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Epidemiology
Original research
Anaemia among lactating and non-lactating women in low-income and middle-income countries: a comparative cross-sectional study
  1. Dagmawi Chilot1,2,
  2. Fantu Mamo Aragaw3,
  3. Daniel Gashaneh Belay4,5,
  4. Melaku Hunie Asratie6,
  5. Mequanint Melesse Bicha7,
  6. Adugnaw Zeleke Alem8
  1. 1Center for Innovative Drug Development and Therapeutic Trials for Africa (CDT-Africa), Addis Ababa University College of Health Sciences, Addis Ababa, Ethiopia
  2. 2Department of Human Physiology, University of Gondar College of Medicine and Health Sciences, Gondar, Ethiopia
  3. 3Department of Epidemiology and Biostatistics, University of Gondar, Gondar, Ethiopia
  4. 4Epidemology, University of Gondar College of Medicine and Health Sciences, Gondar, Ethiopia
  5. 5Human Anatomy, University of Gondar College of Medicine and Health Sciences, Gondar, Ethiopia
  6. 6Women and Family Health, University of Gondar, Gondar, Ethiopia
  7. 7Department of Gynecology and Obstetrics, University of Gondar College of Medicine and Health Sciences, Gondar, Ethiopia
  8. 8Epidemiology and Biostatistics, University of Gondar College of Medicine and Health Sciences, Gondar, Ethiopia
  1. Correspondence to Dagmawi Chilot; dagimchilot21{at}gmail.com

Abstract

Objective This study aimed to assess the prevalence and determinants of anaemia among lactating and non-lactating women in low-income and middle-income countries (LMICs).

Design Comparative cross-sectional study.

Setting LMICs.

Participants Reproductive-age women.

Primary outcome Anaemia.

Methods Data for the study were drawn from the recent 46 LMICs Demographic and Health Surveys (DHS). A total of 185 330 lactating and 827 501 non-lactating women (both are non-pregnant) who gave birth in the last 5 years preceding the survey were included. STATA V.16 was used to clean, code and analyse the data. Multilevel multivariable logistic regression was employed to identify factors associated with anaemia. In the adjusted model, the adjusted OR with 95% CI and a p value <0.05 was reported to indicate statistical association.

Result The prevalence of anaemia among lactating and non-lactating women was found at 50.95% (95% CI 50.72, 51.17) and 49.33% (95% CI 49.23%, 49.44%), respectively. Maternal age, mother’s educational status, wealth index, family size, media exposure, residence, pregnancy termination, source of drinking water and contraceptive usage were significantly associated determinants of anaemia in both lactating and non-lactating women. Additionally, the type of toilet facility, antenatal care visit, postnatal care visit, iron supplementation and place of delivery were factors significantly associated with anaemia in lactating women. Besides, smoking was significantly associated with anaemia in non-lactating women.

Conclusions and recommendations The prevalence of anaemia was higher in lactating women compared with non-lactating. Almost half of the lactating and non-lactating women were anaemic. Both individual-level and community-level factors were significantly associated with anaemia. Governments, non-governmental organisations, healthcare professionals and other stakeholders are recommended to primarily focus on disadvantageous communities where their knowledge, purchasing power, access to healthcare facilities, access to clean drinking water and clean toilet facilities are minimal.

  • EPIDEMIOLOGY
  • Health policy
  • Maternal medicine
  • NUTRITION & DIETETICS

Data availability statement

Data are available upon reasonable request. Data are available in a public, open-access repository.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi.org/10.1136/bmjopen-2022-069851

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    Strengths and limitations of this study

    • The findings were supported by large datasets covering 46 low-income and middle-income countries.

    • We employed multilevel analysis which is an appropriate methodology for such data.

    • The data were collected using a common internationally acceptable methodological procedure.

    • Demographic and Health Surveys (DHS) used a cross-sectional survey design, and the causal relationship between anaemia and the independent variables cannot be established.

    • We did not include important covariates such as dietary intake, or comorbidity as the DHS did not collect information on these variables.

    Background

    Anaemia, which particularly affects young children and pregnant women, occurs when the number of red blood cells is lower than the normal range.1 According to the WHO, over half a billion (29.9%) women of reproductive age and about 269 million children were anaemic in 2019.2 Anaemic individuals can show a range of symptoms including fatigue, weakness, dizziness and drowsiness.3 4 It is measured in terms of haemoglobin content as haemoglobin is the primary oxygen-carrying molecule within red cells.5 Although nutritional deficiency (particularly iron deficiency) is the common cause of anaemia, inherited conditions, infectious diseases and chronic diseases also cause the problem.6–8 Reproductive age anaemia has been associated with maternal and child morbidities including placental abruption, low birth weight, preterm and miscarriage.9–12

    Maternal anaemia continues to be a major public health problem worldwide and remains more concentrated in low/middle-income countries (LMICs).13 14 The WHO has established the Global Nutrition Target to reduce reproductive-age women’s anaemia by half by 2030.15 The socioeconomic, demographic and household factors including inadequate antenatal care (ANC) visits, lack of postnatal care (PNC) visits, higher parity, lack of education, contraceptive usage, rural residency, underweight and nutritional status have been reported as major determinants of anaemia in LMICs settings.16–20

    Several studies have revealed that anaemia prevalence is not uniform among lactating and non-lactating mothers.21 22 Lactating mothers are considered a more vulnerable group than non-lactating mothers as they are physiologically and nutritionally at a higher risk of iron deficiency and could acquire anaemia during their pregnancy.23 24 Besides, lactating mothers require highly nutritious food to produce an adequate quantity of milk in addition to their needs.25 This is even higher in LMICs where insufficient dietary intake, micronutrient deficiencies and infection are common.26 27 The problem also extends to their child and affects its immunity, cognitive development and learning ability.28 29

    Improving lactating and non-lactating women’s health is critical to preserve a healthy generation. Previous studies have examined anaemia using survey data,30 however, those studies were country-specific and did not compare anaemia in both lactating and non-lactating mothers. Thus this study aimed to assess the prevalence and determinants of anaemia among lactating and non-lactating mothers in LMICs. Identifying the more vulnerable groups is vital to prioritise and design appropriate targeted intervention programmes to reduce anaemia.

    Methods

    Study population and data source

    This study was based on the most recent Demographic and Health Surveys (DHS) dataset in 46 LMICs. We appended 46 countries’ DHS data to investigate factors associated with anaemia in women of reproductive age in LMICs. The DHS uses the same standardised data collection procedures, sampling, questionnaires and coding, which makes the results comparable across countries.

    The DHS used two stages of stratified sampling technique to select the study participants and to assure national representativeness. First, census enumeration areas are selected from each sampling stratum using a probability proportional to the size of the number of households in each enumeration area. In the second stage, households are sampled using systematic random sampling from each enumeration area, which forms the survey clusters. A detailed description of the DHS sampling design and data collection procedures has been found in each country’s DHS report. A total of 185 330 lactating and 827 501 non-lactating women of reproductive age were interviewed.

    Study variables and measurement

    Outcome variable

    The outcome variable was the anaemia status of lactating and non-lactating women (both were non-pregnant) and it was measured based on the altitude-adjusted haemoglobin level. Anaemia in non-pregnant women was operationalised as a categorical variable by predefined cut-off points as not anaemic (haemoglobin level ≥12 g/dL), mild (haemoglobin level 10–11.9 g/dL), moderate (haemoglobin level 7–9.9 g/dL) and severe (haemoglobin level <7 g/dL) anaemia. Women with mild, moderate and severe anaemia were levelled as anaemic because of very small numbers of cases in the category of severe anaemia. Hence, non-pregnant mothers with haemoglobin levels <12 g/dL were considered anaemic and coded as ‘1’ whereas not anaemic were coded as ‘0’.

    Independent variables

    Based on previous literature, theoretical and practical significance, the independent variables for this study were the age of women, educational status of women, marital status, mass media exposure, accessing healthcare, working status, terminated pregnancy, birth order, household wealth status, number of household members, sex of household head, parity, source of drinking water, type of toilet facility, smoking status, residence for all childbearing age women and contraceptive for non-pregnant women and iron supplementation, number of antenatal care, place of delivery and PNC.

    Operational definitions

    Media exposure was generated from women’s responses to the questions related to the frequency of listening to the radio, watching television and reading newspapers in a week. It is categorised as ‘yes’ if women had exposure to at least one type of media; radio, newspaper or television, and ‘no’ otherwise.

    Accessing healthcare was generated from the DHS questions; getting the money needed for treatment (problematic/not problematic), distance to a healthcare facility (problematic/not problematic), having to take transport (problematic/not problematic) and not wanting to go alone (problematic/not problematic). It was categorised as ‘problematic’ if a woman faces at least one problem while if a woman did not report none of the above problems were considered as ‘not problematic’.

    Data processing and analysis

    Data analysis was carried out with STATA V.14.2 software. Descriptive analysis was carried out using cross-tabulations and summary statistics. Determinants of anaemia were identified using multilevel binary logistic regression because DHS data are hierarchical, that is, individuals were nested within communities. Separate models were fitted for lactating and non-lactating mothers since the prevalence of anaemia was different among different categories.

    Variables with a p value <0.2 in the bivariable analysis were considered in the multivariable multilevel binary logistic regression models. Finally, results for the multivariable analysis have been presented as adjusted OR (AOR) and its 95% CIs to measure the strength and significance of the association. The variance inflation factor (VIF) test was done to check multicollinearity and was not found because all variables have VIF<5.

    Patient and public involvement statement

    As our study used secondary analysis of DHS data, participants and the public were not involved in the study design or planning of the study. The study participants were not consulted to interpret the results and write or editing of this document for readability or accuracy.

    Results

    Background characteristics of study participants

    A weighted sample of 1 012 831 (185 330 lactating and 827 501 not currently lactating) childbearing-age women in LMICs was included in this study. A larger proportion of lactating (40.62%) and not lactating (46.87%) women had attended secondary education. More than half of lactating and non-lactating respondents, respectively used improved drinking water sources (62.97%, 68.71%), improved toilet facilities (57.82%, 63.85%), had media exposure (70.05%, 79.83%), not used contraceptive (52.85%, 55.17%) (table 1).

    View this table:
    Table 1

    Weighted frequency distribution of study participants in low-income and middle-income countries, 2010–2020

    Prevalence of anaemia among reproductive-age women in LMICs

    The prevalence of anaemia among lactating mothers was found at 50.95% (95% CI 50.72, 51.17), which varied widely from country to country, ranging from 11.71% in Rwanda to 60.49% in India. The prevalence of anaemia in non-lactating mothers was 49.33% (95% CI 49.23%, 49.44%), ranging from a low of 13.02% in Guatemala to a high of 63.43% in Maldives (table 2).

    View this table:
    Table 2

    Survey years and prevalence of anaemia among reproductive-age women in low-income and middle-income countries

    Factors associated with anaemia among reproductive-age women

    We performed the overall model which includes both lactating and non-lactating women and the odds of anaemia were 1.11 times higher among lactating women as compared with non-lactating women (AOR=1.11, 95% CI 1.10, 1.13). Variables including the age of mothers, educational status, wealth index, family size, media exposure, residence, pregnancy termination, source of drinking water and using contraceptives were significantly associated with anaemia in both lactating and non-lactating women. While the type of toilet facility, ANC, PNC, iron supplementation and place of delivery were factors significantly associated with anaemia in lactating women. Besides, smoking was significantly associated with anaemia in non-lactating women (table 3).

    View this table:
    Table 3

    Multi-level binary logistic regression of factors associated with anaemia among women in low-income and middle-income countries, Demographic and Health Surveys 2010–2020

    Discussion

    The prevalence of anaemia among lactating and non-lactating mothers was found at 50.95% (95% CI 50.72, 51.17) and 49.33% (95% CI 49.23%, 49.44%), respectively. This finding is lower than studies done in India,31 Vietnam,32 Myanmar33 and India.28 But higher than studies conducted in China34 and Ethiopia.35 The possible reason for the variation in anaemia prevalence between countries might be due to geographical, cultural and dietary differences.36

    This study found that lactating women aged 25–34 years, and 35–49 years, were less likely to be anaemic than women aged 15–24 years. The finding is consistent with studies done in Ethiopia.37 38 However, this finding was contradictory to a study done in Iran.39 Also, non-lactating women in the age group of 35–49 years were more likely to be anaemic than non-lactating women aged 15–24 years. The discrepancy between age groups could be explained by the fertility differences that occur in each age group, indicating the chance of having multiple pregnancies is higher among older women and could result in iron deficiency.39

    It was found that women who did not have formal education had higher odds of anaemia than those with higher education in both lactating and non-lactating women. This was consistent with other studies conducted in Ethiopia,37 Manipur40 and Benin.41 Education may help women to gain knowledge, which in turn could help them to follow healthy lifestyles such as good nutrition and better health-seeking habits. Another possible explanation could be that higher education may lead to a better socioeconomic status.

    Our study also revealed that mothers from poorer, middle, rich and richest households had lower odds of having anaemia than mothers from the poorest households. This finding was in agreement with previous studies conducted in Nepal, Ethiopia,42 East Africa,26 Myanmar33 and Nepal.43 One possible explanation is that lactating mothers with higher socioeconomic status are more likely to eat a balanced diet and purchase a variety of iron-rich foods, which would help the mothers increase their nutrient intake, especially those important for haematopoiesis.44

    The study also reported that both lactating and non-lactating women living in urban residences were more likely to develop anaemia than women living in rural residences. This was comparable with the study done in India.45 The finding was contradictory to a study done in Ethiopia.46 The possible reason might be women in rural areas could have greater access to and usage of Teff and other iron-containing cereals, which can lower their risk of nutritional anaemia.47 It is also possible that women who migrate to urban centres face a higher risk of anaemia due to unsanitary environmental conditions and insufficient consumption of nutrient-rich foods.48 The WHO report has also mentioned that non-nutritional causes, environmental and socioeconomic differences between urban and rural residents could contribute to the problem.36

    This study found the prevalence of anaemia was more prevalent in households that have unimproved toilets and unimproved water facilities in both lactating and non-lactating women. The finding is similar to studies done in Uganda,49 Rwanda50 and Myanmar.51 The possible justification is that an unimproved latrine facility would expose women to helminthic infection, foodborne and waterborne diseases, resulting in anaemia.52–54

    The current study found that lactating mothers who delivered at home had a higher risk of anaemia than mothers who delivered at a health facility. This finding is consistent with research conducted in East Africa26 and India.31 A possible justification might be the risk of haemorrhage among lactating mothers who had delivered at health institutions is lower and can be easily managed, which can contribute to the reduction of anaemia among women who gave birth at a health facility.

    Furthermore, lactating mothers who received ANC had a lower risk of anaemia. This is comparable to the Indian study.55 Frequent contact with healthcare providers could help them to take iron properly and receive other diet-related information which can reduce the problem to a great extent. Terminated pregnancy was also one of the determinant factors of anaemia in both lactating and non-lactating women. The finding is supported by a study done in Ethiopia.24 Women who have had their pregnancies terminated may be subjected to frequent blood loss, which in turn, results in low haemoglobin levels in the blood.

    The odds of anaemia were lower in lactating mothers who were using family planning. This finding is in line with studies done in Ethiopia23 56 and East Africa.26 This is because women who use modern contraceptive methods avoid pregnancy and childbirth complications, potentially lowering the risk of anaemia caused by frequent loss of blood.47 Furthermore, using some modern contraception methods may help to reduce menstrual bleeding which may decrease the chance of developing anaemia.57

    Family size was another factor that increases the risk of anaemia in both lactating and non-lactating mothers. The finding was also consistent with a study done in Ethiopia,58 East Africa47 and Benin.41 This could be explained by the fact that larger family sizes occur when women give birth, exposing them to blood loss, which results in low haemoglobin levels in the blood.59

    Having PNC was also one of the determinant factors for anaemia among lactating mothers. The possible justification might be women attending PNC service might have the chance to get nutritional advice. Smoking was also one of the factors responsible for anaemia among non-lactating mothers, the finding is supported by a study done in Ethiopia.24

    Our study has several strengths and limitations. It used large nationally representative samples with appropriate statistical modelling. The use of large nationally representative data and multilevel analysis helps to provide more robust estimates of observed associations as well as enhance the generalisability of the results. Although this study used a nationally representative dataset and appropriate model, the results should be interpreted in light of some limitations. First, this study used cross-sectional data, which does not provide itself with the establishment of a temporal relationship between the factors and outcome variables. Second, we did not incorporate important covariates such as dietary intake, other comorbid conditions and energy expenditure, as the DHS did not collect information on these variables.

    Conclusion

    The prevalence of anaemia was higher in lactating women compared with non-lactating. Almost half of the lactating and non-lactating women were anaemic. Both individual-level and community-level factors were significantly associated with anaemia. Governments, non-governmental organisations, healthcare professionals and other stakeholders are recommended to primarily focus on disadvantageous communities where their knowledge, purchasing power, access to healthcare facilities, access to clean drinking water and clean toilet facilities are minimal.

    Data availability statement

    Data are available upon reasonable request. Data are available in a public, open-access repository.

    Ethics statements

    Patient consent for publication

    Ethics approval

    Not applicable.

    Acknowledgments

    The authors acknowledge the Measure DHS for providing us with the data set.

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    Footnotes

    • Contributors Conceptualisation: DC. Study design: DC, FMA, DGB, MHA, MMB, AZA. Execution: DC, FMA, DGB, MHA, MMB, AZA. Acquisition of the data: DC, FMA, DGB, MHA, MMB, AZA. Analysis and interpretation: DC, FMA, DGB, MHA, MMB, AZA. Writing: DC, FMA, DGB, MHA, MMB, AZA. Review and editing: DC, FMA, DGB, MHA, MMB, AZA. Guarantor: DC.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.