JOURNAL OF TROPICAL PEDIATRICS, VOL. 60, NO. 3, 2014
Factors Associated with Malnutrition among Tribal Children in India: A Non-Parametric Approach by Avijit Debnath, and Nairita Bhattacharjee Department of Economics, Assam University, Silchar, Assam 788 011, India Correspondence: Avijit Debnath. Tel: +919435567735; Fax: 91-03842-270802; E-mail .
Summary The purpose of this study is to identify the determinants of malnutrition among the tribal children in India. The investigation is based on secondary data compiled from the National Family Health Survey3. We used a classification and regression tree model, a non-parametric approach, to address the objective. Our analysis shows that breastfeeding practice, economic status, antenatal care of mother and women’s decision-making autonomy are negatively associated with malnutrition among tribal children. We identify maternal malnutrition and urban concentration of household as the two risk factors for child malnutrition. The identified associated factors may be used for designing and targeting preventive programmes for malnourished tribal children. Key words: scheduled tribe, child malnutrition, classification and regression tree model, India.
Introduction Scheduled tribes (STs) comprise 8.2% of India’s population [1], and they mostly reside in forests and hilly terrains isolated from the other elite communities. In comparison with the wider community in India, this community has historically been subject to major social disadvantage and exclusion [2]. Recognizing their special needs, the Government of India has introduced several pro-ST policies and programmes; yet, the condition of the tribal community is far poorer than all of India, on average, in terms of most socioeconomic indicators. For instance, the National Family Health Survey-3 [3] reported that ST children have the poorest nutritional status in the nation. Malnutrition among children is considered as the key risk factor for adolescents’ illness, and it is responsible for about one-third deaths of children globally [4]. It also affects physical and mental development, resulting in lower levels of educational attainment [5]. Moreover, children affected by severe or chronic malnutrition also go on to suffer from diminished functional and intellectual capacity as adults [6]. There have been several studies that have tried to understand child malnutrition and its proximate determinants in India [7–9]. But in aggregate studies, the specific issues of various small communities are not properly and adequately addressed, and this is so true for the STs. There is a second level of problem that is related to the fact that existing studies have based their findings on statistical regression analysis [7,8]. These models have their own assumptions and predefined underlying relationships
between dependent and independent variables. If these assumptions are violated, such models lead to an erroneous estimation of parameters and a biased conclusion. Given this background, the present study seeks to identify the major factors associated with malnutrition among the tribal children in India using a non-parametric approach. Methods The investigation is based on secondary data compiled from the National Family Health Survey-3 [3], which is based on a nationally representative sample of 109 041 households and 124 385 women. Originally, the sample covers 99% of India’s population living in all 29 states. However, for our purpose, we have considered 24 states because data on relevant variables are not available for other states. Definition of variables used in the present study are given in Appendix Table A1. A classification and regression tree (C&RT) model has been used to analyse data. C&RT is a nonparametric technique, and thus it is free from restrictive assumptions of conventional statistical methods. Here, the dependent variable is split into a series of left and right child nodes derived from the primary nodes. When the split is terminated, child nodes are determined as terminal nodes. In general, the development of a C&RT model consists of four basic steps: specifying the criteria for predictive accuracy, selecting splits, determining when to stop splitting and selecting the ‘right-sized’ tree. Please refer to [10] for more details regarding the modelbuilding process of C&RT. Statistical analysis was
ß The Author [2014]. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected] doi:10.1093/tropej/fmt106 Advance Access published on 9 January 2014
211
A. DEBNATH AND N. BHATTACHARJEE
done using the statistical software STATISTICA 10 for Windows.
package
of malnutrition has been found to be lowest in the northeastern region and highest in the central region. Among states, the highest incidence and the lowest incidence of malnutrition have been observed in Madhya Pradesh and Assam, respectively. Rajasthan, Gujarat, Meghalaya, Uttar Pradesh, Jharkhand and Madhya Pradesh have been identified as states where the incidence is higher than the national average.
Results Prevalence of child malnutrition in India: spatial variation We observed significant spatial variation in malnutrition among tribal children (Fig. 1). The incidence
India South KER KAR ANP West MAHA GUJ GOA Northeast TRI SK NAG MIZ MEGH MAN AS AP East WB OR JHK Central UP MP CHH North UTT RAJ JK HP 0
5
10
15
20
25
30
35
40
45
Incidence of Malnutrition (in %) FIG. 1. Spatial variation of child malnutrition. Note: North (NR) includes Himachal Pradesh (HP), Jammu and Kashmir (JK), Rajasthan (RAJ) and Uttaranchal (UTT). Central (CR) includes Chhattisgarh (CHH), Madhya Pradesh (MP) and Uttar Pradesh (UP). East (ER) includes Jharkhand (JHA), Orissa (OR) and West Bengal (WB). Northeast (NER) includes Arunachal Pradesh (AP), Assam (AS), Manipur (MAN), Meghalaya (MEGH), Mizoram (MIZO), Nagaland (NAG), Sikkim (SK) and Tripura(TR). West (WR) includes Goa (GOA), Gujarat (GUJ) and Maharashtra (MAHA). South (SR) includes Andhra Pradesh (ANDRA), Karnataka (KAR) and Kerala (KER). 212
Journal of Tropical Pediatrics
Vol. 60, No. 3
A. DEBNATH AND N. BHATTACHARJEE
ID=1
N=24 Mu=20.67 Var=103.16 WI
5.65 N=11
N=13
Mu=28.08
Mu=14.40
Var=46.34
Var=65.54
AC
MM
26.95 N=3
ID=5
46.50 N=10
ID=7
N=3
Mu=37.96
Mu=24.37
Mu=11.87
Mu=22.86
Var=11.21
Var=9.13
Var=52.56
Var=15.80
URB 38.95 N=6
ID=9
N=4
Mu=8.50
Mu=16.92
Var=11.18
Var=72.04
FIG. 2. Regression tree. Note: We allowed splitting to continue until all terminal nodes became pure or contained no more cases than 5% of the sizes of one or more classes. Factors affecting nutritional status of ST children Figure 2 describes the typical output of a tree model analysis. The analysis proceeds in a divisive way. On top of the figure, there is the root node of the tree model, which contains all the 24 available observations with a mean value of around 20%. At the outset, the malnutrition status is divided into two groups, according to the value of the wealth index (WI). Subsequently, node 2 is further split according to the variable ‘antenatal care (AC)’ and node 3 is separated according to the variable ‘maternal malnutrition status (MM)’. In total there are five terminal nodes. These terminal nodes also called ‘leaves’ contain the main information conveyed by our tree model analysis. To determine which variables are the most important, each variable is ranked as to the order of its importance in Table 1. As provided in Table 1, breastfeeding practice (BF) turned out to be the most important variable, followed by WI and AC, in predicting incidence of child malnutrition in the ST community in India. The variable ‘urban concentration of household’ got the last place in the ranking. ‘Importance’ in Table 1 gives us an overall expression of the importance of a variable among all the splits in the tree. Note that the variable with the highest importance Journal of Tropical Pediatrics
Vol. 60, No. 3
TABLE 1 Ranking of key variables affecting child malnutrition Variable
Importance
BF WI AC MM DMA URB
1.00 0.96 0.96 0.86 0.58 0.38
value (WI) is not necessarily the variable used for the first splits; rather, it draws its importance from its participation in many splits in the tree [11]. It may also be mentioned that unlike a linear regression model, a variable in a regression tree modelling can be considered highly important even if it never appears in the tree structure [12]. Thus, although decision-making autonomy of women, used as a proxy of women empowerment, and breastfeeding practice do not appear in the regression tree (Fig. 2), yet they turn out to be two important variables affecting child malnutrition in the tribal community. 213
A. DEBNATH AND N. BHATTACHARJEE
Discussion We observed a considerable variation in incidence of malnutrition among the tribal children across major regions and states in India. The highest incidence and lowest incidence have been observed in Madhya Pradesh and Assam, respectively. Our C&RT analysis shows that the most important factor that influences child malnutrition is BF. This result is supported by other empirical studies that have found that breastfeeding is crucial to improve the health status of children [13]. WI has been identified as the second important variable affecting child malnutrition, and it has a number of interactions with other variables. We found that in states where a relatively higher percentage (at least 26%) of mothers receive AC from a health professional, incidence of child malnutrition is less (node 5) even if those households do not belong to the highest wealth quintile. This clearly shows that if the medical support provided to women, especially during pregnancy, is improved, the risk of child malnutrition may be minimized even when the target group does not belong to a very affluent economic class. Consistent with our findings, there are studies that have found that inadequate AC during pregnancy is a significant predictor for delivery of a low-birth-weight neonate [14]. Maternal health and place of residence are the two other variables that interact with WI to determine child malnutrition. It has been observed that states where higher percentages (more than 47%) of women suffer from malnutrition, the incidence of child malnutrition is quite high (node 7) even if those states happen to be one where a relatively higher percentage of ST households belong to the highest wealth quintile. This indicates that if the economic status of the household is not conditioned by better maternal health, the risk of child malnutrition does not get reduced. In agreement with our findings, other scholars [5,6,14] also observed that maternal nutritional status and WI are positively associated with childhood nutrition. It may further be noted that states with a relatively better economic status of STs and lower incidence of maternal malnutrition exhibit lowest incidence of child malnutrition (8.5%), provided comparatively lower percentage of households reside in an urban location (node 8). But if a higher proportion of them reside in an urban location, the incidence of malnutrition increases to around 17% (node 9). This might seem little unusual, given the fact that urban locations generally have better disease prevention forces. But we have to remember that one has to have enough resources to purchase those comforts of urbanization. There are at least two reasons that may be put in favour of our findings. First, ST people who migrate to urban areas in search of work generally have lower purchasing power, and hence it becomes difficult for them to get better access to quality health care, improved 214
water and sanitation systems and derive benefits from urbanization. Second, people living in rural locations, especially those who belong to a lower social class, do get some cost-free health facilities from the government and hence may fight malnutrition in a better manner. For instance, India has recently launched the National Rural Health Mission with the primary goal of improving the availability of and access to quality health care for people residing in rural areas. The present study suggests that malnutrition among the tribal children is the result of a nexus of multiple factors. Besides programmes that offer direct nutritional support, emphasis should also be given to improvement of BF, economic status, maternal nutrition and access to medical care especially during pregnancy. In contrast to the conventional thought, we found that the condition of tribal children is far more critical in urban locations, and hence, policymakers aimed at improving health of these children should not assume that people from urban locations require lesser attention, especially those who belong to a lower social class. References 1. India Census. 2011. Provisional Population Totals Paper of 2011. http://www.censusindia.gov.in/ (14 January 2013, date last accessed). 2. Dunn D. Gender inequality in education and employment in the scheduled castes and tribes of India. Popul Res Policy Rev 1993;12:53–70. 3. International Institute for Population Sciences (IIPS) and Macro International. National Family Health Survey (NFHS-3), 2005 06: India. Mumbai: IIPS. http://www.rchiips.org/nfhs/ (12 June 2012, date last accessed). 4. WHO. Levels and Trends in Child Mortality, Report 2010. http://www.childmortality.org/files_v11/download/Levels%20and%20Trends%20in%20Child% 20Mortality%20Report%202010.pdf (24 December 2012, date last accessed). 5. Ahmed T, Roy S, Alam N, et al. Determinants of undernutrition in children under 2 years of age from rural Bangladesh. Indian Pediatr 2012;49:821–24. 6. Aaron L, Robert EK, Carlos HD, et al. Effects of maternal nutrition on infant health: implications for action: Report of An International Workshop, Panajachel, Guatemala, March 12–16, 1979. J Trop Pediatr 1982;28:273–86. 7. Pal S. An analysis of childhood malnutrition in rural India: role of gender, income and other household characteristics. World Dev 1999;27: 1151–71. 8. Radhakrishna R, Ravi C. Malnutrition in India: trends and determinants. Econ Polit Wkly 2004;39: 671–76. 9. Kanjilal B, Mazumdar PG, Mukherjee M, et al. Nutritional status of children in India: household socio-economic condition as the contextual determinant. Int J Equity Health 2010;9:1–22. Journal of Tropical Pediatrics
Vol. 60, No. 3
A. DEBNATH AND N. BHATTACHARJEE
10. Breiman L, Friedman JH, Olshen RA, et al. Classification and Regression Trees. 1st edn. CA: Wadsworth, 1984. 11. Nisbet R, Elder J IV, Miner G. Handbook of Statistical Analysis and Data Mining Applications London: Academic Press, 2009. 12. Speybroeck N, Berkvens D, Mfoukou-Ntsakala A, et al. Classification trees versus multinomial models in
the analysis of urban farming systems in Central Africa. Agric Syst 2004;80:133–49. 13. Kumar D, Goel NK, Mittal PC, et al. Influence of infant-feeding practices on nutritional status of underfive children. Indian J Pediatr 2006;73:417–21. 14. Nair NS, Rao RP, Chandrashekar S, et al. Socio-demographic and maternal determinants of low birth weight: a multivariate approach. Indian J Pediatr 2000;67:9–14.
Appendix TABLE A1 Description of variables Variable
Definition
Child malnutrition (Mu)
Percentage of children aged
Factors Associated with Malnutrition among Tribal Children in India: A Non-Parametric Approach by Avijit Debnath, and Nairita Bhattacharjee Department of Economics, Assam University, Silchar, Assam 788 011, India Correspondence: Avijit Debnath. Tel: +919435567735; Fax: 91-03842-270802; E-mail .
Summary The purpose of this study is to identify the determinants of malnutrition among the tribal children in India. The investigation is based on secondary data compiled from the National Family Health Survey3. We used a classification and regression tree model, a non-parametric approach, to address the objective. Our analysis shows that breastfeeding practice, economic status, antenatal care of mother and women’s decision-making autonomy are negatively associated with malnutrition among tribal children. We identify maternal malnutrition and urban concentration of household as the two risk factors for child malnutrition. The identified associated factors may be used for designing and targeting preventive programmes for malnourished tribal children. Key words: scheduled tribe, child malnutrition, classification and regression tree model, India.
Introduction Scheduled tribes (STs) comprise 8.2% of India’s population [1], and they mostly reside in forests and hilly terrains isolated from the other elite communities. In comparison with the wider community in India, this community has historically been subject to major social disadvantage and exclusion [2]. Recognizing their special needs, the Government of India has introduced several pro-ST policies and programmes; yet, the condition of the tribal community is far poorer than all of India, on average, in terms of most socioeconomic indicators. For instance, the National Family Health Survey-3 [3] reported that ST children have the poorest nutritional status in the nation. Malnutrition among children is considered as the key risk factor for adolescents’ illness, and it is responsible for about one-third deaths of children globally [4]. It also affects physical and mental development, resulting in lower levels of educational attainment [5]. Moreover, children affected by severe or chronic malnutrition also go on to suffer from diminished functional and intellectual capacity as adults [6]. There have been several studies that have tried to understand child malnutrition and its proximate determinants in India [7–9]. But in aggregate studies, the specific issues of various small communities are not properly and adequately addressed, and this is so true for the STs. There is a second level of problem that is related to the fact that existing studies have based their findings on statistical regression analysis [7,8]. These models have their own assumptions and predefined underlying relationships
between dependent and independent variables. If these assumptions are violated, such models lead to an erroneous estimation of parameters and a biased conclusion. Given this background, the present study seeks to identify the major factors associated with malnutrition among the tribal children in India using a non-parametric approach. Methods The investigation is based on secondary data compiled from the National Family Health Survey-3 [3], which is based on a nationally representative sample of 109 041 households and 124 385 women. Originally, the sample covers 99% of India’s population living in all 29 states. However, for our purpose, we have considered 24 states because data on relevant variables are not available for other states. Definition of variables used in the present study are given in Appendix Table A1. A classification and regression tree (C&RT) model has been used to analyse data. C&RT is a nonparametric technique, and thus it is free from restrictive assumptions of conventional statistical methods. Here, the dependent variable is split into a series of left and right child nodes derived from the primary nodes. When the split is terminated, child nodes are determined as terminal nodes. In general, the development of a C&RT model consists of four basic steps: specifying the criteria for predictive accuracy, selecting splits, determining when to stop splitting and selecting the ‘right-sized’ tree. Please refer to [10] for more details regarding the modelbuilding process of C&RT. Statistical analysis was
ß The Author [2014]. Published by Oxford University Press. All rights reserved. For Permissions, please email: [email protected] doi:10.1093/tropej/fmt106 Advance Access published on 9 January 2014
211
A. DEBNATH AND N. BHATTACHARJEE
done using the statistical software STATISTICA 10 for Windows.
package
of malnutrition has been found to be lowest in the northeastern region and highest in the central region. Among states, the highest incidence and the lowest incidence of malnutrition have been observed in Madhya Pradesh and Assam, respectively. Rajasthan, Gujarat, Meghalaya, Uttar Pradesh, Jharkhand and Madhya Pradesh have been identified as states where the incidence is higher than the national average.
Results Prevalence of child malnutrition in India: spatial variation We observed significant spatial variation in malnutrition among tribal children (Fig. 1). The incidence
India South KER KAR ANP West MAHA GUJ GOA Northeast TRI SK NAG MIZ MEGH MAN AS AP East WB OR JHK Central UP MP CHH North UTT RAJ JK HP 0
5
10
15
20
25
30
35
40
45
Incidence of Malnutrition (in %) FIG. 1. Spatial variation of child malnutrition. Note: North (NR) includes Himachal Pradesh (HP), Jammu and Kashmir (JK), Rajasthan (RAJ) and Uttaranchal (UTT). Central (CR) includes Chhattisgarh (CHH), Madhya Pradesh (MP) and Uttar Pradesh (UP). East (ER) includes Jharkhand (JHA), Orissa (OR) and West Bengal (WB). Northeast (NER) includes Arunachal Pradesh (AP), Assam (AS), Manipur (MAN), Meghalaya (MEGH), Mizoram (MIZO), Nagaland (NAG), Sikkim (SK) and Tripura(TR). West (WR) includes Goa (GOA), Gujarat (GUJ) and Maharashtra (MAHA). South (SR) includes Andhra Pradesh (ANDRA), Karnataka (KAR) and Kerala (KER). 212
Journal of Tropical Pediatrics
Vol. 60, No. 3
A. DEBNATH AND N. BHATTACHARJEE
ID=1
N=24 Mu=20.67 Var=103.16 WI
5.65 N=11
N=13
Mu=28.08
Mu=14.40
Var=46.34
Var=65.54
AC
MM
26.95 N=3
ID=5
46.50 N=10
ID=7
N=3
Mu=37.96
Mu=24.37
Mu=11.87
Mu=22.86
Var=11.21
Var=9.13
Var=52.56
Var=15.80
URB 38.95 N=6
ID=9
N=4
Mu=8.50
Mu=16.92
Var=11.18
Var=72.04
FIG. 2. Regression tree. Note: We allowed splitting to continue until all terminal nodes became pure or contained no more cases than 5% of the sizes of one or more classes. Factors affecting nutritional status of ST children Figure 2 describes the typical output of a tree model analysis. The analysis proceeds in a divisive way. On top of the figure, there is the root node of the tree model, which contains all the 24 available observations with a mean value of around 20%. At the outset, the malnutrition status is divided into two groups, according to the value of the wealth index (WI). Subsequently, node 2 is further split according to the variable ‘antenatal care (AC)’ and node 3 is separated according to the variable ‘maternal malnutrition status (MM)’. In total there are five terminal nodes. These terminal nodes also called ‘leaves’ contain the main information conveyed by our tree model analysis. To determine which variables are the most important, each variable is ranked as to the order of its importance in Table 1. As provided in Table 1, breastfeeding practice (BF) turned out to be the most important variable, followed by WI and AC, in predicting incidence of child malnutrition in the ST community in India. The variable ‘urban concentration of household’ got the last place in the ranking. ‘Importance’ in Table 1 gives us an overall expression of the importance of a variable among all the splits in the tree. Note that the variable with the highest importance Journal of Tropical Pediatrics
Vol. 60, No. 3
TABLE 1 Ranking of key variables affecting child malnutrition Variable
Importance
BF WI AC MM DMA URB
1.00 0.96 0.96 0.86 0.58 0.38
value (WI) is not necessarily the variable used for the first splits; rather, it draws its importance from its participation in many splits in the tree [11]. It may also be mentioned that unlike a linear regression model, a variable in a regression tree modelling can be considered highly important even if it never appears in the tree structure [12]. Thus, although decision-making autonomy of women, used as a proxy of women empowerment, and breastfeeding practice do not appear in the regression tree (Fig. 2), yet they turn out to be two important variables affecting child malnutrition in the tribal community. 213
A. DEBNATH AND N. BHATTACHARJEE
Discussion We observed a considerable variation in incidence of malnutrition among the tribal children across major regions and states in India. The highest incidence and lowest incidence have been observed in Madhya Pradesh and Assam, respectively. Our C&RT analysis shows that the most important factor that influences child malnutrition is BF. This result is supported by other empirical studies that have found that breastfeeding is crucial to improve the health status of children [13]. WI has been identified as the second important variable affecting child malnutrition, and it has a number of interactions with other variables. We found that in states where a relatively higher percentage (at least 26%) of mothers receive AC from a health professional, incidence of child malnutrition is less (node 5) even if those households do not belong to the highest wealth quintile. This clearly shows that if the medical support provided to women, especially during pregnancy, is improved, the risk of child malnutrition may be minimized even when the target group does not belong to a very affluent economic class. Consistent with our findings, there are studies that have found that inadequate AC during pregnancy is a significant predictor for delivery of a low-birth-weight neonate [14]. Maternal health and place of residence are the two other variables that interact with WI to determine child malnutrition. It has been observed that states where higher percentages (more than 47%) of women suffer from malnutrition, the incidence of child malnutrition is quite high (node 7) even if those states happen to be one where a relatively higher percentage of ST households belong to the highest wealth quintile. This indicates that if the economic status of the household is not conditioned by better maternal health, the risk of child malnutrition does not get reduced. In agreement with our findings, other scholars [5,6,14] also observed that maternal nutritional status and WI are positively associated with childhood nutrition. It may further be noted that states with a relatively better economic status of STs and lower incidence of maternal malnutrition exhibit lowest incidence of child malnutrition (8.5%), provided comparatively lower percentage of households reside in an urban location (node 8). But if a higher proportion of them reside in an urban location, the incidence of malnutrition increases to around 17% (node 9). This might seem little unusual, given the fact that urban locations generally have better disease prevention forces. But we have to remember that one has to have enough resources to purchase those comforts of urbanization. There are at least two reasons that may be put in favour of our findings. First, ST people who migrate to urban areas in search of work generally have lower purchasing power, and hence it becomes difficult for them to get better access to quality health care, improved 214
water and sanitation systems and derive benefits from urbanization. Second, people living in rural locations, especially those who belong to a lower social class, do get some cost-free health facilities from the government and hence may fight malnutrition in a better manner. For instance, India has recently launched the National Rural Health Mission with the primary goal of improving the availability of and access to quality health care for people residing in rural areas. The present study suggests that malnutrition among the tribal children is the result of a nexus of multiple factors. Besides programmes that offer direct nutritional support, emphasis should also be given to improvement of BF, economic status, maternal nutrition and access to medical care especially during pregnancy. In contrast to the conventional thought, we found that the condition of tribal children is far more critical in urban locations, and hence, policymakers aimed at improving health of these children should not assume that people from urban locations require lesser attention, especially those who belong to a lower social class. References 1. India Census. 2011. Provisional Population Totals Paper of 2011. http://www.censusindia.gov.in/ (14 January 2013, date last accessed). 2. Dunn D. Gender inequality in education and employment in the scheduled castes and tribes of India. Popul Res Policy Rev 1993;12:53–70. 3. International Institute for Population Sciences (IIPS) and Macro International. National Family Health Survey (NFHS-3), 2005 06: India. Mumbai: IIPS. http://www.rchiips.org/nfhs/ (12 June 2012, date last accessed). 4. WHO. Levels and Trends in Child Mortality, Report 2010. http://www.childmortality.org/files_v11/download/Levels%20and%20Trends%20in%20Child% 20Mortality%20Report%202010.pdf (24 December 2012, date last accessed). 5. Ahmed T, Roy S, Alam N, et al. Determinants of undernutrition in children under 2 years of age from rural Bangladesh. Indian Pediatr 2012;49:821–24. 6. Aaron L, Robert EK, Carlos HD, et al. Effects of maternal nutrition on infant health: implications for action: Report of An International Workshop, Panajachel, Guatemala, March 12–16, 1979. J Trop Pediatr 1982;28:273–86. 7. Pal S. An analysis of childhood malnutrition in rural India: role of gender, income and other household characteristics. World Dev 1999;27: 1151–71. 8. Radhakrishna R, Ravi C. Malnutrition in India: trends and determinants. Econ Polit Wkly 2004;39: 671–76. 9. Kanjilal B, Mazumdar PG, Mukherjee M, et al. Nutritional status of children in India: household socio-economic condition as the contextual determinant. Int J Equity Health 2010;9:1–22. Journal of Tropical Pediatrics
Vol. 60, No. 3
A. DEBNATH AND N. BHATTACHARJEE
10. Breiman L, Friedman JH, Olshen RA, et al. Classification and Regression Trees. 1st edn. CA: Wadsworth, 1984. 11. Nisbet R, Elder J IV, Miner G. Handbook of Statistical Analysis and Data Mining Applications London: Academic Press, 2009. 12. Speybroeck N, Berkvens D, Mfoukou-Ntsakala A, et al. Classification trees versus multinomial models in
the analysis of urban farming systems in Central Africa. Agric Syst 2004;80:133–49. 13. Kumar D, Goel NK, Mittal PC, et al. Influence of infant-feeding practices on nutritional status of underfive children. Indian J Pediatr 2006;73:417–21. 14. Nair NS, Rao RP, Chandrashekar S, et al. Socio-demographic and maternal determinants of low birth weight: a multivariate approach. Indian J Pediatr 2000;67:9–14.
Appendix TABLE A1 Description of variables Variable
Definition
Child malnutrition (Mu)
Percentage of children aged
