The Co-Occurrence of Overweight and Micronutrient Deficiencies or Anemia among Women of Reproductive Age in Malawi

Elizabeth C Rhodes; Parminder S Suchdev; K M Venkat Narayan; Solveig Cunningham; Mary Beth Weber; Katie Tripp; Carine Mapango; Usha Ramakrishnan; Monique Hennink; Anne M Williams

doi:10.1093/jn/nxaa076

. 2020 Apr 9;150(6):1554–1565. doi: 10.1093/jn/nxaa076

The Co-Occurrence of Overweight and Micronutrient Deficiencies or Anemia among Women of Reproductive Age in Malawi

Elizabeth C Rhodes ^1,^✉, Parminder S Suchdev ^1,^2,^3,⁴, K M Venkat Narayan ^1,², Solveig Cunningham ^1,², Mary Beth Weber ^1,², Katie Tripp ³, Carine Mapango ⁵, Usha Ramakrishnan ^1,², Monique Hennink ², Anne M Williams ^1,³

PMCID: PMC7269723 PMID: 32271925

ABSTRACT

Background

In low-resource settings, urbanization may contribute to the individual-level double burden of malnutrition (DBM), whereby under- and overnutrition co-occur within the same individuals.

Objective

We described DBM prevalence among Malawian women by urban-rural residence, examined whether urban residence was associated with DBM, and assessed whether DBM prevalence was greater than the prevalence expected by chance given population levels of under- and overnutrition, which would suggest DBM is a distinct phenomenon associated with specific factors.

Methods

We analyzed nationally representative data of 723 nonpregnant women aged 15–49 y from the 2015–2016 Malawi Micronutrient Survey. DBM was defined as co-occurring overweight or obesity (OWOB) and ≥1 micronutrient deficiency or anemia. We used Poisson regression models to examine the association between urban residence and DBM and its components. The Rao-Scott modified chi-square test compared the observed and expected DBM prevalence.

Results

Nationally, 10.8% (95% CI: 7.0, 14.5) of women had co-occurring OWOB and any micronutrient deficiency and 3.4% (95% CI: 1.3, 5.5) had co-occurring OWOB and anemia. The prevalence of co-occurring OWOB and any micronutrient deficiency was 2 times higher among urban women than rural women [urban 32.6 (24.1, 41.2) compared with rural 8.6 (5.2, 11.9), adjusted prevalence ratio: 2.0 (1.1, 3.5)]. Co-occurring OWOB and anemia prevalence did not significantly differ by residence [urban 6.9 (0.6, 13.2) compared with rural 3.0 (0.8, 5.3)]. There were no statistically significant differences in observed and expected prevalence estimates of DBM.

Conclusions

This analysis shows that co-occurring OWOB and any micronutrient deficiency was higher among women in urban Malawi compared with rural areas. However, our finding that co-occurring OWOB and any micronutrient deficiency or anemia may be due to chance suggests that there may not be common causes driving DBM in Malawian women. Thus, there may not be a need to design and target interventions specifically for women with DBM.

Keywords: double burden, malnutrition, overweight, obesity, anemia, micronutrient deficiency, women, Africa, Malawi

Introduction

Low- and middle-income countries (LMICs) are experiencing a rapid increase in overweight, obesity, and diet-related noncommunicable diseases in the face of persisting undernutrition (1–4). The coexistence of under- and overnutrition, termed the ‘double burden of malnutrition’ (DBM), can occur in populations (e.g. high prevalence of undernutrition and overweight or noncommunicable diseases in a country), households (e.g. an overweight adult with a stunted child), and individuals (e.g. simultaneous overweight and micronutrient deficiencies in a person) (5). Addressing the DBM is a high priority on the UN global health agenda, given the adverse health consequences of malnutrition and negative impact on developing economies due to lost economic productivity (1, 6). An essential first step toward reducing DBM is to understand the prevalence and causes of DBM at every level, which in turn, can inform the development and targeting of interventions. Yet, many previous studies have investigated DBM at the population (2, 4, 7) or household levels (8–11), whereas comparatively little research has focused on individual-level DBM, especially in sub-Saharan Africa (12).

Urbanization may be a key driver of DBM within individuals in LMICs (13). Urbanization and attendant economic growth are thought to expand access to processed foods, increase exposure to food marketing, and reduce physical activity through more motorized transportation and sedentary occupations (13–16). These macrolevel changes in environment and lifestyle are believed to lead to a nutrition transition characterized by decreased energy expenditure and shifts away from diets composed of legumes, coarse grains, and vegetables and toward energy-dense, nutrient-poor diets composed of foods high in refined carbohydrates and added sugars. This nutrition transition is viewed as a major contributor to the rise in prevalence of overweight, obesity, and noncommunicable diseases (13, 14). At the same time, it has been posited that diets associated with the nutrition transition play a role in the continued problem of micronutrient deficiencies in LMICs, since processed and packaged foods are often low in micronutrients (16, 17). Thus, exposure to an urban environment may shape lifestyle behaviors, such as high consumption of energy-dense, processed and packaged foods, which may contribute to the co-occurrence of overweight or obesity (OWOB) and micronutrient deficiencies or anemia within individuals. Given the potential role of urbanization in DBM, many empirical studies have investigated the prevalence of conditions of under- and overnutrition among people who reside in urban and rural areas (2, 15). However, since these are ecological studies, their findings cannot be used to make inferences about the co-occurrence of under- and overnutrition within the same individual. Furthermore, few studies have considered that this co-occurrence may be due to chance, with the prevalence of DBM reflecting underlying levels of under- and overnutrition in the population.

Due to the scarcity of micronutrient status data (18–20), previous studies of DBM at the individual level have predominately examined co-occurring OWOB and anemia (12, 17, 21–24), with anemia used to reflect micronutrient deficiencies. Solely assessing co-occurring OWOB and anemia may not provide a clear understanding of individual-level DBM, because anemia is not only associated with micronutrient deficiencies but also nonnutritional factors such as malaria and other infections, inflammation, and hemoglobinopathies (25, 26). As such, analyses of co-occurring OWOB and micronutrient deficiencies are needed to further elucidate the issue of individual-level DBM, particularly in LMICs with a high infection burden (25).

We used nationally representative data based on a sample of nonpregnant women of reproductive age to investigate individual-level DBM in Malawi, an urbanizing sub-Saharan African country with unresolved undernutrition and problems of OWOB (2, 27). Most studies of individual-level DBM have been conducted in middle-income countries, where the nutrition transition is in advanced stages (12). Malawi is a low-income country in the early stages of the nutrition transition (28, 29), and thus offers a different context in which to investigate DBM in individual women. Our objectives were 3-fold. First, we estimated the prevalence of 2 characterizations of DBM at the individual level in Malawian women (1) co-occurring OWOB and anemia, 2) co-occurring OWOB and any micronutrient deficiency) by urban-rural residence. Second, we examined the association between individual-level DBM and residence in urban or rural areas. Finally, we tested whether the prevalence of DBM would differ from that expected by chance.

Methods

Data source and study population

We analyzed nationally representative data from the 2015–2016 Malawi Micronutrient Survey (MNS), which was conducted in co-ordination with the 2015–2016 Malawi Demographic and Health Survey (MDHS) (30, 31). The MDHS used the 2008 Malawi Population and Housing Census and employed a 2-stage cluster sampling design in which clusters were selected using a probability proportional to population size approach and then 30 households per urban cluster and 33 households per rural cluster were selected by applying an equal probability systematic selection method. Additional details of the MDHS sampling methodology are presented elsewhere (31). For the MNS, a subsample of MDHS clusters were randomly selected, and 20 households per urban cluster and 22 per rural cluster were included (30). In each household, eligible participants (defined as usual members of the household who spent the night in that household before the survey) were invited to participate. Of 830 sampled women of reproductive age (15–49 y), we excluded women who were pregnant (n = 34), women for whom pregnancy status was not available (n = 18), and women who had missing (n = 10) or biologically implausible BMI values (beyond 5 SD from reference means; n = 3) (15), missing values on anemia or micronutrient status (n = 41), or missing values on demographic variables used in our analysis (n = 1). Our final analytic sample consisted of 723 nonpregnant women. The proportion of missing or biologically implausible BMI values was 1.8%, indicating good anthropometric data quality (32). All available sociodemographic characteristics (age, wealth, education, residence) among women in our analytic sample and those who were excluded from this analysis were similar. Ethical approval for the MDHS and MNS was granted by The National Health Sciences Research Committee in Malawi. Community leaders also granted consent of MDHS and MNS activities.

Characterizations of DBM at the individual level

We examined 2 characterizations of DBM at the individual level in Malawian women: 1) co-occurring OWOB and anemia and 2) co-occurring OWOB and any micronutrient deficiency. For analyses examining any micronutrient deficiency in women, we created a micronutrient deficiency index that was a composite measure of a woman's micronutrient status. The micronutrient index was comprised of 5 micronutrients (zinc, iron, folate, vitamin B-12, vitamin A), and women with 1 or more deficiency in these micronutrients were categorized as having ≥1 micronutrient deficiency. In subanalyses, we examined co-occurring OWOB and single micronutrient deficiencies. Given that in some cases cut-offs above those used to define deficiency of micronutrients are clinically meaningful, we also examined co-occurring OWOB and folate insufficiency (which is associated with risk of neural tube defects), co-occurring OWOB and vitamin A insufficiency, and co-occurring OWOB and vitamin B-12 depletion.

Measurement of OWOB, anemia, and micronutrient deficiencies

Weight was measured with a digital floor scale (SECA brand) to the nearest 0.1 kg, and a wooden stadiometer [ShorrBoard^® (Weigh and Measure, LLC)] was used to measure height to the nearest 0.1 cm. We used weight and height measurements to calculate BMI and classify women into 3 weight categories, using the WHO recommended cut-off points: underweight (BMI <18.5 kg/m²), normal weight (BMI 18.5–24.9), and OWOB (BMI 25–29.9 [overweight], ≥30 [obesity]) (33). A BMI ≥25 indicates excess weight and has been shown to be associated with increased risk of adverse health outcomes, making it appropriate to collapse BMI categories of OWOB (17).

We measured deficiencies in zinc, iron, folate, vitamin B-12, and vitamin A using serum from venous blood samples, which were collected from consenting participants, using venipuncture into 1 trace element-free and a second EDTA-containing tube. Anemia was tested in the field using the HemoCue^® 301 (UNICEF Supply catalogue) and defined as hemoglobin (adjusted for altitude and smoking) <12.0 g/dL in nonpregnant women (34). Zinc deficiency was defined as a serum zinc concentration <70 μg/dL for morning fasted samples, <66 μg/dL for morning nonfasting samples, and <59 μg/dL for afternoon nonfasting samples (35). Iron deficiency was defined as inflammation-adjusted ferritin <15 μg/L. We adjusted ferritin concentrations for C-reactive protein (CRP) and α-1-acid glycoprotein (AGP) concentrations using the Biomarkers Reflecting Inflammation and Nutritional Determinants of Anemia internal (country-specific) regression-corrected approach (36, 37). Folate deficiency was based on risk of megaloblastic anemia defined as serum folate concentration <6.8 nmol/L (38). Vitamin B-12 deficiency was defined as serum vitamin B-12 concentration <150 pmol/L (39). Vitamin A deficiency was defined as retinol-binding protein (RBP) <0.46 μmol/L calibrated to equal retinol <0.7 μmol/L using regression analysis (40, 41). Folate insufficiency was defined as RBC folate <748 nmol/L (42). Vitamin B-12 depletion was defined as serum vitamin B-12 concentration <220 pmol/L (39). Vitamin A insufficiency was defined as RBP ≥0.46 to <0.86 μmol/L calibrated to equal retinol ≥0.7 to <1.05 μmol/L using regression analysis (41).

Statistical analysis

All analyses were conducted in SAS 9.4 (SAS Institute, Inc.) and used survey weights to generate results representative of women in Malawi. Survey procedures in SAS were used to account for the complex sampling design (strata and clusters). We generated descriptive statistics for sociodemographic characteristics of women (residence, education level, household wealth index, age).

We calculated the prevalence of OWOB, anemia, any micronutrient deficiency, co-occurring OWOB and anemia, and co-occurring OWOB and any micronutrient deficiency. In a bivariate analysis, we examined the association between residence and OWOB, using the Rao-Scott modified chi-square test. We then calculated unadjusted and adjusted (for household wealth index, education level, continuous age in years) prevalence ratios to examine the association between residence and OWOB, using a log-binomial regression model performed with PROC GENMOD (43). We evaluated multicollinearity among our independent variables by assessing variance inflation factors. We conducted similar analyses to assess the association between residence and each of the following outcomes: anemia, any micronutrient deficiency, co-occurring OWOB and anemia, and co-occurring OWOB and any micronutrient deficiency.

The Rao-Scott modified chi-square test was used to compare the observed and expected prevalence of co-occurring OWOB and anemia. The expected prevalence of co-occurring OWOB and anemia was estimated as the product of OWOB prevalence and anemia prevalence. We also compared the observed and expected prevalence of co-occurring OWOB and anemia stratified by residence. We carried out a similar analysis to compare the observed and expected prevalence of co-occurring OWOB and any micronutrient deficiency. For these analyses, underweight women were excluded, and normal weight women served as the reference category.

In subanalyses, we repeated the analyses described above to examine the co-occurrence of OWOB and deficiencies in single micronutrients (i.e. OWOB and zinc deficiency, OWOB and iron deficiency, OWOB and folate deficiency, OWOB and vitamin B-12 deficiency, OWOB and vitamin A deficiency). We also examined the co-occurrence of OWOB and single micronutrient insufficiencies (i.e. OWOB and folate insufficiency, OWOB and vitamin B-12 depletion, OWOB and vitamin A insufficiency). In addition, we estimated the prevalence of micronutrient deficiencies and insufficiencies in women who were underweight, normal weight, and OWOB.

Results

Sociodemographic characteristics of women are provided in Table 1. 79.0% had less than a secondary education, and the largest age category of women was aged 20–29 y with 38.6%. The proportion of women residing in urban areas and rural areas was 9.1% and 90.9%, respectively.

TABLE 1.

Sociodemographic characteristics of nonpregnant women of reproductive age in Malawi¹

	Total (n = 723)	Urban (n = 117)	Rural (n = 606)
Residence
Urban	9.1 (1.5, 16.8)	—	—
Rural	90.9 (83.2, 98.5)	—	—
Education
<Secondary	79.0 (73.2, 84.8)	35.6 (18.0, 53.2)	83.3 (79.0, 87.6)
≥Secondary	21.0 (15.2, 26.8)	64.4 (46.8, 82.0)	16.7 (12.4, 21.0)
Wealth tertile²
Low	35.8 (28.7, 42.9)	10.8 (0.0, 27.7)	38.3 (31.0, 45.6)
Middle	32.2 (27.2, 37.3)	9.7 (3.7, 15.7)	34.5 (29.4, 39.5)
High	32.0 (24.4, 39.6)	79.5 (66.3, 92.7)	27.2 (20.3, 34.1)
Age, y
15–19	20.2 (16.3, 24.0)	14.4 (7.2, 21.6)	20.7 (16.7, 24.8)
20–29	38.6 (32.7, 44.5)	57.0 (34.9, 79.0)	36.8 (31.0, 42.5)
30–39	24.7 (20.4, 29.0)	14.2 (6.6, 21.9)	25.8 (21.2, 30.3)
40–49	16.5 (13.5, 19.6)	14.4 (2.2, 26.6)	16.7 (13.6, 19.9)
Weight status
Underweight³	9.2 (6.9, 11.5)	13.7 (4.1, 23.3)	8.7 (6.4, 11.1)
Normal⁴	76.3 (71.7, 81.0)	51.9 (39.5, 64.4)	78.8 (74.4, 83.1)
Overweight⁵	10.7 (7.3, 14.1)	20.8 (7.8, 33.7)	9.7 (6.4, 13.0)
Obesity⁶	3.7 (1.8, 5.6)	13.6 (3.7, 23.5)	2.8 (1.1, 4.4)

Open in a new tab

Based on data from the 2015–2016 Malawi Micronutrient Survey.

Data are weighted percent and 95% CIs.

Prevalence estimates accounted for the complex sampling design.

Wealth tertiles were created from a wealth index, a variable that is derived from principal component analysis and serves as an indicator of household wealth.

Underweight defined as BMI <18.5 kg/m².

⁴

Normal weight defined as BMI 18.5–24.9 kg/m².

⁵

Overweight defined as BMI 25–29.9 kg/m².

⁶

Obesity defined as ≥30 kg/m².

Prevalence of OWOB, anemia, and micronutrient deficiencies

The national prevalence of OWOB, anemia, and any micronutrient deficiency was 14.5%, 19.9%, and 72.8%, respectively (Table 2). Table 2 and Figure 1 display the prevalence of OWOB, anemia, and any micronutrient deficiency stratified by residence. In bivariate analyses, the prevalence of OWOB in women was higher in urban areas than in rural areas [urban: 34.4% (95% CI: 25.8, 43.0), rural: 12.5% (95% CI: 8.8, 16.2)] (Table 2). The prevalence of anemia among women in urban and rural areas was 17.8% (95% CI: 6.4, 29.2) and 20.2% (95% CI: 16.5, 23.8), respectively. The prevalence of any micronutrient deficiency was high in both urban and rural areas [urban: 87.8% (95% CI: 74.3, 100.0), rural: 71.3% (95% CI: 66.3, 76.3)]. In adjusted regression analyses, OWOB was not statistically more prevalent among women in urban areas compared with women in rural areas [adjusted prevalence ratio (aPR): 1.5 (95% CI: 0.9, 2.5)] (Table 3). The prevalence of anemia did not significantly differ among women in urban areas and those in rural areas [aPR: 0.7 (95% CI: 0.4, 1.4)]; however, there was a higher prevalence of any micronutrient deficiency among women in urban areas compared with women in rural areas [aPR: 1.3 (95% CI: 1.0, 1.5)].

TABLE 2.

Weighted prevalence estimates of single conditions of malnutrition and co-occurring overweight or obesity and anemia or micronutrient deficiencies among nonpregnant women of reproductive age in Malawi, according to residence (n = 723)

	Total	Urban (n = 117)	Rural (n = 606)	P value¹
Single malnutrition conditions
Overweight²	10.7 (7.3, 14.1)	20.8 (7.8, 33.7)	9.7 (6.4, 13.0)	0.15
Obesity²	3.7 (1.8, 5.6)	13.6 (3.7, 23.5)	2.8 (1.1, 4.4)	0.09
OWOB²	14.5 (10.6, 18.4)	34.4 (25.8, 43.0)	12.5 (8.8, 16.2)	0.02
Anemia³	19.9 (16.5, 23.4)	17.8 (6.4, 29.2)	20.2 (16.5, 23.8)	0.69
Micronutrient deficiencies
≥1 micronutrient deficiency⁴	72.8 (67.7, 77.8)	87.8 (74.3, 100.0)	71.3 (66.3, 76.3)	0.15
Zinc deficiency⁵	61.8 (54.8, 68.9)	82.8 (66.2, 99.5)	59.7 (52.7, 66.8)	0.15
Iron deficiency⁶	16.0 (12.6, 19.4)	16.2 (7.5, 24.9)	16.0 (12.3, 19.7)	0.96
Folate deficiency⁷	7.6 (4.7, 10.5)	4.5 (0.0, 9.3)	7.9 (4.8, 11.1)	0.36
Vitamin B-12 deficiency⁸	12.4 (8.7, 16.2)	0.4 (0.0, 1.0)	13.6 (9.6, 17.6)	0.02
Vitamin A deficiency⁹	0.3 (0.0, 0.8)	—	0.4 (0.0, 0.9)	—
Micronutrient insufficiencies
Folate insufficiency¹⁰	80.9 (75.3, 86.6)	98.3 (96.0, 100.0)	79.2 (73.3, 85.1)	0.02
Vitamin B-12 depletion¹¹	39.6 (33.2, 46.0)	19.6 (6.7, 32.4)	41.6 (34.8, 48.5)	0.05
Vitamin A insufficiency¹²	5.0 (2.8, 7.2)	1.0 (0.0, 2.4)	5.4 (3.1, 7.8)	0.07
Co-occurring OWOB and anemia
OWOB + anemia	3.4 (1.3, 5.5)	6.9 (0.6, 13.2)	3.0 (0.8, 5.3)	0.36
Co-occurring OWOB and micronutrient deficiencies
OWOB + ≥1 micronutrient deficiency	10.8 (7.0, 14.5)	32.6 (24.1, 41.2)	8.6 (5.2, 11.9)	0.02
OWOB + zinc deficiency	9.8 (6.1, 13.6)	31.3 (22.1, 40.4)	7.7 (4.4, 11.0)	0.03
OWOB + iron deficiency	2.6 (1.0, 4.1)	2.5 (0.0, 5.3)	2.6 (0.9, 4.3)	0.97
OWOB + folate deficiency	1.8 (0.8, 2.9)	1.5 (0.0, 3.2)	1.8 (0.7, 3.0)	0.73
OWOB + vitamin B-12 deficiency	0.8 (0.2, 1.5)	—	0.9 (0.2, 1.7)	—
OWOB + vitamin A deficiency	0.1 (0.0, 0.3)	—	0.1 (0.0, 0.3)	—
Co-occurring OWOB and micronutrient insufficiencies
OWOB + folate insufficiency	12.5 (8.9, 16.0)	34.2 (25.7, 42.8)	10.3 (7.1, 13.5)	0.02
OWOB + vitamin B-12 depletion	4.4 (2.0, 6.7)	1.6 (0.0, 3.8)	4.6 (2.0, 7.2)	0.21
OWOB + vitamin A insufficiency	0.0 (0.0, 0.1)	—	0.0 (0.0, 0.1)	—

Open in a new tab

Based on data from the 2015–2016 Malawi Micronutrient Survey.

Data are weighted proportions and 95% CIs.

Prevalence estimates accounted for the complex sampling design.

—, For prevalence estimates with 0 unweighted cases. For P value that cannot be calculated.

Based on Rao-Scott modified chi-square test.

Overweight defined as BMI 25–29.9 kg/m². Obesity defined as ≥30 kg/m². Overweight or obesity defined as BMI ≥25.0 kg/m².

Anemia defined as hemoglobin (adjusted for altitude and smoking) <12.0 g/dL for nonpregnant women.

⁴

≥1 micronutrient deficiency defined as a deficiency in 1 or more of the following micronutrients: zinc, iron, folate, vitamin B-12, vitamin A.

⁵

Zinc deficiency defined as serum zinc concentration <70 μg/dL for morning fasted samples, <66 μg/dL for morning nonfasting samples, and <59 μg/dL for afternoon nonfasting samples.

⁶

Iron deficiency defined as inflammation-corrected ferritin <15 μg/L.

⁷

Folate deficiency was based on risk of megaloblastic anemia defined as serum folate concentration <6.8 nmol/L.

⁸

Vitamin B-12 deficiency defined as serum vitamin B-12 concentration <150 pmol/L.

⁹

Vitamin A deficiency defined as retinol-binding protein <0.46 μmol/L calibrated to equal retinol <0.7 μmol/L using regression analysis.

¹⁰

Folate insufficiency defined as RBC folate concentration <748 nmol/L.

¹¹

Vitamin B-12 depletion defined as serum vitamin B-12 concentration <220 pmol/L.

¹²

Vitamin A insufficiency defined as RBP ≥0.46 to <0.86 μmol/L calibrated to equal retinol ≥0.7 to <1.05 μmol/L using regression analysis.

OWOB, overweight or obesity.

Prevalence of overweight or obesity, anemia, and any micronutrient deficiency and 2 characterizations of the individual-level double burden of malnutrition by residence (n = 723). Individual level-double burden of malnutrition defined 2 ways: co-occurring overweight or obesity and anemia, and co-occurring overweight or obesity and any micronutrient deficiency. Based on data from the 2015–2016 Malawi Micronutrient Survey. Urban (n = 117), Rural (n = 606). Prevalence estimates accounted for the complex sampling design. Survey weights were used to calculate nationally representative prevalence estimates. Overweight or obesity defined as BMI ≥25.0 kg/m². Anemia defined as hemoglobin (adjusted for altitude and smoking) <12.0 g/dL for nonpregnant women. Any micronutrient deficiency defined as a deficiency in 1 or more of the following micronutrients: zinc, iron, folate, vitamin B-12, vitamin A. Zinc deficiency defined as serum zinc concentration <70 μg/dL for morning fasted samples, <66 μg/dL for morning nonfasting samples, and <59 μg/dL for afternoon nonfasting samples. Iron deficiency defined as inflammation-corrected ferritin <15 μg/L. Folate deficiency was based on risk of megaloblastic anemia defined as serum folate concentration <6.8 nmol/L. Vitamin B-12 deficiency defined as serum vitamin B-12 concentration <150 pmol/L. Vitamin A deficiency defined as retinol-binding protein <0.46 μmol/L calibrated to equal retinol <0.7 μmol/L using regression analysis. OWOB, overweight or obesity.

TABLE 3.

Associations between urban residence and single conditions of malnutrition, co-occurring overweight or obesity and micronutrient deficiencies, and co-occurring overweight or obesity and micronutrient insufficiency among nonpregnant women of reproductive age in Malawi (n = 723)

	Unadjusted PR (95% CI) (ref = rural)	P value	Adjusted PR (95% CI)¹ (ref = rural)	P value
Single malnutrition conditions
OWOB ²	2.8 (1.9, 4.0)	<0.0001	1.5 (0.9, 2.5)	0.10
Anemia³	0.9 (0.5, 1.7)	0.71	0.7 (0.4, 1.4)	0.31
≥1 micronutrient deficiency⁴	1.2 (1.0, 1.5)	0.01	1.3 (1.0, 1.5)	0.02
Zinc deficiency⁴	1.4 (1.1, 1.7)	<0.01	1.5 (1.1, 1.9)	<0.01
Folate insufficiency⁵	1.2 (1.1, 1.3)	<0.0001	1.2 (1.1, 1.3)	<0.0001
Co-occurring OWOB and micronutrient deficiencies
OWOB + ≥1 micronutrient deficiency	3.8 (2.4, 6.1)	<0.0001	2.0 (1.1, 3.5)	0.02
OWOB + zinc deficiency	4.1 (2.4, 6.8)	<0.0001	2.0 (1.1, 3.8)	0.03
Co-occurring OWOB and micronutrient insufficiency
OWOB + folate insufficiency	3.3 (2.2, 4.9)	<0.0001	1.9 (1.2, 3.0)	0.01

Open in a new tab

Based on data from the 2015–2016 Malawi Micronutrient Survey.

Data are prevalence ratios (PRs) (95% CI). Reference for PRs is rural residence.

Adjusted for education (<secondary, ≥secondary), household wealth index (low, middle, high), and age in years (continuous).

Overweight or obesity defined as BMI ≥25.0 kg/m².

Anemia defined as hemoglobin (adjusted for altitude and smoking) <12.0 g/dL for nonpregnant women.

⁴

≥1 micronutrient deficiency defined as a deficiency in 1 or more of the following micronutrients: zinc, iron, folate, vitamin B-12, vitamin A.

Zinc deficiency defined as serum zinc concentration <70 μg/dL for morning fasted samples, <66 μg/dL for morning nonfasting samples, and <59 μg/dL for afternoon nonfasting samples.

Iron deficiency defined as inflammation-corrected ferritin <15 μg/L.

Folate deficiency was based on risk of megaloblastic anemia defined as serum folate concentration <6.8 nmol/L.

Vitamin B-12 deficiency defined as serum vitamin B-12 concentration <150 pmol/L.

Vitamin A deficiency defined as retinol-binding protein <0.46 μmol/L calibrated to equal retinol <0.7 μmol/L using regression analysis.

⁵

Folate insufficiency defined as RBC folate concentration <748 nmol/L.

OWOB, overweight or obesity.

The prevalence of single micronutrient deficiencies was highly variable (Table 2). The prevalence of zinc deficiency was highest (61.8%), followed by inflammation-adjusted iron deficiency (16.0%), vitamin B-12 deficiency (12.4%), folate deficiency (7.6%), and vitamin A deficiency (0.3%). In bivariate analyses, the prevalence of vitamin B-12 deficiency in women was lower in urban areas than in rural areas [urban: 0.4% (95% CI: 0.0, 1.0), rural: 13.6% (95% CI: 9.6, 17.6)]. Zinc deficiency prevalence was high in both urban and rural areas, and in adjusted analysis, zinc deficiency prevalence was slightly higher in women in urban areas compared with those in rural areas [aPR: 1.5 (95% CI: 1.1, 1.9)]. The prevalence of iron deficiency was similar in urban [16.2% (95% CI: 7.5, 24.9)] and rural [16.0% (95% CI: 12.3, 19.7)] areas. Folate deficiency prevalence was 4.5% (95% CI: 0.0, 9.3) in urban areas and 7.9% (95% CI: 4.8, 11.1) in rural areas. As the prevalence of vitamin A deficiency was very low, vitamin A deficiency prevalence in urban compared with rural areas could not be evaluated.

Prevalence estimates of folate insufficiency, vitamin B-12 depletion, and vitamin A insufficiency were 80.9%, 39.6%, and 5.0%, respectively. The prevalence of folate insufficiency in women was higher in urban areas than in rural areas [urban: 98.3% (95% CI: 96.0, 100.0), rural 79.2% (95% CI: 73.3, 85.1)]. Prevalence estimates of vitamin B-12 depletion and vitamin A insufficiency were not significantly different between urban and rural areas [vitamin B-12 depletion: urban: 19.6% (95% CI: 6.7, 32.4), rural: 41.6% (95% CI: 34.8, 48.5); vitamin A insufficiency: urban: 1.0% (95% CI: 0.0, 2.4), rural: 5.4% (95% CI: 3.1, 7.8)]. In adjusted regression analysis, women in urban areas had a slightly higher prevalence of folate insufficiency than women in rural areas [aPR: 1.2 (95% CI: 1.1, 1.3)] (Table 3).

Prevalence of co-occurring OWOB and anemia or any micronutrient deficiency

The prevalence of DBM among Malawian women, defined as co-occurring OWOB and anemia was 3.4% (95% CI: 1.3, 5.5), and defined as co-occurring OWOB and any micronutrient deficiency was 10.8% (95% CI: 7.0, 14.5). The prevalence of co-occurring OWOB and anemia in women was 6.9% (95% CI: 0.6, 13.2) in urban areas and 3.0% (95% CI: 0.8, 5.3) in rural areas. The prevalence of co-occurring OWOB and any micronutrient deficiency was higher among women in urban areas than in rural areas [urban: 32.6% (95% CI: 24.1, 41.2), rural: 8.6% (95% CI: 5.2, 11.9)] (Table 2, Figure 1).

Unadjusted analysis indicated that women in urban areas had a higher prevalence of co-occurring OWOB and any micronutrient deficiency compared with women in rural areas [PR: 3.8 (95% CI: 2.4, 6.1)] (Table 3). The strength of this association was attenuated in an adjusted model but remained statistically significant; co-occurring OWOB and any micronutrient deficiency was twice as prevalent for women in urban areas compared with rural areas [aPR: 2.0 (95% CI: 1.1, 3.5)]. Low prevalence of co-occurring OWOB and anemia prevented modeling its association between urban and rural residence.

In subanalyses of single micronutrient deficiencies and OWOB, the prevalence estimates were as follows: 9.8% for co-occurring OWOB and zinc deficiency; 2.6% for co-occurring OWOB and iron deficiency; 1.8% for co-occurring OWOB and folate deficiency; 0.8% for co-occurring OWOB and vitamin B-12 deficiency; and 0.1% for co-occurring OWOB and vitamin A deficiency (Table 2). The prevalence estimates of co-occurring OWOB and folate insufficiency, co-occurring OWOB and vitamin B-12 depletion, and co-occurring OWOB and vitamin A insufficiency in women were 12.5%, 4.4%, and 0.0%, respectively. In bivariate analyses, the prevalence of co-occurring OWOB and zinc deficiency was higher among women in urban areas than rural areas [urban: 31.3% (95% CI: 22.1, 40.4), rural: 7.7% (95% CI: 4.4, 11.0)]. Similarly, the prevalence of co-occurring OWOB and folate insufficiency was higher among women in urban areas than rural areas [urban: 34.2% (95% CI: 25.7, 42.8), rural: 10.3% (95% CI: 7.1, 13.5)]. The prevalence estimates of co-occurring OWOB with other single micronutrient deficiencies (iron deficiency, folate deficiency) did not differ significantly among women according to residence (Table 2). Similarly, the prevalence of co-occurring OWOB and vitamin B-12 depletion did not differ significantly among women in urban areas and women in rural areas (Table 2). As such, differences in the prevalence estimates of co-occurring OWOB with these single micronutrient deficiencies and insufficiencies were not assessed further. Differences in the prevalence estimates of co-occurring OWOB and vitamin B-12 deficiency, co-occurring OWOB and vitamin A deficiency, and co-occurring OWOB and vitamin A insufficiency were also not assessed in unadjusted and adjusted analyses due to no unweighted cases with these co-occurring forms of malnutrition in urban areas. The higher prevalence of co-occurring OWOB and zinc deficiency [PR: 4.1 (95% CI: 2.4, 6.8)] and co-occurring OWOB and folate insufficiency [PR: 3.3 (95% CI: 2.2, 4.9)] in urban areas compared with rural areas persisted in adjusted analyses [co-occurring OWOB and zinc deficiency: aPR: 2.0 (95% CI: 1.1, 3.8), co-occurring OWOB and folate insufficiency: aPR: 1.9 (95% CI: 1.2, 3.0)].

In secondary analyses to determine if the prevalence of anemia and micronutrient deficiencies or insufficiencies differed according to weight category (underweight, normal weight, and OWOB), there were no apparent differences (Figure 2, Supplemental Table 1). The exception was vitamin A insufficiency, which was highest in underweight women [underweight: 11.5% (95% CI: 1.8, 21.3), normal weight: 5.2% (95% CI: 2.7, 7.6), OWOB: 0.2% (95% CI: 0.0, 0.7)].

Anemia and micronutrient deficiencies and insufficiencies according to weight status (underweight, normal weight, overweight or obesity) in Malawian women of reproductive age (n = 723). Based on data from the 2015–2016 Malawi Micronutrient Survey. Urban (n = 117), Rural (n = 606). Prevalence estimates accounted for the complex sampling design. Survey weights were used to calculate nationally representative prevalence estimates. Overweight or obesity defined as BMI ≥25.0 kg/m². Anemia defined as hemoglobin (adjusted for altitude and smoking) <12.0 g/dL for nonpregnant women. Any micronutrient deficiency defined as a deficiency in 1 or more of the following micronutrients: zinc, iron, folate, vitamin B-12, vitamin A. Zinc deficiency defined as serum zinc concentration <70 μg/dL for morning fasted samples, <66 μg/dL for morning nonfasting samples, and <59 μg/dL for afternoon nonfasting samples. Iron deficiency defined as inflammation-corrected ferritin <15 μg/L. Folate deficiency was based on risk of megaloblastic anemia defined as serum folate concentration <6.8 nmol/L. Vitamin B-12 deficiency defined as serum vitamin B-12 concentration <150 pmol/L. Vitamin A deficiency defined as retinol-binding protein <0.46 μmol/L calibrated to equal retinol <0.7 μmol/L using regression analysis. Folate insufficiency defined as RBC folate concentration <748 nmol/L. Vitamin B-12 depletion defined as serum vitamin B-12 concentration <220 pmol/L. Vitamin A insufficiency defined as retinol-binding protein ≥0.46 to <0.86 μmol/L calibrated to equal retinol ≥0.7 to <1.05 μmol/L.

Observed versus expected prevalence of co-occurring OWOB and anemia or any micronutrient deficiency

We investigated whether the prevalence of DBM would differ from that expected by chance, assuming the conditions were independent. The observed prevalence of co-occurring OWOB and anemia was 3.7%, which did not differ significantly from the expected prevalence (3.1%, P = 0.49). The observed and expected prevalence of co-occurring OWOB and any micronutrient deficiency was 11.9% and 11.7%, respectively (P = 0.82). There were no statistically significant differences in observed and expected prevalence estimates of DBM, by either definition (co-occurring OWOB and anemia or co-occurring OWOB and any micronutrient deficiency), or in analyses stratified by residence (Table 4).

TABLE 4.

Comparison of the observed prevalence of co-occurring overweight or obesity and anemia and co-occurring overweight or obesity and micronutrient deficiencies with the prevalence expected by chance among nonpregnant women of reproductive age in Malawi (n = 660)

	Observed co-occurrence (%)	Expected co-occurrence (%)	P value¹
Co-occurring OWOB and anemia
OWOB + anemia², ³	3.7	3.1	0.49
Urban	8.0	5.5	0.45
Rural	3.3	2.7	0.52
Co-occurring OWOB and micronutrient deficiencies
OWOB + ≥1 micronutrient deficiency⁴	11.9	11.7	0.82
Urban	37.8	37.0	0.54
Rural	9.4	9.8	0.65
OWOB + zinc deficiency⁵	10.8	9.9	0.30
Urban	36.2	35.0	0.40
Rural	8.4	8.2	0.77
OWOB + iron deficiency⁶	2.8	2.4	0.48
Urban	2.9	4.8	0.26
Rural	2.8	2.1	0.26
OWOB + folate deficiency⁷	2.0	1.2	0.07
Urban	1.7	2.0	0.66
Rural	2.0	1.1	0.05
OWOB + vitamin B-12 deficiency⁸	0.9	2.0	0.01
Urban	—	0.2	—
Rural	1.0	1.9	0.04
OWOB + vitamin A deficiency⁹	0.1	0.1	0.58
Urban	—	—	—
Rural	0.1	0.1	0.53
Co-occurring OWOB and micronutrient insufficiencies
OWOB + folate insufficiency¹⁰	13.8	12.9	0.18
Urban	39.7	39.0	0.22
Rural	11.3	10.8	0.49
OWOB + vitamin B-12 depletion¹¹	4.8	6.5	0.09
Urban	1.9	9.0	0.05
Rural	5.1	5.8	0.43
OWOB + vitamin A insufficiency¹²	0.04	0.7	<0.001
Urban	—	0.2	—
Rural	0.04	0.6	0.001

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Based on data from the 2015–2016 Malawi Micronutrient Survey.

Data are weighted proportions.

Prevalence estimates accounted for the complex sampling design.

Women who were underweight were excluded from the analyses.

—, for prevalence estimates, 0 unweighted cases. For chi-square test statistic and P value, cannot be calculated.

Based on Rao-Scott modified chi-square test.

Overweight or obesity defined as BMI ≥25.0 kg/m².

Anemia defined as hemoglobin (adjusted for altitude and smoking) <12.0 g/dL in pregnant women.

⁴

≥1 micronutrient deficiency defined as a deficiency in 1 or more of the following micronutrients: zinc, iron, folate, vitamin B-12, vitamin A.

⁵

Zinc deficiency defined as serum zinc concentration <70 μg/dL for morning fasted samples, <66 μg/dL for morning nonfasting samples, and <59 μg/dL for afternoon nonfasting samples.

⁶

Iron deficiency defined as inflammation-corrected ferritin <15 μg/L.

⁷

Folate deficiency was based on risk of megaloblastic anemia defined as serum folate concentration <6.8 nmol/L.

⁸

Vitamin B-12 deficiency defined as serum vitamin B-12 concentration <150 pmol/L.

⁹

Vitamin A deficiency defined as retinol-binding protein <0.46 μmol/L calibrated to equal retinol <0.7 μmol/L.

¹⁰

Folate insufficiency defined as RBC folate concentration <748 nmol/L.

¹¹

Vitamin B-12 depletion defined as serum vitamin B-12 concentration <220 pmol/L.

¹²

Vitamin A insufficiency defined as retinol-binding protein ≥0.46 to <0.86 μmol/L calibrated to equal retinol ≥0.7 to <1.05 μmol/L.

OWOB, overweight or obesity.

In subanalyses, the observed prevalence estimates were lower than the expected prevalence estimates for co-occurring OWOB and vitamin B-12 deficiency and co-occurring OWOB and vitamin A insufficiency (P = 0.01 and P <0.001, respectively). In analyses stratified by residence, these findings held among women in rural areas (P = 0.04 and P = 0.001, respectively). The observed prevalence of co-occurring OWOB and folate deficiency was slightly higher than the expected prevalence among women in rural areas (P = 0.05). We did not find statistically significant differences in observed and expected prevalence estimates of other co-occurring OWOB and single micronutrient deficiencies and insufficiencies.

Discussion

Nationally, the prevalence of co-occurring OWOB and anemia was very low, but >1 in 10 women had co-occurring OWOB and any micronutrient deficiency. The prevalence of co-occurring OWOB and anemia did not significantly differ by residence, whereas the prevalence of co-occurring OWOB and any micronutrient deficiency was 2-fold higher among women in urban areas compared with women in rural areas. The larger prevalence of co-occurring OWOB and any micronutrient deficiency in urban areas may be driven by the larger prevalence of OWOB and micronutrient deficiencies among urban women, given that we did not detect women with OWOB to be more or less likely to have micronutrient deficiencies in tests of independence, and based on the observation that the majority of micronutrient deficiencies affected women across all weight categories equally.

The prevalence of co-occurring OWOB and anemia in Malawian women was lower than the prevalence observed in other LMICs. For example, 1 study found that 9% of women in a region of southern India had co-occurring OWOB and anemia, though the results are not directly comparable since BMI cut-offs for Asian populations were used (24). Studies from Latin American countries have found 12% (Guatemala) to 14% (Brazil) of women with co-occurring OWOB and anemia (23). It is not surprising that co-occurring OWOB and anemia is higher in Guatemala and Brazil than that observed in Malawi, given the very high prevalence of both OWOB and anemia in these Latin American countries: in Guatemala, 40.6% of women have OWOB and 24.5% of women have anemia; in Brazil 64.9% have OWOB and 30.6% have anemia (23). The level of co-occurring OWOB and anemia observed in a given country may be related to its stage within the nutrition transition; the nutrition transition is thought to be advanced in some countries in Latin America (22, 44) and in the early stages in sub-Saharan African countries like Malawi (28, 29).

The prevalence of co-occurring OWOB and any micronutrient deficiency was nearly 3 times that of co-occurring OWOB and anemia. Notably, this finding was largely due to the high prevalence of zinc deficiency. In previous studies on DBM, anemia has been used to reflect micronutrient deficiencies, such as deficiencies of iron, folate, and vitamin A (17, 24). Although research supports the role of micronutrient deficiencies in the etiology of anemia, nonnutritional factors, such as chronic disease, genetic blood diseases, malaria, and inflammation are not only known to be associated with anemia, but may contribute even more to the development of anemia than nutritional causes in some settings (25, 26, 45, 46). Furthermore, there is now strong evidence that the contribution of micronutrient deficiencies to anemia varies by setting (25, 45, 46). Context-specific determinants of anemia need to be understood to assess whether anemia is an appropriate proxy for micronutrient deficiencies, as well as to better understand estimates of co-occurring OWOB and anemia. Moreover, given that anemia does not reflect micronutrient deficiencies uniformly across contexts, making direct comparisons of prevalence estimates of co-occurring OWOB and anemia across LMICs is likely problematic. Additional studies that examine co-occurring OWOB and anemia or micronutrient deficiencies in other LMICs are needed to explore the extent to which the prevalence estimates of these 2 characterizations of DBM may differ by setting.

We found that the prevalence of co-occurring OWOB and micronutrient deficiencies in women largely consisted of the prevalence of co-occurring OWOB and zinc deficiency, as the prevalence of other single micronutrient deficiencies (iron, folate, vitamin B-12, vitamin A) was relatively low. Our results are consistent with a large, cross-sectional analysis from Vietnam, where the prevalence of co-occurring OWOB and zinc deficiency (12.2%) was highest, and the prevalence of co-occurring OWOB and iron deficiency and prevalence of co-occurring OWOB and vitamin B-12 deficiency were low (2.3% and 3.0%, respectively) (47). Similar to the high levels of zinc deficiency found in women in Vietnam (67%), Malawian women bear a high burden of zinc deficiency (48). Research investigating co-occurring OWOB and micronutrient deficiencies in other LMICs is warranted. In LMICs such as Mexico and Cameroon, for example, there may be even higher levels of co-occurring OWOB and micronutrient deficiencies in women than that found in Malawian women, as both Mexico and Cameroon have larger burdens of OWOB (11, 27) than Malawi and high prevalence of micronutrient deficiencies (25).

Overall, the patterning of indicators of malnutrition by residence observed in our study is consistent with previous studies. Sub-Saharan Africa, along with South Asia, are the only regions in the world where OWOB remains concentrated in urban areas (16, 49). Simultaneously, cross-sectional studies have shown that intakes of micronutrients, such as vitamin B-12 and folate, are low among urban women in sub-Saharan Africa (50). Constrained potentially by the survey design, we did not observe a significant association between urban residence and co-occurring OWOB and anemia in Malawian women. By contrast, a pooled analysis of nationally representative data for 30 sub-Saharan African countries found that women in periurban and urban areas had significantly higher odds of concurrent OWOB and anemia than women in rural areas (periurban, OR: 1.18; urban, OR: 1.43) (12).

Perhaps 1 of the most salient findings of our analysis was that the national prevalence of individual-level DBM observed in Malawi was consistent with chance association, and this relationship persisted when examined by residence. Other studies in LMICs to evaluate OWOB and anemia in women have reported similar results (23, 24, 51). To our knowledge, few studies have tested the independence of OWOB and micronutrient deficiencies in women. The statistical independence of over- and undernutrition at the individual level among Malawian women is consistent with results examining the interplay of over- and undernutrition at the household level. For example, using 121 datasets from 54 countries, Dieffenbach and Stein found that households with children aged 2–5 y with stunting were not more or less likely to have mothers with OWOB, leading them to conclude that this characterization of household-level DBM was a “statistical artifact,” rather than a distinct entity (52). Subsequent research in Latin America found that in 5 of 6 countries (Brazil, Colombia, Ecuador, Guatemala, and Mexico), the observed prevalence of a child with stunting aged under 5 y and a woman with OWOB within the same household was significantly lower than the prevalence expected by chance, but all absolute differences were small and not considered to be practically meaningful (23).

Collectively, these findings suggest that there may not be shared drivers of over- and undernutrition, which can inform health and nutrition programming and policy in Malawi that is targeted at reducing DBM among women. Although Malawi has experienced steep declines in anemia from 2001 to 2015–2016, multiple forms of malnutrition still exist at the population level. The country is faced with the complex challenge of addressing OWOB, while continuing to combat micronutrient deficiencies and anemia. Accordingly, evidence-based approaches to reducing OWOB that are tailored to the Malawian context may be more consistent with current programming and investments in health and nutrition in Malawi than programs focused specifically on DBM. Reducing either component of DBM will in essence reduce DBM prevalence. Since the national prevalence of OWOB among women in Malawi is still relatively low compared with other LMICs (4, 53), the Malawi government has the opportunity to focus health resources on the prevention of OWOB alongside evidence-based interventions to reduce the current burden of OWOB. An emphasis on OWOB prevention is essential if Malawi is to avoid increases in the prevalence of OWOB and its consequences such as type 2 diabetes and cardiovascular diseases, which are costly to individuals, health care systems, and economies (3). Programming to reduce anemia and iron deficiency is also necessary, and this applies across the weight spectrum. With the optimism for double-duty actions to simultaneously reduce both undernutrition and OWOB and diet-related noncommunicable diseases (54, 55), Malawi may be an appropriate context to test the efficacy of these actions before making national program shifts without an adequate evidence base.

A major strength of this study was its examination of co-occurring OWOB and micronutrient deficiencies, and being able to contrast it with co-occurring OWOB and anemia. Since micronutrient status is not routinely assessed in population-based surveys (19, 20, 25), due to the complexity and expense of collecting and analyzing specimens for micronutrient biomarkers (20), evaluations of co-occurring OWOB and micronutrient deficiencies are rare. By including micronutrient deficiencies in the definition of individual-level DBM, we broaden the undernutrition component of the DBM definition and add to the evidence base that choice of DBM definition will substantially affect the prevalence estimates of DBM (56). A related strength was that we examined 5 different micronutrient deficiencies. Another strength of this study was that we evaluated the statistical independence of conditions of malnutrition, which yielded policy-relevant insights that can inform nutrition programming in the context of DBM.

There were also limitations of the analysis. Although all prevalence estimates are representative at the national level, the MNS was not designed to be representative at the residence level (urban-rural). Indeed, the lack of micronutrient status data that are representative at the residence level in Malawi, as well as most LMICs, limits assessment of urban-rural differences in DBM. Still, examining the individual-level DBM by residence using available data did highlight important findings, such as higher deficiencies of some micronutrients in urban areas that may be due to changes in diet patterns in urban versus rural areas. When micronutrient status data that are representative at the residence level become available, similar analyses can be done to corroborate (or contradict) this evidence. Another limitation was the inability to statistically detect small, but potentially meaningful, differences in DBM and its components across urban and rural areas. Therefore, we urge readers to notice differences in the prevalence estimates and recognize that with 80% power, a design effect of 2, and α = 0.05, the median minimal detectable difference between urban and rural prevalence estimates was 15 percentage points. In addition, although there are gradations of urbanicity (57), our analysis used a dichotomous urban-rural measure for residence. Research using measures with improved ability to capture degrees of urbanization is warranted, particularly given the growth of periurban areas in LMICs (12, 58). Another challenge of this study was that estimates for co-occurring OWOB and vitamin A deficiency and co-occurring OWOB and vitamin A insufficiency were based on a small number of unweighted cases, and thus, must be interpreted with caution. Furthermore, we used RBP as an indicator of vitamin A status, although the WHO recommends retinol (41). In some scenarios RBP is well correlated with plasma retinol (40), but as RBP can be influenced by adiposity and protein energy malnutrition it is not a reliable vitamin A biomarker (59). Finally, dietary data for women were not available to explore the relationship between dietary patterns and women's nutritional status. Analyses of food consumption data may help explain differences (or similarities) in women's nutritional status according to residence. For example, such analyses could uncover reasons why zinc deficiency was higher in women in urban areas compared with their rural counterparts, such as differential intakes of total zinc, lower consumption of higher bioavailable animal-source zinc, or higher intake of phytate, which inhibits zinc absorption (60–62).

In conclusion, this study is 1 of the few to investigate DBM at the individual level in women in sub-Saharan Africa (12) and to include micronutrient deficiencies as part of the DBM definition, which is critical for understanding and addressing DBM. Our results provide evidence for public health practitioners and policymakers, which suggests that individual-level DBM may not require its own set of interventions. Instead, scaling up evidence-based interventions that address OWOB, micronutrient deficiencies, and anemia separately could help reduce the burden of DBM among women in Malawi.

Supplementary Material

nxaa076_Supplemental_File

Click here for additional data file.^{(32.8KB, docx)}

Acknowledgments

The 2015–2016 Malawi Micronutrient Survey (MNS) was implemented by the Malawi National Statistical Office (NSO), Community Health Services Unit (CHSU) of the Ministry of Health, and Department of Nutrition, HIV and AIDS (DNHA) with funding from Irish Aid, World Bank, and Emory Global Health Institute, coordination from UNICEF, and technical assistance from the CDC, ICF, and Emory University. We thank all the survey team members for conducting the 2015–2016 MNS and Malawian women for their participation. Finally, we acknowledge the Nutritional Biomarkers Branch of the CDC, Division of Laboratory Sciences in the National Center for Environmental Health for laboratory technical assistance, analysis of biological specimens, and review of this manuscript.

The authors’ contributions were as follows—ECR, PSS, and AMW: designed the research; ECR: analyzed the data; PSS, AMW, and KMVN: supervised the data analysis; ECR: wrote the first draft of the manuscript; PSS, KMVN, SC, MBW, KT, CM, UR, MH, and AMW: contributed to the final draft of the manuscript; and all authors provided critical input and read and approved the final manuscript.

Notes

Financial support for the Malawi Micronutrient Survey was provided by the Government of Malawi, United States Agency for International Development (USAID), United Nations Children's Fund(UNICEF), Irish Aid, World Bank, and Emory Global Health Institute. Financial support for analysis of biological specimens to assess vitamin B-12 and folate status was provided by the National Center on Birth Defects and Developmental Disabilities of the CDC. ECR received salary support from the NIH National Heart, Lung, and Blood Institute (grant number R01HL125442) and NIH Fogarty International Center (grant number R25Tw09337). MBW was supported by the NIH National Institute of Diabetes and Digestive and Kidney Diseases (grant number P30DK111024).

Author disclosures: The authors report no conflicts of interest.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.

Supplemental Table 1 is available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/jn/.

Abbreviations used: aPR, adjusted prevalence ratio; DBM, double burden of malnutrition; LMICs, low- and middle-income countries; MDHS, Malawi Demographic and Health Survey; MNS, Malawi Micronutrient Survey; OWOB, overweight or obesity; RBP, retinol-binding protein.

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PERMALINK

The Co-Occurrence of Overweight and Micronutrient Deficiencies or Anemia among Women of Reproductive Age in Malawi

Elizabeth C Rhodes

Parminder S Suchdev

K M Venkat Narayan

Solveig Cunningham

Mary Beth Weber

Katie Tripp

Carine Mapango

Usha Ramakrishnan

Monique Hennink

Anne M Williams

ABSTRACT

Background

Objective

Methods

Results

Conclusions

Introduction

Methods

Data source and study population

Characterizations of DBM at the individual level

Measurement of OWOB, anemia, and micronutrient deficiencies

Statistical analysis

Results

TABLE 1.

Prevalence of OWOB, anemia, and micronutrient deficiencies

TABLE 2.

FIGURE 1.

TABLE 3.

Prevalence of co-occurring OWOB and anemia or any micronutrient deficiency

FIGURE 2.

Observed versus expected prevalence of co-occurring OWOB and anemia or any micronutrient deficiency

TABLE 4.

Discussion

Supplementary Material

Acknowledgments

Notes

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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