaemia, exceeds 5.0%of the population (Jouet, 1989).
According to the National Family Health Survey Round 4
(NFHS-4), 53%of women aged 15–49 and 23%of men aged
15–49 in India are anaemic (<13.0 g/dl) (International Insti-
tute for Population Sciences (IIPS) and ICF, 2017. National
1
Department of Mathematical Demography & Statistics, International
Institute for Population Sciences, Mumbai, India
2
Indian Institute of Health Management Research (IIHMR), Jaipur, India
Corresponding author:
Himani Sharma, Department of Mathematical Demography & Statistics,
International Institute for Population Sciences, 400088, Mumbai, India.
Email: himani.sharma446@gmail.com
Nutrition and Health
1–8
ªThe Author(s) 2021
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0260106020982348
journals.sagepub.com/home/nah
Family Health Survey (NFHS-4), 2015-16: India. Mumbai:
IIPS). Anaemia can be caused by several factors. Iron defi-
ciency (>60%) is the leading cause globally (Kassebaum,
2016). In India, despite the population having assess to a rich
diversity of food, for example, consumption of cereals,
fruits and following metabolic regulations, anaemia is
widespread (Nair and Iyengar, 2009). Iron deficiency can
hamper cognitive development and growth among children,
can affect work capacity and productivity among individuals,
and has implications for pregnancy among women.
Anaemia isa serious health problemin India as many of the
states fail to provide healthy statistics to the concerned pop-
ulation. The nutritional indicators are not encouraging as
several problems such as undernutrition, malnutrition and
anaemia persist in the country, especially in the Empowered
Action Group (EAG) states. All eight EAG states, that is,
Bihar, Jharkhand, Uttar Pradesh, Uttarakhand, Madhya Pra-
desh, Chhattisgarh, Odisha (formerly Orissa) and Rajasthan
have a high prevalence of anaemia among men. Government
of India hasinitiated several programmes,such as the National
Nutritional Anaemia Control Programme, Weekly Iron and
Folic Acid Supplementation, and the National Iron Plus Ini-
tiative (NIPI), to combat anaemia, however, the situation
remains alarming as these programmes have failed to address
the problem and no significant change in the prevalence of
anaemia has been observed (Bhatia et al., 2018).
Studies across the world and particularly in India have
been dedicated to understanding and analysing the issue of
anaemia as a major health problem among women, ado-
lescents and young children. A plethora of literature pro-
viding significant knowledge regarding anaemia in women
of reproductive age and young children is available both on
and offline (Luo et al., 2019; Habte et al., 2013; Rammohan
et al., 2012; WHO/UNICEF, 1999). But very few have
focused on how anaemia is a serious health concern among
men. One such recent study from the purview of Indian
men showed that almost a quarter of men aged 15–54 years
have some degree of anaemia. The study concluded that
anaemia among men is a substantial public health issue in
India that has thus far received little attention from either a
research or policy perspective. The study also highlighted
significant variation in the prevalence of anaemia among
men across states and districts, indicating the importance of
targeting resources and relevant programmes to those areas
in greatest need (Didzun et al., 2019). Though the recent
work on anaemia in men was carried out at a national and
district level, no study in India has focused on the issue of
anaemia in the disadvantaged states, commonly known as
the EAG states. As previosuly stated, the Government of
India classifies Bihar, Jharkhand, Uttar Pradesh, Uttarak-
hand, Rajasthan, Madhya Pradesh, Chhattisgarh and Odi-
sha as the EAG states. The current study focused on the
EAG states as these constitute approximately 46%of the
total population and also the majority of the people living
below the poverty line (Office of the Registrar General
Census Commissioner, 2011). Moreover, the EAG states
present the worst scenarios in health-related outcomes
contributing to the highest disease burden in the country
(Ministry of Health and Family Welfare, 2011).
Therefore, the aims of the study were to determine the
prevalence of anaemia among Indian men in the EAG states;
analyse its geographic and socio-demographic determinants;
and identify the factors associated with anaemia. This study
also examined how the prevalence of anaemia in EAG states
differs from non-EAG states and the rest of India as a whole.
Methods
Sample
The study utilized national, representative, cross-sectional
survey data obtained from the National Survey Health Sur-
vey, round 4 (NFHS-4) conducted in 2015–16 under the
stewardship of the Ministry of Health and Family Welfare,
Government of India. The survey provided information on
population, health, nutrition, abortion, sexual behaviour,
HIV/AIDS knowledge, and domestic violence for India and
its states, union territories and districts. The clinical, anthro-
pometric and biochemical components of NFHS-4 are
designed to provide estimates of the prevalence of malnutri-
tion, anaemia, hypertension, HIV, and high blood glucose
levels through a series of biomarker tests and measure-
ments.The survey covered all 29 states states and 7 union
territories and was designed to provide representative esti-
mates for each of the 640 districts in the country, in addition
to being representative of each of the states and the country
as a whole (International Institute for Population Sciences
(IIPS) and ICF, 2017). The NFHS-4 used a stratified two-
stage sampling procedure. A total of 628,900 households
were selected, of which 601,509 were successfully inter-
viewed giving a response rate of 98%. From a random
15%subsample of households, men aged 15–54 were invited
to participate giving an individual response rate of 91.9%(n
¼112,122). The NFHS-4 used a questionnaire that was
distributed to all the members of the household and to vis-
itors who stayed there the night before the interview.
This was based on a de facto census that allocates
individuals to the geographical area where they were
present at a specified time, in this case, the night before the
interview. We limited our analysis to 106,527 men who
were usually residents and slept the previous night in the
household. The biomarker questionnaire covered height,
weight and haemoglobin for children, and measurements of
height, weight, haemoglobin, blood pressure, and random
blood glucose for women age 15–49 and (in the state
module subsample of households only) men aged 15–54
years.
Anaemia testing
In the NFHS-4, with the consent of the participants, blood
specimens for anaemia testing were collected by the health
investigators. Samples were drawn from a drop of blood
taken from a finger prick and collected in a microcuvette.
2Nutrition and Health XX(X)
Haemoglobin analysis was conducted on site with a
battery-operated portable HemoCue Hb 201þanalyser
(HemoCue, Sweden). For more details on how haemoglo-
bin was measured, refer to National Family Health Survey
(NFHS-4), 2015–16 (IIPS and ICF, 2017). Following
WHO’s recommendations, men were categorised as anae-
mic in any form if their haemoglobin concentration was
lower than 13.0g/dL, mildly anaemic if it was 12.0–12.9 g/dL,
moderately anaemic if it was 9.0–11.9 g/dL, and severely
anaemic if it was lower than 8.9 g/dL.
Independent variables
The predictors included age, household wealth, men’s
education, place of residence, marital status, caste, religion,
body mass index (BMI) and dietary pattern. Age was
grouped as 15–19, 20–24, 25–29, 30–34, 35–39, 40–44,
45–49 and 50–54 years. Household wealth was calculated
in the survey by combining household amenities, assets,
and durables and then by characterizing the households in a
range from the poorest to the richest, corresponding to
wealth quintiles ranging from the lowest to the highest. The
men’s educational level was categorized according to
number of years’ schooling: no education, primary, sec-
ondary, and higher education. Marital status was categor-
ized as currently married or never married (also included
formerly and ever married). Caste was divided into three
categories: scheduled caste (SC), scheduled tribe (ST),
other backward class (OBC), and other castes. Religion was
categorized as Hindu and non-Hindu (including Muslim,
Christian, Sikh, Buddhist/Neo-Buddhist, Jain, Jewish,
Parsi/Zoroastrian, no religion, and other). BMI was
included in our analysis as broad categories (<16.0 kg/m
2
,
16.0–17.9 kg/m
2
, 18.0–22.9 kg/m
2
,23.0–24.9kg/m
2
,25.0–
27.4 kg/
2
,27.5–29.9kg/
2
,and30.0 kg/m
2
)underthe
rationale that broad BMI groups could be assessed virtually
without needing exact measurements. For dietary pattern, an
index was created using the variables available for the diet of
the men in the survey. The dietary diversity of the men at the
time of the survey was taken as a proxy measure of diet
diversity. The question asked ‘how often do you yourself eat
the following food items: daily, weekly, occasionally, or
never?’ The food items were milk or curd, pulses or beans,
dark green leafy vegetables, fruits, eggs, fish, chicken or
meat. The diet score or index was created from the men’s
self-reported frequency of consumption categorizing it as
daily (3), weekly (2), occasionally (1) or never (0). In the
next step, the scores for the variables were added and then
assigned to one of three categories: low, medium, and high,
in which high depicted a greater variety of food items con-
sumed while low depicted the opposite.
The current study employed bivariate analysis to
examine differences in anaemia among men by the socio-
economic predictors recorded in the study; statistical
dependence was evaluated with the chi-square test. Binary
logistic regression was used to determine the key predictors
of anaemia among men in the EAG states. P-values of less
than 0.05 were considered significant and all analyses were
carried out using Stata (Version 15); the choropleth map
(Figure 2) was created in ArcGIS (Version 10.3).
Results
Figure 1 depicts the prevalence of anaemia among men in
the EAG states. In India, about 23.4%of men suffer from
any anaemia, whereas this figure is 25.6%for men in the
EAG states. The EAG states were found to have a higher
burden of any anaemia than the national average. Mild
anaemia was most prevalent among the EAG states fol-
lowed by moderate and severe. Severe anaemia was
reported to be highest in Uttar Pradesh (1.6%) followed by
Bihar (1.3%); the lowest prevalence of severe anaemia was
found in Rajasthan (0.7%) and Uttarakhand (0.8%). In
terms of both moderate and mild anaemia, Bihar (14.4%
and 17.4%respectively) and Jharkhand (13.2%and 16.6%
respectively) led with the highest prevalence. The lowest
prevalence of moderate and mild anaemia among men was
observed in Uttarakhand (6.4%and 8.3%).
The prevalence of anaemia among men in the EAG
states according to socioeconomic and socio-demographic
characteristics is presented in Table 1. A significant per-
centage of anaemia prevalence (25.6%) was found in all the
EAG states; the highest being found in Bihar (33.3%)fol-
lowed by Jharkhand (31.1%) and Odisha (29.5%). The
highest prevalence of anaemia among men was recorded in
the 15–19 years age group across all EAG states, ranging
from highest in Bihar (37.9%) to the lowest in Uttarakhand
(22.0%). A similar pattern of anaemia prevalence was
found in the older 45–49 years and 50–54 years age groups
in all the EAG states. All the EAG states except Uttarak-
hand followed a uniform pattern in which the highest
and lowest prevalence of anaemia were found among the
poorest and richest men respectively. In Uttarakhand,
the lowest prevalence was found among men belonging to
the poorest wealth category (9.8%) followed by men
belonging to the middle wealth index category (19.9%).
Education and anaemia were found to have a negative
relationship: an increase in former led to a decrease in the
latter. Among all the EAG states except Uttarakhand, the
highest prevalence of anaemia was found among illiterate
men, whereas the lowest was among the more highly
educated men. Men residing in rural areas of the EAG
states had a higher prevalence of anaemia compared to their
urban counterparts; the highest was in Bihar followed by
Jharkhand, Odisha and Madhya Pradesh. Anaemia was
found to be higher among never-married men in the states
of Madhya Pradesh, Chhattisgarh, Uttar Pradesh, Uttarak-
hand and Odisha, whereas in the states of Bihar, Jharkhand
and Rajasthan, higher prevalence was found among cur-
rently married men. A preponderance of anaemia was
found among men belonging to the SC/ST category in all
states except Bihar, where it was higher among men of the
OBC and other categories. The majority of EAG states
showed a higher prevalence of anaemia among Hindu men:
Kumar et al. 3
this was highest in Bihar (33.5%) followed by Madhya
Pradesh (25.7%) and Uttar Pradesh (24.9%). In terms of
body mass index, a preponderance of anaemia was found
among men with a BMI score <16.0 across all EAG states;
the highest prevalence was observed in Odisha (65.6%)
followed by Jharkhand (49.2%), Uttar Pradesh (44.4%) and
Bihar (42.9). Lastly, prevalence of anaemia was con-
siderably higher among men who consumed a diet poor in
variety and nutrients in all the EAG states.
Figure 2 presents the spatial representation of anaemia
prevalence among men in the EAG states. The choropleth
map depicts prevalence through use of different shades of
red: the darkest shade represents the highest prevalence
(range 25.67–33.30), the lightest the lowest one (range
15.52–17.65). Three states – Bihar, Jharkhand and Odisha –
are shown to have the highest prevalence of anaemia
among men followed by Uttar Pradesh, Madhya Pradesh
and Chhattisgarh. The lowest prevalence was found in
Rajasthan and Uttarakhand.
The results of the binary logistic regression model
showing the odds ratio (95%confidence interval (CI)) of
factors associated with anaemia among men aged 15–54
years are presented in Table 2. The likelihood of anaemia
among men was lower in the 20–24 years age group (OR:
0.67; CI: 0.625–0.729), followed by those aged 25–29 years
(OR: 0.70; CI: 0.642–0.770), 30–34 years (OR: 0.73; CI:
0.662–0.809) and 35–39 years (OR: 0.77; CI: 0.697–0.856).
However, it was higher in the 50–54 years age group (OR:
1.12; CI: 1.001–1.249) than in the age group 15–19 years.
The odds of anaemia among men decreased with an increase
in the wealth quintile from poorer (OR: 0.92; CI: 0.864–
0.972) to richest (OR: 0.76; CI: 0.692–0.835) compared with
the poorest wealth quintile. Men with primary (OR: 0.91; CI:
0.847–0.980), secondary (OR: 0.86; CI: 0.812–0.920) and
higher education (OR: 0.74; CI: 0.674–0.808) had lower
odds of suffering with anaemia than men with no education.
Men from rural areas reported higher odds (OR: 1.07; CI:
1.012–1.138) of anaemia than those residing in urban areas.
In comparison to men belonging to others castes, ST caste
men reported higher odds of anaemia (OR: 1.62; CI: 1.493–
1.766). Muslim men (OR: 0.91; CI: 0.842–0.981) were less
prone to have anaemia compared with their Hindu coun-
terparts. BMI was found to have a significant negative effect
on the prevalence of anaemia: with an increase in BMI, the
prevalence of anaemia decreased significantly. Further, men
with a more varied and enriched diet pattern (OR: 0.89; CI:
0.837–0.943) were less likely to report anaemia. The like-
lihood of anaemia in Chhattisgarh (OR: 0.51; CI: 0.459–
0.562), Jharkhand (OR: 0.83; CI: 0.753–0.909), Madhya
Pradesh (OR: 0.66; CI: 0.615–0.716), Odisha (OR: 0.82; CI:
0.752–0.900), Rajasthan (OR: 0.45; CI: 0.411–0.494), Uttar
Pradesh (OR: 0.65; CI: 0.607–0.701) and Uttarakhand (OR:
0.44; CI: 0.384–0.504) was lower than in Bihar.
Discussion
The results of the study show that the prevalence of anae-
mia among men was reported to be higher than the national
average. A similar pattern of anaemia prevalence among
men was observed across all EAG states. Among these,
0
5
10
15
20
25
30
35
Severe Moderate Mild Any anaemia
Figure 1. Prevalence of anaemia by type among men in the EAG states and India, NFHS-4.
4Nutrition and Health XX(X)
Table 1. Prevalence (%) of anaemia in the EAG states according to socioeconomic and socio-demographic characteristics, NFHS-4, 2015–16.
Predictors
Bihar Jharkhand MP Chhattisgarh UP Uttarakhand Rajasthan Odisha EAG States
Anaemia NAnaemia NAnaemia NAnaemia NAnaemia NAnaemia NAnaemia NAnaemia NAnaemia N
Age (in years)
15–19 37.9 1184 35.6 677 36.3 1700 28.0 603 31.5 2770 22.0 425 22.3 1131 30.7 614 32.0 9104
20–24 25.9 826 27.2 612 22.5 1581 18.1 564 18.8 2194 11.0 297 15.7 996 21.2 544 20.6 7614
25–29 28.0 743 24.3 562 22.2 1427 20.2 563 20.3 1863 12.0 264 14.5 877 26.5 567 21.5 6866
30–34 31.1 663 25.0 477 23.1 1280 17.3 498 21.7 1471 15.1 246 17.0 766 23.7 625 22.8 6026
35–39 34.6 663 27.1 492 20.8 1192 20.1 441 21.3 1492 12.3 231 15.4 738 31.1 578 23.5 5827
40–44 29.7 549 35.1 419 22.4 979 24.2 372 24.3 1291 18.9 197 17.3 589 31.2 537 25.2 4933
45–49 38.9 551 39.2 372 28.1 972 29.8 352 25.7 1227 16.2 191 17.7 548 35.2 510 29.0 4723
50–54 41.0 423 41.3 244 28.8 746 31.3 291 30.0 865 13.9 173 21.6 409 39.2 404 31.8 3555
Wealth index
Poorest 37.0 2262 37.8 1666 33.5 2693 27.5 1119 27.6 2992 9.8 85 27.5 927 41.6 1442 32.9 13,186
Poorer 34.5 1426 30.2 817 24.9 2151 23.8 925 27.0 2922 21.3 353 21.8 1319 29.6 1149 27.5 11,062
Middle 29.0 972 26.6 566 25.2 1632 25.1 548 22.9 2543 19.9 543 15.8 1217 23.3 820 23.4 8841
Richer 26.6 668 21.2 411 22.1 1604 19.1 500 23.1 2310 12.5 481 13.3 1200 18.9 551 20.9 7725
Richest 24.0 274 21.4 395 18.9 1797 16.4 592 18.6 2406 12.4 562 12.8 1391 16.1 417 17.5 7834
Educational level
No education 40.4 1211 36.0 749 32.4 1646 28.6 546 26.5 2163 13.9 158 23.2 918 46.0 684 31.8 8075
Primary 34.6 684 35.6 472 25.9 1745 23.7 608 25.0 1669 18.7 210 19.0 744 35.2 751 27.3 6883
Secondary 31.2 2973 30.9 2155 25.8 5306 22.8 2096 25.0 7216 16.2 1271 17.9 3317 26.2 2425 25.3 26,759
Higher 27.4 734 19.0 479 16.8 1180 17.6 434 18.7 2125 12.1 385 11.4 1075 17.4 519 18.2 6931
Place of residence
Urban 24.4 1072 23.2 1076 21.7 3085 18.6 1007 20.9 4140 14.5 688 15.3 1788 17.3 969 20.3 13,825
Rural 35.2 4530 34.1 2779 27.5 6792 24.6 2677 25.7 9033 16.1 1336 18.6 4266 32.6 3410 27.6 34,823
Marital status
Never married 33.1 1967 30.7 1306 29.3 3358 23.5 1277 25.5 5387 17.4 830 17.5 2117 25.6 1435 26.3 17,677
Currently married 33.2 3635 30.9 2549 23.8 6519 22.8 2407 23.3 7786 14.2 1194 17.7 3937 31.3 2944 25.2 30,971
Caste
SC/ST 32.8 1168 38.2 1631 31.7 3802 26.7 1797 26.5 3318 15.7 509 21.3 2155 37.0 2055 29.3 16,435
OBC 33.0 3479 27.4 1773 22.1 4538 20.6 1571 23.5 7086 15.4 539 15.7 2734 25.4 1578 24.4 23,298
Others 34.0 955 20.5 451 22.0 1537 17.6 316 23.2 2769 15.4 976 15.6 1165 19.7 746 22.8 8915
Religion
Hindu 33.5 4868 30.5 2829 25.7 9130 23.5 3506 24.9 10,574 15.1 1771 17.9 5458 29.4 4139 25.9 42,275
Non-Hindu 31.2 734 31.9 1026 24.6 747 11.3 178 21.4 2599 17.5 253 14.9 596 29.8 240 23.9 6373
Body mass index
<16.0 42.9 194 49.2 138 41.6 468 41.8 96 44.4 499 29.8 46 35.1 234 65.6 116 43.5 1791
16.0–17.9 42.3 851 37.5 544 31.1 1660 29.8 510 32.5 2020 19.0 209 24.4 831 43.7 536 33.5 7161
18.0–22.9 32.7 3128 32.1 2273 25.6 5554 24.2 2204 23.1 7297 16.9 1052 17.1 3205 31.3 2380 25.5 27,093
23.0–24.9 29.8 691 20.0 472 20.2 1102 13.2 479 18.8 1565 10.7 357 13.6 943 20.5 626 19.6 6235
25.0–27.4 22.1 438 24.4 274 16.6 676 12.6 257 16.7 1077 12.6 218 13.2 483 17.4 413 17.3 3836
>= 27.5 29.26 285 17.1 151 17.3 411 15.7 134 19.6 680 10.8 139 12.4 345 15.8 300 19.0 2445
Missing 24.94 15 35.5 3 58.3 6 52.4 4 27.9 35 43.2 3 9.8 13 17.5 8 26.6 87
Dietary pattern
Low 36.8 2385 34.2 1548 27.4 4795 25.6 1373 24.8 6621 19.0 540 19.0 4590 37.4 786 26.0 21,881
Medium 34.9 1467 31.8 1431 24.2 3066 22.6 1548 23.5 4334 16.5 887 13.4 1134 31.4 1816 26.0 16,440
High 28.0 1750 23.2 876 23.7 2016 19.3 763 24.1 2218 10.4 597 14.9 330 24.3 1777 24.1 10,327
Total 33.3 5602 31.1 3855 25.7 9877 23.1 3684 24.2 13,173 15.5 2024 17.7 6054 29.5 4379 25.6 48,648
Note: EAG: Empowered Action Group; NFHS: National Family Health Survey; UP: Uttar Pradesh; MP: Madhya Pradesh; SC: Scheduled caste; ST: scheduled tribe; OBC: Other backward class
5
Bihar and Jharkhand showed the highest prevalence of
anaemia, whereas Uttarakhand had the lowest. Age
emerged as a significant determining factor for anaemia
among men: the higher the age, the higher the chance of
men suffering from anaemia. A recent study in India found
that anaemia prevalence among men was more common
among those with a lower household wealth and level of
education, residing in rural areas, who were smokers or
consumers of smokeless tobacco, who had a BMI of less
than 18.0kg/m
2
, and who were living in districts with a
lower primary school completion rate and level of urbani-
zation (Didzun et al., 2019). Wealth and education were
negatively associated with men having anaemia. Con-
versely, a rural place of residence, being married and
belonging to a deprived caste were positively associated
with the likelihood of having anaemia among men in the
EAG states.
The National Nutrition Policy of 1993 stated that the
nutritional status of a population is critical to the devel-
opment and wellbeing of a nation (Government of India,
Department of Women and Child Development, Ministry
of Human Resource Development, 1974). Since then,
several policies and programmes have been drafted and
initiated in India to ensure food security, providing ade-
quate nutrition to the population and reducing the problems
of undernutrition and anaemia in particular. The underlying
feature of most of the programmes is their focus on chil-
dren, women or adolescent boys or girls. For instance, the
NIPI, introduced in 2013, aimed to reduce the prevalence of
anaemia among adolescents, and women of reproductive
age who were not pregnant or lactating. Another
Figure 2. Choropleth map depicting the spatial pattern of anaemia among men in the EAG states, NFHS-4.
Table 2. Adjusted odds ratio for anaemia by socioeconomic
and socio-demographic characteristics in EAG states, India,
2015–16.
Covariates Odds ratio (95% confidence interval)
Age (in years)
15–19 Ref.
20–24 0.67*** (0.625–0.729)
25–29 0.7*** (0.642–0.77)
30–34 0.73*** (0.662–0.809)
35–39 0.77*** (0.697–0.856)
40–44 0.85*** (0.763–0.942)
45–49 1.01 (0.905–1.117)
50–54 1.12** (1.001–1.249)
Wealth index
Poorest Ref.
Poorer 0.92*** (0.864–0.972)
Middle 0.85*** (0.797–0.914)
Richer 0.81*** (0.75–0.88)
Richest 0.76*** (0.692–0.835)
Educational level
No education Ref.
Primary 0.91** (0.847–0.98)
Secondary 0.86*** (0.812–0.92)
Higher 0.74*** (0.674–0.808)
Place of residence
Urban Ref.
Rural 1.07** (1.012–1.138)
Marital status
Never married Ref.
Currently married 1.02 (0.955–1.098)
(continued)
6Nutrition and Health XX(X)
comprehensive programme, Anaemia Mukt Bharat, tar-
geted a 50%reduction in anaemia prevalence among young
children (6–59 months), adolescents (15–19 years), and
women of reproductive age (15–49 years) (Bhatia et al.,
2018).
The findings of the current study have several important
implications. First, approximately a quarter of men aged
15–54 reported having anaemia, both in India in general
and in the EAG states, indicating it is as considerable a
problem as it is among women and children. Second, the
EAG states exhibited a uniform picture of anaemia pre-
valence, though some variation still exists. Bihar showed
the worst picture, irrespective of the severity, whereas
Uttarakhand had the lowest incidence of any anaemia
amongst all the EAG states.
This study contributes several insights regarding the
burden of anaemia among men in the EAG states. Anaemia
is more prevalent among illiterate men, those who belong
to the poorest families, those who reside in rural areas and
among men from the ST population. The study also
emphasized that anaemia is significantly lower among men
who have a suitable diet intake. Focusing only on the EAG
states, this study gauged the severity of anaemia as a public
health problem. Several initiatives need to be undertaken
by the government to target these states in terms of nutri-
tion and diet. Among the EAG states, Bihar stands at the
most critical end of the scale, followed by Uttar Pradesh
and Madhya Pradesh. We suggest that initiatives should be
undertaken at the state level, be it towards targeting the
household or improving the quality and diversity of food in
the workplace. This could be carried out by setting up
special screening centres in the workplace and a dedicated
food court offering healthy, high-fibre food, providing a
comprehensive range of choice, at least to those who can
afford it. For men engaged in non-organized sectors (e.g.
low wages, irregular work), screening should be under-
taken at regular intervals in order to track their hae-
moglobin levels. The government needs to develop
effective programmes, specifically targeting the India’s
male population with anaemia and the EAG states in par-
ticular; alternatively, men could be covered under the
umbrella of programmes delivered for women and
children.
Availability of data and materials
The study is based on a large-scale secondary data set,
National Family Health Survey (NFHS-4), 2015–16: India,
conducted by the International Institute for Population
Sciences. The data is available in the public domain and
can be accessed from the official site of The DHS Program:
Demographic and Health Surveys (https://dhsprogram.
com).
Author’s contribution
PK and HS wrote the initial draft of the manuscript with
contributions from KKP. PK conducted the data analysis,
wrote the results while HS contributed to the introduction,
results and discussion section. All authors reviewed, com-
mented and edited subsequent drafts of the manuscript. All
authors approved the submission of the manuscript to the
Nutrition and Health Journal, and consent to the publication
of this manuscript.
Declaration of conflicting interests
The authors declared no potential conflicts of interest with
respect to the research, authorship, and/or publication of
this article.
Ethical approval
This study was a secondary analysis of the National Family
Health Survey, 2015–16 data set. Thus, no additional ethi-
cal approval was required. The protocol for the NFHS–4
survey, including the content of all the survey question-
naires, was approved by the IIPS Institutional Review
Board and the ICF Institutional Review Board.
Table 2. (continued)
Covariates Odds ratio (95% confidence interval)
Caste
Others Ref.
SC 0.96 (0.89–1.034)
ST 1.62*** (1.493–1.766)
OBC 0.94* (0.886–1.004)
Religion
Hindu Ref.
Muslim 0.91** (0.842–0.981)
Others 0.99 (0.869–1.147)
Body mass index (g/dL)
<16.0 Ref.
16.0–17.9 0.63*** (0.566–0.702)
18.0–22.9 0.46*** (0.414–0.507)
23.0–24.9 0.36*** (0.322–0.408)
25.0–27.4 0.33*** (0.29–0.377)
>¼27.5 0.37*** (0.323–0.432)
Missing 0.48*** (0.294–0.794)
Dietary pattern
Low Ref.
Medium 0.96 (0.914–1.008)
High 0.89*** (0.837–0.943)
EAG states
Bihar Ref.
Chhattisgarh 0.51*** (0.459–0.562)
Jharkhand 0.83*** (0.753–0.909)
Madhya Pradesh 0.66*** (0.615–0.716)
Odisha 0.82*** (0.752–0.9)
Rajasthan 0.45*** (0.411–0.494)
Uttar Pradesh 0.65*** (0.607–0.701)
Uttarakhand 0.44*** (0.384–0.504)
Note: Ref.: reference category; EAG: empowered action group; SC:
scheduled caste; ST: scheduled tribe; OBC: other backward class.
*p< 0.1, **p< 0.05, ***p< 0.01.
Kumar et al. 7
Funding
The authors received no financial support for the research,
authorship, and/or publication of this article.
ORCID iD
Himani Sharma https://orcid.org/0000-0001-8317-2737
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