Abstract
Women’s micronutrient deficiency, also named hidden hunger, remains a serious public health concern of low- and middle-income countries. To reduce micronutrient deficiencies, Sustainable Development Goal was designed to provide universal access to essential health and nutrition services to women of reproductive age. Recent evidence indicates that multiple micronutrient deficiencies often co-occur. But little is known about their extent, geographic variation, and underlying determinants among non-pregnant women of reproductive age, a critical gap that limits targeted interventions. Therefore, mapping the most affected women is critical to tailor interventions targeting needy segments of the population. We analyzed individual record and coordinates data from the Ethiopian National Micronutrient Survey 2015 to examine the spatial distribution of multiple micronutrient deficiencies. Vitamin A, folate, vitamin B12, zinc, iron, and iodine were micronutrients considered in the analysis. Weighted sample of 1450 non-pregnant women aged 15–49 years was included. Global spatial autocorrelation and hotspot analysis were performed to display geographic equity in multiple micronutrient deficiencies. A multi-scale geographical weighted regression analysis was also performed to identify factors explaining geographic disparities in the distribution of multiple micronutrient deficiencies. Two-fifths (41.3%; 95% CI 37.9–44.8) of women were found to have multiple micronutrient deficiencies in Ethiopia. There were geographical variations in the distribution of multiple micronutrient deficiencies among women in Ethiopia (Global Moran’s I value of 0.11 (p < 0.0087)). Higher proportions of multiple micronutrient deficiencies were observed in the Eastern parts of the country. Dire Dawa City Administration, Harari, and the Northern, Eastern, and Southwest parts of the Somali region had higher predicted multiple micronutrient deficiency levels. Hotspot areas with significantly higher rates of multiple micronutrient deficiencies were observed in southern Afar, Dire Dawa, Harari, Eastern Oromia, and Southern Somali regions. Multiple micronutrient deficiencies affect a substantial proportion of non-pregnant women of reproductive age in Ethiopia, highlighting a critical public health concern. The findings demonstrate significant regional variation and identify key predictors, including rural residence, breastfeeding status, and lack of multivitamin supplementation. These findings underscore public health efforts must integrate multiple micronutrient supplementation into maternal health services. In addition, designing targeted interventions that can reduce the high burden of hidden hunger and geographic inequity. Furthermore, addressing the micronutrient requirements of breastfeeding women and rural communities are critical step toward reducing multiple micronutrient deficiencies across Ethiopia.
Supplementary Information
The online version contains supplementary material available at 10.1038/s41598-026-58922-z.
Keywords: Women, Multiple micronutrient deficiency, Geographic equity, Ethiopia
Subject terms: Diseases, Health care, Medical research, Risk factors
Introduction
Micronutrients are essential vitamins and minerals required in small quantities as dietary components1. Despite adequate stores of these nutrients before conception being essential to improve reproductive health and meet the increased physiological demands during pregnancy, numerous reports indicate a high prevalence of micronutrient deficiencies (MNDs) among women of reproductive age (WRA) globally, especially in low- and middle-income countries2–4. These deficiencies affect an estimated 2 billion people worldwide. While vitamin A, folate, iron, iodine, and zinc deficiencies are among the most common, other MNDs also contribute to the global burden5,6. Although micronutrient deficiency affects all age groups, women tend to be one of the most susceptible due to their elevated nutritional requirements7. As such, deficiencies in women are related to several adverse outcomes: increased susceptibility to infections8, birth defects, blindness, cognitive impairment, reduced productivity, and even death9,10.
Micronutrient deficiencies in women are common in poor-resource settings like Ethiopia. The global burden of deficiency in at least one of three micronutrients is 69% for non-pregnant women of reproductive age (NPWRA), translating to approximately 1.2 billion affected individuals11. The estimates per economic strata and geographic region also showed high levels of micronutrient deficiencies, particularly in South Asia and Sub-Saharan Africa, but also in countries of all income levels. Nine in 10 women in several countries in South Asia and Sub-Saharan Africa have at least one micronutrient deficiency11. The problem is widely distributed among rural residents, breastfeeding mothers, those who did not received multivitamin supplementation and those who have an infection8. Considering this high burden of hidden hunger, a global roadmap called Sustainable Development Goal (SDG) has been designed to address the high burden of the problem (SDG3)12. In alignment with SDG, Ethiopia has also developed a health sector transformation plan (HSTP II), a strategic framework to reduce the burden, with a focus on vulnerable groups such as adolescents, and pregnant/lactating women13.
Ethiopia has also developed, endorsed, and implemented the national nutrition program II14, the food and nutrition policy15, and the food and nutrition strategy16 resulting in a major reduction of women classified as thin from 30% in 2000 to 22% in 201617. However, hidden hunger remains a serious concern in Ethiopia, with two out of every three women deficient in one or more micronutrients18.
Strategies such as dietary improvement, food fortification, biofortification, and supplementation have proven effective in reducing maternal undernutrition and child morbidity, neurocognitive impairment, and mortality19,20. However, in Ethiopia, despite the well-documented magnitude and consequences of micronutrient deficiencies among NPWRA, these interventions remain under-prioritized, fragmented, and limited in scale21.
Moreover, evidence suggests that individuals in developing countries often experience multiple micronutrient deficiencies simultaneously22. Yet, most studies, including meta-analyses, have focused on individual deficiencies, leaving the extent of co-occurring deficiencies unclear and largely speculative23,24. Data specific to NPWRA is limited25, and few studies have considered infection and inflammation biomarkers or the simultaneous occurrence of multiple deficiencies with no spatial component26. Importantly, very limited evidence exists on whether these deficiencies are distributed equitably across geographic regions, despite Ethiopia’s substantial regional diversity in socioeconomic conditions, dietary patterns, and health service access. Understanding geographic equity is essential for identifying which regions are disproportionately affected and for guiding targeted, region-specific interventions.
Therefore, this study aimed to assess the inflammation-adjusted magnitude of multiple micronutrient deficiencies, evaluate geographic equity by examining their spatial distribution across regions, and identify contributing factors among women of reproductive age in Ethiopia using nationally representative data.
Methods and materials
Study setting and design
This study utilized data from the Ethiopian National Micronutrient Survey (ENMS), conducted across Ethiopia, a country located in the Horn of Africa between 3° and 15° north latitude and 33° and 48° east longitude. Ethiopia covers approximately 1.1 million square kilometers, with dramatic topographic variation ranging from 4550 m above sea level to 110 m below sea level. As of the most recent estimates, Ethiopia has a population of approximately 130 million, with nearly 30% comprising younger age groups27. Additional details are available in the main ENMS report28.
Data source and survey description
The ENMS was the primary data source for this study. This large population based cross-sectional survey was conducted between March and July 2015, with representative samples drawn from nine regions and two city administrations. The survey aimed to estimate the prevalence of anemia, iron deficiency, vitamin A deficiency, vitamin B12 deficiency, folate deficiency, zinc deficiency, iodine deficiency, and the adequacy of iodized salt in Ethiopia using established biomarkers. It specifically assessed micronutrient deficiencies among children (6–59 months), school-aged children (5–14 years), and non-pregnant women of reproductive age (15–49 years). Data were collected from eligible households through a structured, pre-tested modular questionnaire, anthropometric measurements, and biological specimen collection. The overarching purpose of the national micronutrient survey was to generate critical evidence to guide policymakers and program implementers in designing and executing nutrition interventions aimed at reducing malnutrition and micronutrient deficiencies in Ethiopia. Detail information can be found elsewhere28.
Source and study population
The source population for this study consisted of all WRA (15–49 years) residing in Ethiopia. The study population was drawn from individuals living within selected Enumeration Areas (EAs). Women were excluded from the final analysis only if they were pregnant at the time of data collection or if biomarker data for serum micronutrients and acute-phase proteins were missing.
Sample size determination and sampling procedure
Sampling technique and procedure
The ENMS employed a two stage stratified cluster sampling design across the nine regions and two city administrations of Ethiopia. In the first stage, EAs were selected from the national sampling frame provided by the Central Statistical Agency using probability proportional to size. In the second stage, a complete household listing was conducted within each selected EA, and 11 households were randomly selected using simple random sampling.
Selected households were visited by trained data collectors, and accessibility was assessed to ensure feasibility of biological sample collection. Inaccessible households were replaced through random selection from the remaining listed households. Household identifiers and eligibility information were preloaded onto data collectors’ smartphones to minimize selection bias. From the selected households, women of reproductive age (15–49 years) were systematically sub-sampled28.
Sample size determination
To estimate the prevalence of multiple micronutrient deficiencies among WRA, we used the sample size from the National Survey. Specifically, to maximize statistical power and ensure national representativeness, we utilized the ENMS sample of 1707 non-pregnant WRA. Further details can be found in the ENMS report28.
Operational definitions, biomarker and cutoffs used for each nutrient
Micronutrient deficiencies were defined using WHO and Biomarkers of Nutrition for Development project criteria: vitamin A deficiency as serum retinol < 0.70 µmol/L29; vitamin B12 deficiency as serum B12 < 150 pmol/L30; folate deficiency as RBC folate < 342 nmol/L31. Iron deficiency was defined as serum ferritin < 15 µg/L32; zinc deficiency as serum zinc < 10.7 µmol/L33; and iodine deficiency as urinary iodine concentration < 100 µg/L34. Inflammation was defined as C-reactive protein (CRP) > 5.0 mg/L and/or alpha-1-acid glycoprotein (AGP) > 1.0 g/L35. A woman was considered having multiple micronutrient deficiency if she had more than one micronutrient deficiency simultaneously.
The not taking or lack of multivitamins variable refers to any form of daily or weekly micronutrient supplementation reported by the participants.
Data collection methods and measurements
Anthropometric and household food security measurements
As part of the EMNS protocol, height was measured to the nearest 0.1 cm using a portable stadiometer, with participants standing upright in the Frankfurt plane. Weight was measured to the nearest 0.1 kg using a calibrated digital solar scale with participants wearing light clothing. Mid-upper arm circumference was measured to the nearest 0.1 cm using a non-stretchable measuring tape. Body mass index was calculated as weight in kilograms divided by height in meters squared.
Within the EMNS, household wealth status was constructed using principal component analysis based on asset ownership and housing characteristics, and households were categorized into quintiles. Household food insecurity was assessed using the Household Food Insecurity Access Scale which classifies households as food secure, mildly food insecure, moderately food insecure, or severely food insecure based on experiences during the preceding four weeks36. Women’s dietary diversity was assessed within EMNS using a 24-hour recall covering ten food groups; consumption of five or more food groups was considered adequate dietary diversity37,38.
Biological sample collection, processing, and storage
Under the EMNS, venous blood and urine samples were collected from eligible non-pregnant women aged 15–49 years. Venous blood was drawn by trained EMNS phlebotomists into three vacutainers as per the EMNS blood collection protocol39: a trace-element free tube for zinc, retinol, and folate analysis; a serum tube for ferritin, vitamin B12, CRP, and AGP. The time of last meal and blood draw was recorded for each participant by EMNS field staff.
Samples collected through EMNS were transported immediately to temporary field laboratories in cold boxes maintained below 8 °C. Blood samples were centrifuged within 30 min of collection, and serum and plasma were aliquoted into cryovials and stored at − 20 °C using portable freezers according to EMNS procedures. Urine samples were stored frozen at − 18 °C. Temporary field laboratories were established by EMNS in each enumeration area or, where electricity was unavailable, within field vehicles. More information can be found in the EMNS report28.
Laboratory analysis of infections, inflammation, and micronutrients
As part of EMNS implementation, urine dipstick analysis was conducted in the field by EMNS laboratory teams, and urinary iodine concentration was determined at EPHI using the Sandell Kolthoff reaction. Edible salt samples were tested for iodine using rapid test kits in the field, with quantitative iodine content determined by titration at EPHI28.
Within EMNS laboratory procedures, CRP and AGP concentrations were measured using immunoturbidimetric methods on a Cobas Integra 6000 analyzer35. Iron status was assessed using serum ferritin biomarkers40,41.
Serum retinol was measured using high-performance liquid chromatography (HPLC)42. Serum zinc concentration was measured by atomic absorption spectrophotometry, with non-fasting zinc deficiency defined according to established cutoffs43. Folate status was assessed using microbiological assays of serum and red blood cell (RBC) folate31. Vitamin B12 was measured using electrochemiluminescence immunoassay28.
The ENMS analyzed the following biomarkers: ferritin (iron), retinol (vitamin A), zinc, serum vitamin B12, RBC folate, and urinary iodine concentration (UIC). For biomarkers known to be influenced by the acute phase response specifically ferritin, retinol, and zinc, which are well established as sensitive to inflammation the ENMS applied an inflammation adjustment using CRP and AGP. This adjustment used the regression correction approach recommended by the BRINDA project, and was consistently applied across all inflammation sensitive biomarkers. Although vitamin B12, folate, and urinary iodine concentration are generally considered unaffected by inflammation, the ENMS performed sensitivity analyses to examine the influence of CRP and AGP on these biomarkers for methodological consistency. These analyses showed that adjusting for CRP and AGP did not substantially change their distributions or deficiency prevalence estimates. Therefore, the final deficiency classifications for vitamin B12, folate, and iodine were based on unadjusted concentrations44.
Analysis
Data analysis was conducted using STATA version 17 and ArcGIS Pro version 3.4.2. Unlike the original survey report, which presented micronutrient indicators using biomarker specific sample sizes, this analysis used a dataset restricted to women with complete biomarker information. This ensured a consistent analytical sample across all micronutrients. Descriptive statistics were used to summarize the socio-demographic characteristics of the study population and to estimate the prevalence of multiple micronutrient deficiencies among women of reproductive age. Women were considered to have multiple micronutrient deficiencies if they had two or more deficiencies among the six micronutrients assessed.
To evaluate geographic equity, spatial analysis was performed to examine whether multiple micronutrient deficiencies were distributed evenly across regions or concentrated in specific geographic areas.
Global Moran’s I was used to assess whether the spatial distribution of multiple micronutrient deficiencies exhibited spatial autocorrelation across enumeration areas45. Values near + 1 indicate strong clustering of similar values, values near − 1 indicate spatial dispersion, and values close to 0 suggest a random spatial pattern46. Evidence of clustering was interpreted as potential geographic inequity in the distribution of deficiencies. Thereby, we tested the null hypothesis that the spatial distribution of multiple micronutrient deficiencies is random across the study area.
Spatial interpolation was conducted to estimate the prevalence of multiple micronutrient deficiencies in unsampled areas of Ethiopia using data from sampled enumeration areas47. This method fits a semivariogram model to the observed data and uses the spatial structure to optimally weight neighboring data points, enabling visualization of geographic inequities in unsampled locations.
Hotspot analysis using the Getis-Ord Gi* statistic was performed to identify statistically significant clusters of high (hotspots) and low (cold spots) prevalence of multiple micronutrient deficiencies48. Identifying these clusters provides insight into geographic inequities by highlighting areas with disproportionately high or low burdens.
To further investigate geographic equity, we employed a two-step regression approach beginning with a global model and transitioning to a local model to account for spatial heterogeneity. Ordinary Least Squares (OLS) regression was first used to identify predictors of multiple micronutrient deficiencies49. Spatial dependence was assessed using Global Moran’s I, confirming the need for spatially adaptive modeling. Model assumptions were evaluated, including expected relationships among explanatory variables, statistical significance of predictors, random distribution of residuals, normality of residuals (Jarque-Bera test), multicollinearity (Variance Inflation Factor < 7.5), and model fit indicated by a strong R-squared value50,51.
After validating the OLS model, Multiscale Geographically Weighted Regression (MGWR) version 3.4.2 was applied to explore spatially varying relationships at a 95% confidence level52. MGWR allows each covariate to operate at its own spatial scale using covariate-specific bandwidths, providing a nuanced assessment of geographic inequities in the determinants of multiple micronutrient deficiencies53. Model performance was compared using the corrected Akaike Information Criterion (AICc) and adjusted R-squared, with the best-fitting model used to interpret spatial variation in deficiency prevalence49.
Data access and ethical considerations
The original national survey received ethical approval from the National Research Ethics Review Committee within the Ministry of Science and Technology, Ethiopia (Reference 3.10/433/06). During the data collection period, official request letters were sent to each region, and approval was granted. Before participation in the survey, informed consent was taken from the head of household of all selected households. In addition, the dataset contained no personal identifiers such as names or household addresses. Geographic identifiers were limited to regional, zonal, and district levels, in accordance with Ethiopia’s administrative divisions. Each enumeration area was assigned a numeric code, but these codes were not linked to specific names or locations, ensuring confidentiality and data protection. To our current analysis, we obtained Ethical clearance from the College of Medicine and Health Sciences Specialized Hospital, University of Gondar, Institutional Research Ethics Review Committee (CMHSSH-UOG-IRERC/52/11/2024). As the study involved secondary data analysis of anonymized national survey data, individual informed consent was not required. EPHI, through the national data management center, provided access to the dataset. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki.
Questionnaire based data collection
Data were collected by the EMNS using a structured and pretested questionnaire. The instrument was initially developed in English and subsequently translated into Amharic by a language expert as part of the EMNS protocol. The questionnaire captured information on socio-demographic and economic characteristics, household food security, women’s dietary practices, history of illness and supplementation, and water, sanitation, and hygiene (WASH) conditions.
Under the EMNS implementation, a total of 130 personnel were engaged in data collection, including 41 interviewers, 23 supervisors, 46 phlebotomists, and 23 laboratory technicians. All field staff received intensive training tailored to their roles, including separate sessions on interviewing techniques, anthropometric measurements, and biological sample collection and laboratory procedures. Household visits were conducted by EMNS field teams to administer questionnaires. If eligible respondents were absent, up to two revisits were made with prior appointments. Households were recorded as refusals if eligible participants were unavailable after repeated visits, without replacement28.
Results
Socio-demographic and economic characteristics of study participants
A total of 1450 WRA were included in this study. Participants ranged in age from 15 to 49 years, with a mean age of 28.64 years and a standard deviation of ± 8.95 years. In terms of educational attainment, about half (53.24%) had some level of schooling. Only a small percentage (6.34%) had achieved higher education (Table 1).
Table 1.
Socio-demographic and economic characteristics of study participants (n = 1450).
| Characteristics | Unweighted N (%) | Weighted* % (95% CI) |
|---|---|---|
| Age of the women (Years) | ||
| < 20 | 277 (19.1) | 18.77 (16.67–21.06) |
| 20–34 | 757 (52.21) | 49.63 (46.60-52.66) |
| 35–49 | 416 (28.69) | 31.61 (29.03–34.29) |
| Educational level of the women | ||
| No education | 679 (46.83) | 45.26 (41.60-49.01) |
| Primary education | 435 (30.00) | 28.53 (25.47–31.80) |
| Secondary education | 244 (16.83) | 19.35 (16.62–22.41) |
| Higher education | 92 (6.34) | 6.86 (5.22–8.96) |
| Religion of the head of the household | ||
| Orthodox | 697 (48.07) | 54.01 (49.70-58.24) |
| Muslim | 283 (19.52) | 14.45 (11.78–17.61) |
| Protestant/Roman Catholic | 470 (32.41) | 31.55 (27.61–35.77) |
| Educational level of the head of the household | ||
| No education | 713 (49.17) | 47.28 (42.86–51.73) |
| Primary education | 437 (30.14) | 28.52 (25.06–32.26) |
| Secondary education | 178 (12.28) | 14.83 (12.22–17.89) |
| Higher education | 122 (8.41) | 9.37 (6.88–12.63) |
| Residence | ||
| Urban | 483 (33.31) | 38.65 (33.29–44.31) |
| Rural | 967 (66.69) | 61.35 (55.69–66.71) |
| Regions | ||
| Tigray | 183 (12.62) | 18.66 (16.16–21.45) |
| Afar | 110 (7.59) | 10.83 (8.17–14.21) |
| Amhara | 217 (14.97) | 13.95 (11.66–16.62) |
| Oromiya | 197 (13.59) | 9.52 (7.50–12.00) |
| Somali | 104 (7.17) | 8.15 (6.90–9.59) |
| Benishangul Gumuz | 101 (6.97) | 0.88 (0.55–1.38) |
| SNNPR** | 170 (11.72) | 9.81 (7.97–12.02) |
| Gambela | 116 (8.00) | 4.02 (3.08–5.22) |
| Hareri | 69 (4.76) | 4.87 (3.73–6.33) |
| Addis Ababa | 130 (8.97) | 16.20 (13.81–18.91) |
| Dire Dawa | 53 (3.66) | 3.13 (2.44-4.00) |
| Number of family members in the household | ||
| < 5 | 820 (56.55) | 58.94 (55.20-62.58) |
| >=5 | 630 (43.45) | 41.06 (37.42–44.80) |
| Wealth index | ||
| Poorest | 284 (19.59) | 16.29 (13.53–19.49) |
| Poor | 316 (21.79) | 19.29 (16.34–22.62) |
| Middle | 296 (20.41) | 18.75 (15.88-22.00) |
| Rich | 284 (19.59) | 20.77 (17.08-25.00) |
| Richest | 270 (18.62) | 24.91 (21.13–29.11) |
*Analysis adjusted for enumeration area and women sampling weights.
**SNNPR; Southern Nations, Nationalities and Peoples Region.
Concentration and burden of individual micronutrient deficiencies
The prevalence of zinc deficiency was 31.9%, with a median serum zinc concentration of 11.9 µmol/L (interquartile range/IQR: 4.0). Folate deficiency was observed in 21.0% of women, with a median RBC folate concentration of 511.3 nmol/L (IQR: 39.0). Vitamin B12 deficiency affected 18.7%, with a median serum B12 concentration of 260.2 pmol/L (IQR: 192.0). Iron deficiency was present in 9.7%, with a median serum ferritin concentration of 57.7 µg/L (IQR: 59.0). The mean serum retinol concentration was 1.47 ± 0.44 µmol/L, and vitamin A deficiency was observed in 3.0% of women. Iodine deficiency was the most prevalent, affecting 59.0%, with a median urinary iodine concentration of 95.2 µg/L (IQR: 91.2).
Burden of multiple micronutrient deficiencies
The overall weighted prevalence of multiple micronutrient deficiencies (MMND) among WRA in Ethiopia was 41.31% (95% CI 37.91–44.79). About one-third (28.16%; 95% CI 25.41–31.09) of women had two micronutrient deficiencies, while one-tenth (10.0%, 95% CI 8.41–11.83) were deficiency for three micronutrients. Women deficient for four and five micronutrients were 2.78% (95% CI 1.92–3.95), and 0.40% (95% CI 0.17–0.94), respectively.
Spatial analysis of multiple micronutrient deficiencies
Spatial autocorrelation
The global spatial autocorrelation analysis revealed a statistically significant clustering of multiple micronutrient deficiencies across Ethiopia, as indicated by a Global Moran’s I value of 0.11 (p < 0.0087) (Fig. 1).
Fig. 1.
Global spatial autocorrelation result of multiple micronutrient deficiencies among women of reproductive age in Ethiopia. Generated by ArcGIS (Arcmap) version 10.7. https://desktop.arcgis.com/en.
Geographic equity of multiple micronutrient deficiencies
Hotspot analysis revealed clear geographic inequities in the distribution of multiple micronutrient deficiencies. Southern Afar, Dire Dawa, Harari, Eastern Oromia, and Southern Somali emerged as hotspot regions, where women experienced significantly higher burdens of multiple deficiencies. In contrast, Addis Ababa, Amhara, Benishangul-Gumuz, and Gambela were coldspot regions, with substantially lower burdens (Fig. 2a). Kriging interpolation further highlighted these inequities, showing predicted prevalence ranging from 33 to 44%. The highest predicted burdens were concentrated in Dire Dawa, Harari, and large parts of the Somali region, while the lowest burdens were observed in Gambela, Benishangul-Gumuz, Western Oromia, and southwestern Ethiopia (Fig. 2b).
Fig. 2.
Hotspot analysis (a) and kriging interpolation (b) of multiple micronutrient deficiencies among women of reproductive age in Ethiopia.
Spatial regression of multiple micronutrient deficiency predictors
OLS regression showed that breastfeeding and rural residence were positively associated with multiple micronutrient deficiencies. The effect of lack of multivitamin supplementation varied spatially, indicating that its influence differs across regions another marker of geographic heterogeneity and inequity. The Koenker (BP) statistic confirmed spatial non-stationarity, justifying the use of MGWR. The MGWR model substantially improved model fit (AICc reduced from 957.21 (OLS) to 675.59 (MGWR); adjusted R² increased from 0.358 to 0.503), indicating that relationships between predictors and deficiencies vary across geographic contexts. MGWR identified rural residence, breastfeeding, and lack of multivitamin supplementation as key predictors, with spatially varying coefficients that reflect region-specific inequities. The Jarque-Bera test produced a p-value of 0.0943, indicating that the residuals were normally distributed and the OLS predictions were unbiased (Table 2).
Table 2.
Summary of model diagnostic results for OLS and MGWR analyses of determinants of spatial variation in the distribution of women’s multiple micronutrient deficiencies, Ethiopia (EMNS, 2015 (n = 1450).
| Variable | Coefficients | Robust t-statistics | Robust probability | VIF |
|---|---|---|---|---|
| Intercept | 0.252 | 1.65 | 0.100625 | – |
| Toilet availability (No) | 0.034903 | 3.123 | 0.052453 | 1.97 |
| Raised inflammation (Yes) | 0.031204 | 2.117 | 0.053675 | 1.27 |
| Rural residence | 0.093 | 2.28 | 0.023574 | 1.88 |
| Breast feeding | 0.202 | 2.94 | 0.003562 | 1.66 |
| Lack of multivitamin supplementation | 0.155 | 3.18 | 0.001665 | 1.85 |
| Model diagnostics (diagnostic criteria) | Magnitude | p-value | ||
|---|---|---|---|---|
| Number of data points (no of observations) | 297 | – | ||
| Joint F-statistics | 42.31 | 0.000000 | ||
| Joint Wald statistics | 118.68 | 0.000000 | ||
| Koenker (BP) statistics | 39.41 | 0.000000 | ||
| Jarque–Bera statistics | 4.72 | 0.094298 | ||
| Model comparison of OLS and MGWR model fit/performance (Fitness parameter) | ||||
|---|---|---|---|---|
| Akaike’s information criterion (AICc) for OLS | 957.21 | |||
| R Squared for OLS | 0.367 | |||
| Adjusted R-squared for OLS | 0.358 | |||
| Akaike’s information criterion (AICc) for GWR, (MGWR) | 691.963 (675.591) | |||
| R Squared for GWR (MGWR) | 0.508 (0.557) | |||
| Adjusted R-squared for GWR (MGWR) | 0.445 (0.508) | |||
Spatial variation of Beta coefficients for key predictors
The mean and median beta coefficients for breastfeeding and rural residence were positive, indicating a consistent positive association with multiple micronutrient deficiencies. In contrast, the coefficients for lack of multivitamin supplementation were both positive and negative, suggesting a spatial heterogeneity effect on multiple micronutrient deficiencies (Table 3).
Table 3.
Summary statistics for MGWR parameter estimates describing the spatially varying relationships between multiple micronutrient deficiencies and predictors among women of reproductive age in Ethiopia (EMNS, 2015 (n = 1450)).
| Explanatory variable | Mean | Standard Deviation | Minimum | Median | Maximum |
|---|---|---|---|---|---|
| Intercept (Scaled) | −0.0519 | 0.1531 | −0.4865 | −0.0139 | 0.5278 |
| Rural residence (Scaled) | 0.1495 | 0.0475 | 0.0728 | 0.1495 | 0.2179 |
| Breast feeding (Scaled) | 0.1321 | 0.0189 | 0.1060 | 0.1242 | 0.1726 |
| Lack of multivitamin supplementation (Scaled) | 0.2213 | 0.2192 | −0.1066 | 0.2537 | 0.5378 |
The beta coefficient for breastfeeding exhibited substantial spatial variation, indicating that its relationship with multiple micronutrient deficiencies is not uniform across the study area. A strong positive association was observed in Gambela, Oromia, SNNPR, and Somali Regions. In contrast, the lowest coefficients were also concentrated in Tigray, Amhara, and Afar Regions (Fig. 3a). Regarding rural residence, a strong positive association with multiple micronutrient deficiencies was concentrated in most parts of Amhara, Benishangul Gumuz, Oromia, Gambela, and SNNPR. Conversely, low beta coefficients were concentrated in some parts of Tigray, Amhara, Afar, Oromia, SNNPR, and Somali Regions (Fig. 3b). Moreover, lack of multivitamin supplementation showed a positive influence in Tigray, Amhara, and Afar Regions. Whereas, it showed a protective influence in Gambela, the Western parts of SNNP, and Oromia regions (Fig. 3c).
Fig. 3.
The spatial mapping of the local regression coefficient of breastfeeding (a) rural residence (b) and lack of multivitamin supplementation (c) in Ethiopia.
Discussion
Two-fifths of women were found to have multiple micronutrient deficiencies in Ethiopia. There were geographical inequalities in the distribution of multiple micronutrient deficiencies among women in Ethiopia. Higher proportions of multiple micronutrient deficiencies were observed in the Eastern parts of the country. Hotspot areas with significantly higher rates of multiple micronutrient deficiencies were observed in southern Afar, Dire Dawa, Harari, Eastern Oromia, and Southern Somali regions.
Two-fifth of Ethiopian women of reproductive age were found to have two or more micronutrient deficiencies, though Ethiopia has made progress through policy reforms, such as the HSTP II and Food and Nutrition Policy (2019)13,15. The current finding was in line with the regional burden of hidden hunger in sub-Saharan Africa and other low- and middle-income countries11. This similarity might be due to women’s limited access to a diverse diet, healthcare access, and socioeconomic conditions in the region6,54. The high burden of hidden hunger among WRA in Ethiopia suggests that current supplementation strategies may be insufficient and call a national multiple micronutrient supplementation. Importantly, the substantial geographic variation observed in this study highlights that these deficiencies are not evenly distributed across the country, pointing to significant geographic inequities that require region specific strategies rather than uniform national interventions. Thus, addressing the high burden is critical for maternal health, thereby advancing Ethiopia’s progress toward SDG 2.2 and SDG 3.112.
The prevalence of deficiencies in two or more micronutrients among women of reproductive age in Ethiopia is notably lower than that reported in Pakistan (69%) and India (57%)11. In Pakistan and India, the higher prevalence may be attributed to heavy reliance on cereal-based diets with limited fortification coverage and challenges in implementing and enforcing nutrition policies55. Ethiopia’s intermediate position suggests both progress and persistent gaps. Although the prevalence is lower than in South Asia, it remains alarmingly high and, importantly, unevenly distributed across regions, indicating substantial geographic inequities. These disparities highlight the need for context specific strategies that address the underlying structural and environmental drivers of multiple micronutrient deficiencies.
The spatial analysis further demonstrated significant geographic clustering of multiple micronutrient deficiencies among Ethiopian women. This spatial dependence mirrors findings from other low and middle income countries, where geographic disparities in nutritional status often reflect regional differences in food security, healthcare access, environmental conditions, and socioeconomic development6,54. The presence of statistically significant clusters indicates that deficiencies are not randomly distributed, but instead concentrated in specific geographic areas. Such clustering underscores the existence of geographic inequity, where women in certain regions face higher nutritional risks due to contextual disadvantages.
The hotspot analysis provided deeper spatial insight by identifying statistically significant clusters of high and low prevalence of multiple micronutrient deficiencies. Hotspot regions including Southern Afar, Dire Dawa, Harari, Eastern Oromia, and Southern Somali showed markedly higher burdens, consistent with previous national nutrition assessments that have documented high levels of undernutrition, poor dietary diversity56, and lower serum calcium and magnesium concentrations in these areas57.
This pattern may be attributable to socioeconomic, geographic, and sociocultural differences across regions, as well as variations in livelihood systems, dietary practices, food availability, and access to healthcare. The convergence of environmental hardship, weak service delivery systems, and sociocultural barriers likely creates a perfect storm that exacerbates nutritional vulnerability in these areas58. Furthermore, population displacement and the presence of nomadic populations in hotspot regions such as Somali and Afar may further constrain access to stable food supplies and essential health services. Collectively, these factors highlight pronounced geographic inequities, whereby women in certain regions experience greater nutritional disadvantage. This evidence underscores the need for region-specific strategies tailored to the distinct structural and environmental challenges driving these disparities or geographic inequity.
This study highlights the spatial heterogeneity of multiple micronutrient deficiency risk factors across Ethiopia, revealing distinct geographic patterns in the influence of breastfeeding, rural residence, and lack of multivitamin supplementation. Breastfeeding showed a strong positive association with multiple micronutrient deficiencies. The observed association between breastfeeding and multiple micronutrient deficiencies likely due to increased postpartum nutritional requirements, variations in dietary intake and supplementation, differences in access to health services, socioeconomic conditions, feeding herself and her baby, and other unmeasured confounders. In regions where maternal diets are limited in diversity or where food insecurity is more severe, breastfeeding may therefore reflect underlying maternal depletion rather than protection. The spatial variation observed in this study suggests that women in certain regions face compounded nutritional vulnerabilities, reinforcing the presence of geographic inequities in maternal micronutrient status.
Rural residence was also positively associated with multiple micronutrient deficiencies, consistent with global evidence showing that rural populations face higher nutritional risks due to limited access to fortified foods, healthcare services, and nutrition education59. In South Asia and Sub-Saharan Africa, rural women are disproportionately affected by hidden hunger, a pattern often intensified by seasonal food insecurity, low maternal literacy, and constrained access to social and health services11,60. These findings indicate that rural women in Ethiopia experience disadvantages that heighten their vulnerability to micronutrient deficiencies. Addressing these disparities is therefore essential for advancing geographic equity, as rural women require targeted interventions to ensure their micronutrient needs are adequately met.
Lack of multivitamin supplementation was positively associated with multiple micronutrient deficiencies in several regions, highlighting persistent gaps in coverage and access. Although supplementation is globally recognized as a cost-effective strategy to reduce micronutrient deficiencies, uptake remains low in many low-resource settings due to barriers such as limited availability, weak supply chains, low awareness, and inconsistent program implementation61. The spatial variation observed in this study indicates that these barriers are not evenly distributed across Ethiopia. Instead, women in certain regions face reduced access to supplementation, reinforcing geographic inequities in both exposure and vulnerability to micronutrient deficiencies. These findings underscore the need for geographically tailored interventions that address region-specific constraints and ensure equitable access to supplementation programs.
Despite the robust findings, this study has some limitations. First, the data were collected several years ago, and the nutritional landscape in Ethiopia may have changed since then, particularly in regions that have experienced substantial socio-economic, political, or environmental shifts. As a result, the current geographic distribution of multiple micronutrient deficiencies may differ from the patterns observed in this analysis. Second, not all potentially relevant variables were available in the dataset, which may have limited our ability to fully capture the complex determinants of micronutrient deficiencies. These constraints should be considered when interpreting the findings, especially in the context of assessing geographic equity.
Conclusion
This study demonstrates that multiple micronutrient deficiencies are a significant public health concern among Ethiopian WRA, affecting more than 41% of the population. The burden is unevenly distributed across geographic regions, with rural residence, breastfeeding, and lack of multivitamin supplementation contributing to observed disparities. Spatial analysis revealed distinct hotspots and coldspots, underscoring the need for targeted, region-specific interventions. The findings highlight the urgency of addressing multiple micronutrient deficiencies through coordinated national and regional strategies. Without timely intervention, these deficiencies will continue to compromise maternal health, and their reproductive capacities.
To effectively reduce the burden, programs should prioritize region specific approaches, including targeted micronutrient supplementation and food fortification initiatives, particularly in hotspot areas such as Dire Dawa, Harari, and the Southern Somali region. Integrating micronutrient screening and supplementation into maternal and child health services especially for breastfeeding women is essential to meet increased nutritional demands. Strengthening public health interventions and expanded access to healthcare can also play a critical role in mitigating multiple micronutrient deficiencies. In addition, culturally tailored awareness campaigns promoting dietary diversity and the importance of micronutrients, particularly in rural communities with high prevalence rates, may further enhance nutritional outcomes.
Finally, conducting longitudinal studies to explore causal pathways and evaluate intervention effectiveness especially in regions with pronounced spatial variability is crucial. Increased government and donor investment in nutrition-sensitive programs, with a strong emphasis on equity and regional disparities, will be vital to reducing the burden of micronutrient deficiencies among Ethiopian women of reproductive age.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to forward heartfelt thanks to Ethiopian Public Health Institute (EPHI) for allowing us to use the Ethiopian National Micronutrient Survey dataset.
Author contributions
MS, NW, AT and SMA designed and developed the proposal. MS, NW, AT and SMA involved in the design, data cleaning, statistical analysis, and prepared the original draft of the manuscript. MS, NW, AT, SMA and MT were involved in writing the Manuscript. All authors contributing to subsequent reviews, editing, read and approved the final draft of the manuscript.
Funding
No funding was received for this work.
Data availability
The datasets analyzed could be available from the corresponding author upon reasonable request.
Declarations
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The datasets analyzed could be available from the corresponding author upon reasonable request.