Anaemia among lactating and non-lactating women in low-income and middle-income countries: a comparative cross-sectional study

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.

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).

Table 1.

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

Variables	Category	Lactating mothers (%) (N=185 330)	Non-lactating mothers (%) (N=827 501)
Age (in years)	15–24	69 451 (37.47)	277 039 (33.48)
	25–34	92 483 (49.90)	212 413 (25.67)
	35–49	23 396 (12.62)	338 048 (40.85)
Women’s education	Not educated	47 709 (25.74)	178 999 (21.63)
	Primary	42 641 (23.01)	149 857 (18.11)
	Secondary	75 290 (40.62)	387 876 (46.87)
	Higher	9691 (10.62)	110 765 (13.39)
Wealth status	Poorest	47 072 (25.40)	137 666 (16.64)
	Poorer	42 035 (22.68)	157 100 (18.98)
	Middle	37 325 (20.14)	169 908 (20.53)
	Richer	33 207 (17.92)	179 242 (21.66)
	Richest	25 692 (13.86)	183 584 (22.19)
Marital status	Never in union	4986 (2.69)	254 722 (30.78)
	Married	174 877 (94.36)	516 936 (62.47)
	Widowed/divorced/separated	5468 (2.95)	55 842 (6.75)
Working status	Not working	54 838 (54.44)	189 670 (54.10)
	Working	45 898 (45.56)	160 941 (45.90)
Family size	≤5	48 094 (25.95)	333 400 (40.29)
	5–10	119 878 (64.68)	451 553 (54.57)
	>10	17 358 (9.37)	42 548 (5.14)
Parity	Nulipara	–	297 605 (35.96)
	Primiparous	56 376 (30.42)	85 810 (10.37)
	Multiparous	100 620 (54.29)	364 886 (44.09)
	Grand multiparous	28 334 (15.29)	79 198 (9.57)
Media exposure	No	55 492 (29.95)	166 871 (20.17)
	Yes	129 787 (70.05)	660 500 (79.83)
Number of ANC visits	<4	75 498 (40.74)	45 055 (33.82)
	≥4	109 823 (59.26)	88 154 (66.18)
Birth order	1	56 376 (30.42)	85 810 (16.19)
	2–5	111 423 (60.12)	399 178 (75.33)
	>5	17 531 (9.46)	44 906 (8.47)
Sex of household head	Male	153 160 (82.64)	656 222 (79.30)
	Female	32 170 (17.36)	171 279 (20.70)
Residence	Urban	48 135 (25.97)	307 044 (37.11)
	Rural	137 195 (74.03)	520 456 (62.89)
Accessing healthcare	Not problematic	85 905 (46.36)	452 992 (54.75)
	Problematic	99 405 (53.64)	374 453 (45.25)
Iron supplementation	No	31 654 (17.08)	23 586 (17.71)
	Yes	153 674 (82.92)	109 596 (82.29)
Terminated pregnancy	No	159 074 (85.83)	720 854 (87.11)
	Yes	26 255 (14.17)	106 640 (12.89)
Health insurance	No	149 741 (82.63)	599 589 (73.88)
	Yes	31 482 (17.37)	211 955 (26.12)
Contraceptive use	No	97 944 (52.85)	456 521 (55.17)
	Yes	87 386 (47.15)	370 971 (44.83)
Type of source of drinking water	Improved	116 711 (62.97)	568 567 (68.71)
	Unimproved	68 619 (37.03)	258 934 (31.29)
Smoking	No	183 050 (99.57)	817 446 (99.39)
	Yes	794 (0.43)	5052 (0.61)
Type of toilet facility	Improved	107 158 (57.82)	528 352 (63.85)
	Unimproved	78 172 (42.18)	299 149 (36.15)
Place of delivery	Home	32 971 (17.79)	20 891 (15.69)
	Health facility	152 352 (82.21)	112 294 (84.31)
Postnatal care visits	No	95 371 (52.79)	64 887 (49.86)
	Yes	85 278 (47.21)	65 255 (50.14)

ANC, antenatal care.

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).

Table 2.

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

World region	Survey year	Lactating (N)	Not lactating (N)	Prevalence of anaemia
				Lactating mothers (%)	Not-lactating (%)
South Asia
Maldives	2016–2017	948	5492	60.44	63.43
India	2019/2021	99 831	560	60.49	56.44
Nepal	2016	1426	4758	45.79	39.31
East Asia and the Pacific
Cambodia	2014	1581	9105	51.42	44.36
Myanmar	2014–2015	1834	10 232	47.76	46.31
Timor-Leste	2016	769	3196	25.58	22.06
Europe and Central Asia
Albania	2017–2018	673	9440	22.14	21.57
Armenia	2015–2016	351	5261	15.95	13.25
Tajikistan	2017	1906	7986	45.75	40.12
Kyrgyz Republic	2012	1325	6161	39.17	34.43
Middle East and North Africa
Egypt	2014	1481	4985	28.09	24.45
Jordan	2017–2018	872	5493	45.30	43.15
West and Central Africa
Burkina Faso	2010	3028	4560	50.43	47.92
Benin	2017/2018	2331	4846	56.84	58.08
Central Democratic Congo	2013/2014	3450	4925	31.71	38.76
Cote d’Ivoire	2011/2012	1166	2949	50.01	53.32
Cameroon	2018	1374	4855	39.96	39.68
Congo	2011/2012	1232	3660	51.10	54.94
Mauritania	2021	1723	5005	54.61	56.44
Gabon	2012	727	4008	49.59	62.35
Ghana	2014	1059	3262	45.19	41.52
Gambia	2019/2020	1363	4064	46.51	43.60
Guinea	2018	1333	3440	40.09	45.03
Liberia	2019/2020	879	2872	47.61	43.63
Mali	2018	1845	2670	62.51	63.88
Nigeria	2018	3817	9326	59.69	57.24
Niger	2011	2094	2254	43.05	47.72
Sierra leone	2018	1543	5292	52.88	44.73
Senegal	2012	1518	3686	46.62	56.46
Togo	2013–2014	1281	3103	44.07	49.51
Eastern and Southern Africa
Burundi	2016–2017	2955	4976	44.96	36.28
Ethiopia	2016	4657	9281	28.26	21.53
Lesotho	2014	485	2675	24.90	27.67
Madagascar	2021	4904	6421	30.39	24.96
Malawi	2015–2016	1845	5276	29.43	33.87
Mozambique	2011	4201	7921	53.11	54.37
Namibia	2013	584	3368	21.88	20.45
Rwanda	2019–2020	1818	5033	11.71	13.59
South Africa	2016	235	2514	28.81	33.57
Eastern and Southern Africa
Tanzania	2015	3495	8477	46.28	42.30
Uganda	2016	1544	3874	33.79	30.96
Zambia	2018	3005	9165	27.61	32.06
Zimbabwe	2015	1636	7020	23.27	27.54
Latin America and Caribbean
Guatemala	2014–2015	4618	19 544	16.37	13.02
Haiti	2016–2017	1134	7891	50.04	48.82
Honduras	2011–2012	3244	17 070	14.36	15.27
Overall		185 330	827 501	50.95 (50.72, 51.17)	49.33 (49.23, 49.44)

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).

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

Variables	Category	Lactating mothers N=185 330	Non-lactating mothers N=827 501
		AOR (95% CI)	AOR (95% CI)
Age	15–24	1	1
	25–34	0.95 (0.93, 0.97)**	0.97 (0.95, 0.98)**
	35–49	0.96 (0.94, 0.99)**	1.02 (1.01, 1.04)*
Women’s education	Not educated	1.42 (1.36, 1.48)**	1.25 (1.23, 1.28)**
	Primary	0.99 (0.95, 1.04)	1.05 (1.03, 1.07)**
	Secondary	1.14 (1.09, 1.18)**	1.15 (1.13, 1.17)**
	Higher	1	1
Wealth status	Poorest	1.76 (1.70, 1.83)**	1.66 (1.64, 1.69)**
	Poorer	1.54 (1.49, 1.61)**	1.46 (1.43, 1.48)**
	Middle	1.25 (1.20, 1.29)**	1.22 (1.20, 1.24)**
	Richer	1.12 (1.07,1.17)**	1.05 (1.04, 1.07)**
	Richest	1	1
Family size	≤5	1	1
	5–10	1.05 (1.03, 1.08)**	1.06 (1.05, 1.07)**
	>10	1.23 (1.18, 1.28)**	1.45 (1.42, 1.48)**
Media exposure	No	1	1
	Yes	0.95 (0.93, 0.98)**	0.94 (0.93, 0.96)**
Sex of household head	Male	1	1
	Female	1.01 (0.98, 1.03)	0.99 (0.98, 1.01)
Residence	Rural	1	1
	Urban	1.03 (1.00, 1.07)**	1.02 (1.00, 1.04)*
Accessing healthcare	Not problematic	1	1
	Problematic	1.01 (0.99,1.03)	1.01 (1.00, 1.03)*
Terminated pregnancy	No	1	1
	Yes	1.05 (1.02, 1.09)**	1.07 (1.05, 1.08)**
Covered by health insurance	Yes	1	1
	No	1.02 (0.99, 1.05)	1.02 (1.01, 1.04)**
Type of source of drinking water	Improved	1	1
	Unimproved	1.97 (1.94, 1.99)**	1.45 (1.43, 1.46)**
Smoking	No	1	1
	Yes	0.96 (0.82, 1.14)	1.89 (1.83, 1.94)**
Type of toilet facility	Improved	1	1
	Unimproved	1.97 (1.94, 1.99)*	1.01 (0.99, 1.02)
Contraceptive use	Yes	1	1
	No	1.25 (1.22, 1.28)**	1.14 (1.13, 1.15)**
Number of ANC	≥4	1
	<4	1.08 (1.06, 1.11)**
Iron supplementation	No	1
	Yes	0.08 (0.05, 0.12)**
Place of delivery	Health facility	1
	Home	1.05 (1.02, 1.08)**
Postnatal care	Yes	1
	No	1.08 (1.06, 1.11)**

*P value <0.05; **p value <0.001.

ANC, antenatal care; AOR, adjusted OR.

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 Ethiopia^{23 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

Not applicable.

Ethics approval

Not applicable.