DOI: https://doi.org/10.21203/rs.2.13856/v2
Background: Malnutrition among mothers and children is a major public health challenge in developing countries like Nepal. Although undernutrition among children has been gradually decreasing, the coexistence of different forms of malnutrition among mothers and children has continued to rise globally. There is a gap in knowledge of the coexistence of such multiple burdens of malnutrition in the Nepalese context. The aims of this study were to explore the coexistence of different forms malnutrition among children and associated factors among the mother-child pairs in the same household.
Methods: A total sample of 2,261 mother-child pairs from the Nepal Demographic and Health Survey (NDHS) 2016 was included in the study. Anthropometric measurements and hemoglobin levels of the children and anthropometric measurements of their mothers were taken. The bivariate and multivariable logistic regression were performed to assess the factors associated with the double burden of malnutrition (DBM) and the triple burden of malnutrition (TBM).
Results: Prevalence of DBM and TBM was 6.60(5.13-8.84) % and 7(5.42-8.99) % respectively in the same household. In the adjusted multivariable logistic regression, mothers with short stature compared to normal height (AOR=4.18, 95% CI: 2.04-8.52), from the richest wealth status compared to poor wealth status (AOR=2.46, 95% CI= 1.17-5.15), from age group of above 35 years compared to 15-24 years (AOR=3.08, 95% CI:1.20-7.86), and those who had attended at least a secondary level of education compared to no education (AOR=2.05, 95% CI: 1.03-4.07) were more likely to suffer from the DBM. Similarly, mothers with short stature compared to normal height (AOR=5.01, 95% CI:2.45-10.24), from the richest wealth status compared to poor wealth status (AOR=2.66, 95% CI=1.28-5.54), age groups of above 35 years compared to 15-24 years (AOR=3.41, 95% CI:1.26-9.17), and those who had attended at least a secondary level of education compared to no education (AOR=2.05, 95% CI: 1.00-4.18) were more likely to suffer from the TBM.
Conclusions: There is a low prevalence of double and triple burden of malnutrition among mother-child pairs in Nepal. Mothers with short stature, belonging to the richest family and in older age were more prone to double and triple burden of malnutrition
The different forms of malnutrition among children and mothers are major public health challenges in low and middle-income countries [1]. Multiple forms of malnutrition consist of double and triple burden of malnutrition. The double burden of malnutrition (DBM) is defined as the coexistence of maternal overweight/ obesity along with child undernutrition within the same household level [2, 3]. However, limited literature defined the triple burden of malnutrition (TBM). In previous studies, the triple burden of malnutrition referred to the coexistence of overnutrition, undernutrition and micronutrient deficiencies [4, 5]. Overnutrition, undernourishment, and micronutrient deficiencies equally increase the risk of various health problems [6]. Child undernutrition is associated with an increased risk of childhood mortality and poor cognitive development [7]. On the other hand, overnutrition plays a significant role in causing various non-communicable diseases, such as high blood glucose, raised blood pressure, central obesity and high lipid profiles [8].Overweight/obesity during pregnancy is positively linked with several adverse maternal and fetal consequences during pregnancy, delivery and the postpartum period [9, 10].
Globally, the prevalence of undernutrition (stunting, wasting and underweight) in children has declined whereas maternal overweight and obesity have increased [6]. A systematic review reported that the prevalence of double burden of malnutrition at the household level varied from 0.0 to 26.8% in different countries and years [11]. Similarly, a study conducted in different low and middle-income countries in Africa, Asia, and Latin America showed the prevalence of overweight/obese mother and stunted child varied from 4–30.6% at the household level [12]. Among the South and Southeast Asian countries, the prevalence of overweight and obesity was observed at 21.3% and 8.6% respectively [9]. In Nepal, the prevalence of stunting, wasting, and underweight has declined in the last decade [13], however, anemia among children aged under five years has been stagnant. Overweight and obesity have increased in all women of all socio-demographic groups [14, 15].
Association of socioeconomic status (SES) and the double burden of malnutrition has been explored in several studies [16, 17]. The maternal overweight/obesity and child undernutrition among the mother-child pairs was found to be due to the interaction of changes related to socio-demographic and economic status, dietary habit and intensity of physical activity [18]. Most of the low and middle-income countries are undergoing economic and nutrition transition [19]. Mothers from wealthy status families are shifted to consuming energy-dense food resulting in overweight/obesity [20]. In addition, various studies have also indicated that the double burden of malnutrition is associated with older aged mothers, mothers having short stature and a higher level of maternal education and wealth [16, 21, 22].
Undernutrition and micronutrient deficiencies are highly prevalent among Nepalese mothers and children under five years of age, however adequate evidence on the co-existence of such a form of malnutrition is lacking in the Nepalese context. The coexistence of different forms of malnutrition among mothers and children has continued to rise globally [23]. According to Popkin’s nutrition transition model, it is explicit that Nepal has moved into the fourth stage of nutrition transition resulting in the rise of the prevalence of overweight and obesity [24]. To our knowledge, overnutrition, undernutrition and micronutrient deficiencies in mother-child pairs within the same household has not been explored using nationally representative data in Nepal so far. The goal of this study was to explore the prevalence and factors associated with the double and triple burden of malnutrition among mother-child pairs in Nepal. Eventually, this study provides important information on the reality of nutritional status among mother-child pairs in the context of Nepal.
Study design and population
This study utilized secondary data from the Nepal Demographic and Health Survey (NDHS) 2016, a nationally representative cross-sectional survey, to explore the prevalence of double and triple burden of malnutrition and associated factors among mother-child pairs. This survey was carried out as part of the DHS program by New ERA under the guidance of the Ministry of Health, Government of Nepal and supported by ICF international and United States Agency for International Development (USAID). The study population for this study were mother-child pairs from the Nepal Demographic and Health Survey 2016.
Sampling strategy
The NDHS 2016 utilized a stratified, two-stage cluster sampling design to provide representatives estimates for seven provinces, three ecological zones, and urban and rural areas. The survey used enumeration areas (EAs) which is a primary sampling unit (PSU) and was selected from 383 wards in both rural (n = 199) and urban (n = 184) areas with probability proportional to size method. In the second stage, 30 households on average within EAs were selected using a systematic sampling technique. A more detailed methodology of the NDHS has been published in the most recent NDHS report [15]. The details of the sample size and exclusion criteria for the selection of the mother-child pairs are presented in Fig. 1.
Fig. 1 Flow chart for sample size selection
Data collection techniques
In this study, we used anthropometric and biochemical indices such as height-for-age, weight-for-height, and weight-for-age and hemoglobin levels to evaluate the nutritional status of 0–59 months child. The WHO Multicenter Growth Reference Study Group, 2006 was used to calculate the anthropometric indicators to evaluate the nutritional status of the child [25]. Children suffering from stunting, wasting and underweight were defined as children with Z-scores below –2 standard deviation (more than 2 standard deviations below the reference median), for height-for-age (HAZ), weight-for-height (WHZ) and weight-for-age (WAZ) respectively. We categorized the blood hemoglobin level as anemic (<11g/dl) and not anemic (≥11gm/dl) for the purpose of analysis. Similarly, we used body mass index (BMI) classification according to WHO for mothers aged 15–49 years. The standard WHO cut-off value was used to determine the normal BMI (18.5 to <24.99kg/m2) and overweight/obesity (>25.0 kg/m2) [26].
Study variables
The detailed plan for data coding and description of the study variables is given in Table 1.
Outcome variables
In order to simplify the analysis of the outcome variables, we dichotomized all dependent variables into presence (coded 1) versus absence (coded 0). We created four different categories of malnutrition such as overweight/obese mother and stunted child (OM/SC), overweight/obese mother and wasted child (OM/WC), overweight/obese mother and underweight child (OM/UC), overweight/obese mother and anemic child (OM/AC) at the same household level. Four different categories were further combined to formed two categories: overweight/obesity mother and undernourished child (stunting or wasting or underweight) which was considered as the double burden of malnutrition (DBM)[12] and the double burden of malnutrition plus anemic child (DBM + anemia) was regarded as the triple burden of malnutrition (TBM) [4, 5].
Independent variable
In this study, we included maternal socio-demographic factors (mother’s age, age at first birth, ethnicity, place of residence, province, education level, occupation, household wealth status, height, iron/folate intake, antenatal care (ANC) visits, parity, delivery by caesarean section), fathers occupation, education and child factors (age of child, child sex, vitamin A consumption, deworming tablet consumption, breastfeeding status, child weight at birth, and total number of children ever born from single mother) as independent variables.
Table 1 Plan for data coding and description of the study variables.
Data analysis
Data were analyzed using STATA/MP version 14.1 (StataCorp LP, College Station, Texas). The ‘svy’ command was used to adjust for EAs and disproportionate sampling weight and non-response. The datasets for women and child files were merged. The prevalence of overweight/obese mother and stunted child (OM/SC), overweight/obese mother and wasted child (OM/WC), overweight/obese mother underweight child (OM/UC), overweight/obese mother and anemic child (OM/AC), double and triple burden of malnutrition were presented as weighted percentage and 95% confidence intervals. The bivariate and multivariable logistic regression model were performed to assess the factors associated with the double and triple burden of malnutrition. To prevent statistical bias in the multivariable logistic regression model, we examined and reported multicollinearity among the predictor variables using variation inflation factors (VIF). In this study, we used “10” as a cut-off value for the maximum level of VIF [27]. Bivariate analysis was performed to assess the association of socio-demographic factors with outcome variables. All variables with statistically significant associations (p<0.05) in bivariate analysis were included in the multivariable regression model. Results were presented as crude odds ratio (COR) and adjusted odds ratio (AOR) with 95% confidence intervals (CI). P-value <0.05 was considered as statistically significant.
Ethical considerations
This study was a secondary analysis of the NDHS 2016 data thus no separate ethical approval was required. However, ethical clearance for the NDHS was obtained from the ethical review board of Nepal Health Research Council and the written informed consent was obtained from each participant as per the standard ethical guidelines of the DHS program. We registered and requested for access to data from the DHS website (URL: https://www.dhsprogram.com/data/available-datasets.cfm) and received an approval to access and download the DHS data file.
Table 2 Socio-demographic characteristics of the study participants
A total of 2,261 mother-child pairs were included in the study (Fig 1). Table 2 shows socio-demographic information of the participants and different forms of malnutrition existing among the mother-child pairs at the same household level in Nepal. The mean (+SD) age of the mothers and age of the child was 26.36(+5.64) years and 29.01(+17.37) months respectively. About one-half of the mothers (49.22%) were in the 25–34 years age groups and more than half of the mothers (52.77%) were below 19 years of age at their first birth. One-third of the mothers (33.68%) and only 15.12% of the fathers did not receive any formal education and approximately one-third of the mothers(32.36%) and more than one-third of the fathers (43.86%) attained secondary level education. The majority of the mothers (46.69%) and only 19.32% of the fathers were involved in agriculture. Slightly more mothers (25.89%) were living in province number 2, More mothers (42.04%) belonged to poor wealth status and the majority of the mothers (90%) had normal height. Slightly more children (22.53%) were below 12 months of age groups, more than half of children (52.71%) were male, slightly more than two-thirds of the child (74.52%) received a vitamin A capsule in the previous six months and more than half of the child (59.82%) were taking deworming. About two-thirds of the child (67.72%) were born with average birth weight.
The prevalence of overweight/obese mother and stunted child (OM/SC) was 8.30(6.32–10.84) %, overweight/obese mother and wasted child (OM/WC) was 1.25(0.74–2.11) %, overweight/obese mother and underweight child (OM/UC) was 3.37(2.34–4.83) % and overweight/obese mother and anemic child (OM/AC) was 18.89(15.43–22.83) %. The prevalence of the DBM was 6.60(5.13–8.84) % and TBM was 7(5.42–8.99) % at the household level.
Table 2 Socio-demographic characteristics of the study participants (N = 2,261)
Table 3. Depicts bivariate and multivariate logistic regression model for the different forms of malnutrition and its associated factors among the mother-child pairs. The following results are the interpretation of the different forms of malnutrition and associated factors.
Prevalence and factors associated with the double burden of malnutrition
In the bivariate logistic regression model, several maternal factors were significantly associated with higher odds of the double burden of malnutrition: mother’s short stature compared to normal height (COR = 3.19, 95% CI: 1.59–6.40), mothers from the richest wealth status compared to poor wealth status (COR = 2.89, 95% CI: 1.50–5.54), mothers whose occupation was services compared to agriculture (COR = 2.82, 95% CI: 1.37–5.81), mother who had attended at least secondary level of education compared to no education (COR = 2.43, 95% CI: 1.24–4.76), mothers whose last delivery done through caesarean section compared to normal delivery (COR = 2.39, 95% CI: 1.18–4.48) and mothers aged of above 35 years compared to 15–24 years (COR = 1.18, 95% CI: 0.38–3.61). In addition, child-related factors that were more likely to increase odds of double burden of malnutrition were as follows: Children who were 36–47 months compared to below 12 months (COR = 2.19, 95 % CI: 1.01–4.73), children with no history of current breastfeeding compared to children being currently breastfed (COR = 1.97, 95% CI: 1.10–3.51) and child’s large size at birth compared to average birth weight (COR = 1.93, 95% CI: 1.13–3.29). Mothers who were 20–29 years of age during first birth of their child compared to above 30 years (AOR = 0.12, 95% CI = 0.03–0.44), mothers living in province number 2 compared to province number 3 (AOR = 0.08, 95% CI: 0.03–0.47), no history of vitamin A intake compared to intake of vitamin A intake among children (COR = 0.45, 95% CI: 0.22–0.92) and no history of deworming compared to deworming among children (COR = 0.54, 95% CI: 0.31–0.91) were found to have lower odds of DBM. Multivariable logistic regression model indicated that mothers with short stature compared to normal height (AOR = 4.18, 95% CI:2.04–8.52), mothers from richest wealth status compared to poor wealth status (AOR = 2.46, 95% CI: 1.17–5.15), age groups of above 35 years compared to 15–24 years of age groups (AOR = 3.08, 95% CI: 1.20–7.86–4.77), mothers with secondary level of education compared to no education (AOR = 2.05, 95% CI: 1.03–4.07) were more likely to have higher odds of DBM. While mothers living in province number 2 compared to province number 3 (AOR = 0.13, 95% CI: 0.03–0.47), were found to had lower odds of DBM (Table 3).
Prevalence and factors associated with the triple burden of malnutrition
Bivariate logistic regression model (Table 3) indicated that the mother’s height of short stature compared to normal height (COR = 4.38, 95% CI: 2.17–8.86),mother’s age groups of above 35 compared to 15–24 years (COR = 3.11.19, 95% CI: 1.34–7.22), had child’s age group of 24–35 months compared to below 12 months (COR = 2.85, 95 % CI: 1.33–6.11), had the richest wealth status compared to poor wealth status (COR = 2.61, 95% CI: 1.36–5.02), mothers who worked in services compared to agriculture (COR = 2.61, 95% CI: 1.36–5.02), having at least secondary level of education compared to no education (COR = 2.09, 95% CI: 1.05–4.16), and mothers who had no history of current breastfeeding (COR = 1.93, 95% CI: 1.07–3.47) were more likely to have higher odds of TBM. Likewise, results in the multivariable logistic regression model shows mothers short stature compared to normal height (AOR = 5.01, 95% CI: 2.45–10.24), mothers age groups of above 35 years compared to 15–24 years (AOR = 3.41, 95% CI: 1.26–9.17), mothers from the richest wealth status compared to poor wealth status (AOR = 2.66, 95% CI = 1.28–5.54), and mothers who attended at least secondary level of education compared to no education (AOR = 2.05, 95% CI: 1.00–4.18) were found to had higher odds of TBM. Furthermore, mothers living in province number 2 compared to province number 3 (AOR = 0.11, 95% CI: 0.03–0.41), children with no history of vitamin A intake compared to vitamin A intake (COR = 0.40, 95% CI: 0.19–0.86), and no history of deworming drug intake (COR = 0.49, 95% CI: 0.28–0.86) were found to had lower odds of TBM (Table 3).
Table 3 Bivariate and multivariable analysis of double and triple burden of malnutrition among mother-child pairs and its associated factors (n = 2,261).
The present study revealed the coexistence of double and triple burden of malnutrition within the same household in Nepal. The prevalence of DBM was 6.60% which is higher than that of the neighboring country Bangladesh. A study by Emdadul S et al., [3] found that the maternal over and child undernourishment (MOCU) was 4.9% and Das et al., [12] reported that in Bangladesh the proportion of coexistence of overweight/obese mother and underweight or stunted or wasted child (OWOBM/USWC) was 6.3%. The double burden of malnutrition was 11% in Indonesia which was higher than in most of the South Asian countries [3, 11, 12]. It has been noted that the overweight/obesity of mothers is associated with the nutrition transition situation that contributes to a positive energy balance which means higher intake of energy-dense food and less energy expenditure[28]. Tendency to consume calorie-dense food with more saturated fat, trans fat and a sedentary lifestyle results in reproductive-aged women gaining weight [11, 19]. On the other hand, the intake of processed food with low nutrient content leads to child undernutrition [12, 29].
This study shows that short stature in mothers was strongly associated with the risk of DBM. This result is consistent with that of Oddo et al., [30] who reported that maternal short stature and older age had higher odds of DBM compared to those normal height and younger age groups. Similarly, these findings are also supported by Ferreira et al., who found that higher BMI was significantly associated with short stature mothers which reflect the vicious cycle of malnutrition that is more prone to the risk of a stunted child [31]. Stunting is an intergenerational phenomenon that transfers from mother to child and contributes to small for gestational age (SGA) babies. Malnourished mother is likely to have a low birth weight baby in the first 1000 days of life [18, 32]. Emdadul et al., and Mamun et al., reported the maternal over and child undernutrition were significantly associated with higher income and wealthier family [1, 3] which is consistent with our findings. This result was also validated by other similar studies [9, 33, 34]. Similarly, the current study found that mothers who had wealthy status were positively associated with DBM. This is because those in the wealthy family may have increased intake of energy-dense food such as processed food trans-fat and sedentary lifestyle [30]. Moreover, our study also found that mothers from the lowest wealth status were likely to be protective against maternal over and child undernutrition. The findings are in line with the results of the previous study conducted in Bangladesh and Indonesia [30]. Our results revealed that mothers who were above 35 years were found to be at higher risk of double burden of malnutrition. This result is consistent with Emdadul et al., and Wong et al., who suggested that the prevalence of overweight/obesity was higher in the older age groups compared to younger groups [3, 33]. Mothers who attended at least a secondary level of education had a higher risk of having a double burden of malnutrition. This finding is supported by Rai et al., [35] who revealed that women who had primary/secondary level of education were more likely to be at risk of overweight/obesity. However, mothers with higher levels of education were protective against maternal and child double burden of malnutrition in Indonesia [30]. Another study suggested that the relationship between education and overweight/obesity is complex and varies from country to country [36]. In developing countries, this association could be partly due to the educational status of women, getting sedentary lifestyle jobs and being unaware of the health consciousness of having overweight/obesity [35]. In the case of the mother having poor health and nutritional knowledge, it leads to women being less sensitive to child and her nutritional status in terms of food choices and barriers such as food cost, accessibility, availability, lack of cooking skills [18]. As we found that DBM was more prevalent in mothers who attended a lower level of education, therefore, providing nutrition education during pregnancy could bridge this nutritional knowledge gap in Nepal [37].
This study found that mothers from province number 2 were less likely to have DBM compared with that of province 3. A possible reason could be mothers from province number 2 had less likely to be overweight/obesity [35, 38]. The prevalence of overweight/obesity was higher (35%) in province number 3 which is Kathmandu, the largest city (and capital) of the country [38]. Maternal overweight/obesity and undernourished child among mother-child pairs and its associated factors has not been explored yet after the country has undergone federal administrative provinces in Nepal.
Our study shows that the prevalence of TBM among mother-child pairs was 7%. Maternal overweight/obesity and undernourished child and its associated factors have been explored in most of the Latin American and South Asian countries like Guatemala, Colombia, Brazil, Malaysia, Indonesia, and Bangladesh [3, 30, 39–41]. However, in mother-child pairs, the coexistence of three forms of malnutrition has not yet been examined. Thus, to our knowledge, this study is the first to present the coexistence of overnutrition in mother with undernutrition and anemia in child of the same household. A higher rate of TBM than DBM could have happened because more than half (53%) of the children aged 6–59 months were found to be anemic in Nepal as per NDHS [15]. The overall prevalence of anemia among the less than 59 months of children is 54.2% in developing countries [1]. Despite declining undernutrition among children, micronutrient deficiency anemia remains one of the intractable public health problems in South Asia [42]. Despite limited evidence available so far on TBM, maternal overweight/obesity is a crucial factor in contributing to child anemia. The plausible mechanism for the phenomenon of TBM has not been examined clearly. A possible reason could be that maternal overweight/obesity is a risk factor for anemia in their offspring. Maternal obesity and excessive gestational weight gain posed a higher risk of low neonatal iron status [43]. In obese mothers, impaired iron transfer to the fetus resulted in lower serum iron as well as transferrin saturation in cord blood as compared to normal-weight mothers [43, 44]. The upregulation of hepcidin under proinflammatory conditions in overweight/obese mothers leads to impaired iron transfer to the placenta resulting in iron deficiency in the newborn [43].
Our study had some limitations. First, the study could not establish the causal pathway of the association between the predictors and explanatory variables. Second, dietary intake of mothers and children were not assessed. Data on the outcome measure of maternal overweight/obesity such as dietary intake, physical activity level, health, and nutrition status during pregnancy were not available. Third, the nutritional status of the mother was assessed using BMI only. BMI is less accurate than other methods such as waist-hip ratio, bioelectrical impedance technique, skinfold thickness, and DEXA methods to assess the type of overweight/obesity. Despite these limitations, the strengths of this study were the use of a population-based nationally representative sample. This study provided information on the combination of overweight/obese mother and undernourished child plus anemia with associated factors among mother-child pairs in the same household. These findings can provide relevant information to prioritize nutrition intervention programs in Nepal.
In conclusion, our study revealed a low prevalence of DBM and TBM in Nepal. Our results found that mothers having short stature, mothers from the richest family and older mothers are more prone to double and triple burden of malnutrition. The study results clearly suggest the need for the prevention of the short maternal stature through the promotion of the first 1000 days approach particularly focusing on the prevention of girl’s child stunting to break the intergenerational cycle of malnutrition. Also, nation-wide effective implementation of maternal health promotion interventions and nutrition education program would be a good strategy to prevent overweight/obesity and stunting among children under five years of age in Nepal. Likewise, our study findings also indicate that wealthier families should not be neglected in the prevention strategies of double and triple burden of malnutrition. The nutrition-sensitive and specific interventions need to be scaled up throughout the country for the timely prevention of DBM and TBM among the Nepalese mothers and children. Further research is needed to identify the causes and associated risk factors of the double and triple burden of malnutrition which helps to pave the way for sustainable prevention of the multiple forms of malnutrition in Nepal.
ANC: Antenatal care; AOR: Adjusted odds ratio; BMI: Body mass index; CI: Confidence interval; COR: Crude odds ratio; DBM: Double burden of malnutrition; DEXA: Dual-energy X-ray absorptiometry; DHS: Demographic and Health Surveys; EAs: Enumeration areas; NDHS: Nepal Demographic and Health Survey; OM/AC: Overweight/obese mother anemic child; OM/SC: Overweight/obese mother stunted child; OM/UC: Overweight/obese mother underweight child; OM/WC: Overweight/obese mother wasted child; SGA: Small for gestational age, TBM: Triple burden of malnutrition; PSU: Primary sampling unit
This study was a secondary analysis of the NDHS 2016 data thus no separate ethical approval was required. However, ethical clearance for the NDHS was obtained from the ethical review board of Nepal Health Research Council and the written informed consent was obtained from each of the participants as per the standard ethical guidelines of the DHS program.
Not applicable
Dataset used in this study is publicly available from the DHS website (URL:https://www.dhsprogram.com/data/available-datasets.cfm). Dataset modified for use in this paper is available upon reasonable request to the corresponding author.
The authors have declared that no competing interest exists.
This research received no specific financial support from any funding agencies.
Dev Ram Sunuwar
Roles: Research design, conceptualization idea, data extraction, data analysis, interpretation, software, writing an original draft, writing review and editing.
Devendra Raj Singh
Roles: Data analysis, interpretation, writing an original draft, writing review and editing.
Pranil Man Singh Pradhan
Roles: Supervision on writing original draft, reviewing and editing
We would like to thank the DHS program, ICF international for providing us the NDHS 2016 data set for analysis. We would also like to thank Mr. Umesh Ghimire from New ERA, who supported us in data compilation and management.
1 Department of Nutrition and Dietetics, Armed Police Force Hospital, Kathmandu, Nepal
2 Department of Public Health, Asian College for Advance Studies, Purbanchal University, Lalitpur, Nepal
3Southeast Asia Development Actions Network (SADAN), Lalitpur, Nepal
4Department of Community Medicine and Public Health, Institute of Medicine, Tribhuvan University, Kathmandu, Nepal
Table 1 Plan for data coding and description of the study variables.
Study variables |
Coding category for analysis |
Outcome variables |
|
Stunting (HAZ) |
0=normal HAZ/not stunted (HAZ -2SD and above) 1=stunted(HAZ<-2SD) |
Wasting (WHZ) |
0=normal WHZ/not wasted (WHZ-2SD to +2SD) 1=Wasted (WHZ <-2SD) |
Underweight(WAZ) |
0=Normal WAZ/not underweight (WHZ-2SD and above) 1=underweight (WAZ<-2SD) |
Child anemia |
0= normal/not anemic (hemoglobin level >11g/dl), 1=anemic (hemoglobin level <11g/dl) |
Mothers BMI (Continuous, calculated using measured height and weight) |
0 = Normal (18.5–24.9 kg/m2) 1 = Overweight/Obese (≥25 kg/m2) |
Double burden of malnutrition (DBM):overweight/obese mothers was paired with her child having one form of undernourished(stunted or wasted or underweight) |
0=normal or not overweight/obese mother and not undernourished child (stunted or wasted or underweight) 1=overweight/obese mother and undernourished child (stunted or wasted or underweight) |
Triple burden of malnutrition (TBM):overweight/obese mothers was paired with her child having one form of undernourished(stunted or wasted or underweight) plus anemic child |
0=normal or not overweight/obese mother and not undernourished child (stunted or wasted or underweight) plus not anemic child 1= overweight/obese mother and undernourished child (stunted or wasted or underweight) plus anemic child |
Table 2 Socio-demographic characteristics of the study participants (N=2,261)
Variables |
Mean+SD |
|
Mother's age (years) |
26.36+5.64 |
|
Child's age (months) |
29.01+17.37 |
|
Mother's age at 1st birth (year) |
19.83+3.34
|
|
Maternal factors |
characteristics |
n(%)a |
Age groups (years)
|
15-24 25-34 >35 |
951(41.14) 1,104(49.22) 206(9.64) |
Age at 1st birth (years)
|
<19 20-29 >30 |
1,190(52.77) 1,039(45.73) 32(1.50) |
Province |
Province 1 Province 2 Province 3 Province 4 Province 5 Province 6 Province 7 |
305(16.27) 467(25.89) 227(15.61) 232(8.01) 382(18.93) 341(6.57) 307(8.71) |
Education |
No education Primary Secondary higher |
744(33.68) 423(19.53) 744(32.36) 350(14.42) |
Occupation |
Agriculture No job Services |
1,120(46.69) 855(40.35) 286(12.95) |
Wealth status |
Poor Middle Rich |
1,052(42.04) 476(22.02) 733(35.94) |
Height |
Normal height Short stature |
2,046(90) 215(10) |
Delivery by CS
|
No Yes |
2071(90.2) 190(9.8) |
Father's occupation
|
Agriculture No job Services |
422(19.32) 952(41.9) 869(38.78) |
Father's education
|
No education Primary Secondary higher |
310(15.12) 509(22.79) 1000(43.86) 422(18.24) |
Child factors |
|
|
Child's age (months)
|
<12 months 13-23 months 24-35 months 36-47 months 48-59 months |
514(22.53) 435(19.18) 427(18.45) 452(20.11) 433(19.73) |
Child's sex
|
Male Female |
1193(52.71) 1068(47.29) |
Vitamin A
|
Yes No |
1688(74.52) 573(25.48) |
Deworming
|
Yes No |
1399(59.82) 862(40.18) |
Currently breastfeeding |
Yes No |
1824(78.8) 437(21.2) |
Birth weight
|
Average Large Small |
1510(67.72) 350(15.72) 396(16.61) |
OM/SC (n=1027) |
Not stunted Stunted |
949(91.7) 78(8.302) |
OM/UC(n=1138) |
Not underweight Underweight |
1100(96.63) 38(3.37) |
OM/WC(n=1350) |
Not wasted Wasted |
1335(98.75) 15(1.253) |
OM/AC(n=751) |
Not anemic Anemic |
625(81.11) 126(18.89) |
DBM(n=1529) |
Normal OWOBM/UC |
1437(93.4) 92(6.60) |
TBM(n=1381) |
Normal OWOBM/UC/AC |
1293(93) 88(7) |
a Frequency are unweighted; percentage are weighted
OM/SC: Overweight/obese mother and stunted child
OM/WC: Overweight/obese mother and wasted child
OM/UC: Overweight/obese mother and underweight child
OM/AC: Overweight/obese mother and anemic child
DBM: Double burden of malnutrition (Overweight/obese mother and undernourished child (stunted or wasted or underweight) at the same household
TBM: Triple burden of malnutrition (Overweight/obese mother and undernourished and anemic child) at the same household
CS: Caesarean section
Table 3 Bivariate and multivariable analysis of double and triple burden of malnutrition among mother-child pairs and its associated factors (n=2,261).
Maternal factors |
DBM |
|
TBM |
||
|
COR(95% CI) |
AOR(95% CI)b |
|
COR(95% CI) |
AOR(95% CI)c |
Age group 15-24 25-34 >35 |
1 1.61(0.87-2.98) 2.59(1.13-5.94)** |
1 1.59(0.83-3.03) 3.08(1.20-7.86)** |
|
1 1.64(0.88-3.08) 3.11(1.34-7.22)** |
1 1.96(1.04-3.71)* 3.41(1.26-9.17)** |
Age at 1st birth <19 years 20-29 years Above 30 years |
0.13(0.03-0.45)** 0.12(0.03-0.44)** 1 |
|
|
0.16(0.04-0.62)** 0.16(0.03-0.64)** 1 |
0.34(0.66-1.83) 0.18(0.03-1.06) 1 |
Province Province 1 Province 2 Province 3 Province 4 Province 5 Province 6 Province 7 |
0.89(0.40-2.00) 0.08(0.02-0.29)*** 1 0.73(0.31-1.71) 0.51(0.21-1.22) 0.24(0.07-.75)* 0.13(0.04-0.41)*** |
1.20(0.50-2.85) 0.13(0.03-0.47)*** 1 0.97(0.36-2.61) 0.65(0.25-1.72) 0.47(0.14-1.58) 0.24(0.07-0.86)* |
|
0.89(0.38-2.03) 0.10(0.03-0.33)*** 1 0.71(0.29-1.70) 0.57(0.23-1.38) 0.28(0.09-0.87)* 0.14(0.04-0.45)*** |
1.08(0.47-2.49) 0.11(0.03-0.41)*** 1 0.85(0.31-2.32) 0.65(0.27-1.59) 0.50(0.15-1.67) 0.23(0.06-0.82)* |
Education No education Primary Secondary higher |
1 1.14(0.55-2.34) 2.43(1.24-4.76)* 1.83(0.81-4.10) |
1 1.04(0.49-2.22) 2.05(1.03-4.07)* 1.04(0.43-2.49) |
|
1 1.09(0.53-2.27) 2.09(1.05-4.16)* 1.57(0.67-3.71) |
1 1.06(0.46-2.41) 2.05(1.00-4.18)* 1.43(0.53-3.84) |
Occupation Agriculture No job Services |
1 1.42(0.75-2.68) 2.82(1.37-5.81)* |
1 1.19(0.58-2.42) 1.34(0.63-2.86) |
|
1 1.33(0.70-2.52) 2.60(1.26-5.36)* |
1 1.10(0.53-2.26) 1.33(0.59-2.96) |
Wealth status Poor Middle Rich |
1 1.15(0.48-2.75) 2.89(1.50-5.54)*** |
1 1.42(0.54-3.71) 2.46(1.17-5.15)** |
|
1 0.98(0.38-2.49) 2.61(1.36-5.02)*** |
1 1.61(0.58-4.40) 2.66(1.28-5.54)*** |
Height Normal height Short stature |
1 3.19(1.59-6.39)*** |
1 4.18(2.04-8.52)*** |
|
1 4.38(2.17-8.86)*** |
1 5.01(2.45-10.24)*** |
Delivery by CS No Yes |
1 2.39(1.18-4.84)* |
1 1.44(0.68-3.04) |
|
1 1.95(0.90-4.25) |
|
Father's occupation Agriculture No job Services |
1 0.98(0.41-2.32) 1.24(0.53-2.87) |
|
|
1 1.01(0.40-2.52) 1.27(0.52-3.07) |
|
Father's education No education Primary Secondary higher |
1 1.81(0.58-5.75) 1.49(0.54-4.10) 1.64(0.56-4.80) |
|
|
1 1.84(0.54-6.17) 1.36(0.46-4.00) 1.51(0.48-4.71) |
|
Child factors |
|
|
|
|
|
Child's age <12 months 13-23 months 24-35 months 36-47 months 48-59 months |
1 1.63(0.80-3.30) 2.15(1.04-4.46)* 2.19(1.01-4.73)* 1.76(0.79-3.89) |
1 0.85(0.33-2.20) 1.06(0.39-2.86) 1.18(0.43-3.24) 0.77(0.26-2.26) |
|
1 2.13(0.99-4.57) 2.85(1.33-6.11)* 2.75(1.21-6.24)* 2.27(0.98-5.28) |
1 1.24(0.47-3.34) 1.60(0.56-4.57) 1.71(0.58-5.02) 1.17(0.36-3.75) |
Child's sex Male Female |
1 0.84(0.53-1.33) |
|
|
1 0.78(0.49-1.26) |
|
Vitamin A Yes No |
1 0.45(0.22-0.92)* |
1 0.70(0.29-1.69) |
|
1 0.40(0.19-0.86)* |
1 0.85(0.34-2.14) |
Deworming Yes No |
1 0.54(0.31-0.91)* |
1 0.71(0.32-1.56) |
|
1 0.49(0.28-0.86)* |
1 0.79(0.35-1.74) |
Currently breastfeeding Yes No |
1 1.97(1.10-3.51)* |
1 1.34(0.64-2.79) |
|
1 1.93(1.07-3.47)* |
1 1.24(0.59-2.60) |
Birth weight Average Large Small |
1 1.93(1.13-3.29)* 1.43(0.78-2.60) |
1 1.57(0.85-2.92) 1.46(0.77-2.75) |
|
1 1.61(0.90-2.87) 1.53(0.84-2.81) |
|
DBM: Double burden of malnutrition (Overweight/obese mother and undernourished child at the same household)
TBM: Triple burden of malnutrition (Overweight/obese mother and undernourished and anemic child at the same household)
1: reference category
COR: crude odds ratio, AOR: adjusted odds ratio
*p < 0.05, **p < 0.02, ***P<0.001
b This model was adjusted for mother's age groups, province, education, occupation, wealth status, mother's height, delivery by CS, child's age, vitamin A intake in the last 6 months, deworming, currently breast feeding, and birth weight
c This model was adjusted for mother's age groups, mother's age at 1st birth, province, education, occupation, wealth status, mother's height, child's age, vitamin A intake in the last 6 months, deworming, and currently breast feeding