Factors associated with stunting among childrenaged 0 to 59 months from the central regionof Mozambique

nutrients Article Factors Associated with Stunting among Children Aged 0 to 59 Months from the Central Region of Mozambique Loida María García Cruz 1,2, Gloria González Azpeitia 1, Desiderio Reyes Súarez 1, Alfredo Santana Rodríguez 2, Juan Francisco Loro Ferrer 3and Lluis Serra-Majem 4,5,* 1Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain; [email protected] (L.M.G.C.); [email protected] (G.G.A.); [email protected] (D.R.S.) 2 Clinical Genetics Unit, Complejo Hospitalario Insular-Materno Infantil, 35016 Las Palmas de Gran Canaria, Spain; [email protected] 3Department of Clinical Sciences, University of Las Palmas de Gran Canaria, 35016 Las Palmas de Gran Canaria, Spain; [email protected] 4Ciber OBN (CB06/03), Instituto Carlos III, Spanish Government, 28029 Madrid, Spain 5 Research Institute of Biomedical and Health Sciences, University of Las Palmas de Gran Canaria/Nutrition Without Borders, 35016 Las Palmas de Gran Canaria, Spain *Correspondence: [email protected]; Tel.: +34-928-453-454; Fax: +34-928-453-475 Received: 3 March 2017; Accepted: 4 May 2017; Published: 12 May 2017 Abstract: The objective of this study was to identify the major socio-demographic, health, and environmental determinants of stunting among children aged 0–59 months from the Tete province (Mozambique) and offering useful information for future healthcare strategies and interventions. A case-control study was conducted among 282 (162 boys; 120 girls) children under five years of age from the central region of Mozambique between 1 May and 3 June 2014. Children with stunting ( HAZ < −2 SD according to the WHO Child Growth Standards in 2006) were considered as cases and those who had a Z-score < − 2 SD were considered as controls. We collected data related to mothers and children and their environment, and they were assessed in two groups to find a possible association. The software used for data analysis was the SPSS ® (version, 21.0) using descriptive statistics, t-test, ANOVA, chi-square analyses, bivariate comparisons, and stepwise multiple logistic regression analysis. The results showed that birth weight, mother’s educational status, maternal occupation, living in a rural area, family size, number of children under five years of age in the household, cooking with charcoal, inhabiting wooden or straw housing or housing without proper floors, overall duration of breastfeeding as well as duration of exclusive breastfeeding, and time of initiation of complementary feeding were significantly related to stunting. Thus, appropriate nutritional intervention programmes considering these determinants and the dissemination of knowledge at the population level related to undernutrition are necessary to ameliorate the children´s nutritional status. Keywords: undernutrition; growth retardation; stunting; nutritional assessment; mozambique 1. Background Undernutrition is a major public health problem that increases the global health burden of premature mortality and morbidities during childhood [ 1 , 2 ]. It accounts for 45% of all deaths in children under five years of age. More than two million children under five years of age die each year due to undernutrition around the world [3,4]. Nutrients 2017,9, 491; doi:10.3390/nu9050491 www.mdpi.com/journal/nutrients Nutrients 2017,9, 491 2 of 16 The prevalence of stunting (chronic undernutrition) (< − 2 standard deviation (SD) of low-height-for-age) remains one of the main problems of public health, and a significant proportion of people suffer from moderate or severe undernutrition during early childhood, especially in developing countries, as is the case in Mozambique. The prevalence of stunting is the conventional anthropometric measure that reflects long-term chronic undernutrition, failure of linear growth and multifactorial social deprivation, a long-term response to the prolonged deprivation of food and/or presence of disease. Undernutrition refers to a state resulting from a relative or absolute deficiency of one or more essential nutrients [ 5 – 9 ]. The three main indicators used to define undernutrition, that is to say, stunting, underweight, and wasting, represent different nutritional problems for the child and are measured by the indices of height-for-age, weight-for-height, and weight-for-age, respectively. The Lancet series on maternal and child undernutrition reported critical associations between stunting (length/height-for-age Z-score < −2 SD) at age two years and long-term consequences [10,11]. Undernutrition in the first 1000 days post conception represents an important wasting of human potential. In these crucial days, the building blocks are established for the development of the brain and for future growth. Any alteration in this stage has long-term implications, and the damage caused by undernutrition in the early years of life is largely irreversible. In unfavorable environmental conditions, the majority of children will remain locked in their acquired growth channel. The school performance of under-nourished children is reduced. They have lower work capacity and productivity as adults. Later in life they have an increased likelihood of being overweight and developing associated chronic diseases such as cardiovascular disease, diabetes, cancer, and mental health disorders. Although there has been a decrease in prevalence, it has not been accompanied by a commensurate decrease in the number of stunted children due to the increasing population [ 12 – 18 ]. Over a 10-year period (1990–2010), Africa was the only region where the number of stunted children under five years increased. Projections to 2025 show that the increasing trend is likely to remain [ 3 , 8 ]. ‘The UNICEF’s Annual Reports’ consistently show the prevalence of stunting in Sub-Saharan Africa to be around 30–40%, varying per country or region or urban versus rural environment [ 17 , 19 – 22 ]. According to ‘The 2016 Global Nutrition Report’, the prevalence of stunting in Mozambique in children under the age of five years is still high (43.3%) while the underweight (19%) and wasting (5.9%) categories are decreasing [23]. Even though the problem of stunting in Mozambique has been well documented, its specific determinants are not clearly understood. In addition, case-control or cohort studies have not been conducted to identify the risk factors of growth retardation in the Tete province. There is also inconsistency across studies regarding the determinant factors behind stunting in childhood. Therefore, this study attempts to investigate the major socio-economic, demographic, health, and environmental determinants of stunting among infants aged 0–59 months from the central region of Mozambique, i.e., Tete province. The research results obtained would be helpful to healthcare providers in designing and implementing appropriate interventions to improve the growth of children in the central region of Mozambique and other similar regions in the countr, and will enable the government and non-governmental agencies to formulate appropriate policies and initiate intervention strategies for the wellbeing of the population. 2. Materials and Methods 2.1. Study Area and Population A case-control study was conducted among children under five years of age between 1 May and 3 June 2014 in the Tete province, Mozambique. The province is located in the central region of Mozambique, bordering Zambia on the North and Zimbabwe on the East, and is the location of the huge Cahora Bassa Dam and the city of Tete. It covers an area of 100,724 km 2 . According to ‘The 2014 Annual National Institute of Statistics’s Report’ [ 24 ], the province has a population of 2,500,000 individuals; approximately 20.6% are under the age of five. Nutrients 2017,9, 491 3 of 16 The health system in Mozambique is provided by the Ministry of Health through hospitals, health centres, and health posts. There are three levels of organization of health; the national, provincial, and, finally, the district level. The lowest level of care is provided by health posts. Between the central hospitals and the health posts there are other types of health dispensing units such as health centers, rural hospitals, and provincial or general hospitals [ 25 ]. Undernutrition is a very common problem in the region, and the prevalence of stunting is 52% [ 22 ]. The study population included children aged 0 to 59 months from two healthcare centres of districts 2 and 3 at the data collection period. 2.2. Sample Size Calculation and Sampling Procedure The sample size was calculated using OpenEpi (Open Source Epidemiologic Statistics for Public Health) version 3.5.4 [ 26 ] using formula of two population proportions by assuming the proportion of stunting to be 43.3% based on results from ‘The UNICEF’s Annual Report’ (52% in the Tete province) [ 22 ]. In this regard, a 5% level of significance, a power of 80%, and a ratio case to control of 1:2 was assumed. The total sample size was 282 (102 cases and 180 controls). Based on the above assumptions, with an additional 15% added for non-response, the total sample size was 324 children with 110 for cases and 214 for controls. We used a conservative design effect of two to adjust for a complex survey design. The reason for this is that two steps were required to reach or identify the study participants. A consecutive sampling technique was used to select the participants until the calculated sample size was attained. All children aged 0 to 59 months visiting healthcare centres 2 and 3 during the data collection period were measured for their height for age z-score and categorized as stunted or not stunted. Under-nourished children were first identified and then selected as cases. The cases were stunted children aged 0 to 59 months with height-for age z-scores (HAZ) below − 2 SD according to the World Health Organization (WHO) Child Growth Standards in 2006 [ 10 ]. They were categorized as severely stunted if the HAZ was < − 3 SD. The controls were children aged 0 to 59 months without stunting, apparently healthy and living in the community where cases resided. They are considered to be apparently healthy if the mothers/caretakers perceived the child to be healthy and declared no symptoms of disease such as fever, coughing, difficulty of breathing, or diarrhoea and without undernutrition selected from the same healthcare centres. Children with mental retardation, physical challenges, serious illnesses, preterm infants (less than 37 weeks of gestation), children suffering from wasting and an exacerbation of chronic undernourishment, and those with mild chronic undernutrition were excluded from the study. After measurements, mothers of the respondents were interviewed by the principal investigator based on a validated questionnaire. Only children accompanied by their own mothers were recruited to avoid recall bias. 2.3. Data Collection and Quality Assurance A two-day training session was given to nine data collectors and one supervisor. The training focused on the questionnaire and the anthropometric measurements. The questionnaire included the demographic characteristics of the children and their caregivers/mothers such as the sex of the child, the age of the child and their mother, the level of education, occupation, and care practices. To minimize recall bias and measurement bias we have offered instructions to the data collector and a supervisor through a two-day training session focused on the survey and anthropometric measurements. The initial design of the survey was conducted in Spanish, based on the literature developed for similar purposes and finally translated into Portuguese (the official language) after being adapted to the local context. It also included a physical examination and an anthropometric measurement. Both open and closed questions were used. On average, the interview lasted 30–40 min. The data collection instruments were tested previously and validated. The height and weight of each child were recorded using the standard procedure [27]. Nutrients 2017,9, 491 4 of 16 The height of the subject was recorded with an accuracy of 0.10 cm with the help of a rod out of the anthropometer, with the head held in the Frankfort horizontal plane. Height was measured for children above two years of age, while length for children below two years using a length/height board. The weight of the children was taken with a portable weighing scale with an accuracy close to 0.1 kg, using minimum clothing and with bare feet, and the device was calibrated frequently. 2.4. Data Processing and Analysis The data were analyzed statistically using the Statistical Package for Social Sciences (SPSS ® Inc., Chicago, IL, USA; version 21.0). The descriptive statistical analysis of the data obtained is described in terms of mean and standard deviation (SD). The homogeneity of variance was tested using Levene’s test of equal variances. For all anthropometric variables, it was observed that the p-value was statistically significant (p< 0.05). Normality was tested and also specifies gender using the Shapiro-Wilk test for each one of the anthropometric variables, and it was noted that the p-values were not statistically significant (p> 0.05). An independent sample t-test was performed to assess gender differences in the anthropometric variables. Analysis of variance (ANOVA) using the Scheffépost-hoc test was applied to evaluate the mean differences by age in the anthropometric variables. Chi-square analysis ( χ2 ) was used to assess the differences in the age-specific and general prevalence of stunting between the sexes. A binary logistic regression analysis was conducted to estimate the odds ratios (OR), and 95% confidence intervals (CIs) were used to evaluate the possible differences and the associated risk factors among children with height-for-age z-scores less than − 2 SD (HAZ < − 2 SD) and normal height-for-age z-scores (HAZ > − 2 SD). The binary logistic regression analysis allowed the creation of categorical dependent variables, and the odds were obtained by comparing these scors with the reference category in the analysis of the univariate independent model. To create dichotomous dependent variables (stunted growth versus normal), the normal code is coded as (0) and the delay of growth was coded as (1) in the regression models. The multiple logistic regression analysis by stages (in advance) in the conditional model was also carried out to determine the most effective predictive variables from the variables considered in the binary logistic regression analysis. A stepwise backward elimination approach was applied, and collinearity was tested in the final model and reported. The odds ratios with 95% CIs were calculated in order to assess the adjusted risk of independent variables, and those with p< 0.05 were retained in the final model [28,29]. 2.5. Ethical Considerations Ethical approval was provided by the district health authorities and the Ethics Committee of the Tete Regional Hospital, Mozambique. All the participants involved in the study were informed about the nature of the study, the research objectives, and theconfidentiality of the data. The participation of the subjects was completely voluntary in nature, and verbal consent was obtained from each mother before their children were recruited into the study. All the subjects were free from any physical deformities and not suffering from any disease at the time of data collection. To avoid any selection bias, the subjects were examined for any nutritional deficiencies and related disorders. Any previous histories related to medical and surgical episodes were also taken into consideration during the time of examination. Persons eligible to participate in the study were not offered a monetary incentive for participation. In the cases where the respondent was illiterate, we asked a literate person from the community to read out the consent form and explain it to the head of the family. Then we obtained the thumb print of the respondent. In those cases, the person who read the consent form also signed as a witness. The research procedures were consistent with the Declaration of Helsinki [ 30 ]. Interviews were administered after obtaining informed consent. The protocol was reviewed by a small group of experts who had experience working in centers of recovery and nutritional education and was amended based on their recommendations. Nutrients 2017,9, 491 5 of 16 3. Results 3.1. Sociodemographic Characteristics The size of the sample was 282 patients (n= 282) from 13 districts. Of these, 69.2% (n= 195) lived in an urban area and 22.7% (n= 64) in a rural area. Regarding distribution by sex, 57.4% of the sample and of the cases were boys, with an average age of 42 months ± SD of 18.3 (range: 0–59). Differences observed between percentages of stunting and normally nourished children according to sex were statistically significant (p< 0.05). As to the children’s age, it was higher in the group of stunting. The mean ages ± SD for the cases and controls was 43.3 ± 18.5 and 40.9 ± 18.2, respectively (p= 0.31). When they were grouped by age intervals, a higher percentage of stunting was shown in those under six and those over 24, but the results were not significant either (p> 0.05). The mean pregnancies ± SD for the cases and controls were 3.6 ± 1.8 and 1.6 ± 0.9, respectively, while their mean birth weights ± SD were 2.8 ± 0.4 and 3.1 ± 0.3 kg, respectively (p< 0.001). The findings highlighted that there were significant associations between undesirable growth of the subjects and their mothers’ educational level (p< 0.001). The mean age of the mothers in their first pregnancy ± SD for the cases and controls was 16.8 ±1.8 and 20.5 ±2.5, respectively (p< 0.05) (Table 1). Table 1. Distribution of stunting status by characteristics of mothers and children and their environment (N= 282). Data is expressed in frequencies (%) or average ±SD (standar deviation). Chronically Malnourished Normally Nourished Total p-Value N= 102 N= 180 N= 282 n(%) n(%) n(%) X±σX±σX±σ Sex Male 79 (77.5) 83 (46.1) 162 (57.4) 0.001 *** Female 23 (22.5) 97 (53.9) 120 (42.6) Age (months) 43.3 ±18.5 40.9 ±18.2 - 0.31 Area of origin Urban 20 (19.6) 175 (97.2) 195 (69.1) 0.001 *** Periurban 20 (19.6) 3 (1.7) 23 (8.2) Rural 62 (60.8) 2 (1.1) 64 (22.7) Reason for consultation Respiratory infection 17 (16.7) 10 (5.6) 27 (9.6) 0.001 *** Gastrointestinal infection 43 (42.2) 90 (50.0) 133 (47.2) Fever 33 (32.4) 78 (43.3) 111 (39.4) Weight control and vaccination 9 (8.8) 2 (1.1) 11 (3.9) Routine checkups a16 (15.7) 157 (82.7) 173 (61.3) 0.001 *** Completed vaccination schedule b56 (54.9) 179 (99.4) 235 (83.3) 0.001 *** Age of beikost c <6 months 75 (73.5) 54 (30) 129 (45.7) 0.001 *** ≥6 months 27 (26.5) 126 (70) 153 (54.3) Time in months Cereals d5.51 ±2.42 7.76 ±2 - 0.001 *** Fruit 7.01 ±3.02 7.92 ±2.04 - 0.003 ** Vegetables 7.5 ±3.31 8.28 ±2.1 - 0.02 * Cow’s milk 0.93 ±4.22 1.04 ±4.56 - 0.85 Cow’s meat (beef) 13.3 ±6.74 11.48 ±9.33 - 0.06 Fish 11.85 ±5.04 13.93 ±5.51 - 0.002 ** Chicken 13.4 ±5.75 15.32 ±5.34 - 0.01 * Eggs 9.97 ±5.37 10.39 ±4.43 - 0.48 Yoghurt 0.9 ±3.24 1.06 ±2.76 - 0.66 Legumes 7.77 ±4.06 8.18 ±2.37 - 0.35 Juices 3.83 ±5.51 6.99 ±3.43 - 0.001 *** Nutrients 2017,9, 491 6 of 16 Table 1. Cont. Chronically Malnourished Normally Nourished Total p-Value N= 102 N= 180 N= 282 n(%) n(%) n(%) X±σX±σX±σ Siblings < 5 years old 78 (76.5) 13 (7.2) 91 (32.3) 0.001 *** Type of home Straw and wood 45 (44.1) 35 (19.4) 80 (28.4) 0.001 *** Clay bricks 53 (52) 79 (43.9) 132 (46.8) Others 4 (3.9) 66 (36.7) 70 (30.6) Type of floor Soil 98 (96.1) 63 (35) 161 (57.1) 0.001 *** Concrete 4 (3.9) 117 (65) 121 (42.9) Access to drinking water - 52 (28.9) 52 (18.4) 0.001 *** Cooking fuel * Coal 19 (18.6) 128 (71.1) 147 (52.1) 0.001 *** Wood 83 (81.4) 30 (16.7) 113 (40.1) Gas - 22 (12.2) 22 (7.8) *p< 0.05 (Chi-square), ** p< 0.01, *** p< 0.001; a Those who have visited the healthcare centre at least once in the last year have been considered; b Completed vaccination schedule for the child’s age according to the Ministry of Health in Mozambique; c Children aged six months or over were considered; d Cereals consumed in form of ‘xhima’ or ‘papinha’. 3.2. Feeding Habits and Practices and Findings in the Examination of Malnourished Children Both the duration of breastfeeding (17.4 ± 5.4 and 11.4 ± 6.1 months) and the introduction of beikost (7.6 ± 2.3 and 5.3 ± 2.3 months) were higher in the group of normally nourished against the group of malnourished, being significant in both cases (p< 0.001). The early incorporation of these foods was associated with a higher risk of suffering stunting, and the observed differences were statistically significant, except for eggs and legumes. Moreover, the frequency of intake of foods was also analysed. No relevant differences were observed between groups in regards to the consumption of foods such as yoghurt, pasteurized milk, vegetables, potatoes, chicken, and fish. Only a small percentage had yoghurt regularly and pasteurized milk with sugar occasionally. The majority ate vegetables, potatoes, chicken, and fish every day on a regular basis. However, there were statistically significant differences observed regarding the intake of other foods. The results obtained from the comparison of housewives withthose who work outside the home, showed that the condition of having a job outside the home increases the risk of a child suffering undernutrition (p< 0.05). The most frequent findings in physical examination of malnourished children were changes in the skin (60.8%), loss of muscle mass (57.8%), alterations of the oral mucosa (57.8%), alterations of the subcutaneous cellular tissue (56.9%), alterations in hair and nails (48%), ocular alterations (47.1%), distended abdomen (30.4%), and neurological disorders (16.7%) (Table 1). 3.3. Characteristics of the Environment Other relatives lived at home with the malnourished in 65.7% of the cases, whilst only in 9.4% of the control group had relatives living with them (p< 0.001) (Table 1). 3.4. Simple and Multiple Logistic Regression Analyses The results of the univariate binary logistic regression analysis and the associations of sociodemographic variables on the prevalence of stunting among children are shown in Table 2. The following factors remained and were incorporated into the regression model; the child ´ s age in months, birth weight, mother’s educational status, maternal occupation, living in a rural area, family size, number of children under five years of age in the household, cooking with charcoal, and inhabiting wooden or straw housing or housing without proper floors (Table 2). Nutrients 2017,9, 491 7 of 16 Finally, the factors associated with stunting were examined in a multiple logistic regression model. A stepwise backward trend was retained in the final model. The odds ratios with 95% CIs were calculated in order to assess the adjusted risk of independent variables, and those with p< 0.05 were retained in the final model. It was observed that the child’s sex, birth weight, living in a rural area, having siblings under five years of age, living in houses made of straw and wood with soil floor, and homes where other relatives lived too, maintain statistically significant differences (p< 0.05). Sociodemographic factors were significantly associated with chronic undernutrition in children. The relationship between gender and chronic undernutrition was statistically significant [Adjusted Odds Ratio (AOR) = 4.57, 95% CI = (2.06–10.12), p< 0.05]. Birth weight was significantly associated with stunting [AOR = 19.99, 95% CI = (5.8–68.85), p< 0.001] and reported a lower risk of stunting. Children living in urban areas significantly reported a lower risk of stunting than those in rural areas [AOR = 138.0, 95% CI = (32.38–587.80), p< 0.001]. Table 2. Binary logistic regression (BLR) analysis and associations of sociodemographic variables with stunting. Binary Logistic Regression Analysis Univariate Model † p-Value Cases Controls Crude OR 95% IC N= 102 N= 180 n(%) n(%) Sex Male 79 (77.5) 83 (46.1) 4.01 (2.32; 6.95) 0.001 *** Female (ref.) 23 (22.5) 97 (53.9) - Area of origin Urban 40 (39.2) 178 (98.9) 137.95 (32.38; 587.65) 0.001 *** Rural (ref.) 62 (60.8) 2 (1.1) - - Birth weight ≤2.50 kg 36 (35.3) 4 (2.2) 23.86 (8.18; 69.65) 0.001 *** >2.50 kg (ref.) 66 (64.7) 175 (97.8) - - Mothers’ education No education/primary 102 (100) 123 (68.3) - - ≥Secondary (ref.) - 57 (31.7) - - Mother occupation Housewife (ref.) 76 (74.5) 167 (92.8) - - Others 26 (25.5) 13 (7.2) 0.23 (0.11; 0.47) 0.304 Extended family household Yes 67 (65.7) 17 (9.4) 18.36 (9.63; 35) 0.001 *** No (ref.) 35 (34.3) 163 (90.6) - Siblings under 5 years old Yes 78 (76.5) 13 (7.2) 41.75 (20.19; 86.33) 0.001 *** No (ref.) 24 (23.5) 167 (92.8) - - Type of home Straw and wood 45 (44.1) 35 (19.4) 3.27 (1.91; 5.60) 0.002 *** Others (ref.) 57 (55.9) 145 (80.6) Type of floor Soil 98 (96.1) 63 (35.0) 45.5 (15.99; 129.46) 0.001 *** Concrete (ref.) 4 (3.9) 117 (65.0) - - Access to drinking water Yes (ref.) - 52 (28.9) 0.56 (0.50; 0.625) - No 102 (100) 128 (71.1) - Cooking fuel Coal (ref.) 19 (18.6) 150 (83.3) - - Wood 83 (81.4) 30 (16.7) 0.05 (0.024; 0.086) 0.001 *** *** p< 0.001; ref.: reference category; OR—odds ratio; CI—confidence interval; † —binary logistic regression analysis considering effect of one explanatory/predictor variable. Children living in households with other family members [AOR = 17.3, 95% CI = (7.62–39.12), p= 0.001 ] and living in households with other children less than five years of age were more likely to develop stunting [AOR = 28.42, 95% CI = (11.93–67.70), p< 0.001]. Similarly, stunting was also related to living in households which used charcoal for cooking, households made of straw and wood [ AOR = 3.10, 95% CI = (1.53–6.26), p= 0.002 ], or those households which did not have a proper floor [AOR = 17.26, 95% CI = (5.87–50.75), p= 0.001] (Table 3). Nutrients 2017,9, 491 8 of 16 Table 3. Unadjusted and adjusted odds ratios (OR) (95% CI) for stunted children aged 0–59 months. Cases Controls Crude Odds Ratio OR Adjusted Odds Ratio aaOR p-Value N= 102 N= 180 n(%) n(%) 95% CI 95% CI Sex Male 79 (77.5) 83 (46.1) 4.01 (2.32; 6.95) 4.57 (2.06; 10.12) 0.001 *** Female (ref.) 23 (22.5) 97 (53.9) - - Area of origin Urban 40 (39.2) 178 (98.9) 137.95(32.38; 587.65) 138 (32.38; 587.80) Rural (ref.) 62 (60.8) 2 (1.1) - - 0.001 *** Birth weight ≤2.50 kg 36 (35.3) 4 (2.2) 23.86 (8.18; 69.65) 19.99 (5.80; 68.85) 0.001 *** >2.50 kg (ref.) 66 (64.7) 175 (97.8) - - Mothers’ education No education/1 a102 (100) 123 (68.3) - - - ≥Secondary (ref.) - 57 (31.7) - - Mother occupation Housewife (ref.) 76 (74.5) 167 (92.8) - - Others 26 (25.5) 13 (7.2) 0.23 (0.11; 0.47) 2.37 (0.46; 12.21) 0.304 Extended family household Yes 67 (65.7) 17 (9.4) 18.36 (9.63; 35) 17.27 (7.62; 39.12) 0.001 *** No (ref.) 35 (34.3) 163 (90.6) - Siblings under 5 years old Yes 78 (76.5) 13 (7.2) 41.75 (20.19; 86.33) 28.42 (11.93; 67.70) 0.001 *** No (ref.) 24 (23.5) 167 (92.8) - - Type of home Straw and wood 45 (44.1) 35 (19.4) 3.27 (1.91; 5.60) 3.10 (1.53; 6.26) 0.002 ** Others (ref.) 57 (55.9) 145 (80.6) Type of floor Soil 98 (96.1) 63 (35.0) 45.5 (15.99; 129.46) 17.26 (5.87; 50.75) 0.001 *** Concrete (ref.) 4 (3.9) 117 (65.0) - - Access to drinking water Yes (ref.) - 52 (28.9) 0.56 (0.50; 0.625) - No 102 (100) 128 (71.1) - Cooking fuel Coal (ref.) 19 (18.6) 150 (83.3) - - Wood 83 (81.4) 30 (16.7) 0.05 (0.024; 0.086) 0.055 (0.024; 0.124) 0.001 *** ** p< 0.01, *** p< 0.001; CI—confidence interval; significance considered when p< 0.05 and if the OR and adjusted odds ratios (aOR) estimate did not cross the null for 95% CI. aOR adjusted for child‘s sex and area of origin. 4. Discussion Linear growth is the best overall indicator of children’s well-being and provides an accurate marker of inequalities in human development. This is tragically reflected in the millions of children throughout the world who do not achieve their full potential of linear growth due to suboptimal health conditions and inadequate nutrition and childcare [ 31 ]. The nutrition assessment of the vulnerable segments of the population should be emphasised, not only for the identification of nutritional risks but also for the improvement of existing health situations. Currently, the nutritional scenario of the developing countries has been changing radically over the past two decades, experiencing the double burden of malnutrition (both under and overnutrition) due to changes in socio-economic and demographic transition, dietary habits, lifestyle modification, and increasing risks of non-communicable diseases [32,33]. This study assessed the child growth situation in the central region of Mozambique (Tete province) and identified sociodemographic, health, and environmental variables as important determinants of stunting of children under five in the Tete province. The findings showed that the overall prevalence of stunting was observed to be 36.2% using the proposed WHO reference (WHO 2006). The rate of 52% stunting in the Tete province is much higher than the 40% critical public health thresholds. This estimation is almost similar to the 43% level reported by the 2007 Demographic Health Survey (DHS) for this age group, suggesting that not much has changed between 2007 and 2016 [34]. Nutrients 2017,9, 491 9 of 16 The study findings depicted the sex of child to be a strong determinant of childhood stunting. Previous studies from Sub-Saharan Africa have reported mixed findings on the effects of sex, with some suggesting that males are more affected by undernutrition compared to females, while others reported otherwise [ 35 – 38 ]. In this study, males were more likely to be stunted. This is in line with the findings of a meta-analysis of DHS of 16 Sub-Saharan African countries [ 39 ], as well as the 2011 Mozambique DHS [ 40 ] and the 2010 DHS in Eastern Africa [ 41 ]. Studies in Sub-Sahara African countries have reported girls to be more recognized, first because of their high value in agriculture and secondly due to the fact that they are seen as an investment, especially among the low socio-economic class, thus leading to more care and dietary preferential treatment [ 39 ]. Considering that over 80% of Mozambique is agriculturally based and that the majority of the population is socio-economically constrained, i.e. an estimated 61% are below the poverty line of $1.25 per day [ 42 ], this notion may explain why males are more malnourished. Additionally, epidemiological evidence depicts boys to be biologically more vulnerable to morbidity [ 43 , 44 ], and, in a setting like Mozambique where morbidity incidences are high, this probably exerts considerable effects on boys. Childhood stunting was found to progressively rise with an increase in age up to the age of 24 months. A similar trend has been reported in a number of previous studies in developing countries [18,37,45–48]. The decreasing immune protective effects of breast milk coupled with increasing exposure to contaminated complementary foods, culminating in the onset of infectious diseases along with increasing nutrient requirements, explains the trend [ 49 – 52 ]. In line with a number of studies from settings like Central-Eastern Africa (Kenya) [ 36 ], Turkey [ 53 ], and Eastern Africa (Burundi) [ 41 ] along with the recent lancet series, which put children born with low birth weight at a 20% risk of stunting [ 3 ], low birth weight children in this study were at a higher risk of stunting. Research shows that low birth weight babies are born with low reserves of vital growth nutrients; vitamin A, zinc, and iron [54]. Therefore, they depend on breast milk to cover these deficiencies. However, as the amount of these nutrients present in breast milk mainly depends on maternal intake and nutritional status, low birth weight children are at a risk of not meeting the recommended amounts if maternal intake is inadequate. Additionally, smaller than average babies experience feeding problems, a factor involved in undernutrition, even in this study [ 55 ]. A number of studies have shown maternal education to provide protective effects against all under-nutrition indicators in children [ 36 , 45 , 46 , 56 – 60 ]. The findings of this study are inconsistent with the results of the studies in terms of growth retardation and underweight children, with which no association was found. Particularly, the results disagree with the 10 years schooling threshold reported in a recent study conducted across the three countries of Malawi, Zimbabwe, and Tanzania [ 61 ], as protective effects were only found for secondary/tertiary education with no effects for primary completion. As reported in other settings [ 59 , 62 ], 10 years of schooling may represent an opportunity of achieving a higher paying job, meaning better access to the market and leading to better feeding practices. Additionally, these mothers may have better child and healthcare knowledge, more health seeking behaviours, lower fertility rates, and access to better medical care. Certainly, in this study, a significant proportion of children whose mothers had secondary/tertiary education followed a diet that met the recommended dietary diversity, in comparison with their counterparts whose mothers were uneducated. The study findings depict no association between maternal occupation and any of the undernutrition indicators. This is contrary to a recent systematic review on determinants of undernutrition in Sub-Saharan Africa [ 38 ], as well as other studies from Malaysia [ 63 ] and Ethiopia [ 64 ], but in agreement with findings of Adekanmbi and Kayode [ 45 ] from Nigeria. As the majority of mothers are in the agricultural sector, which is largely on the subsistence level, it could be that there contribution towards the household income is too low to influence household decisions like feeding practices. Children of undernourished mothers are more likely to be stunted. Similar findings have been documented in Tanzania [ 56 ] and other low-income countries such as Nigeria [ 45 ] and Ethiopia [ 18 , 58 ]. Though a group of evidence shows that maternal nutritional status has no effect on the composition Nutrients 2017,9, 491 16 of 16 61. Makoka, D.; Masibo, P.K. Is there a threshold level of maternal education sufficient to reduce child undernutrition? Evidence from Malawi, Tanzania and Zimbabwe. BMC Pediatr. 2015 ,15, 96. [CrossRef] [PubMed] 62. Limwattananon, S.; Tangcharoensathien, V.; Prakongsai, P. Equity in maternal and child health in Thailand. Bull. World Health Organ. 2010,88, 420–427. [CrossRef] [PubMed] 63. Shuhaimi, F.; Muniandy, N.D. The association of maternal employment status on nutritional status among children in selected kindergartens in Selangor, Malaysia. Asian J. Clin. Nutr. 2012,4, 53–66. [CrossRef] 64. Negash, C.; Whiting, S.J.; Henry, C.J.; Belachew, T.; Hailemariam, T.G. Association between Maternal and Child Nutritional Status in Hula, Rural Southern Ethiopia: A Cross Sectional Study. PLoS ONE 2015 ,10, e0142301. [CrossRef] [PubMed] 65. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks; World Health Organization: Geneva, Switzerland, 2009; Available online: http://www.who.int/iris/ handle/10665/44203 (accessed on 10 May 2017). 66. Darteh, E.K.M.; Acquah, E.; Kumi-Kyereme, A. Correlates of stunting among children in Ghana. BMC Public Health 2014,14, 504. [CrossRef] [PubMed] 67. Correia, L.L.; Silva, A.C.; Campos, J.S.; Andrade, F.M.; Machado, M.M.; Lindsay, A.C.; Leite, Á.J.; Rocha, H.A.; Cunha, A.J. Prevalence and determinants of child undernutrition and stunting in semiarid region of Brazil. Rev. Saude Publica 2014,48, 19–28. [CrossRef] [PubMed] 68. Dewey, K.G. Impact of breastfeeding on maternal nutritional status. In Protecting Infants through Human Mil; Springer: Berlin, Germany, 2004; pp. 91–100. 69. Dewey, K.G.; Cohen, R.J. Does birth spacing affect maternal or child nutritional status? A systematic literature review. Matern. Child Nutr. 2007,3, 151–173. [CrossRef] [PubMed] 70. Greene, M.E.; Merrick, T. Poverty Reduction: Does Reproductive Health Matter? Available online: http://documents.worldbank.org/curated/en/709821468137992945/Poverty-reduction-doesreproductive-health-matter (accessed on 10 May 2017). 71. Cleland, J.; Bernstein, S.; Ezeh, A.; Faundes, A.; Glasier, A.; Innis, J. Family planning: The unfinished agenda. Lancet 2006,368, 1810–1827. [CrossRef] 72. Fink, G.; Gunther, I.; Hill, K. The effect of water and sanitation on child health: Evidence from the demographic and health surveys 1986–2007. Int. J. Epidemiol. 2011,40, 1196–1204. [CrossRef] [PubMed] 73. Checkley, W.; Gilman, R.H.; Black, R.E.; Epstein, L.D.; Cabrera, L.; Sterling, C.R.; Moulton, L.H. Effect of water and sanitation on childhood health in a poor Peruvian peri-urban community. Lancet 2004 ,363, 112–148. [CrossRef] 74. Humphrey, J.H. Child undernutrition, tropical enteropathy, toilets, and handwashing. Lancet 2009 ,374, 1032–1035. [CrossRef] 75. United Nations Children’s Fund (Executive Board). Mozambique-UNICEF Country Programme of Cooperation, 2017–2020. Economic and Social Council, 2016. Available online: http://www.un.org/ ga/search/viewm_doc.asp?symbol=E/ICEF/2016/13 (accessed on 10 April 2017). © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).