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Dietary diversity and iron deficiency anemia among a cohort of singleton pregnancies: a cross-sectional study | BMC Public Health

Dietary diversity and iron deficiency anemia among a cohort of singleton pregnancies: a cross-sectional study | BMC Public Health

Study design and recruitment

A cross-sectional study was conducted between August and December 2022 on pregnant mothers attending the Ministry of Health (MOH) antenatal care clinics located in maternal and child health centers in Northern Jordan.

Jordanian healthy mothers aged between 19–45 years old, who attended the MOH antenatal care clinics in Northern Jordan and pregnant with a singleton pregnancy (66 women in each pregnancy trimester) were included in this study. Whereas pregnant mothers diagnosed with pre-eclampsia, gestational diabetes, autoimmune disorders, chronic diseases (such as diabetes mellitus, liver, and renal diseases), hyperemesis gravidarum, and women with unknown pre-pregnancy weight (weight of the mother at conception or two weeks prior to conception or through the first two weeks of gestation) were excluded from this study.


“The sample size was calculated using the infinite population equation n = z2pq/d2. Where n stands for sample size, z is the value of the 95% confidence level, P is the estimated average prevalence, q is 1-p, and d is the accepted error which is the precision around the population mean” [26]. The prevalence of low-birth-weight newborns (13.8%) in Jordan according to Islam et al., 2020 [27] was used. Thus, the sample size required was:

$$\mathrm n={(1.96)}^2(0.138)\;(0.862)/\;{(0.05)}^2=182.8\;\mathrm{women}$$

The sample size was increased by at least 10% to increase the power of analysis and to compensate for excluded subjects from data analysis. Therefore, the total number included in the study was 198.

The 198 pregnant women were randomly selected from MOH antenatal care clinics located in the North of Jordan. Initially, all antenatal care clinics were numbered and entered into SPSS, from which six clinics were randomly selected using the SPSS computer random number generator. The study participants were also randomly selected based on their order number, with those having odd numbers being chosen. The selected pregnant women were divided into three groups based on gestational weeks (GWs): 1st trimester (0–13 weeks), 2nd trimester (14–26 weeks), and 3rd trimester (27–40 weeks), with 66 women in each group. The total number (n = 198) was distributed equally across the six antenatal clinics, with 33 women from each clinic and 11 from each trimester being randomly selected.

Sociodemographic, medical, and antenatal care data

Data was collected directly from pregnant women through personal interviews using a validated questionnaire. Sociodemographic data include the mother’s current age, marriage age, education and employment status of the mother and the husband, family income per month, religion, number of family members, health insurance, place of residence, and type of housing. The following medical data were included: last menstrual period, gestational age, history of chronic diseases, medications, gestational hypertension, gestational diabetes, preeclampsia/eclampsia, miscarriages, stillbirths, low birth weight, parity, spacing, previous deliveries, history of breastfeeding, and food allergies and intolerance. Additionally, information about antenatal care, such as the time of the first visit, the number of visits, the types of assessments, nutrition education, iron, and other micronutrient supplementations, were collected.

We specifically investigated the mother’s current intake of iron supplements, emphasizing details regarding her supplementation routine, whether these supplements were prescribed during her prenatal and or antenatal care, any symptoms experienced due to iron intake, and whether they are taken in conjunction with other supplements. Additionally, we sought information on the frequency of supplement intake and the level of adherence to the recommended regimen. Therefore, the iron supplements intake was categorized into yes, if the pregnant woman started taking at least 30 mg of iron daily after the 13th week of gestation and was complying with the iron supplementation program and/or if the mother was taking iron supplements before pregnancy and continued during pregnancy according to her physician’s prescribed dose. No, if the pregnant mother did not take any iron supplements on a daily basis after the 13th week of gestation.

Gestational age determination

Gestational age was calculated based on the date of the last menstrual period. For pregnant mothers who were not able to remember their last menstrual period date and/or were breastfeeding when they conceived, the gestational age was determined by the obstetrician, using ultrasonic fetal biometrics such as the biparietal diameter, abdominal circumference, and femur length.

Anthropometric data

A stadiometer was used to measure height. Women were barefoot, minimally clothed and were asked to straighten their legs, adhere their heels, put their arms to the side, relax their shoulders and keep their heads in the Frankfort horizontal plane [28]. A beam scale was used to measure actual weight after its calibration and zero-balance check in each measurement. Women would stand without assistance on the scale’s center while being minimally dressed, barefoot and looking straight ahead [28]. Quetelet’s formula [weight (kg)/height (m)2] was used to calculate body mass index BMI [28]. Additionally, the mother’s pre-pregnancy weight was recorded from the mother’s antenatal records. The gestational weight gain was calculated as the variation between the actual weight and the pre-pregnancy weight. Additionally, we considered differences in gestational weight gain based on pre-pregnancy BMI, following the recommendations of the Institute of Medicine. During the first trimester, weight gain may range from 1 to 5 pounds, or remain unchanged. In the second and third trimesters, mothers with a healthy pre-pregnancy weight typically gain between half a pound and 1 pound per week [29].

Biochemical data

A venous blood sample was collected from the participants by the certified laboratory technician and distributed into two tubes: EDTA tube for complete blood count (CBC) analysis and a plain serum separator tube to determine the serum ferritin level. Blood samples were kept refrigerated and then sent to a certified diagnostic laboratory for analysis. To test for external validity of the biochemical blood analysis, two duplicated samples were sent to two different diagnostic laboratories to see the differences in the results. On the other hand, the same blood sample was divided into two tubes, each taking a different code and analysis was performed twice to test for internal validity. All blood analysis was carried out using two levels of different quality samples at the same laboratory and by the same team of laboratory technicians. Hematological parameters including complete blood count (CBC) of white blood cells (WBCs), red blood cells (RBCs), platelets, hemoglobin (Hb), hematocrit or packed cell volume (PCV), and RBCs indices of mean corpuscular volume (MCV), mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), and red cell distribution width (RDW) were determined using MINDRAY (BC-5300) Auto Hematology Analyzer. All Hb concentrations were not adjusted for altitude at sea level because the participants live in areas with altitudes below 1000 m. Serum ferritin was measured by immunoassay using COBAS e 411 analyzer, (Roche Diagnostics, Rotkreuz, Switzerland).

Dietary assessment

A trained dietitian was responsible for collecting the dietary intake from the participating mothers using 24-h recall and a validated quantitative food frequency questionnaire (FFQ) for Jordanian pregnant mothers [30]. Dietary intake was assessed using the minimum dietary diversity score for women (MDD-W) and the prime diet quality score (PDQS).

Each mother was asked to list the foods and the method of preparation, the amount eaten, and the time and place of food intake in the past 24 h. Food models, measuring cups, and spoons were utilized to help the participants estimate the portion sizes of the food and beverages they consumed. The MDD‐W was originally developed by the Food Agriculture Organization (FAO) in 2021 [31] as a population‐level dichotomous indicator to assess the sufficiency of micronutrients for reproductive-age women living in resource‐limited environments. The previous day’s consumption of at least five out of ten food groups is defined as the MDD-W indicator. The ten food groups include starchy staples, peas and beans, nuts and seeds, dairy (milk and milk products), flesh foods (meat, fish, poultry), eggs, dark green vegetables rich in vitamin A, other fruits and vegetables rich in vitamin A, other vegetables, and other fruits. Based on the 24-dietary recall data, each of the consumed food groups receive 1 point, and the summation of the total points is identified as the MDD-W out of ten. The cumulative dietary diversity score was categorized into two outcome variables: dietary diversity (consuming > 5 food groups) and no dietary diversity (consuming < 5 food groups).

The PDQS is mainly composed of fourteen healthy food groups (dark green vegetables, carrots, cruciferous vegetables, other vegetables, whole citrus fruits, other fruits, whole grains, nuts and seeds, legumes, low‐fat dairy, eggs, fish, poultry, and liquid vegetable oils) and seven unhealthy food groups which include (red meat, processed meat, potatoes, refined grains, and baked goods, fried foods eaten away from home, sugar‐sweetened beverages, and ice cream and desserts). Other fruits and vegetables rich in vitamin A such as pumpkin, passion fruit, apricots, and mango were also included in the carrots group [32]. Based on the main component of mixed dish, it was either assigned to the healthy or unhealthy PDQS food group. The total number of weekly servings from each food item was calculated by the summation of the daily servings consumed from each food item included in each food group and then multiplied by seven. Pregnant mothers who consumed food items were grouped into either healthy or unhealthy PDQS food groups. Based on the total food serving(s) consumed per week from both the healthy and unhealthy food groups, a score for each food group was allocated as the following: healthy food groups: 2 points for 4 + serving/week, 1 point for 2–3 serving/week, and 0 points for 0–1 serving/week. Unhealthy food groups: 0 points for 4 + serving/week, 1 point for 2–3 serving/week, and 2 points for 0–1 serving/week. The sum of the scores for each food group was used to get the overall PDQS score.

Statistical analysis

The collected data were double entered, checked, and analyzed using SPSS statistical package (IBM, SPSS version 25, 2017). Descriptive statistics were performed using frequencies and percentages to describe the categorical variables. Means and standard deviations (SD) were used to describe continuous variables. The nonparametric Kolmogorov–Smirnov test was performed to examine all continuous variables for normal distribution. A Student t-test for independent variables was performed to detect any significant differences between the means of the normally distributed continuous variables. The numeric variables of gestational age, Hb, serum ferritin, MDD-W, and PDQS were converted into categorical variables according to international and/or laboratory cutoff values. The associations between dichotomous and categorical variables were assessed using Pearson’s Chi-square (χ2) and Fisher’s exact tests.

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