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Dietary Iron Consumption Estimates Among Women
Of Reproductive Age In Kersa, Eastern Ethiopia:
Cross-Sectional Study
Nega Assefa
Haramaya University College of Health and Medical Sciences
Yasir Younis Abdullahi ( yasdire@gmail.com )
VS Hospital https://orcid.org/0000-0003-3402-0029
Aklilu Abraham
Haramaya University College of Health and Medical Sciences
Elena C Hemler
Harvard University T H Chan School of Public Health
Isabel Madzorera
Harvard University T H Chan School of Public Health
Yadeta Dessie
Haramaya University College of Health and Medical Sciences
Kedir Teji Roba
Haramaya University College of Health and Medical Sciences
Wafaie W Fawzi
Harvard University T H Chan School of Public Health
Research
Keywords: Women of reproductive age (WRA), Adjusted prevalence ratios (APRs), 95% condence intervals
(CIs), iron bioavailability, meat, poultry, sh
Posted Date: October 12th, 2021
DOI: https://doi.org/10.21203/rs.3.rs-955227/v1
License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read
Full License
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Abstract
Dietary iron inadequacy is a public health concern in developing countries. Women of reproductive age
(WRA) are the most at risk for this micronutrient deciency due to biological, socio-cultural, and dietary
factors. This analysis aimed to assess estimated dietary intakes of iron (including heme and non-heme)
and estimate bioavailability of dietary iron intake in Ethiopian women of reproductive age in Kersa district,
Eastern Ethiopia.
A total of 1140 randomly selected women from households in Kersa participated in this study. We used a
non-quantitative food frequency questionnaire to assess total dietary iron consumption in WRA. Adjusted
prevalence ratios (APRs) and 95% condence intervals (CIs) were computed using modied Poisson
regression to evaluate factors for inadequate dietary iron intake.
The median usual iron consumption was 24.7 mg/d and 41.8 % of WRA were at risk for iron inadequacy.
The following factors were associated with a greater likelihood for the risk of iron inadequacy: seasonal
(APR 1.56; 95% CI 1.36-1.80) and part-time (APR 1.75; 95% CI 1.45-2.12) agricultural employment, market
food source (APR 1.30; 95% CI 1.14-1.49), old age (APR 1.29; 95% CI 1.05-1.60) and low women’s dietary
diversity (APR 2.34; 95% CI 1.88-2.91).
Two-fths of women had an inadequate dietary iron intake. Improving dietary diversity and food security,
fortifying staple foods that have low iron bioavailability, and increasing animal-based foods and fruit
consumption with meals would help to decrease the burden of iron dietary inadequacy and deciency in
WRA.
Introduction
The human body needs iron (
Fe
), which is a trace mineral that is essential for the synthesis of
Heme
, a
constituent of hemoglobin - an oxygen carrier in the body, iron-containing enzymes, and for neural
development in the fetus and during childhood [1, 2]. Dietary iron intake is composed of both heme (from
meat, poultry, and sh) and non-heme iron (from cereals, legumes, fruits, and vegetables). Bioavailability is
high for heme compared to non-heme iron. It is estimated that a quarter of heme-iron and one-tenth of non-
heme iron is bioavailable from the diet [3, 4]. Iron is actively absorbed in the gut and transported by
transferrin to the bone marrow for producing blood products and is reversibly stored as ferritin [5, 6]. The
body’s balance of iron depends upon its reserve, loss, and absorption (bioavailability and intake).
Iron deciency occurs when the diet cannot supply enough iron to cover the body’s physiological
requirements. This can occur due to dietary intake is quantitatively inadequate, reduced absorption, or
increased demands for iron for example among women of reproductive age (WRA) during pregnancy or due
to menstruation [7, 8]. Iron deciency is the most frequently encountered micronutrient malnutrition in the
world [9]. Although the global prevalence of iron deciency in non-pregnant women has decreased from 33–
29% in the past decade [10], it is still a signicant public health concern as it affects up to 500-600 million
people, the majority in low- and middle-income countries (LMICs) [11, 12]. Inadequate iron intake can cause
iron deciency anemia (IDA), and IDA prevalence is high in WRA (because of loss in menstruation) in
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developing countries [13, 14]. It also leads to reduced work capacity, loss of appetite, weakened immunity
and puts pregnancies at risk contributing to 20% of all perinatal and maternal deaths [15, 16]. IDA can also
disrupt the metabolic process and affect behavioral and cognitive function, with severe consequences for
children [5, 17].
The mean dietary iron intake among WRA reported in Kenya, Nigeria, and South Africa ranged from 3.8 to
97.8 mg/d and around 34–100% of the WRA had inadequate intakes [13]. Studies suggest that only 8–12%
of the WRA in Ethiopia have inadequate
Fe
intake, however, most of the staples consumed in Ethiopia
including iron-rich foods such as teff have low bioavailability of iron [13, 18]. Additionally, the prevalence of
maternal and child anemia in Ethiopia at 23% and 56% is among the highest globally [19]. Therefore,
understanding the role of dietary intake in iron status among Ethiopians is of critical importance.
Several factors contribute to inadequate intake of dietary iron in Ethiopia. As dietary intake in Ethiopia is
mostly plant-based with limited diversity and consumption of animal source foods, the presence of anti-
nutritional factors has made the bioavailability of iron signicantly low [18]. Additionally, poverty, illiteracy
and limited knowledge of nutrition, and poor lifestyle choices are also contributing factors [5, 20]. Studies in
other LMICs have also shown that high cost and limited access to micronutrient-rich foods, low
acceptability of fortied foods, fasting, and sub-optimal dietary practices inuence dietary iron intake in
WRA [21].
Little is known about the dietary iron intake of Ethiopian women and the factors inuencing it. Past research
is outdated, and there are considerable discrepancies between regions. This study aims to estimate the
usual dietary iron consumption and its components in Ethiopian WRA in the Kersa district in eastern
Ethiopia. These ndings will help to estimate the risk of inadequate iron intake among WRA in the area, and
the factors associated with estimated dietary iron inadequacy. The ndings help health ocials to
collaborate with stakeholders in improving the food security and fortication policy of locally available
foods in Ethiopia.
Methods And Methods
Study design and settings
This study took place in the Kersa Health Demographic Surveillance system (Kersa HDSS) eld research
site, in Oromia region of eastern Ethiopia. Kersa HDSS includes 24 kebeles (the lowest administrative unit in
Ethiopia), out of the 38 kebeles in the district [22]. Of the HDSS Kebeles, 21 are rural. The 2016 national
census reported that the population of Kersa district was 170,816, of which 50% were women and 6.7%
were urban dwellers [23].
We conducted a cross-sectional survey among households in Kersa HDSS from August 1 to September 30,
2019 [22]. The total sample size for the study was 1,200 women. Study participants were selected using a
random selection method from a sampling frame derived from the Kersa HDSS database. Households with
at least one married WRA (15-49 years old) or women head of the households were included in the sampling
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frame. If more than one WRA lived in the household and was present at the interview, one woman was
randomly selected using a number generator. We excluded pregnant women from the study.
Data collection tool
The participants responded to the questionnaire; which had ve sections, including information on socio-
demographic characteristics, health information, food choices, and cooking practices, food security, food
expenditures, homestead food production, and dietary intake. Data were collected via interviewer-
administered tablet-based questionnaires, using an Open Data Kit (ODK) platform.
Sociodemographic and anthropometric assessment
Household wealth index was calculated from principal component analysis including 10 items describing
the household asset ownership, crowding, housing roof or oor quality, and water and sanitation facilities.
The wealth index was used to group women using tertiles (the poor, middle, and rich) [24]. Women’s
employment status was classied according to the Ethiopian Demographic and Health survey dened
categories [23]. Women who were classied as entirely employed had a skilled and stable job and had been
working at least for 7 days before the survey. Hard labor or agricultural employment was categorized as
partial and seasonal based on monthly employment and experience before the survey.
Height and weight of WRA were measured to the nearest centimeter (cm) and kilograms (kg) using a
stadiometer and standard weighing scale [25]. We calculated body mass index (BMI) as weight in kilograms
divided by height in meters squared. Based on BMI, individuals were classied using standard cutoffs as
underweight (<18.5 kg/m2), normal weight (18.5–24.9 kg/m2), or overweight/obese (≥ 25 kg/m2).
Overweight was classied as BMI 25-29.9 kg/m2, and obesity BMI ≥30 kg/m2 [26].
Dietary assessment:
Participants’ dietary intake was assessed using a 69-item non-quantitative, food frequency questionnaire
(FFQ), locally adapted from a semi-quantitative FFQ version validated for use among urban Tanzanian
adults [27]. The FFQ included locally available foods and varieties and an option to specify other foods. The
participants were asked the number of days they consumed each food out of the past seven days. The
number of food items consumed per week was divide by seven to derive daily consumption. If a participant
reported consuming a particular food with respective once twice, thrice and every day, a week, the frequency
of consumption recorded would be 0.14, 0.29, 0.43, and 1. Portion sizes and frequencies of intake were not
collected. The mean portion sizes for each food item were computed from previous population-based study
protocol [19, 28]. The Ethiopian Food Composition table and other studies were used to capture iron
composition data of reported foods [29–31].
Diet diversity was assessed using the minimum dietary diversity for women (MDD-W) indicator [32]. We
grouped foods consumed by women (based on the FFQ) into ten non-overlapping food groups according to
Food Agriculture Organization (FAO) guidance [33, 34]. The 10 food groups are 1) starchy staples, 2) pulses
3) nuts and seeds 4) dairy products 5) esh foods 6) eggs 7) dark green leafy vegetables 8) vitamin-a rich
fruits and vegetables 9) other vegetables, and 10) other fruits [34]. Foods made from grains cereals roots
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and tubers are grouped into starchy staples. Poultry and all meat products were categorized as esh foods
and all milk products as dairy [33]. If a participant reported daily consuming at least one of the food items
in a particular food group, it is scored one and if a participant didn’t consume any of the food items, it is
scored zero. The scores are then summed into a dietary diversity score (DDS-W, range 0-10). We categorized
women as meeting minimum dietary diversity (MDD) if they consumed at least ve food groups (DDS ≥5)
out of the 10 groups, as a proxy for micronutrient adequacy [33, 35, 36].
The usual iron intake of participants was estimated by multiplying the mean portion size and iron
composition for each reported food item with its converted daily consumption. Heme and non-heme iron
intake were estimated based on assumptions that 40% of animal food sources had heme iron and 60% of
animal, and all plant source foods and dairy contained non-heme iron [37]. We estimated iron bioavailability
after food intake assuming that 25% of all heme-iron and 10% non-heme would be bioavailable on average
after consuming a cooked meal or ripe food [37]. We calculated the usual iron, heme and, non-heme iron
intake in milligrams per day.
We calculated the risk of inadequate dietary iron intake based on consumption less than the age- and sex-
specic Estimated Average Requirement (EAR) for iron for WRA. We assumed a low bioavailability of iron
absorption (5%). A cut-off of < 22.4 mg/d was used to dene inadequate intake of dietary iron [38].
Data analysis
Data were analyzed using STATA 16. We characterized sociodemographic characteristics of the study
population using means, and standard deviations (SDs) and medians, and interquartile ranges for variables
that were not normally distributed for continuous variables; and, counts and percentages for categorical
variables. Data points with more than 50% missing data and with usual amounts (outliers) were removed
from the analysis. Wilcoxon rank-sum test was used to evaluate signicant differences in medians for the
outcome measures. We describe dietary iron intake by specic food type and women’s characteristics. We
evaluated for multi-collinearity among the independent variables using the covariance matrix.
Modied Poisson regression models were used to evaluate associations between the independent variables
and risk of dietary
F
e inadequacy (0 = no risk of
F
e dietary inadequacy, 1 = risk of
F
e inadequacy) based on
EAR-derived cut-offs for WRA. Covariates were selected based on signicance in univariate analysis at p <
0.2. Crude Prevalence Ratios (CPR) and Adjusted Prevalence Ratio (CPR) and 95% CI were calculated. The
statistical association level was p < 0.05 to identify independent variables associated with the risk of dietary
iron inadequacy.
Results
The study included 1,123 WRA from the community. Forty-seven households refused to participate in the
study, making a response rate of 96%. We excluded 52 missing observations and 25 outlier observations
from the analysis.
The median age of participants was 30 years (Inter Quantile Range (IQR) 26; 30) and half of the participants
had never attended school. Most participants (96%) were Muslims and played a spouse and head role of
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the household. Even though a majority of WRA (67.1%) worked full time, more than half of the households
were in the poor wealth index category. The median weight and height for WRA were 51 kg (IQR 47.8; 56.0)
and 157 cm (IQR 154.5; 161.1), respectively. Most study participants (90.7%) had under-ve children in their
household with a median age of 36 months (IQR 23; 48). The median number of previous pregnancies
reported was four (IQR 3; 6). Many women (76.0%) reported using household products as a primary source
of food and travels more than half a kilometer (0.7 km) for reaching a food source. The median dietary
diversity score was 4.0 (IQR 3.0; 5.0) and 34.6% of women met minimum dietary diversity (Table 1).
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Table 1
Sociodemographic, reproductive characteristics, and food source, and diversity study participants women of
reproductive age, Kersa, Eastern Ethiopia (N=1123)
Variables N Values
Woman’s age (years) 1123
16- 25 267 (23.8)
26- 35 633 (56.4)
≥ 36 223 (19.8)
Highest Education 1123
Never attended school 609 (54.2)
Did not nished rst grade 99 (8.8)
Completed 10 grade and more 415 (37.0)
Partner Highest Education 1123
Never attended school 571 (50.8)
Did not nished rst grade 84 (7.5)
Completed 10 grade and more 468 (41.7)
Religion 1123
Muslim 1079 (96.1)
Orthodox 33 (2.9)
Other b11 (1.0)
Employment type 1123
Full-time 753 (67.1)
Part-time 102 (9.1)
seasonal 268 (23.8)
Occupational status of women 1123
Farmer 925 (82.4)
Trade 44 (3.9)
Professional/technical 61 (5.4)
Other d93 (8.3)
Role in the household 1123
Head of the HH 141 (12.5)
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Variables N Values
Spouse 971 (86.5)
Another c11 (1.0)
Wealth index 1123
Poor 639 (56.9)
Middle 255 (22.7)
Rich 229 (20.4)
Weight a (kg) 1123 51.0 (47.8; 56.0)
Height a (cm) 1123 157.0 (154.5;
161.1)
Body mass index
Underweight 1123 188 (16.7)
Normal 868 (77.3)
Overweight 67 (6.0)
Family size #1123 5.9 ± (3.0)
Has an under 5 children 1123 1018 (90.6)
Age of Under 5 children a*1018 36.0 (23.0; 48.0)
Number of previous pregnancies #1123 4.45 ± (2.4)
Source of household food 1123
Household production 853 (76.0)
Street Vendor and local market 270 (24.0)
Food source distance from Household # e 284 0.7 ±(1.4)
Women’s DDS a f 1123 4.0 (3; 5)
Minimum dietary diversity 1123
Optimum 389 (34.6)
Estimated daily iron intake (mg/d) a1123 24.7 (17.7; 33.2)
Estimated daily non-heme iron consumption (mg/d) a1123 24.7 (17.7; 33.1)
Estimated daily heme-iron consumption (mg/d) a1123 0.0 (0.0; 0.0)
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Variables N Values
Estimated heme-iron consumption after consuming animal products
(mg/d) a g
114 0.3 (0.1; 0.6)
Estimated relative bioavailable Iron a1123 2.5 (1.8; 3.3)
Low usual Iron intake 1123 381 (33.9)
Values are mean ± SD, median [IQR], or frequency (percent).
a = Median (25th ; 75th percentile)
#= Mean ±(SD)
b = Protestant, Jehovah-witness
c = sister, daughter, aunt
d = un skilled and manual labor, clerical
e = 284 observations
f = total score = 10
* = age in Months
g = 114 observations
Usual iron, non-heme, heme, and bioavailable iron intake
The median dietary iron intake estimated in this study was 24.7 mg/day: 95% CI (23.9– 25.5). The mean
heme and non-heme iron intakes respectively were 0.05 (95% CI 0.04-0.06) and 24.7 mg/d (95% CI 23.9-
25.5). Around 2.5 mg/d (95% CI 2.4-2.6) of iron is estimated to be bioavailable after consumption. Total
Fe
consumption distribution was positively skewed. 41.8% of women of reproductive age were at risk of iron
inadequacy based on a cut-off for Fe inadequacy of EAR. Heme-Fe contributes less than one percent of the
total iron intake. The median daily iron intake for women who met minimum dietary diversity (5+ food
groups) and those with low dietary diversity was 32.7 (95% CI 31.1 – 34.3) and 21.6 (95% CI 20.8- 22.4)
mg/d, respectively Figure 1-3
Food frequency distribution and total iron consumption
Table 2 shows the amounts of total and bioavailable dietary
Fe
consumption was lower in women whose
ages were 36 or above than the other groups. The median intake of all types of iron was higher in women
meeting criteria for minimum dietary diversity, as well as in women in the overweight category and among
those in the highest SES. The mean dietary intake of all types of iron was higher in a household that
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depended on a household food source. Participants who reported concurrent intake of esh foods and fruits
daily had the highest amount of dietary iron intake across all groups.
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Table 2
Stratied dietary Iron, heme, non-heme, and bioavailable iron of women of reproductive age in Kersa,
Eastern Ethiopia, 2019
Variable N (%) Iron
(mg/d) * P-value
b
Non-hem
Iron
(mg/d)
P-value
b
Bioavailable
Iron (mg/d) P-value
Age (in
Years)
16- 25 267
(23.8) 24.6
(17.4;33.5) 24.6
(17.4;33.1) 2.5 (1.7;3.4)
26- 35 633
(56.4) 25.3
(18.6;34.9) 0.01** 25.3
(18.5;34.4) 0.01** 2.5 (1.9;3.5) 0.01**
≥ 36 223
(19.9) 22.0
(15.7;28.6) 22.0
(15.7;28.5) 2.2 (1.6;2.9)
Employment
type
Full-time 753
(67.1) 26.3
(19.3;34.9) 26.3
(19.3;34.9) 2.6 (1.9;3.5)
Part-time 102 (9.1) 20.4
(15.4;30.0) 0.000** 20.4
(15.4;29.9) 0.000** 2.0 (1.5;3.0) 0.000**
Seasonal 268
(23.9) 20.6
(16.6;28.1) 20.6
(16.5;28.1) 2.1 (1.7;2.8)
BMI
Underweight 188
(16.7) 23.0
(17.1;30.1) 23.0
(17.1;30.1) 2.3 (1.7;3.0)
Average 868
(77.3) 24.9
(17.9;33.1) 0.05 24.9
(17.9;33.1) 0.04** 2.5 (1.8;3.3) 0.06
Overweight 67 (6.0) 27.5
(17.5;49.8) 27.5
(17.5;49.4) 2.7 (1.7;5.0)
Wealth Index
Poor 639
(56.9) 22.2
(16.6;27.8) 22.2
(16.6;27.8) 2.2 (1.7;2.8)
Middle 255
(22.7) 29.2
(20.9;41.9) 0.000** 29.2
(20.9;41.9) 0.000** 2.9 (2.1;4.2) 0.000**
Rich 229
(20.4) 30.6
(20.1;47.6) 30.6
(20.0;47.6) 3.1 (2.0;4.8)
Dietary
Diversity
a =114 observations consumed esh foods b = Wilcoxon rank-sum test *median (IQR) ** signicance at
0.05 level
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Variable N (%) Iron
(mg/d) * P-value
b
Non-hem
Iron
(mg/d)
P-value
b
Bioavailable
Iron (mg/d) P-value
Optimum (5+
food groups) 389
(34.6) 32.7
(23.9;49.4) 0.000** 32.4
(23.7;49.0) 0.000** 3.3 (2.4;5.0) 0.000**
Low 734(65.4) 21.6
(15.7;27.7) 21.6
(15.7;27.7) 2.2 (1.6;2.8)
Food source
Household 853
(76.0) 24.9
(18.7;32.5) 0.02** 24.9
(18.6;32.5) 0.02** 2.5 (1.9;3.3) 0.04**
Market 270
(24.0) 22.6
(15.7;39.1) 22.6
(15.7;38.9) 2.3 (1.6;3.9)
Concurrent
Consumption
of esh and
fruit
Yes 39 (3.5) 45.5
(31.1;57.7) 0.000** 45.3
(30.8;57.3) 0.000** 4.6 (3.2;5.8) 0.000**
a =114 observations consumed esh foods b = Wilcoxon rank-sum test *median (IQR) ** signicance at
0.05 level
Table 3 shows the consumption patterns of the study women and the contribution of food groups
consumed to Fe intake. The median intake of iron for women who reported eating beans and peas, and
dairy products was 5.8 mg/d and 4.8 mg/d, respectively. Almost all participants reported consumption of
starchy staples (99.6%) and other vegetables (98.1%) in the previous 7 days; the 2 food groups providing
16.5 and 1.1 mg/d iron (median), respectively. Of the usual
Fe
consumed; starchy staples contributed 66.8%
(16.5/24.7) of the intake. Nuts and seeds, and esh foods were the least consumed food groups and eggs,
and other fruits contributed the least amount of
F
e. Many of the participants (66.5%) reported an intake of
dairy products at least once a week. Participants who reported at least a daily intake of starchy stables had
the highest estimated bioavailable iron after consumption.
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Table 3
Food frequency with median usual Iron intake of women of reproductive age in Kersa, Eastern Ethiopia
Food group Rank/
contribution
Consumed Iron (mg/d) * Bioavailable
Fe
*
1) All starchy staples 1 /66.8% 1119
(99.6) 16.5
(11.7;23.2) 1.6 (1.2;2.3)
Consumed everyday 756 (67.3) 18.3
(12.8;24.1) 1.8 (1.3;2.4)
Consumed ≤6 days in 7 days 363 (32.3) 12.7
(8.8;18.6) 1.3 (0.9;1.9
2) Beans and Peas 2 /23.5% 454 (40.4) 5.8 (2.3;12.6) 0.6 (0.2;1.3)
Consumed everyday 16 (1.4) 20.3
(8.0;29.5) 2.0 (0.8;2.9)
Consumed ≤6 days 438 (39.0) 5.7 (2.3;12.6) 0.6 (0.2;1.3)
3) Nuts and Seeds 6 /3.2% 69 (6.1) 0.8 (0.7;1.0) 0.0 (0.0;0.0)
Consumed everyday 0.0 (0.0) 0 (0; 0) 0.0 (0.0;0.0)
Consumed ≤6 days 69 (6.1) 0.8 (0.7;1.0) 0.0 (0.0;0.0)
4) All Diary 3 /19.4% 747 (66.5) 4.8 (3.2;5.6) 0.5 (0.3;0.6)
Consumed everyday 289 (25.7) 5.6 (5.6;5.6) 0.6 (0.6;0.6)
Consumed ≤6 days 458 (40.8) 3.2 (2.4; 4.8) 0.3 (0.2;0.5)
5) Flesh Foods 8 /2.8% 114 (10.2) 0.7 (0.4; 1.4) 0.4 (0.4;0.7)
Consumed everyday 4 (0.4) 2.5 (2.5; 4.3) 0.4 (0.4;0.7)
Consumed ≤6 days 110 (9.8) 0.7 (0.4; 1.4) 0.1 (0.1;0.2)
6) Eggs 9 /0.4% 128 (11.4) 0.1 (0.1; 0.3) 0.0 (0.0; 0.0)
Consumed everyday 2 (0.2) 0.5 (0.5; 0.5) 0.0 (0.0; 0.0)
Consumed ≤6 days 126 (11.2) 0.1 (0.1; 0.3) 0.0 (0.0; 0.0)
7) Vitamin A-rich dark green leafy
vegetables 4 /7.7% 514 (45.8) 1.9 (1.2; 3.1) 0.2 (0.1;0.3)
Consumed everyday 09 (0.8) 4.3 (4.3; 4.3) 0.4 (0.4;0.4)
Consumed ≤6 days 505 (45.0) 1.9 (1.2; 3.1) 0.2 (0.1;0.3)
8) Other vitamin A-rich vegetables and
fruits 7 /3.2% 344 (30.6) 0.8 (0.3;
1.0)/ 0.07/0.07
* Median/ Inter-Quantile Range (25th ;75th percentile) of daily Iron of those consumed
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Food group Rank/
contribution
Consumed Iron (mg/d) * Bioavailable
Fe
*
Consumed everyday 3 (0.3) 1.8 (0.9; 1.8) 0.2 (0.1;0.2)
Consumed ≤6 day 341 (30.4) 0.8 (0.3; 1.0) 0.1 (0.0;0.1)
9) Other vegetables 5 /4.4% 1102
(98.1) 1.1 (1.1; 1.2) 0.1 (0.1; 0.1)
Consumed everyday 877 (78.1) 1.10 (1.1;
1.2) 0.1 (0.1;0.1)
Consumed ≤6 days 225 (20.0) 0.9 (0.8; 1.0) 0.1 (0.1; 0.1)
10) Other fruits 10 /0.4% 142 (12.6) 0.1 (0.1; 0.3) 0.0 (0.0; 0.0)
Consumed everyday 21 (1.9) 1.5 (1.5; 1.5) 0.2 (0.2;0.2)
Consumed ≤6 days 121 (10.8) 0.1 (0.1; 0.2 0.0 (0.0; 0.0)
* Median/ Inter-Quantile Range (25th ;75th percentile) of daily Iron of those consumed
Factors associated with dietary iron inadequate consumption.
Table 4 shows factors associated with inadequate dietary Fe intake using the EAR cut-off Women’s age was
positively associated with the risk of dietary iron inadequacy. Participants whose aged 36 and higher were
29% more likely (APR 1.29, 95% CI 1.05-1.60) to have iron inadequacy compared to women aged 15 to 25.
Seasonal and part-time agricultural employment, having street food sources, and low dietary diversity was
associated with increased risk of dietary iron inadequacy. Women who had seasonal agricultural
employment were 56% more likely (APR 1.56, 95% CI 1.36-1.80 to have dietary iron inadequacy compared to
those employed full-time. Compared to wealthier households, women in the lowest wealth tertile were 1.2
(APR 1.2 95%CI 0.9-1.4) times as likely to have iron inadequacy. This relationship was statistically
insignicant. Women with low dietary diversity intake were almost two and half times as likely (APR 2.4 CI
1.9-2.9) to have inadequate dietary iron compared to women with optimum dietary diversity (consuming 5
or more food groups daily). We found similar ndings when categorized iron tertiles of iron intake as shown
in Table 5.
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Table 4
Factors Associated with risk of dietary iron inadequacy of women of reproductive age in Kersa, Eastern
Ethiopia, 2019
Risk of iron inadequacy
N (%) CPR 95% CI APR 95% CI P-value
Age
16- 25 108 (40.4) ref
26- 35 245 (38.7) 0.96 0.80-
1.14 1.05 0.88
-1.25 0.6
≥ 36 117 (52.7) 1.30 1.07-
1.57 1.29 1.05-
1.60 0.02**
Employment type
Full-time 260 (34.5) ref ref
Part-time 57 (55.9) 1.62 1.33-
1.97 1.75 1.45-
2.12 0.000**
Seasonal 153 (57.1) 1.65 1.43-
1.91 1.56 1.36-
1.80 0.000**
BMI
Underweight 89 (47.3) 1.15 0.98-
1.37 1.10 0.9-1.3 0.20
Average 355 (40.9) ref ref
Overweigh 26 (38.8) 0.95 0.69-
1.29 1.01 0.76-
1.33 0.95
Wealth Index
Poor 322 (50.4) 1.56 1.27-
1.91 1.17 0.97-
1.42 0.08
Middle 74 (29.0) 0.90 0.69-
1.17 0.84 0.65-
1.08 0.18
Rich 74 (32.3) ref
Number of previous
pregnancies a
4.6(2.4) 1.03 1.00-
1.01 0.99 0.96-
1.02 0.72
Dietary Diversity
a = mean (SD)
CPR = Crude Prevalence Ratio
APR = Adjusted Prevalence Ratio
** = signicant at p = 0.05
Page 16/27
Risk of iron inadequacy
N (%) CPR 95% CI APR 95% CI P-value
Optimum 80 (20.6) ref ref
Low 390 (53.1) 2.58 2.10-
3.18 2.34 1.88-
2.91 0.000**
Food source
Household 338 (39.6) ref ref
Market 132 (48.9) 1.23 1.06-
1.43 1.30 1.14-
1.49 0.000**
a = mean (SD)
CPR = Crude Prevalence Ratio
APR = Adjusted Prevalence Ratio
** = signicant at p = 0.05
Page 17/27
Table 5
Factors Associated with low usual iron consumption of women of reproductive age in Kersa, Eastern
Ethiopia, 2019
Low usual Iron
intake N (%) CPR 95%
CI P-
value APR 95%
CI P-value
Age
16- 25 88 (32.96) 0.81 0.64-
1.02 0.07 0.86 0.67
-1.10 0.2
25- 35 202 (31.91) 0.78 0.64-
0.95 0.01 0.89 0.74
-1.07 0.2
≥ 36 91 (40.8) ref ref
Employment type
Full-time 206 (27.36) ref ref
Part-time 50 (49.02) 1.79 1.42-
2.25 0.000 1.99 1.59-
2.49 0.000**
Seasonal 125 (46.64) 1.70 1.43-
2.03 0.000 1.60 1.35-
1.89 0.000**
BMI
Underweight 72 (38.30) 1.15 0.94-
1.42 0.17 1.10 0.91-
1.33 0.34
Average 288 (33.18) ref ref
Overweight and obese 21 (31.34) 0.94 0.65-
1.36 0.76 1.01 0.73-
1.39 0.95
Wealth Index
Poor 266 (41.63) 1.67 1.31-
2.13 0.000 1.19 0.95-
1.50 0.12
Middle 58 (22.75) 0.91 0.66-
1.26 0.58 0.87 0.64-
1.18 0.27
Rich 57 (24.89) ref
Number of previous
pregnancies a
4.73(2.48) 1.04 1.01-
1.08 0.01 1.01 0.98-
1.05 0.51
Dietary Diversity
a = mean (SD)
CPR = Crude Prevalence Ratio
APR = Adjusted Prevalence Ratio
** = signicant at p = 0.05
Page 18/27
Low usual Iron
intake N (%) CPR 95%
CI P-
value APR 95%
CI P-value
Optimum 53 (13.62) ref ref
Low 328 (44.69) 3.28 2.52-
4.27 0.000 2.96 2.25-
3.91 0.000**
Food source
Household 271 (31.77) ref ref
Market 110 (40.74) 1.28 1.07-
1.53 0.005 1.36 1.16-
1.59 0.000**
a = mean (SD)
CPR = Crude Prevalence Ratio
APR = Adjusted Prevalence Ratio
** = signicant at p = 0.05
Discussion
This study assessed the usual iron, hem, non-heme, and bioavailable iron intake among women of
reproductive age in Kersa, Eastern Ethiopia. The median usual iron consumption was 24.7 mg/d and 41.8 %
of WRA were at risk for iron inadequacy. The following factors were associated with a greater likelihood for
the risk of iron inadequacy: seasonal and part-time agricultural employment, market food source, and low
women’s dietary diversity. Older women in the study were more likely to have inadequate dietary iron intake.
The mean dietary total iron intake in this study was lower than a study that was done in an urban resident
of Gonder, which reported a mean iron dietary of 97.81 mg/d [39]. The size of dietary iron inadequacy was
also high compared to the national report that listed 14% and others that reported iron inadequacy in
women of reproductive age group [40, 41]. This difference could be due to the disparity of malnutrition in
the regions and cities of Ethiopia. The district, like most rural areas, is dependent solely on rainfall and
traditional agricultural means of food productions [42]. Moreover, the study’s accuracy in the estimation of
the total dietary iron intake might be limited due to not measuring biochemical markers of iron amount and
storage and the use of a non-quantitative FFQ which can distinguish between high and low consumers of
nutrients, but is not ideal for measuring absolute intake.
Diets for study participants were plant-based and poor as expected. Most participants consumed starchy
staples and all types of vegetables, and the least food groups consumed were sh, eggs, esh foods, and
fruits. Non-heme iron-made up most of the dietary intake.
The dietary iron inadequacy found in this study was high compared to the recommended cut-off point in a
population [38]. The nding is not surprising Ethiopia has high levels of poverty, food insecurity, and
malnutrition [43]. Malnutrition and hunger are also common in this rural district being a major cause of
Page 19/27
death in children under ve [22]. Many households are also dependent on handouts from Ethiopia’s
Productive Safety Net Program (PSNP) due to chronic malnutrition [44].
Some Sub-Saharan African countries have implemented fortication of cereal and other food products with
iron to meet their high dietary needs [45] There are limited iron-fortied foods or enriched food products yet
available in Ethiopia, and food insecurity has made the matter even worse [40]. Fortifying wheat and
cereals, availing inexpensive nutrient-rich food alternatives, and eliminating hunger have signicantly
improved nutrition and the health status of WRA. The World Health Organization (WHO) currently
recommends iron supplementation for women of reproductive age and children [46], fortication of foods,
and food and nutrition education as a strategy in combating iron deciency [47]. The application and
effectiveness of these strategies in developing countries are affected by economic, socio-cultural, and
infrastructural challenges. Therefore, dietary approaches are a top priority in addressing IDA.
Low iron intake among women of reproductive age impacts the women’s life and future pregnancy and birth
outcomes. Several studies have shown that a woman with low iron intake is at risk for antepartum and
postpartum hemorrhage, which is the number one cause of maternal morbidity and mortality [7].In addition,
women with low iron stores and consumption are more likely to have poor birth outcomes including preterm
birth, stillbirth, and low birth weight babies(ref). Babies that were born from anemic women tended to show
poor physical growth performance, predisposition to infection, and retarded brain development, which
affects eventual schooling and social development [48]. Therefore, it would be an important addressing
problem.
Furthermore, older women had a higher risk of dietary iron inadequacy in this study. Because the need for
higher dietary iron intake for old women increases due to larger families and have more children, they are
the most at risk for iron deciency and insuciency [21]. On the contrary, other studies had shown
traditional social and cultural inequities, younger women are also more likely to impose dietary restrictions
that would decrease the consumption level and inadequacy of iron [49]. This difference could arise from
other unmeasured confounders and the potential introduction of recall bias in the study.
Women that had lower dietary diversity were also found to be at higher risk of dietary iron inadequacy. This
could be explained by poor individuals are more likely to consume foods that are less diverse and healthy
[50]. Furthermore, the mean dietary total iron in our sample with higher dietary diversity had roughly twice
the amount of recommended dietary iron and was statistically different across the subtypes of
Fe
[38].
Having a lower dietary diversity tends to decrease the consumption level of a balanced and healthy diet,
leading to nutrient inadequacy and deciency. This is in line with different studies that describe a strong
relationship between individual dietary diversity score and micronutrient inadequacy [34, 51, 52]. Research
has shown that women meeting minimum dietary diversity for the FAO MDDW are more like to meet their
RDAs/EARs for iron, vitamin A and other micronutrients [53]. It is also worth noting that women’s dietary
diversity varies in the seasonal availability of foods, which was not considered in this study.
In addition, Others have also shown poor individuals had a higher restrain on nancial freedom in the
household that would limit securing and choosing nutrient-rich food for the family. This not only
predisposes families to hunger but also other poor health outcomes [54]. are more likely to consume foods
Page 20/27
that are less diverse and healthy [50]. Furthermore, the mean dietary total iron in our sample with higher
dietary diversity had roughly twice the amount of recommended dietary iron and was statistically different
across the subtypes of
Fe
[38].
Conclusion
This study sheds light on a burden of dietary iron inadequacy in women of reproductive age in Kersa,
Eastern Oromia using the FFQ questionnaire. Even though Teff-based foods and staples have high iron with
low bioavailability [41], their fortication with iron and diversication with other foods would improve
bioavailability and adequacy of iron in women of the reproductive age group. The authors recommend
further study of FFQ with iron biomarkers for accurate identication of iron deciency and insuciency
National nutrition-based surveillance system should be strengthened, as well as further collaboration with
stakeholders to improve and modernize agricultural systems to meet the global sustainable goals.
Ethiopia’s commitment to the fortication of iron, diversication of our diet, and frequent use of animal
source food and fruits may help in decreasing the burden of iron insuciency and increase its
bioavailability and absorption. Improving and identifying dietary iron intake alone is not the most ecient in
identifying anemia, other socio-cultural inequities, women empowerment, other causes of non-nutritional
anemia, and nutrition knowledge were not addressed in this paper.
Abbreviations
DDS; dietary diversity score; EAR: Estimated Average RequirementFFQ; food frequency questionnaire; MDD;
minimum dietary diversity, PSA; principal component analysis, SSA; Sub-Saharan Africa, WRA; women of
reproductive age,
Declarations
Ethics approval and consent to participate
This study was ethically approved by the Institution Health Research Ethical Review Board of the College of
Health and Medical Sciences with reference number SHE/S1M/14.4/708/19.The study procedureswere
also undertaken per the Helenski Declaration. At the time of visit to the household, written informed,
voluntary consent was secured from respondents
Consent for publication
Not Applicable
Availability of data and materials
The datasets used and analyzed during this study are available from the corresponding author on
reasonable request
Competing Interest
Page 21/27
The authors declare no conicts of interest.
Funding
This work was supported by Harvard T.H. Chan School of Public Health, USA.
Authors' contributions
NS, AA, KTR, YD, and WWF designed a concept note. NS, AA, KTR, and YD developed a proposal. NS, AA,
KTR, and YD worked on data generation and eldwork. NS, YYA, EC, and IM performed statistical analysis.
NS, YYA, EC, IM, and WWF developed the manuscript. All authors reviewed, edited, and approved manuscript
the nal manuscript.
Acknowledgments
The authors wish to thank Dr. Sabri Bromage for his valuable comments and suggestions and Harvard
school of public health for their assistance in data collection. The authors would like to thank study
participants, local administrators, and data collectors for facilitating the data collection.
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Figures
Figure 1
This is Figure 1 Total Daily Dietary Iron consumption among women of reproductive age, Kersa, Eastern
Ethiopia, 2019
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Figure 2
This is Figure 2 Total Dietary Iron Consumption Distribution by Dietary Diversity of among women of
reproductive age, Kersa, Eastern Ethiopia, 2019
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Figure 3
This is Figure 3 Daily Hem and non-Hem Iron Consumption among women of of reproductive age, Kersa,
Eastern Ethiopia, 2019
