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BMJ Nutrition, Prevention & Health logoLink to BMJ Nutrition, Prevention & Health
. 2020 Dec 4;3(2):320–338. doi: 10.1136/bmjnph-2020-000182

Spectrum of nutrition-specific and nutrition-sensitive determinants of child undernutrition: a multisectoral cross-sectional study in rural Mozambique

Hirotsugu Aiga 1,2,✉, Marika Nomura 2,3, José Paulo M Langa 4, Mussagy Mahomed 5, Rosa Marlene 6, Albertina Alage 7, Nilton Trindade 8, Dino Buene 9, Hiroshi Hiraoka 10, Shunichi Nakada 10, Edgar Arinde 11, José Varimelo 12, Américo Jeremias Chivale 13
PMCID: PMC7841811  PMID: 33521543

Abstract

Background

Despite an increasing need for multisectoral interventions and coordinations for addressing malnutrition, evidence-based multisectoral nutrition interventions have been rarely developed and implemented in low-income and middle-income countries. To identify key determinants of undernutrition for effectively designing a multisectoral intervention package, a nutrition survey was conducted, by comprehensively covering a variety of variables across sectors, in Niassa province, Mozambique.

Methods

A cross-sectional household survey was conducted in Niassa province, August–October 2019. Anthropometric measurements, anaemia tests of children under 5 years of age and structured interviews with their mothers were conducted. A total of 1498 children under 5 years of age participated in the survey. We employed 107 background variables related to possible underlying and immediate causes of undernutrition, to examine their associations with being malnourished. Both bivariate (χ2 test and Mann-Whitney’s U test) and multivariate analyses (logistic regression) were undertaken, to identify the determinants of being malnourished.

Results

Prevalence rates of stunting, underweight and wasting were estimated at 46.2%, 20.0% and 7.1%, respectively. Timely introduction of solid, semi-solid or soft foods to children of 6–8 months of age was detected as a determinant of being not stunted. Mother–child cosleeping and ownership of birth certificate were a protective factor from and a promoting factor for being underweight, respectively. Similarly, availability and consumption of eggs at the household level and cough during the last 2 weeks among children were likely to be a protective factor from and a promoting factor for being wasted, respectively.

Conclusion

Timely introduction of solid, semi-solid or soft foods could serve as an entry point for the three sectors to start making joint efforts, as it requires the interventions from all health, agriculture and water sectors. To enable us to make meaningful interprovincial, international and inter-seasonal comparisons, it is crucially important to develop a standard set of variables related to being malnourished.

Keywords: nutrition assessment, dietary patterns, malnutrition

What this paper adds?

  • Despite a rapidly increasing need for multisectoral planning and implementations of nutrition-specific and nutrition-sensitive interventions, multisectoral nutrition surveys covering a spectrum of variables across sectors have been rarely conducted globally.

  • Timely introduction of solid, semi-solid or soft foods should be a key intervention for childhood stunting in Niassa province, Mozambique, calling for the joint interventions from health, agriculture and water sectors.

  • To ensure a better-designed package of evidence-based multisectoral interventions, it is recommended that a multi-stakeholder platform proactively work beyond respective sectoral interests in each country so that the initial step could be a joint multisectoral nutrition survey.

Introduction

Undernutrition accounts for 35% of total under-five mortalities globally.1 Thus, malnutrition has been drawing a great deal of attention as a key global development agenda from both developed and developing nations since the launch of the Millennium Development Goals (MDGs) in 2000.2 Under the Sustainable Development Goals, a greater emphasis continues to be placed on the critical need for addressing malnutrition as an unfinished agenda for the post MDG era.3 One of the major reasons that malnutrition remains the unfinished agenda was a significant lack of multisectoral and multistakeholder joint efforts when addressing malnutrition.4 It must be admitted that fragmented efforts previously made by respective sectors (eg, health and agriculture) ended up producing not only inadequate desirable outcomes but also sometimes even intersectoral confusions and conflicts.5 Malnutrition is not an independent issue that could be addressed and resolved by a single sector but a multifaceted complex issue that must be addressed and resolved by multiple sectors (eg, health, agriculture, environment, education, manufacturing and trading).6 7 To encourage and accelerate better integrated or coordinated efforts towards the reduction in prevalence of malnutrition in each country, Scale Up Nutrition (SUN) was launched as a global movement to end malnutrition in 2010. SUN advocates for the importance of and need for multisectoral planning and implementations of necessary interventions, by setting ‘multiple stakeholders come together’ as step 1.8

In Mozambique, where 43%, 15% and 6% of children under 5 years of age suffer from stunting, underweight and wasting, respectively,9 10 undernutrition has been one of the major public health concerns. The prevalence of stunting, in particular, remains extremely high around 42%–43% during the last 12 years, after its reduction from 60% in 1995 to 43% in 2008.11 Also, 2%–3% loss of gross national product in Mozambique is estimated to be attributed to chronic undernutrition.12 Having thoroughly understood the critical need for a multisectoral coordination in addressing high prevalence of malnutrition in the country, the Government of Mozambique (GoM) launched the Technical Secretariat for Food Security and Nutrition (SETSAN), a national multisectoral coordination mechanism for reducing undernutrition as a public health and social problem.13 Yet, despite a series of efforts made by the SETSAN and its participating partners since 2010, the country’s nutritional status has not significantly improved. One of the possible factors to which the inadequate progress in reduction in malnutrition is attributed should be a lack of detailed evidence-based multisectoral programming.

The determinants and underlying causes of malnutrition differ one country to another, and one province to another. Thus, designing a local setting-specific and context-sensitive multisectoral nutrition programme in an evidence-based manner is the key to ensuring more effective and efficient interventions.6 14 Nevertheless, there have been few earlier studies that systematically address the variables related to both nutrition-specific and nutrition-sensitive interventions15 and three underlying causes of undernutrition (household food insecurity, inadequate feeding and caring practices and unhealthy household environment)16 despite its importance and needs.17 18 While some earlier studies employed exclusively the variables related to feeding and caring practices, or water, sanitation and hygiene,19–23 others employed exclusively those related to household food security.24 25 Few employed the variables related to both types of interventions and three types of underlying causes of child undernutrition in a well-balanced manner. The contradiction between an emphasised need for multisectoral interventions and insufficiency of multisectoral nutrition studies is obvious.17 26

To design an evidence-based nutrition programme for Niassa province, the least developed province with the highest malnutrition prevalence in the country, the GoM and Japan International Cooperation Agency jointly conducted a multisectoral nutrition survey in the province. All the ministries responsible for addressing the three types of underlying causes of child undernutrition (ie, Ministry of Health, Ministry of Agriculture and Food Security and Ministry of Public Works, Housing and Water Resources) participated in the survey. No comprehensive multisectoral nutrition survey has been previously conducted in Mozambique. Therefore, the results of the survey will serve as the key foundation not only for designing an upcoming evidence-based multisectoral nutrition programme in Niassa province but also for developing the national technical standard and guidelines for multisectoral nutrition survey. This study is aimed at identifying key nutrition-specific and nutrition-sensitive determinants of child undernutrition, by employing a series of variables across three sectors (health, agriculture and water sanitation and hygiene) in Niassa province. Note that this is the first fully comprehensive multisectoral nutrition household survey in Mozambique.

Methods

Study objectives and study design

A cross-sectional household survey was conducted in two typical rural districts of Niassa province (Majune and Muembe), Mozambique, to estimate prevalence of undernutrition among children under 5 years of age and identify its key determinants in relation to nutrition-specific and nutrition-sensitive interventions across the three sectors (ie, health, agriculture and environment).

Study areas and study group

Majune district is located in the geographic centre of Niassa province and composed of 92 enumeration areas (EAs) for the Census 2017. Muembe district is bordered with Majune district in southeast and composed of 132 EAs. The total populations were estimated at 38 453 and 44 042 in Majune and Muembe, respectively, as of 2017.27 Ajawa is the major ethnic group in the both districts. The most commonly spoken languages in the districts are Ajawa and Macua. Agriculture accounts for the greatest proportion of local industries in the two districts. The both districts are positioned in the extremely rainy highland (annual precipitation 1171 mm and altitude 1500–1600 m). The targets of the study were children under 5 years of age living in Majune and Muembe districts.

Sample size and sampling methods

Demographic and Health Survey 2011 reported 46.8%, 18.2% and 3.7% as the prevalence of stunting, underweight and wasting among children under 5 years of age in Niassa province, respectively.11 Assuming no significant change in those prevalence rates since 2011, the sample sizes were calculated for prevalence of the three types of undernutrition with α (error)=0.05, 1-β (power)=0.80 and d (precision)=0.05, by applying the provincial prevalence as of 2011. This is a reasonable and realistic approach because the aforementioned prevalence rates were the only province-specific ones available and no significant changes were identified at least nationally during the last 12 years.11 As a result, 783 494 and 148 children under 5 years of age were calculated as the sample sizes required for estimating the prevalence of stunting, underweight and wasting, respectively, in the two districts. Then, of the three sample sizes calculated, the greatest one (=783 for stunting) was selected as the common sample size as it satisfied the sample sizes for underweight and wasting, too. Then, a design effect of 1.8 was multiplied, as two-stage sampling was employed for the survey (ie, 783×1.8=1409). Assuming non-response rate of 7.5%28 and cases of unknown child age of 2%–3%, 1556 was determined as the final sample size.

Of a total of 224 EAs (=92+132) in the two districts for the Census 2017, 94 EAs are randomly selected. Then, the number of households having children under 5 years of age to be selected in each of 94 EAs was calculated, by applying probability-proportional-to-size. The list of households for the Census 2017 was not readily available for the both districts.27 Thus, household listing was undertaken in all the 94 selected EAs, to develop the sampling frames from which target households having children under 5 years of age were selected. Then, in proportion to the population size of each selected EA, 7–38 households having children under 5 years of age were randomly selected from the household lists. Two repeated household visits were made, when children under 5 years of age, mothers and other caregivers were either absent or not available upon the initial visits. When a household was totally unavailable despite three visits, a substitute household was adopted by mechanically sampling the next eligible household in the household lists.

By targeting those randomly selected households, anthropometric measurements and anaemia tests of children under 5 years of age and structured interviews with their mothers or other caregivers were conducted during the period from 21 August 2019 to 4 October 2019, the postharvest season in Niassa province.

Anthropometric measurements

Weight measurements were undertaken for the children to the nearest 0.1 kg, using the electronic scale for children and adults (Seca 876, Hamburg, Germany). Their heights were measured to the nearest 0.1 cm, using the stadiometer for children and adults (Seca 213, Hamburg, Germany). Children younger than 2 years of age and unable to stand properly were measured lying down (recumbent length), using the length scale for infants (Seca 416, Hamburg, Germany).

Biochemical tests

Children of 6–59 months of age were tested for anaemia by the certified nurses, using the rapid blood analyzer HemoCue 301 (Quest Diagnostics, Norrköping, Sweden). Table salt available at households was sampled and tested for iodine, by using the field test kit (MBIK001, MBI Kits International, Tamil Nadu, India).

Household interviews and observations

A total of 107 background variables were employed as the potential determinants of undernutrition among children under 5 years of age. These background variables were selected from those representative of nutrition-specific interventions, nutrition-sensitive interventions and enabling environments, which were defined in the framework for actions to achieve optimum fetal and child nutrition and development.15 Those variables were selected so as to be in line with immediate causes and underlying causes of child undernutrition in the UNICEF’s conceptual framework of the determinants of child undernutrition, too.16Of the 107 variables, five were derived from immediate causes in the conceptual framework (ie, disease symptoms). Thirty-eight, 28 and 8 were derived from three underlying causes of undernutrition in the conceptual framework (ie, household food insecurity, inadequate feeding and caring practices and unhealthy household environment, respectively). And, the rest (28 variables) were sociodemographic and socioeconomic variables. Moreover, we attempted to ensure that a series of these variables were consistent with the independent variables employed in earlier studies.26 29–32

The questions on those background variables were included in the structured questionnaire. Of them, the data on type of and travelling time to drinking water source, type of toilet, presence of soap/ash for handwashing, food storage, utensil maintenance and house building materials were collected through enumerators’ direct observations and measurements. The data on other variables were collected through interviews with mothers and caregivers of children under 5 years of age. Of three locally spoken languages (ie, Ajawa, Macua and Portuguise), the most comfortable one for interviewees was selected as the language for an interview.

Data analysis

The data obtained through household interviews, observations, anthropometric measurements, anaemia tests and iodine tests were entered into a microcomputer. By using Anthro,33 z-scores for height-for-age, weight-for-age and weight-for-height were calculated based on the 2009 WHO standard reference population under 5 years of age.34 Those having been assumed to be under 5 years of age by parents but later found to be older by referring to the home-based records were excluded from the analysis. Wealth index was calculated for each household, by applying socioeconomic variables to principal component analysis, to categorise all the households into wealth index quintiles.35 Household Dietary Diversity Score (HDDS) was calculated by summing up the number of 12 food groups available at and consumed by each household during last 24 hours.36 The values for six standard indicators for Infant and Young Child Feeding (IYCF) were calculated, by using the IYCF indicator measurement guide.37

The statistical analyses were conducted, by using SPSS for Windows, V.22 (IBM/SPSS, Chicago, USA).

Bivariate and multivariate analyses were undertaken to identify the determinants of and risk factors for whether being malnourished (dependent variables). While the dependent variables are dichotomous, the independent variables are composed of interval ratio variables and categorical variables. Therefore, two types of bivariate analyses were employed. First, the associations between 94 categorical variables and whether being malnourished were examined, using χ2 test (Fisher’s exact test). Note that a total of 16 dummy variables were created for the mutually exclusive categorical variables having three or more categories (ie, primary income source, primary birth attendant). The category with the greatest frequency was designated as the reference for the dummy variables. Second, the associations between 13 interval ratio variables and whether being malnourished were examined, using a non-parametric method (Mann-Whitney’s U test), as it was expected and actually confirmed in Levene’s test that those variables were not normally distributed.

The background variables significantly associated with being malnourished (p<0.05 in χ2/Fisher’s exact test or Mann-Whitney’s U test) were selected as the possible independent variables for multivariate analyses. Prior to applying them to multivariate analyses, multicollinearity between those possible independent variables was systematically examined. To address possible multicollinearity between two interval ratio variables, those having a variance inflation factor (VIF) smaller than 10 were selected as the independent variables for multivariate analyses. To examine possible multicollinearity between two categorical variables, χ2 test (Fisher’s exact test) was conducted. When a statistical significance (p<0.05) was detected between them, one having a smaller p value with the dependent variable in χ2 test (Fisher’s exact test) was selected as an independent variable. Similarly, to examine possible multicollinearity between interval ratio and categorical variables, Mann-Whitney’s U test was conducted. When a statistical significance (p<0.05) was detected between them, one having a smaller p value with the dependent variables in Mann-Whitney’s U test was selected as an independent variable.

Ethical consideration

An informed consent to participate in the study was obtained in a written form from mothers or caregivers of children under 5 years of age. Children found to suffer from anaemia through blood tests were guided to the nearest health facilities for medical consultations and treatment. A small pack of iodised salt (approximately 5 g) was provided to households as a substitute for table salt sampled for iodine test.

Results

Undernutrition prevalence

Of 1556 sampled children, 58 were excluded from data analysis since their ages were either unknown and difficult to estimate, or found to be 5 years of age or older. Thus, the data collected from 1498 (=1556–58) children under 5 years of age, their mothers and other caregivers were analysed. Of the 1498 children under 5 years of age, boys (736; 49.1%) and girls (762; 50.9%) were almost equally represented. While children of 0–11 months of age (0 year old) account for the largest proportion (25.6%), those 48–59 months of age (4 years old) account for the smallest (11.6%). The prevalence rates of stunting, underweight and wasting were 46.2% (95% CI 43.6% to 48.8%), 20.0% (95% CI 18.0% to 22.1%) and 7.1% (95% CI 5.9% to 8.6%), respectively (table 1).10

Table 1.

Prevalence of undernutrition among children under 5 years of age in comparison with the previous survey

Stunting: height-for-age Underweight: weight-for-age Wasting weight-for-height
n % 95% CI n % 95% CI n % 95% CI
Majune and Muembe district as of 2019
 (+) Severe (z-score <-3) 424 28.3 (26.0 to 30.7%) 135 9 (7.6 to 10.6%) 56 3.6 (3.7 to 4.8%)
 (+) Moderate and severe (z-score <-2) 692 46.2 (43.6 to 48.8%) 300 20 (18.0 to 22.1%) 107 7.1 (5.9 to 8.6%)
 (-) Non-malnourished (z-score ≥ −2) 806 53.8 (51.2 to 56.4%) 1198 80 (77.9 to 82.0%) 1391 92.9 (91.4 to 94.1%)
Niassa province as of 2011*
 (+) Severe (z-score <−3) (n.a.) 24 (n.a.) (n.a.) 5.1 (n.a.) (n.a.) 1.3 (n.a.)
 (+) Moderate and severe (z-score <−2) (n.a.) 46.8 (n.a.) (n.a.) 18.2 (n.a.) (n.a.) 3.7 (n.a.)
 (-) Non-malnourished (z-score ≥−2) (n.a.) 53.2 (n.a.) (n.a.) 81.8 (n.a.) (n.a.) 96.3 (n.a.)
Mozambique as of 2011*
 (+) Severe (z-score <-3) (n.a.) 19.7 (n.a.) (n.a.) 4.1 (n.a.) (n.a.) 2.1 (n.a.)
 (+) Moderate and severe (z-score <-2) (n.a.) 42.6 (n.a.) (n.a.) 14.9 (n.a.) (n.a.) 5.9 (n.a.)
 (-) Non-malnourished (z-score ≥ −2) (n.a.) 57.4 (n.a.) (n.a.) 85.1 (n.a.) (n.a.) 94.1 (n.a.)
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*Mozambique Demographic and Health Survey (National Institue of Health 2011).10

Bivariate analyses

Table 2 shows the results of bivariate analyses between child undernutrition and socioeconomic/demographic status.15 Tables 3–5 show the results of bivariate analyses15 37–39 between child undernutrition and variables related to the three types of underlying causes, that are (1) household food insecurity, (2) inappropriate parental feeding and caring practices and (3) unhealthy household environment. In addition, table 6 shows the results of bivariate analyses between child undernutrition and disease symptoms (including anaemia), as the immediate causes.16 They are also categorised into three types of key interventions: (1) nutrition-specific interventions, (2) nutrition-sensitive interventions and (3) enabling environment in tables 3–6.15 A total of 107 background variables examined on their bivariate relationships with child undernutrition, seven were significantly associated with whether being stunted (p<0.05). Similarly, 5 and 10 background variables were significantly associated with whether being underweight and whether being wasted (p<0.05), respectively. Of the seven variables significantly associated with whether being stunted, two were excluded from the independent variables for the logistic regression model for stunting due to their multicollinearity. For the same reason, 3 of 5 and 7 of 10 variables were excluded from the independent variables for the logistic regression models for underweight and for wasting, respectively.

Table 2.

Bivariate analyses between undernutrition and sociodemographic/economic variables

Background variable Type of intervention and conditions† Stunting (N=1498) Underweight(N=1498) Wasting (N=1498)
Nutrition specific Nutrition sensitive Enabling (+) Stunted (−) Not stunted P value‡ (+) Under weight (−) Not underweight P value‡ (+) Wasted (−) Not wasted P value ‡
Environment
N (%) n (%) n (%) n (%) n (%) n (%)
Categorical variables
Sex
Female – – – 332 48 430 53.3 Ref. 147 49 615 51.3 Ref. 46 43 716 51.5 Ref.
v1: male – – X 360 52 376 46.7 0.038* 153 51 583 48.7 0.478 61 57 675 48.5 0.108
Total 692 100 806 100 300 100 1198 100 107 100 1391 100
Primary income source
Agriculture or crop sales – – – 608 87.9 700 86.8 Ref. 263 87.7 1045 87.2 Ref. 88 82.2 1220 87.7 Ref.
v2: livestock or animal sales – – X 2 0.3 2 0.2 1 1 0.3 3 0.3 1 0 0 4 0.3 1
v3: fishing – – X 3 0.4 3 0.4 1 1 0.3 5 0.4 1 1 0.9 5 0.4 0.359
v4: unskilled wage labour – – X 10 1.4 19 2.4 0.259 3 1 26 2.2 0.244 3 2.8 26 1.9 0.457
v5: skilled wage labour – – X 15 2.2 13 1.6 0.45 7 2.3 21 1.8 0.479 4 3.7 24 1.7 0.134
v6: handicrafts, artisanal works – – X 2 0.3 3 0.4 1 2 0.7 3 0.3 0.263 0 0 5 0.4 1
v7: charcoal production – – X 1 0.1 2 0.2 1 1 0.3 2 0.2 0.489 0 0 3 0.2 1
v8: seller, trader or commercial business – – X 25 3.6 25 3.1 0.666 8 2.7 42 3.5 0.59 5 4.7 45 3.2 0.398
v9: salary wage – – X 23 3.3 36 4.5 0.288 13 4.3 46 3.8 0.74 5 4.7 54 3.9 0.607
v10: begging and assistance – – X 0 0 1 0.1 1 0 0 1 0.1 1 0 0 1 0.1 1
v11: pension and government subsidy – – X 3 0.4 2 0.2 0.667 1 0.3 4 0.3 1 1 0.9 4 0.3 0.31
Total 692 100 806 100 300 100 1198 100 107 100 1391 100
Ownership of properties
v12: land for housing, farming or renting – – X 430 62.1 507 62.9 0.789 188 62.7 749 62.5 1 73 68.2 864 62.1 0.216
v13: electricity – – X 109 15.8 139 17.2 0.444 44 14.7 204 17 0.341 19 17.8 229 16.5 0.687
v14: radio – – X 280 40.5 322 40 0.874 115 38.3 487 40.7 0.47 40 37.4 562 40.4 0.609
v15: television set – – X 66 9.5 94 11.7 0.208 32 10.7 128 10.7 1 15 14 145 10.4 0.255
v16: mobile phone – – X 290 41.9 368 45.7 0.159 118 39.3 540 45.1 0.079 45 42.1 613 44.1 0.761
v17: refrigerator – X X 7 1 19 2.4 0.049‡ 3 1 23 1.9 0.334 6 5.6 20 1.4 0.008**
v18: generator – – X 4 0.6 12 1.5 0.129 1 0.3 15 1.3 0.22 2 1.9 14 1 0.319
v19: air conditioner – – X 4 0.6 3 0.4 0.71 1 0.3 6 0.5 1 1 0.9 6 0.4 0.405
v20: house ownership – – X 604 87.3 687 85.2 0.261 258 86 1033 86.2 0.926 93 86.9 1198 86.1 1
v21: personal computer – – X 4 0.6 8 1 0.403 2 0.7 10 0.8 1 0 0 12 0.9 1
v22: bicycle – – X 318 46 350 43.4 0.348 139 46.3 529 44.2 0.516 40 37.4 628 45.1 0.13
v23: motorbike – – X 119 17.2 149 18.5 0.543 50 16.7 218 18.2 0.557 19 17.8 249 17.9 1
v24: vehicle (car, truck and tractor) – – X 10 1.4 11 1.4 1 2 0.7 19 1.6 0.284 0 0 21 1.5 0.394
Mean SD Mean SD P value§ Mean SD Mean SD P value§ Mean SD Mean SD P value§
Interval and ratio variables
v25: age (year) – – X 2.7 1.29 1.6 1.11 <0.001** 3 1.3 1.9 1.23 <0.001** 2 1.42 2.1 1.33 0.274
v26: birth order in sibling (Nth child) – – X 3 2.23 3.1 2.11 0.207 2.8 2.1 3.1 2.18 0.093 3.2 2.17 3 2.16 0.195
v27: total number of household members (person) – – X 5.7 2.74 5.7 2.35 0.556 5.7 2.41 5.7 2.57 0.67 6.1 2.46 5.7 2.54 0.064
v28: wealth quintile (Nth quintile) – – X 3 1.38 3.1 1.43 0.182 3 1.38 3 1.41 0.724 3.2 1.47 3 1.4 0.207
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*p<0.05, **p<0.01. Categorisation based on the previous review (Black et al. 2013).15

†Categorisation based on the previous review (Black et al. 2013).

‡χ2 test (Fisher’s exact test)

§Mann-Whitney U test.

Table 3.

Bivariate analyses between undernutrition and food security variables

Background variable Type of intervention and conditions† Stunting (N=1498) Underweight (N=1498) Wasting (N=1498)
Nutrition specific Nutrition sensitive Enabling environment (+) Stunted (−) Not stunted P value‡ (+) Under weight (−) Not underweight P value‡ (+) Wasted (-) Not wasted P value‡
N (%) n (%) n (%) n (%) n (%) n (%)
Categorical variables
Food availability and consumption
v29: cereal – X – 685 99 779 99.1 0.794 298 99.3 1186 99 0.749 107 100 1377 99 0.617
v30: white roots and tubers – X – 362 52.3 392 48.6 0.162 152 50.7 602 50.3 0.949 51 47.7 703 50.5 0.616
v31: vegetables (vitamin A rich, leafy, and others) – X – 570 82.4 670 83.1 0.732 251 83.7 989 82.6 0.732 94 87.9 1146 82.4 0.183
v32: fruits (vitamin A rich and others) – X – 84 12.1 71 8.8 0.041* 35 11.7 120 10 0.398 8 7.5 147 10.6 0.409
v33: meats (organ and flesh) – X – 82 11.8 111 13.8 0.28 32 10.7 161 13.4 0.212 16 15.9 177 12.7 0.548
v34: eggs – X – 75 10.8 82 10.2 0.674 28 9.3 129 10.8 0.528 4 3.7 153 11 0.014*
v35: fish and seafood – X – 265 38.3 285 35.4 0.259 121 40.3 429 35.8 0.16 34 31.8 516 37.1 0.299
v36: legumes, nuts and seeds – X – 389 56.2 409 50.7 0.038* 172 57.3 626 52.3 0.121 46 43 752 54.1 0.034*
v37: milk and milk products – X – 24 3.5 30 3.7 0.89 11 3.7 43 3.6 1 4 3.7 50 3.6 0.791
v38: oils and fats – X – 270 39 324 40.2 0.672 116 38.7 478 39.9 0.742 39 36.4 555 39.9 0.539
v39: sweets – X – 135 19.5 160 19.9 0.896 56 18.7 239 19.9 0.685 13 12.1 282 20.3 0.043*
v40: spices, condiments and beverages – X – 146 21.1 161 20 0.608 59 19.7 248 20.7 0.749 18 16.8 289 20.8 0.385
Self-production of crops
v41: maize – X – 636 98.5 726 98.5 1 278 98.9 1084 98.4 0.784 94 96.9 1268 98.6 0.178
v42: rice – X – 166 25.7 207 28.1 0.332 74 26.3 299 27.1 0.822 30 27 343 26.7 0.406
v43: sorghum – X – 135 20.9 146 19.8 0.639 68 24.2 213 19.3 0.081 25 25.8 256 19.9 0.19
v44: cassava – X – 271 42 287 38.9 0.272 119 42.3 439 39.8 0.454 33 34 525 40.8 0.199
v45: wheat – X – 1 0.2 3 0.4 0.628 0 0 4 0.4 0.588 0 0 4 0.3 1
v46: yams – X – 143 22.1 151 20.5 0.469 53 18.9 241 21.9 0.289 15 15.5 279 21.7 0.159
v47: pumpkin – X – 266 41.2 282 38.3 0.271 120 42.7 428 38.8 0.246 35 36.1 513 39.9 0.519
v48: spinach and other green leafy vegetables – X – 61 9.4 63 8.5 0.573 30 10.7 94 8.5 0.292 11 11.3 113 8.8 0.36
v49: vitamin A rich fruits (mango, apricot, papaya and peach) – X – 45 7 51 6.9 1 19 6.8 77 7 1 11 11.3 85 6.6 0.094
v50: banana – X – 14 2.2 20 1.7 0.603 5 1.8 29 2.6 0.52 2 2.1 32 2.5 1
v51: other fruits (orange, water melon and melon) – X – 12 1.9 16 2.2 0.707 7 2.5 21 1.9 0.484 3 3.1 25 1.9 0.441
v52: pea and beans – X – 354 54.8 382 51.8 0.28 160 56.9 576 52.3 0.18 55 56.7 881 53 0.527
v53: nuts and other legumes X 169 26.2 183 24.8 0.578 71 25.3 281 25.5 1 26 26.8 326 25.3 0.719
Ownership of agricultural assets
v54: farmland – X – 641 92.6 727 90.2 0.098 280 93.3 1088 90.8 0.206 96 89.7 1272 91.4 0.48
v55: home garden – X – 8 1.2 9 1.1 1 5 1.7 12 1 0.358 3 1.7 14 1 0.116
v56: milk cow, cattle and bull – X – 4 0.6 1 0.1 0.188 2 0.7 3 0.3 0.263 1 0.9 4 0.3 0.31
v57: horse, donkey and mule – X – 0 0 1 0.1 1 0 0 1 0.1 1 0 0 1 0.1 1
v58: goat – X – 20 2.9 26 3.2 0.765 13 4.3 33 2.8 0.188 3 2.8 43 3.1 1
v59: chicken and other poultry – X – 226 32.7 247 30.6 0.404 107 35.7 366 30.6 0.096 36 33.6 437 31.4 0.666
Mean SD Mean SD P value§ Mean SD Mean SD P value§ Mean SD Mean SD P value§
Interval and ratio variables
v60: number of months without maize during last 12 months (mo) X – – 0.6 1.73 0.7 1.93 0.147 0.6 1.77 0.6 1.86 0.483 0.8 1.92 0.6 1.84 0.361
v61: number of months without cassava during last 12 months (mo) X – – 2.9 4.19 2.7 4.18 0.268 3.2 4.4 2.7 4.1 0.093 3.4 4.51 2.7 4.15 0.262
v62: number of months without rice during last 12 months (mo) X – – 2.9 4.42 3 4.39 0.464 3 4.5 2.9 4.4 0.958 3.4 4.63 2.9 4.38 0.241
v63: household dietary diversity score (pt) c X – – 4.5 2.01 4.3 2.1 0.069 4.4 1.97 4.4 2.08 0.312 4.1 1.97 4.4 2.06 0.06
v64: total number of meals yesterday (meal) X – – 2.7 0.53 2.7 0.5 0.873 2.7 0.48 2.7 0.52 0.353 2.7 0.5 2.7 0.51 0.806
v65: farmland size (ha) X – – 288 1010 352 1067 0.463 379 1352 308 949 0.939 522 1722 307 969 0.145
v66: home-garden size(m2) X – – 205 363 130 162 0.306 121 212 184 295 0.364 200 259 158 279 0.407
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*p<0.05, **p<0.01.

†Categorisation based on the previous review (Black et al. 2013).15

‡χ2 test (Fisher’s exact test)

§Mann-Whitney U test.

Table 4.

Bivariate analyses between undernutrition and feeding/caring practice variables

Background variable Type of intervention and conditions† Stunting (N=1498) Underweight(N=1498) Wasting (N=1498)
Nutrition specific Nutrition sensitive Enabling environment (+) Stunted (−) Not stunted P value‡ (+) Under weight (−) Not underweight P value‡ (+) Wasted (−) Not wasted P value‡
N (%) n (%) n (%) n (%) n (%) n (%)
Categorical variables
Food preparation process
v67: rinse vegetable and fruit with safe water – X – 548 79.2 655 81.3 0.329 248 82.7 955 79.7 0.291 89 83.2 1114 80.1 0.528
: Cook meat thoroughly till meat juice is clear – X – 409 59.1 483 59.9 0.752 181 60.3 711 59.3 0.793 75 70.1 817 58.7 0.024*
v69: store leftovers in cool places§ – X – 127 18.4 144 17.9 0.84 51 18.8 220 18.4 0.616 17 15.9 254 18.3 0.604
v70: store staple food in container(s) with cover¶ – X – 192 27.7 253 31.4 0.126 84 28 361 30.1 0.481 35 32.7 410 29.5 0.51
v71: store utensils in cabinet after cleaning – X – 95 13.7 107 13.3 0.82 48 16 154 12.9 0.157 23 21.5 1391 92.9 0.018*
v72: iodised table salt X – 531 76.7 618 76.7 1 218 72.7 931 77.7 0.067 75 70.1 1074 77.2 0.059
Food preparation conditions
v73: clean cooking fuel†† – X – 0 0 2 0.2 0.503 0 0 2 0.2 1 0 0 2 0.1 1
v74: indoor cooking facility‡‡ – X – 368 53.2 485 60.2 0.07 161 53.7 692 57.8 0.216 68 63.6 785 56.4 0.158
Infant and young child feeding
v75: breastfed in 1 hour after birth (n=754)§§ X – – 223 96.1 490 93.9 0.228 69 93.2 644 94.7 0.587 53 91.4 660 94.8 0.234
v76: exclusively breastfed (n=156)¶¶ X – – 22 73.3 95 75.4 0.817 6 66.7 111 75.5 0.692 10 66.7 107 75.9 0.53
v77: continued breastfeeding at 1 year (n=104)*** X – – 35 94.6 54 80.6 0.078 14 100 75 83.3 0.212 7 87.5 82 85.4 1
v78: introduction of solid, semi-solid and/or soft foods (n=132)††† X – – 13 54.2 84 77.8 0.023* 4 50 93 75 0.207 5 50 92 75.4 0.129
v79: at least four of seven food groups consumed (n=605)‡‡‡ X – – 18 8.9 27 6.7 0.329 6 9.2 39 7.2 0.614 2 4.5 43 7.7 0.763
v80: minimum meal frequency (n=605)§§§ X – – 34 16.8 68 16.9 1 9 13.8 93 17.2 0.6 9 20.5 93 16.6 0.53
Pre- and post-birth care
v81:≥4 antenatal care visits – X – 314 45.4 345 42.8 0.322 50 46.7 609 43.8 0.614 189 27.3 236 29.3 0.421
v82: facility-based delivery – X – 345 49.9 421 52.2 0.378 155 51.7 611 51 0.947 53 49.5 713 51.3 0.764
v83: low birth weight (<2500 gram at birth) X – – 89 12.9 99 11 0.262 45 15 132 11.1 0.072 18 16.8 159 11.5 0.119
v84: mother–child cosleeping X X – 636 91.9 777 96.4 <0.001** 265 88.3 1148 95.8 <0.001** 87 90.7 1316 94.6 0.123
Primary attendant for the child’s birth
Skilled birth attendant (physician, nurse and midwife) – – – 596 86.1 703 87.2 Ref. 254 84.7 1045 87.2 Ref. 95 88.8 1204 86.6 Ref.
v85: traditional birth attendant – X – 33 5.1 35 4.7 0.709 12 4.4 56 5 0.875 1 1 67 5.2 0.084
v86: no health worker attended – X – 12 1.9 13 1.7 0.843 5 1.8 20 1.8 1 278 40.2 163 20.2 <0.001**
v87: Do not know do not remember – X – 51 7.4 55 6.8 0.687 29 9.7 77 6.4 0.059 9 8.4 97 7 0.556
Total 692 100 806 100 0.687 300 100 1198 100 107 100 1391 100
Ownership of home-based records
v88: child vaccination card/handbook X – – 368 53.2 436 54.1 0.755 139 46.3 665 55.5 0.005* 42 39.3 762 54.8 0.002*
v89: maternal health card/handbook X – – 72 10.4 85 10.5 1 36 12 121 10.1 0.343 14 13.1 143 10.3 0.33
v90:child health card/handbook X – – 125 18.1 127 15.8 0.24 60 20 192 16 0.102 14 2 15 1.9 0.346
v91: maternal and child health card/handbook X – – 14 2 15 1.9 0.853 8 2.7 21 1.8 0.346 3 2.8 26 1.9 0.457
v92: birth certificate X – – 51 7.4 53 6.6 0.61 30 10 74 6.2 0.030* 13 12.1 91 6.5 0.045*
v93: any home-based record(s) X – – 581 84 679 84.2 0.887 247 82.3 1013 84.6 0.377 88 82.2 1172 84.3 0.583
Mean SD Mean SD P value¶¶¶ Mean SD Mean SD P value¶¶¶ Mean SD Mean SD P value¶¶¶
Interval and ratio variables
v94: mother’s current height (cm) X – – 153.7 7.02 154.2 7.5 0.020* 153.7 6.66 154.1 7.43 0.117 154.3 6.3 154 7.35 0.676
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*p<0.05,

†Categorisation based on the previous review (Black et al. 2013).15

‡χ2 test (Fisher’s exact test)

§Cool places include: (i) refrigerator and (ii) under shadow.

¶Materials of containers include: (i) plastic and (ii) metal.

**p<0.01.

††Clean cooking fuel includes: (i) electricity; (ii) gas; and (iii) solar energy (WHO 2016).38

‡‡Indoor cooking facility includes: (i) kitchen in a house and (ii) kitchen in a separate building. (Malla and Timilsina).39

§§Applicable only for children 0–24 months of age (n=754) (WHO 2010).37

¶¶Applicable only for children 0–5 months of age (n=156) (WHO 2010).37

***Applicable only for children 12–15 months of age (n=104) (WHO 2010).37

†††Applicable only for children 6–8 months of age (n=132) (WHO 2010).37

‡‡‡Seven food groups are composed of: (i) grains/roots/tubers; (ii) legumes/nuts; (iii) milk products; (iv) flesh foods; (v) eggs; and (vi) other fruits and vegetables. Applicable only for children 6–23 months of age (n=605) (WHO 2010).37

§§§Minimum meal frequency is defined as: (i) 2 (meal/day) for breastfed children 6–8 months of age; (ii) 3 (meal/day) for breastfed children 9–23 months of age; and (iii) 4 (meal/day) for non-breastfed children 6–23 months of age. Applicable only for children 6–23 months of age (n=605) (WHO 2010).37

¶¶¶Mann-Whitney U test.

Table 5.

Bivariate analyses between undernutrition and household environment variables

Background variable Type of intervention and conditions† Stunting (N=1498) Underweight(N=1498) Wasting (N=1498)
Nutrition specific Nutrition sensitive Enabling environment (+) Stunted (−) Not stunted P value ‡ (+) Under-weight (−) Not underweight P value ‡ (+) Wasted (−) Not wasted P value‡
N (%) n (%) n (%) n (%) n (%) n (%)
Categorical variables
Type of water source for drinking and cooking
Not improved type of source of water§ – – – 264 38.2 307 38.1 Ref. 108 36 463 38.6 Ref. 38 35.5 533 38.3 Ref.
v95: improved type of source of water¶ – X – 428 61.8 499 61.9 1 192 64 735 61.4 0.425 69 64.5 858 61.7 0.606
Total 692 100 806 100 300 100 1198 100 107 100 1391 100
Availability of water at water source
On and off†† – – – 119 17.2 166 20.6 Ref. 51 17 234 19.5 Ref. 14 13.1 271 19.5 Ref.
v96: 24 hours a day ‡‡ – X – 573 82.8 640 79.4 0.099 249 83 964 80.5 0.366 93 86.9 1120 80.5 0.124
Total 692 100 806 100 300 100 1198 100 107 100 1391 100
Type of sanitation facility and excreta disposal
Not improved type of sanitation facility §§ – – 638 92.2 752 93.3 Ref. 269 89.7 1121 93.6 Ref. 100 93.5 1290 92.7 Ref.
v97: improved type of sanitation facility ¶¶ – X – 54 7.8 54 6.7 0.424 31 10.3 77 6.4 0.024* 7 6.5 101 7.3 1
Total 692 100 806 100 300 100 1198 100 107 100 1391 100
Domestic water treatment
Inappropriate or no water treatment – – – 528 76.3 589 73.1 Ref. 226 75.3 891 74.4 Ref. 78 72.9 1039 74.7 Ref.
v98: appropriate water treatment – X – 164 23.7 217 26.9 0.171 74 24.7 307 25.6 0.767 29 27.1 352 25.3 0.73
Total 692 100 806 100 300 100 1198 100 107 100 1391 100
Hand washing practices
v99: wash hand with soap or ash after toilet – X – 269 38.9 327 40.6 0.525 117 39 479 40 0.792 42 39.3 554 39.8 1
v100: wash hand with soap or ash before cooking – X – 263 38 315 39.1 0.709 114 38 464 38.7 0.842 41 38.3 537 38.6 1
v101: wash hand with soap or ash before eating – X – 249 36 304 37.7 0.519 115 38.3 438 36.6 0.593 40 37.4 513 36.9 0.918
Mean SD Mean SD P value *** Mean SD Mean SD P value*** Mean SD Mean SD P value***
Interval and ratio variables
v102: total time for water collection (min) ††† – X – 57.6 58.7 54.4 60.2 0.096 55 58.5 56 59.8 0.638 67.9 73.8 55 58.3 0.277
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*p<0.05, **p<0.01.

†Categorisation based on the previous review (Black et al. 2013).15

‡χ2 test (Fisher’s exact test)

§Types of not improved source of water include: (i) unprotected well; (ii) unprotected spring; (iii) surface water (eg, river, lake and reservoir); (iv) vendor-provided water (eg, truck and cart); and (v) bottled water.

¶Types of improved source of water include: (i) piped private household connection indoor/in yard; (ii) public standpipe; (iii) protected well (protected hand-dug well); (iv) protected spring; and (v) rain water collection.

**p<0.01.

††For instance, water is available only when public water attendant is on duty.

‡‡Includes 37 cases of ‘Do not know/do not remember’.

§§Types of not improved sanitation facilities include: (i) flush toilet not connected to sewerage system; (ii) latrine without slab; (iii) joint installation with other households; and (vi) outdoor defecation.

¶¶Types of improved sanitation facilities include: (i) flush toilet connected to sewerage system/septic tank; (ii) ventilated latrine/pit; (iii) toilet connected to pit/latrines with slab; and (iv) other non-sewered sanitation systems.

***Mann-Whitney U test.

†††The number of minutes spent reaching a water source and waiting there was measured by making enumerators physically walk.

Table 6.

Bivariate analyses between undernutrition and disease symptoms (including micronutrient deficiency)

Background variable Type of intervention and conditions† Stunting (N=1498) Underweight (N=1498) Wasting (N=1498)
Nutrition specific Nutrition sensitive Enabling environment (+) Stunted (−) Not stuntd P value ‡§ (+) Under weight (−) Not underweight P value ‡ (+) Wasted (−) Not wasted P value‡
N (%) n (%) n (%) n (%) n (%) n (%)
Categorical variables
Low birth weight
(−) Not low birth weight:≥2500 [g) – – – 601 67.1 714 89 Ref. 255 85 1060 88.9 Ref. 89 83.2 1226 88.5 Ref.
v103: (+) Low birth weight:<2500 (g) X – – 89 12.9 88 11 0.262 45 15 132 11.1 0.072 18 16.8 159 11.5 0.119
Total 660 100 773 100 283 100 1150 100 105 100 1328 100
Anaemia§
(-) Without anaemia: haemoglobin concentration ≥110 (g/L) – – – 291 45.5 309 49.9 Ref. 125 44.8 475 48.5 Ref. 39 44.8 561 47.9 Ref.
v104: (+) With anaemia: haemoglobin concentration <110 (g/L) X – – 348 54.5 310 50.1 0.128 154 55.2 504 51.5 0.278 48 55.2 610 52.1 0.657
Total 660 100 773 100 283 100 1150 100 105 100 1328 100
Diarrhoea during the last 2 weeks
(−) Without diarrhoea – – – 463 70.2 528 68.3 Ref. 200 70.7 791 68.8 Ref. 67 63.8 924 69.6 Ref.
v105: (+) With diarrhoea X – – 197 29.8 245 31.7 0.456 83 29.3 359 31.2 0.566 38 36.2 404 30.4 0.228
Total 660 100 773 100 283 100 1150 100 105 100 1328 100
Cough during the last 2 weeks
(-) Without cough – – – 386 58 436 55.8 Ref. 155 53.6 667 57.6 Ref. 48 45.3 774 57.8 Ref.
v106: (+) with cough X – – 279 42 345 44.2 0.424 134 46.4 490 42.4 0.232 58 54.7 566 42.2 0.014*
Total 665 100 781 100 289 100 1157 100 106 100 1340 100
Fever during the last 2 weeks
(−) Without cough – – – 386 58 436 55.8 Ref. 155 53.6 667 57.6 Ref. 48 45.3 774 57.8 Ref.
v107: (+) with fever X – – 270 40.7 393 59.3 0.065 116 40.6 435 37.6 0.377 45 42.5 506 37.8 0.351
Total 663 100 781 100 286 100 1158 100 106 100 1338 100
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*Categorisation based on the previous review (Black et al. 2013).15

†χ2 test (Fisher’s exact test)

‡A total of 1258 children were tested for anaemia as a result of exclusion of 240 children (=138 children under 6 months of age+102 children having rejected blood sampling).

§p<0.05, **p<0.01.

Multivariate analyses

As the results of bivariate analyses and multicollinearity testing, five, two and three background variables were employed as the independent variables for the logistic regression models for stunting, underweight and wasting, respectively. Simultaneous variable entry was applied to logistic regression analyses. Table 7 shows their results. Timely introduction of solid, semi-solid or soft foods to children was the only independent variable whose OR was significant (p<0.05) in the logistic regression model for stunting. This implies that introduction of solid, semi-solid or soft foods to children at the age of 6–8 months is likely to have reduced the risk of becoming stunted by 68.3% (= (1–0.317)×100). In the logistic regression model for underweight, a significant OR (p<0.05) was detected for both two independent variables, that is, mother–child cosleeping and ownership of birth certificate. Mother–child cosleeping is likely to have reduced the risk of becoming underweight by 66.7% (= (1–0.333)×100). Those having birth certificate are 1.656 times more likely to be underweight. Of 113 birth certificate holders, 103 (91.2%) owned it as the only home-based record. A birth certificate does include not the data and information related to maternal and child health but exclusively the name, sex and date of birth of a child and his/her parents’ names.40 Thus, ownership of birth certificate implies either absence or extreme lack of opportunities for parents to practice self-monitoring and self-care of their maternal and child health. Two independent variables whose ORs were significant (p<0.05) were detected in the logistic regression model for wasting (availability and consumption of eggs generally household members and cough during the last 2 weeks). It was found availability and consumption of eggs were protective against becoming wasted, by indicating 91.6% (= (1–0.184)×100) reduction of risk of becoming wasted. Cough during the last 2 weeks was highly associated with being wasted, by producing a greater OR (ie, adjusted OR=1.713).

Table 7.

Logistic regressions on being malnourished with background variables

Logistic regression model Type of intervention and conditions† Adjusted 95% CI P value
Nutrition specific Nutrition sensitive Enabling environment OR
Logistic regression for stunting
v1: sex (dummy variable for ‘male’) – – X 1.482 0.573 to 3.830 0.417
v17: ownership of refrigerator – X X 0 0 0.999
v25: age (year) – – X 6.617 0.017 to 2550.7 0.534
v32: availability and consumption of fruits (vitamin A rich and other fruits) – X – 1.001 0.198 to 5.055 0.999
v79: introduction of solid, semi-solid and/or soft foods X – – 0.317 0.124 to 0.812 0.017 *
Logistic regression for underweight
v84: mother–child cosleeping X X – 0.333 0.212 to 0.524 <0.001 **
v92: ownership of birth certificate X – – 1.656 1.057 to 2.596 0.028 *
Logistic regression for wasting
v34: availability and consumption of eggs – X – 0.184 0.045 to 0.754 0.019 *
v86: delivery not attended by health workers X – – 1.45 0.330 to 6.383 0.623
v106: cough during the last 2 weeks X – – 1.713 1.128 to 2.603 0.012 *
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*p<0.05, **p<0.01.

†Categorisation based on the previous review (Black et al. 2013).15

Discussion

All the three types of undernutrition prevalence rates are at the similar level to both provincial and national prevalence as of 2011 (table 1). This implies that there has been probably neither an improvement nor a worsening in prevalence of all three forms of child undernutrition during 8 years from 2011 to 2019. In particular, very high prevalence of stunting (46.2%) we identified is in line with its Mozambique’s national trend that prevalence of stunting stays around 42%–43% during the last 12 years.11 This should be attributable not only to inadequate multisectoral coordination and fragmented efforts by relevant sectors and stakeholders but also to generally slower progress of reduction in prevalence of stunting than in those of underweight and wasting.41

This study identified timely introduction of solid, semi-solid or soft foods to children as the only significant determinant of or risk factors for being stunted (p<0.05) (table 7). Several studies reported that timely introduction of solid, semi-solid or soft foods at the age of 6–8 months provides children with significant protection against becoming stunted.42–44 Thus, the results of our study are consistent to these earlier studies. Yet, giving solid, semi-solid or soft foods to children 6–8 months of age needs to be supported by mothers’ previous exclusive breastfeeding practices at the age of 0–5 months. In other words, giving solid, semi-solid or soft foods not accompanied by previous exclusive breastfeeding may often involve premature introduction of those foods prior to 6 months of age. A multicountry study in Africa reported that infants suffering from diarrhoea and respiratory infections were significantly likely to be introduced solid, semi-solid or soft foods prematurely between the age of 3 and 5 months.45 As stunting, diarrhoea and respiratory infections are mutually attributable,46 47 the importance of timely introduction of solid, semi-solid or soft foods at the appropriate age must be rehighlighted. This is also because timely introduction of solid, semi-solid or soft foods plays a key role in smoothly responding to an additional energy requirement derived from the increase in child’s activities during 6–8 months of age. In our study, of 97 children 6–8 months of age given solid, semi-solid or soft foods, 79 (81.4%) used to be exclusively breastfed during 0–5 months of age (p<0.05 in χ2/Fisher’s exact test). Thus, a majority of mothers and other caregivers giving solid, semi-solid or soft foods to their children of 6–8 months of age in Majune and Muembe districts have been continuously practising appropriate infant feeding since their childrens’ births.

Mother–child cosleeping serves not just as the general proxy for desirable caring attitude, but rather as a reliable channel that ensures breastfeeding timely and frequently enough during night time and nap time. Several earlier studies indicated that mothers’ physical contacts through cosleeping with their children predict feeding in response to early hunger cues.48–50 Mother–child cosleeping, however, may increase the risks of Sudden Unexpected Death in Infancy (SUDI), through regulating infant’s breathing by the rocking movement of the mother’s chest while breathing.51 Thus, while mother–child cosleeping is generally recommended not only for ensuring timely and adequate breastfeeding but also for facilitating physiological, cognitive and socioemotional development of children, efforts to minimise the risks of SUDI should be carefully made. A typical example of those efforts is to avoid cosleeping on sofa or couch which increases the likelihood of child’s breathing regulation.52 The enumerators employed for this survey rarely observed sofa and couch during household visits. Thus, the risks of SUDI to be derived from mother–infant cosleeping in the two districts should be quite limited.

The ownership of a birth certificate largely implies non-ownership of health-related home-based record (eg, child vaccination, maternal health card, child health card, maternal and child health card). Those having their children’s birth certificates might think it is unnecessary to have health-related home-based records, assuming as if a birth certificate sufficed all requirements as an all-round home-based record unique to their children (eg, eligibility for school enrollment). Of 1498 children under 5 years of age, 341 (22.8%) did not have health-related home-based records (ie, either only birth certificate or no home-based record). In view of the WHO’s recommendation of health-related home-based records as an effective tool for maternal and child health,53 the recent commitment of the Mozambican Ministry of Health to developing a nationally standardised home-based record for maternal and child health is highly valued.

In this study, availability of eggs at households and their consumption generally by household members was measured as a food security variable for calculating HDDS. On the other hand, consumption of eggs by each child under 5 years of age was separately measured as a feeding/caring practice variable for calculating the IYCF minimum diet diversity. Thus, availability and consumption of eggs generally at household level are defined and measured differently from child-specific consumption of eggs, in this study. There is a possibility that eggs might have been consumed exclusively by the household members other than children at some households. In view of this, of 148 studied children of 6–59 months of age whose households had readily available eggs and actually consumed them, 114 (77.0%) actually ate eggs during the last 24 hours (p<0.05 in χ2/Fisher’s exact test). Thus, mothers and caregivers of children of 6–59 months of age in Majune and Muembe districts tend to proactively practice introduction of eggs during complementary feeding, as far as eggs are readily available at households. Several studies reported that early introduction of eggs in complementary feeding significantly improved growth and nutritional status of young children.54 Thus, the results of our study support those earlier studies. Yet, instability of egg production and supplies in Niassa province55 are likely to make it generally difficult for households in Majune and Muember districts to access and consume eggs.

A significantly positive association between having cough during the last 2 weeks and being wasted (adjusted OR = 1.713) implies that appetite reduced by respiratory infections might have caused inadequate food intake and digestion, and thereby acute undernutrition. There are a number of earlier studies on the causality between cough and undernutrition.47 56–58 Particularly, severe acute malnutrition (z-score for weight-for-height < −3) is often accompanied by cough and fever. Thus, the association between having cough and being wasted we identified not only is in line with the results of those earlier studies but also signals a certain need for therapeutic feeding.

Figure 1 shows the hypothetical process of becoming malnourished based on the findings of our study. None of the determinants of and risk factors for whether being malnourished we identified was common across three types of undernutrition (ie, stunting, underweight and wasting) (table 7). This probably does not necessarily indicate that each determinant contributes exclusively to a specific type of undernutrition. For instance, mother–child cosleeping was significantly associated with both being stunted and being underweight in bivariate analyses. Yet, due to possible multicollinearity, it was excluded from the independent variables in the logistic regression model for stunting.

Figure 1.

Figure 1

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Hypothetical process of becoming stunting, underweight and wasted. oOR, adjusted OR. Adapted from Black et al. 2013

Some could be critical about adopting such a great number of independent variables. Yet, in a total absence of a standard set of variables and indicators for identifying the determinants of and risk factors for child undernutrition, taking all the possible variables, was an inevitable choice. In fact, mother–child cosleeping, a variable never employed in earlier studies, was identified as a determinant of and risk factor for whether being underweight in this study. Note that this was one of the key findings we reached by broadly screening all possible determinants.

This study has limitations in generalisability of determinants of and risk factors for child undernutrition due to employment of a cross-sectional study as the study design. We must particularly admit that possible overestimation of household food security status might have prevented this study from thoroughly identifying all the possible food-security-related determinants and risk factors. Generally, diverse food crops (including livestock products) are more available, accessible and affordable during the study period from August to October than yearly average.59 Thus, household food security we measured may not be representative of its year-round status. A further study is necessary to more precisely estimate associations between food security variables and undernutrition, as seasonal variation of food security data is generally greater than that of feeding and caring practice data and household environment data.

Conclusion

This study identified that timely introduction of solid, semi-solid or soft foods to children of 6–8 months of age as a determinant of being not stunted. Mother–child cosleeping and ownership of birth certificate were a protective factor from and a promoting factor for being underweight, respectively. Similarly, availability and consumption of eggs at the household level and cough during the last 2 weeks among children were a protective factor from and a promoting factor for being wasted, respectively.

Note that the aforementioned determinants and risk factors are likely to be very applicable neither to other provinces of Mozambique and other countries nor to different seasons. This is because causality and association between undernutrition and its background factors vary according to local settings and seasons. To enable us to make meaningful interprovincial, international and interseasonal comparisons, it is crucially necessary to develop a standard set of variables and indicators related to immediate and underlying causes of child undernutrition. Yet, it is reality that the numbers and types of independent variables employed for multivariate analysis largely differ between the studies. Some studies employed less than 10 independent variables,29 60 other employed more than 30.26 30 Moreover, the types and definitions of those variables are not consistent and standardised enough to allow us to meaningfully compare the data between the studies. Thus, it is an urgent task to develop an internationally standardised set of variables and indicators. SUN has proposed a standard set of 79 indicators from eight technical domains.61 Yet, they are the indicators appropriate not for identifying determinants and risk factors but rather for monitoring policy milestones and operational progress. Therefore, WHO, as the UN specialised agency responsible for health, should take responsibility for undertaking the task in collaboration with other key partners such as Food and Agriculture Organisation, UNICEF and SUN.

Conducting a multisectoral nutrition survey jointly between several key ministries (ie, typically, ministry of health, ministry of agriculture and ministry of public works) provides them with an invaluable opportunity to open a policy dialogue and further attempt to design, implement, monitor and evaluate a multisectoral nutrition programme in a joint or coordinated manner. Timely introduction of solid, semi-solid or soft foods, a possible key intervention we identified, could serve as a great entry point for the three sectors to start making joint efforts. To appropriately introduce solid, semi-solid or soft foods to a child, food production, water for cooking and feeding practising need to be undertaken by agriculture, water and health sectors, respectively. Though 54 of 61 SUN member countries (88.5%) set up national multistakeholder platform,62 it is not clear whether those platforms facilitate conducting multisectoral nutrition surveys as the joint efforts among the stakeholders. To ensure a better-designed package of evidence-based multisectoral interventions, it is recommended that the multistakeholder platform proactively work beyond sectoral interests in each country and that its initial step be to jointly conduct a multisectoral nutrition survey.

Acknowledgments

The authors gratefully thank Jaime Fernandes, Hannah Danzinger Silva and Maxwell Odhiambo for their support to data collection, and Toshiyasu Murai for his valuable technical supports to data analysis. The authors also thank Japan International Cooperation Agency (JICA) for its funding support to the study. This work is sincerely dedicated to all the children and their parents and caregivers in Niassa province of Mozambique.

Footnotes

Contributors: All the authors made substantial intellectual contributions to the study. HA, MN, JPML, MM, SN and HH conceptualised and designed the study. HA, MN, JPML, MM and EA took responsibility for data collection. HA and MN analysed and interpreted the data. HA drafted and finalised the manuscript. RM, AA, NT, DB, JV and AJC critically commented and revised manuscript. All the authors reviewed and approved the final version of the manuscript.

Funding: This work was supported by Japan International Cooperation Agency (JICA).

Competing interests: None declared.

Patient consent for publication: Not required.

Ethics approval: The study protocol was submitted to the National Committee for Bioethics of Ethics, the Mozambican Ministry of Health, for its ethical clearance. The Committee officially approved the study protocol accordingly (Ref: 279/CNBS/19). An informed consent to participate in both anthropometric measurements and structured household interviews were obtained in written form from the parents of each sampled schoolchild.

Provenance and peer review: Not commissioned; externally peer reviewed by Wondu Garoma Berra, Wollrga University, Ethiopia.

Data availability statement: Data are available upon reasonable request. The anonymised datasets used and analysed during the current study are available from the corresponding author on reasonable request. Please email: hirotsugu.aiga@nagasaki-u.ac.jp for data requests.

References

Articles from BMJ Nutrition, Prevention & Health are provided here courtesy of NNEdPro Global Centre for Nutrition and Health and BMJ Publishing Group

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