The magnitude of anemia and its associated factors among pregnant women in Hawela Tula Sub-city of Hawassa, Hawassa, Ethiopia

Abstract

Introduction:

Anemia is one of the most serious health problems impacting people worldwide. The disease is quiet, moving slowly and producing only a few physical symptoms. Anemia during pregnancy raises the risk of premature birth, low birth weight, and fetal anomalies, and it can have a substantial financial impact on society and families. However, there was a paucity of studies on the magnitude and associated factors of anemia among pregnant women in southern Ethiopia.

Objective:

This study aimed to assess the magnitude and associated factors of anemia among pregnant women attending antenatal care in the Hawella Tula Sub-city of Hawassa City in 2021.

Methods:

Institution-based cross-sectional study was done on 341 randomly selected pregnant women attending antenatal care clinics. Data were obtained using a standardized semi-structured questionnaire. To identify the associated factors for the magnitude of anemia logistic regression model was used with an adjusted odds ratio (AOR) with 95% confidence intervals (CI) was calculated.

Results:

The prevalence of anemia among pregnant women attending antenatal care in health facilities of Hawella Tula Sub-city was 113 (33.7%) with a 95% confidence interval (CI) (28.8, 38.9). Male-headed household (AOR = 2.217, 95% CI: 1.146, 4.286), rural resident (AOR = 3.805, 95% CI: 2.118, 6.838), early marriage below 18 years (AOR = 2.137, 95% CI: 1.193, 3.830), and recurrent of illness during pregnancy (AOR = 3.189, 95% CI: 1.405, 7.241) were associated factors for anemia.

Conclusion:

Anemia prevalence among pregnant women was 113 (33.7%). Anemia among pregnant women was associated with rural residents, early marriage age below 18 years, and repeated illnesses during pregnancy.

Introduction

A hemoglobin concentration below a cutoff determined by age, sex, and pregnancy is referred to as anemia (1). Low blood hemoglobin concentration, or anemia, is a health issue that affects people in high-, middle-, and low-income nations. It has detrimental implications on social and economic development and major health consequences (2). Although blood hemoglobin concentration is the most reliable indication of anemia at the population level, measures of concentration alone do not establish the cause of anemia (3). Anemia during pregnancy is classified as severe if the hemoglobin concentration is less than 7.0 g/dL, moderate if the hemoglobin concentration is between 7.0 and 9.9 g/dL, and mild if the hemoglobin concentration is between 10.0 and 11 g/dL (4).

Anemia is still a major health issue, particularly among pregnant women and girls in less developed countries (5). Anemia is thought to be a sign of both inadequate nutrition and health state (6). An estimated 2 billion people are suffering, representing more than one-third of the world’s population (7). Although nutritional, genetic, and infectious disease variables all contribute to anemia, iron deficiency accounts for 75% of cases (8, 9). Iron deficiency anemia has a detrimental impact on national development since it impairs children’s cognitive development and adult productivity (8). More than half of pregnant women and over one-third of all women suffer from anemia (10–12). In Mali, 63.5% of women of reproductive age suffer from anemia (13). In Somali land, a study found an overall prevalence of anemia among pregnant women at 50.6% (95% CI: 45.40–55.72%) (14).

In Ethiopia, the systematic review and meta-analysis pooled the prevalence of anemia among pregnant women was 26.4% (15). This is manifested by an inadequate supply of iron to the cells following the depletion of stored iron (16). Iron helps us the transfer of oxygen from the lungs to other parts of the body tissues. When iron deficiency happens it affects metabolism, brain development, and growth (17). More pregnant women are affected than other society groups (3, 18).

Anemia may result from several causes, with the most significant contributor being iron deficiency. Approximately 50% of cases of anemia are considered to be due to iron deficiency, but the proportion probably varies among population groups and in different areas, according to the local condition (19). Although the primary cause is iron deficiency, it is seldom present in isolation (20). Because of the significance of this disease worldwide, many countries have seen it cohabit more commonly with a variety of other causes, including hemoglobinopathies, dietary inadequacies, parasite infections, and malaria (21–24).

Severe maternal anemia is strongly associated with maternal mortality and is typically confounded by multiple underlying conditions. Despite this known complexity, iron deficiency is often misidentified as the single contributor to the onset of anemia. Due to their unique roles in the synthesis of hemoglobin and/or the formation of erythrocytes, deficiencies in vitamins A, B₂, B₆, B₁₂, C, D, E, folate, copper, selenium, and zinc can also cause anemia; however, some of these micronutrients may not be significantly responsible for the prevalence of anemia worldwide, infectious diseases such as human immunodeficiency virus (HIV), parasitic infections such as hookworm and schistosomiasis and inherited genetics are additional and often neglected causes of anemia (16, 25–27).

As a result, no new research has been published in this field; however, earlier research has indicated that iron deficiency which is rarely found alone is the main contributing factor. It more often coexists with hemoglobinopathies, dietary deficits, and parasite infections. The management and control of anemia during pregnancy are significantly influenced by the availability of local prevalence information. Aside from the few studies already out in Ethiopia, nothing is currently known about the prevalence of anemia among pregnant women in our research area. To focus on the characteristics in the study region, this study aimed to determine the prevalence of anemia and its associated factors among pregnant women receiving prenatal care in Hawassa municipal administration health facilities.

Materials and methods

Study settings and study periods

The study was conducted at Hawela Tula sub-city of Hawassa City Administration, Sidama Regional State, Southern Ethiopia. The study was conducted from October 15 to December 2, 2021. Hawassa city was located 275 km from Addis Ababa. According to the population census, of 2007, the population of Hawassa City in 2021 was 374,034 out of which 190,757 were males and 183,277 were females (28). The total population of the sub-city is estimated to be 135,793 out of this about 4,698 (3.46%) pregnant women are expected to live in the city. In Hawassa city, there are eight sub-cities and 83 public and private health institutions among them, one public referral and teaching hospital, one General Hospital, two primary hospitals, four private hospitals, six public HC, 17 Health posts, and 51 private clinics given the service.

Study design

A cross-sectional study design was used.

Source and study population

All pregnant mothers living in Hawassa city were the source population. The study population was all systematically randomly selected pregnant women who had ANC follow-up in the selected health facilities.

Inclusion and exclusion criteria

Pregnant women who had earned antenatal care at Hawella Tula Sub-city health facilities during the period under the interview were included; however, those who were seriously ill at the moment of data collection, who were unable to hear and/or speak, and who were unwilling to participate in the interviews were excluded.

Measurement of variables

Outcome variable

The outcome variable in this study is anemia. According to the WHO classifications for hemoglobin concentration values, pregnant mothers were divided into four groups: Hgb level ≥ 11.0 gm/dL is no anemia, Hgb level less than 7.0 gm/dL is severe anemia, Hgb level 7–9.9 gm/dL is moderate anemia and Hgb level 10–10.9 gm/dL is mild anemia (3).

Independent variables

Sociodemographic and socioeconomic variables: age, religion, residence, occupational status, marital status, Educational status, monthly family income, gravidity, parity, gestational age, history of contraceptive use, history of the heavy ministerial cycle, birth interval, family size, nutritional status, hemoglobin level, intestinal parasite infestation, iron folic supplementation, iron supplementation starting period, deworming in the last 6 months, drinking of stimulants (tea, coffee, cocoa, etc.), and lack of sleeping/rest are independent variables.

• Pregnant women’s nutritional status: MUAC less than 23 cm indicates that the pregnant woman has moderate malnutrition, MUAC less the 18.5 cm indicates that the mother has severe malnutrition, and MUAC 23 cm and above is normal (29).

• Pregnant women DDS: Dietary Diversity Score was calculated from a single 24-h recall and all the liquids, and the foods consumed a day before the study were categorized into nine food groups. Consuming a food item from any of the groups was assigned a score of “1” and a score of “0” if the food was not taken. Thus, a Dietary Diversity Score of nine points was calculated by adding the values of all the food groups. After that, it was classified as low (less than or equal to 3) medium (4—6), and high (7—9) (30, 31).

• Dietary variables: include whether tea or coffee is consumed immediately after a meal, the frequency of daily meat eating (more than once per week or less than once per week), fruit and vegetable consumption (more or less than once per day), and milk consumption.

Sample size determination

A single population proportion is used to determine the sample size, P is obtained from similar studies in Adama town 28.1% (32). The sample size was figured out with consideration of 95% Confidence level, 5% precision, and p = 28.1% magnitude of anemia among pregnant women. The non-response rate taken about 10% sample in addition to the calculated “n.” Based on this the formula was presented as follows to calculate “n.”

Where:

• n = the required sample size.

• p = proportion of anemia, which is 28.1%.

• d = estimated marginal error, which is 5%.

• Z 𝛼/_2 = the cross-ponding value of the confidence coefficient at alpha level 0.05 which is 1.96. After adding 10%, non-response allowances the minimum final sample size became 341.

Sampling technique and procedure

Three health institutions were selected by simple random sampling out of a total of six. Based on the family planning clinic attendants or each health center’s past performance, the proportionate sample approach was used to calculate the intended number of clients for each facility. After selecting using a lottery method from 1 to 4 for the first time, every fourth client in each health facility was interviewed to gather the necessary data for the study. This process was repeated every 4 weeks until the necessary sample size was achieved. Individual study participants were chosen using systematic sampling techniques between 1 and the sampling interval.

Data collection tools and procedures

The semi-structured questionnaire was taken from relevant published research and then tailored to the local environment to gather critical data from expecting mothers when they exited ANC. Interviewer-assisted questions were used for socio-demographics and socioeconomic, reproductive history, maternity features, food and nutrition-related characteristics, and other variables (16, 33, 34). Variables such as hemoglobin (Hgb) level and MUAC were extracted from respondents’ charts using their registration numbers. Dietary diversity was assessed using the 24-h dietary recall approach, which was modified from the Food and Agriculture Organization criteria for quantifying household and individual dietary diversity (30, 31). The selected interviewers employed questionnaires and interview procedures to obtain data from expectant mothers at the selected health facilities. The data collection method was undertaken by a team of four senior midwives and two experienced supervisors.

Data quality control

To ensure the accuracy of the results, a mid-upper arm circumference measurement device and a 10% pretested semi-structured questionnaire were utilized. Three days of training on the purpose of the study, how to contact study participants, and how to utilize the questionnaire were given to the data collectors and supervisors. Senior experts with extensive experience performed the laboratory procedures and sample collection. The gathered data were checked for accuracy, clarity, and completeness by the principal investigator and supervisors. Subsequent data collection, this quality check was conducted daily, and any modifications were made before the subsequent data collection measure. The data were cleaned up and cross-checked before analysis. The outcome and independent variables were coded before putting the data into software to ensure data completeness and consistency. The data were then entered into Epi-data version 4.6.2 software and exported to SPSS version 25 software for data analysis.

Data analysis

Demographic information and other factors were presented using descriptive statistics such as frequency, and percentage. Furthermore, data were illustrated with tables, graphs, and texts. The data were analyzed using both bivariate and multivariable logistic regression models to identify associations. In the bivariate analysis, factors having a p value less than 0.25 were chosen for further examination in the multivariable logistic model. To ensure model fitness, the multi-collinearity and goodness of Hosmer and Lemeshow tests were applied. This multivariable approach aims to find significant variables while controlling for any confounding factors. Adjusted Odds Ratios (AORs) were used to measure relationships, along with a 95% confidence interval. Variables having a p value equal to or less than 0.05 were considered statistically significant, indicating a strong association or influence in the context of the research.

Results

Socio-demographic characteristics of the respondents

A total of 335 pregnant mothers among pregnant women attending antenatal care in Hawella Tula Sub-city of Hawassa city administration with a response rate of 98.22% took part in the study. The mean (±SD) age was 27.4 (±4.3) years, which categorized as mainly 188 (56.1%) were 25–29 years. Most of the respondents 246 (73.4%) were headed by males and 234 (69.9%) were urban dwellers. About the educational level of the mothers, 79 (23.6%) had no formal education and 39 (11.6%) of them attended college and above education. About half of the study participants 176 (52.5%) were housewives. About the husband’s educational status, 40 (11.9%) had no formal education. The mean (±SD) family size was 4.2 (±1.9), and the age at first marriage for 112 (33.4%) was below the age of 18 years (Table 1).

Obstetric characteristics of study participants

The number of pregnancies with a mean (±SD) was 3.1 (±1.4) and the majority of the study participants 277 (82.7%) were multi-gravida, 231 (69.0%) were part 2–4, and most of the reports 334 (99.7%) had single pregnancy. About half of the respondents 186 (48.2%) were between 1 and 3 months of gestational age on their first visit and 70 (20.9%) only had a history of abortion. Out of the study participants interviewed, 134 (47.5%) had below 24 months birth interval and 263 (78.5%) had regular iron intake. On dietary habits, 193 (57.6%) had meat consumption and 146 (43.6%) were using tea immediately after having a meal (Table 2).

Health and nutrition service-related factors

Many of the study participants 243 (72.5%) travel less than 30 min to reach the nearest health institution. Nearly half of the study participants, 160 (47.8%) had nutritional counseling. Most of the participants 218 (65.1%) had experience with food fortification and 285 (85.1%) used piped water for drinking. Furthermore, 275 (82.1%) had latrines and 108 (32.2%) had deworming in the last six mothers during pregnancy preparation (Table 3).

The magnitude of anemia in pregnant women

The magnitude of anemia among pregnant women attending antenatal care in health facilities of Hawella Tula Sub-city was 113 (33.7%) with 95% CI (28.8, 38.9). Among these, 4 (1.2%) had severe anemia, 63 (18.8%) had mild anemia, and 46 (13.7%) had moderate anemia (Figure 1).

Figure 1

Factors associated with the prevalence of anemia

The maternal meal frequency, maternal DDS, maternal MUAC, and iron folic supplementation, were not candidate variables for the prevalence of anemia. The sex of the head of household, place of residence, age at first marriage, birth interval, rate of menstrual flow, history of abortion, recurrence of illness during pregnancy, and nutritional counseling were found to be candidate variables for multivariate analysis and be associated with the prevalence of anemia, with p-values <0.25. In the multivariable logistic regression, the pregnant women from male-headed households were about 2.2 times more likely to develop anemia (AOR = 2.217, 95% CI: 1.146, 4.286) as compared with female-headed households. In addition to this rural resident were about 3.8 times more likely to develop anemia (AOR = 3.805, 95% CI: 2.118, 6.838) as compared with urban dwellers. Early marriage below 18 years (AOR = 2.137, 95% CI: 1.193, 3.830) and recurrent illness during pregnancy (AOR = 3.189, 95% CI: 1.405, 7.241) were associated factors for the prevalence of anemia as compared with their counterparts (Table 4).

Discussion

Anemia prevalence was reported to be 33.7% among pregnant women receiving prenatal care at health facilities. It was almost similar to research (36.6%) carried out in western Arsi (35), Ilu Aba Bora zone in southwest Ethiopia (31.5%) (33), and Arba Minch Town in southern Ethiopia (32.8%) (36), and eastern Africa (34.85%) (37). But higher other study findings were conducted in western Ethiopia (23.5%) (38), Southeast Ethiopia (27.9%) (39), Addis Ababa, Ethiopia (21.3%) (40), Azezo health center, Ethiopia (21.1%) (41) and in Gondar town (16.6%) (42). It was also lower in the study conducted in India (84.85%) (43), Nepal (48%) (44), Libya (54.6%) (45), Kenya (57%) (46), Africa (42.17%) (47), and at Bahirdar Felege-Hiwot Referral Hospital Northwestern Ethiopia (45.5%) (48). The observed discrepancy could be explained by differences in sociodemographic factors and geographical variance among research participants, which could be explained by the occurrence of food taboos during pregnancy in specific Ethiopian communities. For example, according to a study conducted in Tigray (12%) (49), and in Addis Ababa (18.2%) (50) women skipped at least one food category during their recent pregnancy. Furthermore, this variation might be attributed to the difference in methods and lifestyle between study participants (51).

Compared to female-headed households, male-headed households are linked to anemia. This outcome was consistent with the Pakistani study (52). Nevertheless, this contradicts data from eastern Africa that was obtained using a multilevel mixed-effects generalized linear model (37). One possible explanation for this discrepancy could be the mother’s workload when she enters the workforce; in particular, the amount of time spent on time-consuming home tasks like child care and breastfeeding tends to decrease (53, 54).

This study demonstrated that there is a significant association between rural resident pregnant women and anemia. Compared to urban residents, those who live in rural areas are more susceptible to anemia. This outcome was in line with the research conducted in rural Sidama (55), and Arba Minch Town, Southern Ethiopia (36), Tigray (56), and Australia (57). The higher risk of anemia among women from rural areas who come to hospitals for labor may be related to a lack of information about adequate nutrition during pregnancy, economic factors, diet differences, or the inaccessibility of healthcare services (40). To reduce severe anemia, women need to be empowered economically, play a great role in decision-making, and receive counseling services for dietary diversification (51). Poor sanitation conditions in rural areas can result in inadequate restrooms and sources of drinking water, as evidenced by consistent reports from the Lao People’s Democratic Republic (27).

This study showed that early marriage below 18 years was associated with anemia. This result was in line with the study in Bahir Dar Felege-Hiwot Referral Hospital Northwestern Ethiopia (48), India (43), and Nepal (44). This is because early marriages below 18 years might be exposed to undernutrition due to maternal demand and pregnancy (47). This could be because adolescence is a period of increased iron demands due to the growth of muscle mass and blood volume in addition to sharing with the developing fetus. Due to menstrual blood loss, young women are more vulnerable to the development of iron deficiency. This evidence has been confirmed by Ethiopian data supporting the 23.8% overall prevalence of anemia among female adolescents showed a substantial correlation between early marriage and childbearing and teenage anemia (58).

There was a strong significant association between recurrent illness during pregnancy and anemia among pregnant women. Those mothers who were infected by recurrent illness were 3.189 times more likely to develop anemia than those who were not recurrently ill during the current pregnancy. This was similar to the study result in western Ethiopia (38), and in Arba Minch Town in southern Ethiopia (36). This could be because of blood loss caused by parasite infestations, which could put women at risk of iron deficiency anemia. Studies from Durame, south Ethiopia, reinforce this evidence (56), Dessie and Yirgalem (59, 60). This may also be linked to the likelihood that environmental contamination of drinking water with parasites that cause anemia increases due to the lack of accessible restrooms, which may raise the risk of anemia (27, 61). This might be happening due to the impacts of lead poisoning in the workplace and environment related to iron metabolism (62).

The nutrition-related factors of maternal DDS, meal frequency, MUAC, and iron-folic acid supplementation during pregnancy are significantly associated variables in this study result. For example, pregnant women who did not take iron supplements during their pregnancy were more likely to develop anemia than those who did. This could be associated with an iron deficiency that occurs during pregnancy as a result of increased iron requirements to supply the mother’s growing blood volume as well as the rapidly growing fetus and placenta. The findings are comparable with studies conducted in Karnataka, India, Uganda, and Ethiopia (63–66).

Pregnant women with a low dietary variety score were more likely to develop anemia than those with an optimal dietary diversity score. This finding was validated by research conducted in Mekele Town, Hosan, and South Ethiopia (34, 67, 68). Inadequate dietary diversification can result in a shortage of micronutrients such as vitamins, minerals, and other necessary substances, which can boost iron bioavailability and modify iron levels. This could be because pregnancy requires biologically high nutrition; it is recommended to adjust one’s diet from the norm.

Study strengths and limitations

Despite this study’s attempt to provide accurate information about the study objective and confidentiality during the interview, there is a chance of recall and social desirability bias in the participants’ number of pregnancies, abortion history, and socioeconomic status. Furthermore, we used the WHO-recommended technique for developing countries, and Hgb levels were not detected using the gold standard method.

Conclusion

The magnitude of anemia among pregnant women was found 33.7%. Factors such as male head of the family, living in a rural area, being married young (before turning 18), and experiencing repeated illnesses while pregnant were found to be associated factors. Emphasis should be placed on gender roles, the proper age for marriage, and nutrition education regarding the consumption of foods high in iron during prenatal care, especially in rural communities.

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