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ORIGINAL ARTICLE
Year : 2018  |  Volume : 62  |  Issue : 2  |  Page : 82-88  

Dynamics in child undernutrition in Bangladesh: Evidence from nationally representative surveys between 1997 and 2014


1 National Centre for Epidemiology and Population Health, Research School of Population Health, College of Medicine, Biology and Environment, The Australian National University, Canberra, Australia
2 School of Management, Faculty of Business, Government and Law, University of Canberra, Canberra, Australia

Date of Web Publication14-Jun-2018

Correspondence Address:
Alice Marion Richardson
Building 62, Mills Road, Australian National University, Canberra, Act 2601
Australia
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijph.IJPH_153_17

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   Abstract 


Background: Bangladesh has been struggling to reduce the prevalence of childhood undernutrition, which impedes physical and mental capability and accelerates morbidity and mortality. Objectives: The objective of the paper is to examine the changes over time in the association between potential covariates and nutritional status of Bangladeshi children. Methods: The study combined and analyzed data from six waves of Demographic and Health Surveys between 1997 and 2014. Multivariable binary logistic regression models have been fitted to data from individual waves. Overall association has been investigated using forest plots, and meta-regression has been utilized to assess the pace of change in the association over time. Results: Parental education and place of residence showed a consistent association with nutritional status of children. Children from parents with no little education were more likely to be undernourished than those from parents with secondary or higher level of education (odds ratio [OR] in 1997 = 3.34, 95% confidence interval [CI] = 2.65–4.22, OR in 2004 = 1.93, 95% CI = 1.58–2.37). On the other hand, gaps in the association of wealth and childhood nutrition have been widening consistently so that in 2014 children from households from the lowest 40% wealth category were 2.66 times (OR = 2.66, 95% CI = 2.13–3.33) as likely as to be undernourished than those from upper 20%. Conclusions: The findings have policy implications in terms of developing programs directed to mothers with a relatively poor socioeconomic background. A specific example would be providing nutritional education in relation to importance of childhood nutrition or cheaper nutritious food.

Keywords: Forest plots, logistic regression, meta-regression, stunting


How to cite this article:
Hasan MM, Quazi A, Richardson AM. Dynamics in child undernutrition in Bangladesh: Evidence from nationally representative surveys between 1997 and 2014. Indian J Public Health 2018;62:82-8

How to cite this URL:
Hasan MM, Quazi A, Richardson AM. Dynamics in child undernutrition in Bangladesh: Evidence from nationally representative surveys between 1997 and 2014. Indian J Public Health [serial online] 2018 [cited 2018 Nov 16];62:82-8. Available from: http://www.ijph.in/text.asp?2018/62/2/82/234498




   Introduction Top


Undernourished children are more likely to suffer from diseases, die at a young age, or achieve lower mental capability throughout their lives. In 2011, one-third of 6.9 million preschooler deaths worldwide were due to malnutrition-related illnesses.[1] Poor nutritional status results in cognitive deficits linked to structural abnormalities of different parts of the brain,[2],[3] and consequently, such children perform worse in school.[4],[5] Despite advances in child health, undernutrition-related morbidity and mortality still remain a major public health challenge in developing countries.[6],[7]

Bangladesh has already achieved or is close to achieving the millennium development goals (MDG) in terms of under-five mortality, child immunization, school enrollment, and use of improved water and sanitation. However, with its very low reduction rate (1.27%/year) in childhood malnutrition, the country has been struggling to achieve the target for nutritional level of children set in the MDG.[8]

In developing countries, the socioeconomic background of parents has been found to influence the nutritional status of children significantly. Higher rates of stunting were reported among rural children in Nigeria and Iran;[9],[10] however, such differences were not obvious in Bangladesh or Nepal.[11] Importantly, rapid urbanization has been shown to cause increased urban poverty resulting in growing childhood undernutrition in the urban areas of Bangladesh.[12],[13] Children from richer families receive better food, care, and access to health facilities, leading to relatively better nutrition than those from poorer families.[13] Studies in the South Asian context documented increased risk for undernutrition for higher ordered births.[14] Nutrition status of Bangladeshi mothers as measured by body mass index (BMI) has shown significant positive association on the nutritional status of their children.[15],[16]

Parental educational attainment is strongly associated with nutrition status of their children and this association may be even more effective if nutrition-sensitive programs are introduced into school curricula.[17],[18] Well-planned campaigns through mass media can produce awareness regarding positive health-related behavioral changes across populations.[19] In Bangladesh and Indonesia, a mother's membership of micro-credit organization has shown significant association on health and nutrition of their children.[20],[21]

Characteristics of children also determine their nutritional status. Compared to infants, higher malnutrition rates have been observed in older children.[22],[23] Gender discrepancy in childhood malnutrition is also not uncommon in South Asia and sub-Saharan Africa.[24],[25] Although the incidence of morbidity showed consistent association with the prevalence of undernutrition of children, the relationship works in both directions.[22],[23] Bangladesh has been achieving significant economic growth resulting in a doubling of her per capita gross domestic product as well as a reduction in the proportion of the population below the poverty line by 17.5% in the recent decades.[26] Rapid development of social capital in contemporary Bangladesh has resulted in wider access to hygienic sanitation and safe drinking water. These positive changes are expected to reduce the overall prevalence of childhood undernutrition in the country. Furthermore, the abovementioned positive changes would minimize the gaps in the prevalence of childhood undernutrition among various socioeconomic subgroups. Studying and comparing information from multiple surveys (in the same country) over years, rather than a single survey, would help investigating the changes in such gaps over time.

In this research, children below − 2 standard deviations (SDs) of the reference population mean height-for-age are categorized as undernourished. This paper aims to examine the change over time in the association between socioeconomic background and nutritional status of children in Bangladesh. To achieve the aim, three objectives were set as follows: (1) to investigate the overall prevalence of undernutrition in Bangladeshi children below the age of 5 years at various time points, (2) to investigate the association of household, parent, and child characteristics with childhood undernutrition, and (3) to investigate changes over time in the direction and magnitude in the association. This study will have implications in deciding upon whether the changes in the predictor variables (happening over time) have been associated with a significant reduction in childhood undernutrition, and whether special intervention is required for specific subgroups.


   Materials and Methods Top


The data

The study utilized cross-sectional and nationally representative Bangladesh Demographic and Health Survey datasets. The surveys adopted multistage cluster sampling techniques to achieve nationally representative samples of ever-married women. Details of the sampling technique can be accessed in reports on respective surveys (http://dhsprogram.com/pubs/). For the sampled women, anthropometric information was recorded for living children aged below 5 years at the time of the surveys: between 4.9% and 9.2% of the children ever born to the sampled women had died before the age of five. The height and weight were measured except for those children who were not at home at the time of survey. Between 5.1% and 12.1% of children ever born were not at home at the time of the survey. Using height and weight, the height-for-age and weight-for-height Z-scores were calculated. Any implausibly low or high score was not reported in the published dataset, which accounts for another 1.9% to 5.9% of children ever born. The first-wave survey (1993–1994) was excluded from the analysis due to unavailability of anthropometric measurements of children. Finally, the analysis was conducted using complete and plausible (as described above) information from 36,252 children (4787 from 1997; 5419 from 2000; 5977 from 2004; 5217 from 2007; 7689 from 2011; and 7162 from 2014).

Dependent variable

Taking age and sex into consideration, the height and weight were converted into Z-scores based on the National Centre for Health Statistics reference population recommended by the World Health Organization. As described in the Introduction, children below – 2SDs of the reference population mean for height-for-age were considered as undernourished. Considering the fact that stunting is less affected than wasting by seasonal variations,[27] the current study defined childhood undernutrition on the basis of stunting.

Predictors (independent variables)

Variables related to the background of children, their parents, and the households they belong to were considered as possible predictors. Children were categorized as aged 0–11 months, 12–23 months, and 24–59 months, and as male or female. Finally, the children were categorized as those suffering or not suffering from at least one of three disease episodes in the last two weeks (short-rapid breath, diarrhea, and associated symptoms). The variable parental education was categorized as those with “no-little” (at best only one parent has completed primary), “primary” (both completed primary or one incomplete primary and other secondary or higher), and “secondary” (otherwise) education. Mother's exposure to media (radio, television, or newspaper) was considered as a dichotomous variable with the levels of “having” or “not having” access to any of these media. Due to unavailability of information regarding respondents' engagement with social media, the variable, exposure to media, did not take this specific media exposure into consideration. Membership status of women with any microcredit organizations (yes or no) was also recorded. Mothers were categorized as malnourished (BMI <18.5) and nourished (BMI >=18.5) to assess the association of a mother's nutrition status on the nutritional status of their children. Birth order of the children was considered in the study with two categories (1–2 and 3+). Based on scores obtained from principal component analysis, the study categorized households into lowest (lowest 40%), middle (next 40%), and upper (upper 20%) wealth groups (see other applications of this scoring [28],[29]).

Bivariate analysis

The dependent variable (stunting) considered in the study is dichotomous with levels nourished and undernourished. The predictor variables are also categorical. Bivariate Chi-square analyses were therefore carried out to investigate and compare the prevalence of undernutrition among the levels of predictors one at a time.

Multivariable logistic regression

Multivariable logistic regression models were fitted to assess the association of selected predictors with childhood nutrition. Separate models were fitted to the data from individual surveys, maintaining the same set of predictor variables each time.

Meta-analysis

A meta-analysis [30] was utilized to combine the effects from individual surveys. Using meta-analysis, the odds ratios (ORs) of undernutrition from individual studies were combined through a weighted average. The effects from individual studies along with the combined effect are represented by forest plots and the QE test of heterogeneity with associated P value. A small P value in the QE test (P < 0.05) indicates significant heterogeneity in the study effects. In such situations, meta-regression [31] can be used to explain heterogeneity in the effect sizes and confidence intervals (CIs) in terms of study characteristics, such as year. Considering the heterogeneity in the study effects, meta-regression is conducted to examine any changes in the association over time due to a particular predictor. The statistical analysis on the data was carried out using version 20.0 of SPSS software (IBM Corp, Armonk, NY, USA)[32] and the Rmeta package [33] of the open source statistical software R (R Foundation for Statistical Computing, Vienna, Austria).[34]


   Results Top


In this section, we discuss the results of the bivariate analysis of each variable against the outcome at each time point, the multiple logistic regression analyses and forest plots at each time point, and the meta-regression combining results from all time points.

Bivariate analysis

Consistent reduction in undernutrition has been observed over time (from 54.6% in 1997 to 29.1% in 2014). Except for gender of child and membership status of mother of micro-credit organizations, all other variables considered in the study showed consistent and significant association on childhood undernutrition [Table 1]. For children aged between 0 and 11 months, the percentage dropped from 22.7 in 1997 to 10.6 in 2014; whereas, for children aged between 24 and 49 months, the percentages were 63.5 and 33.0, respectively. In 1997, the percentages of undernourished children from three educational levels of parents (no-little, primary, and secondary educations) were 60.6, 41.0, and 26.3, respectively; those reduced to 38.6, 27.1, and 14.6, respectively, in 2014. The difference in the percentages of undernourished children from households with lowest and upper wealth groups became 22.8 in 2014 from 10.7 in 1997. Higher percentages of undernourished children were also reported in rural areas (56.2% in 1997–30.4% in 2014) than in urban areas (39.3% in 1997–25.0% in 2014).
Table 1: Percentage distribution of undernourished children over 1997-2014 for background characteristics of children and their parents

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Multivariable logistic regression

Children in the age groups of 12–23 months and 24–59 months possessed significantly higher adjusted odds of undernutrition with respect to those from the reference category (aged 0–11 months) [Table 2]. The odds of undernutrition for children from parents with no-little education with respect to those from parents with secondary education dropped significantly during 1997–2004 and then increased till the latest survey in 2014. With respect to children from secondary educated parents, those from parents with primary education were significantly more likely to be undernourished and the association was consistent over time. Nutritional status of a mother and her children were positively associated; children from undernourished mothers were 1.15 (95% CI = 1.03–1.30) to 1.42 (95% CI = 1.27–1.59) times as likely to be undernourished as those from nourished mothers. The hypothesis that membership of mothers of micro-credit organizations helps improve the nutritional status of their children was not supported by the study. In 1997, children living in rural areas were more likely (OR = 1.21; 95% CI = 0.95–1.53) to be undernourished than those residing at urban areas; however, the relationship reversed from 2004. Children from households with lowest wealth were more (in 1997, OR = 1.21; 95% CI = 1.01–1.46) likely to be undernourished than those from households in the higher wealth categories (OR = 2.66; 95% CI = 2.13–3.33 in 2014).
Table 2: Odds ratio (confidence interval) of undernutrition for children aged below 5 years for the surveys between 1997 and 2014

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Meta-analysis using forest plots

The forest plots representing the ORs for educational attainment of parents, place of residence, and wealth status of households are presented in [Figure 1]. According to the combined association, children from the parents with no-little education were more likely (OR = 2.19; 95% CI = 1.99–2.64) to be undernourished than those from parents with secondary education. Children from households with lowest wealth category were more likely (OR = 1.87; 95% CI = 1.52–2.31) to be undernourished than those from households with upper wealth category. Children from rural settings were less likely (OR = 0.93; 95% CI = 0.83–1.03) to be undernourished than those from urban settings. However, the QE test indicated significant heterogeneity in the study effects, and hence, the combined effects do not provide a representation of individual ORs.
Figure 1: Forest plot representing the individual and combined effect from the six studies (left panel). Right panel represents the trend in the odds ratio for undernutrition over time.

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Meta-regression

Three separate meta-regressions are reported that investigate the effect of time (year) on the OR of undernutrition relating to children from (i) parents with no-little versus secondary or higher educational status, (ii) urban versus rural residence, and (iii) lowest versus upper wealth category [right panels of [Figure 1]. The result indicates that the gaps in the odds of undernutrition among children from parents with no-little versus secondary or higher educational status are reducing over time; however, the pace of change is not statistically significant ([INSDE:1]). A negative and statistically significant change in ORs for undernutrition over time was observed for children from rural areas compared to those residing in urban areas ([INSDE:2]). Finally, a significant positive change in the odds of undernutrition for children from households with lowest wealth category compared to those from households with upper wealth category was evident from the meta-regression analysis ([INSDE:3]).


   Discussion Top


Older children possessed significantly higher risk of undernutrition than the younger. The result is supported by findings [22],[23] which showed that due to improper feeding practices, prevalence of undernutrition was higher in older children. Children from these older age categories need special attention, as they consistently possess higher risk of being undernourished with no improvements over time. Although the incidence of morbidity consistently showed significant association with the prevalence of childhood undernutrition, the association has been seen to work in both directions.[23] Female children were more likely to suffer from undernutrition than males and sex discrimination in childhood undernutrition was increasing in more recent waves of surveys.

By using household resources efficiently and providing better health care, educated parents contribute positively to maintaining better nutrition to their children. This result mirrors other research showing that children from parents with no-little education were significantly more undernourished than the children whose parents have secondary or higher level of education.[17] In the recent years, the odds of undernutrition for children whose parents have no-little education have diminished. These findings suggest that parents with low level of education are being better informed, leading to increase of their awareness about nutritional issues affecting their children. However, children with parents having primary level of education did not experience any reduction in nutritional level. The policy implications emerging from these findings would be to include nutritional aspects into the school curricula toward making nutritionally conscious parents. One study [11] found no difference in child nutrition in urban and rural areas of Bangladesh and Nepal. However, the current study showed that, from 2004, the urban children were more likely to be undernourished than those from rural areas. The fact is supported by others,[13] who concluded that rapid urbanization may increase urban poverty which leads to extremely poor quality of life in urban slums, resulting in increased child undernutrition in urban areas.

This research hypothesized that with decreasing percentages of people below the poverty line in contemporary Bangladesh, the gaps in childhood undernutrition among the wealth categories should reduce over time. In fact, this hypothesis has not been supported in terms of reduction of child undernutrition over time. This suggests that the observed decrease in the percentage of the Bangladeshi population below the poverty line has not changed the size of the differences in proportion of malnourished children between the lowest and upper wealth categories. The reasons for this state of affairs are unknown, which should be subject to further research.

Media has been found to play a critical role in enhancing social awareness among parents about nutritional information.[19] However, the association between exposure to media of a mother and nutritional status of her children was not found to be consistent over time. Undernourished mothers (BMI <18.5) are more likely to suffer from noncommunicable diseases and may fail to provide sufficient feeding and care to their children.[35] The result is supported by the current study where children of undernourished mother were more likely to suffer from undernutrition, and this association was increasing in the recent years.[16]


   Conclusions Top


This study was based on well-recognized, population-based, large-scale repeated surveys in Bangladesh. This study compared the association of predictors with undernutrition over regularly spaced multiple surveys to achieve a rich picture. Stunting was used as the main vehicle to characterize undernutrition.

The widening gaps in child nutrition among the children from households in various wealth categories indicate that the pace of improvement in nutritional level for children in the lowest wealth category was slower than the pace of improvement in nutritional level for those from upper wealth category. There should be specific plans to address this challenging and strategically important issue of national significance. More specifically, intensive collaborative programs initiated and implemented under the Bangladesh Government's public–private partnership program could help reduce this gap in nutritional achievements in contemporary Bangladesh.

The direction in the association of urban–rural residence on child undernutrition has reversed, and since 2004, rural children are performing better than their urban counterparts. This may be due to rapid urbanization which resulted in an urban population increase from 9.4% in 1997 to 22.1% in 2011. This population increase resulted from expansion of urban regions, as well as migration from rural areas. Appropriate programs aimed at increasing the nutrition level of children should be introduced in the newly developed urban areas and existing urban slums. Finally, programs need to be developed to ensure nutritional levels of mothers, who are instrumental in developing healthy children who would enter the workforce as able-bodied persons. These programs may be helpful to break the intergenerational cycle of malnutrition.

Limitations and future research

There are a couple of limitations of this research that will be noted here. The study was based on cross-sectional surveys and as such no direct trend analysis is possible, only the examination of changes over time was conducted here. Furthermore, any seasonal variation in the prevalence of undernutrition was not captured. In addition, some possible important predictors, such as food security and income of the household members, are missing in the analysis because the 1997 dataset does not have any provision for the above variables.

Future research can consider the possible seasonal variations in the pervasiveness of malnutrition toward overcoming uncertainties in nutritional estimates in Bangladeshi children. Furthermore, the percentage of missing data in the surveys varies from year to year. Future research can impute missing data or otherwise account for missing observations to test for the sensitivity of these results to missing observations.

Acknowledgment

The authors thank the respondents to the Bangladesh Demographic and Health Surveys and thank the Demographic and Health Surveys Program for giving permission to access the survey data online.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
   References Top

1.
Bhutta ZA, Salam RA. Global nutrition epidemiology and trends. Ann Nutr Metab 2012;61 Suppl 1:19-27.  Back to cited text no. 1
[PUBMED]    
2.
Wisniewski SL. Child nutrition, health problems, and school achievement in Sri Lanka. World Dev 2010;38:315-32.  Back to cited text no. 2
    
3.
Kitsao-Wekulo P, Holding P, Taylor HG, Abubakar A, Kvalsvig J, Connolly K, et al. Nutrition as an important mediator of the impact of background variables on outcome in middle childhood. Front Hum Neurosci 2013;7:713.  Back to cited text no. 3
    
4.
Hoddinott J, Alderman H, Behrman JR, Haddad L, Horton S. The economic rationale for investing in stunting reduction. Matern Child Nutr 2013;9 Suppl 2:69-82.  Back to cited text no. 4
[PUBMED]    
5.
Nisbett N, Gillespie S, Haddad L, Harris J. Why worry about the politics of childhood undernutrition? World Dev 2014;64:420-33.  Back to cited text no. 5
    
6.
McDonald CM, Olofin I, Flaxman S, Fawzi WW, Spiegelman D, Caulfield LE, et al. The effect of multiple anthropometric deficits on child mortality: Meta-analysis of individual data in 10 prospective studies from developing countries. Am J Clin Nutr 2013;97:896-901.  Back to cited text no. 6
[PUBMED]    
7.
Kouam CE, Delisle H, Ebbing HJ, Israël AD, Salpéteur C, Aïssa MA, et al. Perspectives for integration into the local health system of community-based management of acute malnutrition in children under 5 years: A qualitative study in Bangladesh. Nutr J 2014;13:22.  Back to cited text no. 7
    
8.
de Onis M, Dewey KG, Borghi E, Onyango AW, Blössner M, Daelmans B, et al. The World Health Organization's global target for reducing childhood stunting by 2025: Rationale and proposed actions. Matern Child Nutr 2013;9 Suppl 2:6-26.  Back to cited text no. 8
    
9.
Uthman OA. Decomposing socio-economic inequality in childhood malnutrition in Nigeria. Matern Child Nutr 2009;5:358-67.  Back to cited text no. 9
    
10.
Nouri Saeidlou S, Babaei F, Ayremlou P. Malnutrition, overweight, and obesity among urban and rural children in North of West Azerbijan, Iran. J Obes 2014;2014:541213.  Back to cited text no. 10
[PUBMED]    
11.
Srinivasan CS, Zanello G, Shankar B. Rural-urban disparities in child nutrition in Bangladesh and Nepal. BMC Public Health 2013;13:581.  Back to cited text no. 11
[PUBMED]    
12.
Paciorek CJ, Stevens GA, Finucane MM, Ezzati M; Nutrition Impact Model Study Group (Child Growth). Children's height and weight in rural and urban populations in low-income and middle-income countries: A systematic analysis of population-representative data. Lancet Glob Health 2013;1:e300-9.  Back to cited text no. 12
[PUBMED]    
13.
Giashuddin MS, Kabir M, Hasan M. Economic disparity and child nutrition in Bangladesh. Indian J Pediatr 2005;72:481-7.  Back to cited text no. 13
[PUBMED]    
14.
Raj A, McDougal LP, Silverman JG. Gendered effects of siblings on child malnutrition in South Asia: Cross-sectional analysis of demographic and health surveys from Bangladesh, India, and Nepal. Matern Child Health J 2015;19:217-26.  Back to cited text no. 14
[PUBMED]    
15.
Adekanmbi VT, Kayode GA, Uthman OA. Individual and contextual factors associated with childhood stunting in Nigeria: A multilevel analysis. Matern Child Nutr 2013;9:244-59.  Back to cited text no. 15
[PUBMED]    
16.
Khan RE, Raza MA. Nutritional status of children in Bangladesh: Measuring composite index of anthropometric failure (CIAF) and its determinants. Pak J Commerce Soc Sci 2014;8:11-23.  Back to cited text no. 16
    
17.
Shit S, Taraphdar P, Mukhopadhyay DK, Sinhababu A, Biswas AB. Assessment of nutritional status by composite index for anthropometric failure: A study among slum children in Bankura, West Bengal. Indian J Public Health 2012;56:305-7.  Back to cited text no. 17
[PUBMED]  [Full text]  
18.
Ruel MT, Alderman H; Maternal and Child Nutrition Study Group. Nutrition-sensitive interventions and programmes: How can they help to accelerate progress in improving maternal and child nutrition? Lancet 2013;382:536-51.  Back to cited text no. 18
[PUBMED]    
19.
Wakefield MA, Loken B, Hornik RC. Use of mass media campaigns to change health behaviour. Lancet 2010;376:1261-71.  Back to cited text no. 19
[PUBMED]    
20.
Adams AM, Rabbani A, Ahmed S, Mahmood SS, Al-Sabir A, Rashid SF, et al. Explaining equity gains in child survival in Bangladesh: Scale, speed, and selectivity in health and development. Lancet 2013;382:2027-37.  Back to cited text no. 20
[PUBMED]    
21.
Jalal CS, Frongillo EA. Effect of poverty reduction program on nutritional status of the extreme poor in Bangladesh. Food Nutr Bull 2013;34:402-11.  Back to cited text no. 21
[PUBMED]    
22.
Caulfield LE, Huffman SL, Piwoz EG. Interventions to improve intake of complementary foods by infants 6 to 12 months of age in developing countries: Impact on growth and on the prevalence of malnutrition and potential contribution to child survival. Food Nutr Bull 1999;20:183-200.  Back to cited text no. 22
    
23.
Ortiz J, Van Camp J, Wijaya S, Donoso S, Huybregts L. Determinants of child malnutrition in rural and urban Ecuadorian Highlands. Public Health Nutr 2014;17:2122-30.  Back to cited text no. 23
[PUBMED]    
24.
Khera R, Jain S, Lodha R, Ramakrishnan S. Gender bias in child care and child health: Global patterns. Arch Dis Child 2014;99:369-74.  Back to cited text no. 24
[PUBMED]    
25.
Wamani H, Astrøm AN, Peterson S, Tumwine JK, Tylleskär T. Boys are more stunted than girls in sub-Saharan Africa: A meta-analysis of 16 demographic and health surveys. BMC Pediatr 2007;7:17.  Back to cited text no. 25
    
26.
International Bank for Reconstruction and Development. Bangladesh Data: The World Bank; 2017. Available from: https://www.data.worldbank.org/country/bangladesh. [Last accessed on 2016 Jun 16].  Back to cited text no. 26
    
27.
Jeddere-Fisher K. Protecting and promoting food security and nutrition for families and children in Bangladesh. Available from: http://www.mdgfund.org/sites/default/files/Bangladesh%20-%20Nutrition%20-%20Mid-term%20Evaluation%20Report_0.pdf. [Last accessed on 2016 Jun 16].  Back to cited text no. 27
    
28.
Hasan MM, Richardson A. How sustainable household environment and knowledge of healthy practices relate to childhood morbidity in South Asia: Analysis of survey data from Bangladesh, Nepal and Pakistan. BMJ Open 2017;7:e015019.  Back to cited text no. 28
[PUBMED]    
29.
Kamal MM, Hasan MM, Davey R. Determinants of childhood morbidity in Bangladesh: Evidence from the demographic and health survey 2011. BMJ Open 2015;5:e007538.  Back to cited text no. 29
[PUBMED]    
30.
Hedges LV, Olkin I. Statistical Methods for Meta-Analysis. Oval Road, London: Academic Press Inc.; 1985.  Back to cited text no. 30
    
31.
Thompson SG, Higgins JP. How should meta-regression analyses be undertaken and interpreted? Stat Med 2002;21:1559-73.  Back to cited text no. 31
[PUBMED]    
32.
SPSS. IBM SPSS Statistics for Windows, Version 20.0. New York: IBM Corp.; 2011.  Back to cited text no. 32
    
33.
Lumley T. Rmeta: Meta-Analysis. Package Version 2.16; 2012. Available from: http://www.CRAN.R-project.org/package=rmeta. [Last accessed on 2016 Jun 16].  Back to cited text no. 33
    
34.
Team RC. R: A Language and Environment for Statistical Computing. Vienna, Austria; R Foundation for Statistical Computing. Available from: http://www.R-project.org/. [Last accessed on 2016 Jun 16].  Back to cited text no. 34
    
35.
Zahangir MS, Hasan MM, Richardson A, Tabassum S. Malnutrition and non-communicable diseases among Bangladeshi women: An urban-rural comparison. Nutr Diabetes 2017;7:e250.  Back to cited text no. 35
[PUBMED]    


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