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Associations between feeding practices and growth and neurodevelopmental outcomes at 36 months among children living in low- and low-middle income countries who participated in the BRAIN-HIT trial

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BMC Nutrition20184:19

https://doi.org/10.1186/s40795-018-0228-9

Received: 20 April 2017

Accepted: 12 April 2018

Published: 25 April 2018

Abstract

Background

Feeding practices over the first several years of a child’s life can critically influence development. The purpose of this study was to examine associations between feeding practices and growth and neurodevelopmental outcomes at 36 months of age among children from low- and low-middle-income countries (LMIC).

Methods

We conducted a secondary analysis using data collected from children in India, Pakistan, and Zambia who were enrolled in a randomized controlled trial of a home-based early development intervention program called Brain Research to Ameliorate Impaired Neurodevelopment Home-based Intervention Trial. Qualitative dietary data collected at 36 months was used to assess the modified Minimum Acceptable Diet (mMAD), a measure based on a core indicator developed by the World Health Organization to measure whether young children receive the minimum number of meals recommended and adequate diversity of major food groups in their diet. Regression models were used to assess cross-sectional associations between diet and growth indices, including Z-scores for height-for-age (HAZ), weight-for-age (WAZ), weight-for-height (WHZ), head circumference (HCZ), and Bayley Scales of Infant Development II mental and psychomotor developmental measures at 36 months of age.

Results

Of 371 children, 174 (47%) consumed the mMAD, with significantly higher mean adjusted WHZ than those who did not meet mMAD (0.20 vs − 0.08, p = 0.05). Egg consumption was found to be significantly associated with a decreased risk of wasting [adjusted RR (95% CI): 0.37 (0.15, 0.89), p = 0.03]. HCZ at 36 months did not differ significantly for children who did and did not receive the mMAD.

Conclusion

Meeting the mMAD was associated with better weight-for-height outcomes at 36 months in children in these three LMIC, highlighting the importance of adequate food quantity and quality.

Trial registration

NCT00639184 registered on March 20, 2008.

Keywords

  • Bayley scores of infant development
  • Developmental outcome
  • Stunting
  • Wasting
  • Dietary diversity
  • Low- and middle-income countries

Background

According to the World Health Organization (WHO), approximately 45% of the 5.9 million deaths of children under the age of 5 years in 2015 were linked to undernutrition [1]. While undernutrition may not be the main cause of death for children in this age group, it is an important underlying contributing factor that increases a child’s susceptibility to severe diseases and can have a large impact on a surviving child’s developmental outcome. Several studies have shown that poor young child feeding (YCF) practices are a risk factor for wasting, defined as weight-for-height Z-scores < -2SD, often the result of insufficient amounts of food and/or infectious diseases [24]. Additionally, as many as one in three children under 5 years in low- and low-middle-income countries (LMIC) are stunted (height-for-age Z-scores < -2SD), due to chronic undernutrition [5]. Stunting has not only been associated with increased risk of mortality, but also heightened risk of morbidity, delayed motor development and impaired cognition, potentially affecting long-term health outcomes [68]. Thus, considering the vital role of nutrition in child development, appropriate YCF practices are critical in the physical growth and cognitive development of children living in disadvantaged environments [9].

In 2008, WHO published a set of eight core indicators aimed at improving YCF practices in children 0–23 months of age and, ultimately, child survival [10]. These indicators focus on breastfeeding practices up to 1 year of age and the introduction of complementary foods around 6–8 months, as well as meal frequency and dietary diversity (general indicators of adequate energy and micronutrient intakes, respectively) in children aged 6–23 months.

We used data collected during the Brain Research to Ameliorate Impaired Neurodevelopment Home-based Intervention Trial (BRAIN-HIT) to conduct a secondary analysis to assess associations between YCF practices and growth and developmental outcomes at 36 months of age in children from three LMIC. While the YCF indicators were designed for younger children, this study had the unique opportunity to not only examine potential growth outcomes in slightly older children, but also to explore the relationship between YCF indicators and Bayley Scales of Infant Development – Second Edition (BSID-II) Mental Developmental Index (MDI) and Psychomotor Development Index (PDI) scores. We hypothesized that meeting YCF indicators will have positive associations with growth, and mental and psychomotor developmental outcomes.

Methods

Study population

Children studied were enrolled in the FIRST BREATH trial [11] and participated in the follow-on BRAIN-HIT (ClinicalTrials.gov: NCT00639184). BRAIN-HIT was a parallel-randomized controlled trial conducted in rural communities in India, Pakistan, and Zambia from 2007 to 2010 that aimed to determine whether a home-based, parent-provided early developmental intervention (EDI) plus WHO Enhanced Health Education Counseling (HC) would improve the BSID-II MDI scores at 36 months when compared to HC only in infants who have had birth asphyxia. The trial constituted two treatment populations: (1) babies who had mild-moderate birth asphyxia and were resuscitated via bag and mask ventilation and (2) non-resuscitated babies without perinatal complications who served as the healthy comparison group. Mild-moderate birth asphyxia was defined as insufficient breathing at birth and needing positive pressure ventilation. Infants were eligible for BRAIN-HIT if they met the following criteria: (1) weighed at least 1500 g at birth, (2) had a normal neurological examination (Stage I or II on the Ellis scale), and (3) were willing to participate in an intervention program for 36 months. Infants were ineligible if the mother was not contacted within 7 days of giving birth, younger than 15 years of age, unable/unwilling to participate, or not planning to stay in the study communities for the subsequent three years.

BRAIN implemented a modified version of the WHO Integrated Management of Childhood Illnesses Program (IMCI) [12] for the Health and Safety Counseling curriculum used in both arms of the trial. At enrollment and the 2-week visit, demographics and family resources were noted. At 12-, 24- and 36-months of age, information on family resources, health status and growth measurements were collected as well as neurodevelopmental assessments for BSID-II MDI and PDI scores. Child diet information was collected at 36 months of age. Full details of the BRAIN-HIT protocol have been published [13].

Among the 540 infants screened, 438 (81%) were eligible for participation with 407 (93%) of the eligible infants having mothers who consented to participate in the study [14]. Among infants whose mothers consented, 371 (91%) had completed dietary forms and health evaluations at 36 months and are included in the current analysis (Fig. 1).
Figure 1
Fig. 1

Cohort

Dietary methods and indicators

Young child feeding practices were evaluated during the last visit at 36 months of age by use of a qualitative dietary questionnaire, including number of meals and food groups consumed on an average day. In 2008, WHO published a set of reliable and valid core indicators to assess feeding practices in children up to two years old. Since BRAIN-HIT collected data on dietary practices at 36 months of age only, we applied the WHO recommended diet up to 24 months to the 36 month intake assessment. The three core indicators of interest were modified versions of the Minimum Dietary Diversity (mMDD), Minimum Meal Frequency (mMMF), and Minimum Acceptable Diet (mMAD). Children met the mMDD if they received at least four of the following food groups: (1) grains, roots or tubers; (2) legumes or nuts; (3) dairy products; (4) flesh foods (meat, poultry, or insects); (5) eggs; and (6) fruits or vegetables. BRAIN-HIT did not distinguish between vitamin-A rich and non-vitamin-A rich fruits and vegetables and thus, all fruit and vegetables were combined in one food group. However, the mMDD was considered met if at least four food groups were being regularly consumed in a day, as per the WHO definition. To achieve mMMF, BRAIN-HIT applied the WHO Integrated Management of Childhood Illness (IMCI) program recommendation of three meals with two additional snacks a day for this age group. Finally, children met the mMAD if they met both the mMDD and mMMF.

Demographics

Data were collected on demographic characteristics of the child (sex, premature birth, birth weight, resuscitation status, exclusively breast fed the first 6 months) and mother (socioeconomic status, age, educational level, marital status, parity).

Outcomes

Anthropometric measures

Three anthropometric measures were taken at each visit: length/height, weight, and head circumference. Weight was measured using digital infant weighing scales in Pakistan and analog scales in India and Zambia. Head circumference was measured using a plasticized or fiberglass non-elastic tape measure. Standing height was measured in Zambia while recumbent length was measured in India and Pakistan. In India, children were made to lie supine on clean level floors with their heads resting against the wall and looking straight at the ceiling. Their legs were straightened at the knee with their feet perpendicular to the leg while ensuring their heads, backs, buttocks, and heels were in contact with the floor. Hard boards were then placed against the foot and lengths were measured by placing the measuring tape from the wall to inner edge of the board. Children were properly restrained during the procedure. In Pakistan, recumbent length was measured two times with a Seca 416 infantometer (Perspective Enterprises, Portage, MI). If the two initial measurements differed by more than 0.2 cm, a third measurement was undertaken. Anthropometric instruments were calibrated regularly.

Weight, length/height, and head circumference measurements taken at 36 months of age were used to determine weight-for-age (WAZ), height-for-age (HAZ), weight-for-height (WHZ) and head circumference-for-age Z-scores (HCZ) based on international growth standards developed by WHO for children up to 5 years of age [15]. Lengths were converted to heights by subtracting 0.7 cm as recommended by WHO [16]. Stunting and wasting were defined as HAZ < -2SD and WHZ < -2SD, respectively.

Developmental measures

The BSID-II is a well-validated measure of development in infants aged 1–42 months [17]. Both the BSID-II MDI- and PDI- scores, which measure cognitive development and motor skills, respectively, were used to assess development at 36 months. The BSID-II was used as a main measure for the BRAIN-HIT trial due to its extensive use in a number of LMIC. To verify the validity of BSID-II in the local context, it was pretested at each site and a few items were modified to ensure cultural appropriateness (e.g., image of a sandal in place of a shoe). The BSID-II was administered to each child in the appropriate language using standard material by certified neurodevelopmental evaluators (pediatricians and psychologists familiar with the local language and culture) who were masked to the birth history and intervention group. All evaluators received an intensive 4-day training in the purpose and correct administration of each item.

Statistical analysis

Descriptive statistics were calculated for child and maternal characteristics. Frequencies and percentages were reported for categorical variables with differences in characteristics between sites tested for by chi-square and Fisher’s exact test. Means, standard deviations, medians, minimums and maximums were reported for continuous variables with difference in means tested using the Kruskal Wallis test.

Linear regression models fitting each BSID-II Score index (i.e. MDI and PDI) and anthropometric measures (i.e. WAZ, HAZ, WHZ, and HCZ) were used to estimate adjusted mean scores in groups of children defined by dietary consumption. Models included one dietary indicator at a time as the primary independent variable with site, intervention group, resuscitation status, socioeconomic status, child sex, exclusively breastfed first 6 months, birth weight, preterm status, maternal age, and maternal education level as covariates. Results from the main trial suggested some heterogeneity between neurodevelopmental evaluator scoring. To account for potential confounding due to evaluator, evaluator within site was included as a nested effect in the BSID-II models. Statistical significance for a difference in adjusted means for those with and without each dietary indicator was determined by the F test. Tests of interaction were conducted to assess whether the relationship between the dietary indicator and outcome differed between the three study sites with means shown by site if interactions were significant.

Adjusted relative risks (RR) and 95% confidence intervals (CI) for stunting and wasting were estimated using Poisson regression models with robust variance estimators [18] assuming an independent correlation structure. Relative risks were adjusted for site, intervention group, resuscitation status, and child and maternal characteristics with statistical significance determined by Wald chi-square tests. All analyses were conducted in SAS version 9.4.

Results

Maternal average age among study participants was 24.9 years but differed significantly among each country (India, 22.8 years; Zambia, 25.2 years; Pakistan, 27.4 years; p < 0.01) (Table 1). The majority (97%) of mothers from Pakistan received no formal education, compared to 38% from India and 7% from Zambia (p < 0.01). A larger proportion of women from Pakistan had preterm babies (29%) than from India and Zambia (8% and 3%, p < 0.01). Infants from Zambia had heavier average birth weights (3159 g) compared to India (2694 g) and Pakistan (2517 g) (p < 0.01). Notably, fewer children from Pakistan were exclusively breastfed in the first 6 months (10%) compared to India (83%) and Zambia (96%) (p < 0.01).
Table 1

Study Participant and Maternal Characteristics, Food Group Consumption and Young Child Feeding Index Components by Site

Measures

Subgroup

Statistic

All study participants N = 371

Zambia N = 92

India N = 159

Pakistan N = 120

P-value*

Study participant characteristics

 Intervention group

Early developmental intervention

n (%)

186 (50)

44 (48)

80 (50)

62 (52)

0.86

Control

 

185 (50)

48 (52)

79 (50)

58 (48)

 

 Child sex

Male

n (%)

218 (60)

53 (58)

96 (60)

69 (61)

0.87

Female

 

146 (40)

39 (42)

63 (40)

44 (39)

 

 Premature birth

Yes

n (%)

50 (14)

3 (3)

12 (8)

35 (29)

< 0.01

No

 

318 (86)

87 (97)

146 (92)

85 (71)

 

 Birth weight(g)

 

N

360

92

159

109

 
 

Mean (SD)

2760 (504)

3159 (444)

2694 (367)

2517 (528)

< 0.01

 

Median

2700

3100

2600

2500

 
 

Min, Max

1500, 4500

2000, 4500

1600, 3800

1500, 3600

 

 Resuscitated

Yes

n (%)

154 (42)

35 (38)

80 (50)

39 (33)

< 0.01

No

 

217 (58)

57 (62)

79 (50)

81 (68)

 

 Exclusively breastfed first 6 months

Yes

n (%)

232 (63)

88 (96)

132 (83)

12 (10)

< 0.01

No

 

139 (37)

4 (4)

27 (17)

108 (90)

 

Maternal characteristics

 SES by wealth index terciles

Low (SES score < = − 1.3)

n (%)

176 (47)

28 (30)

74 (47)

74 (62)

< 0.01

Medium (−1.3 < SES score < 2.8)

 

162 (44)

42 (46)

77 (48)

43 (36)

 

High (SES score > =2.8)

 

33 (9)

22 (24)

8 (5)

3 (3)

 

 Maternal age (years)

 

N

371

92

159

120

 
 

Mean (SD)

24.9 (5.3)

25.2 (6.7)

22.8 (3.0)

27.4 (5.2)

< 0.01

 

Median

24

24

22

27

 
 

Min, Max

15, 44

15, 42

18, 32

17, 44

 

 Maternal education (years)

No formal education, illiterate

n (%)

170 (48)

6 (7)

58 (38)

106 (97)

< 0.01

Literate, primary

 

107 (30)

49 (54)

57 (37)

1 (1)

 

Secondary, university

 

76 (22)

35 (39)

39 (25)

2 (2)

 

 Married

Yes

n (%)

349 (98)

86 (93)

154 (100)

109 (100)

< 0.01

No

 

6 (2)

6 (7)

0 (0)

0 (0)

 

 Parity

1 to 2

n (%)

202 (55)

55 (61)

107 (68)

40 (33)

< 0.01

3 to 4

 

98 (27)

16 (18)

48 (30)

34 (28)

 

5+

 

68 (18)

19 (21)

3 (2)

46 (38)

 

Child food consumption at 36 months

 Grains, roots and tubers

Yes

n (%)

371 (100)

92 (100)

159 (100)

120 (100)

N/A

 Fruits and vegetables

Yes

n (%)

341 (92)

92 (100)

141 (89)

108 (90)

< 0.01

No

 

30 (8)

0 (0)

18 (11)

12 (10)

 

 Legumes and nuts

Yes

n (%)

155 (42)

84 (91)

65 (41)

6 (5)

< 0.01

No

 

216 (58)

8 (9)

94 (59)

114 (95)

 

 Eggs

Yes

n (%)

101 (27)

18 (20)

28 (18)

55 (46)

< 0.01

No

 

270 (73)

74 (80)

131 (82)

65 (54)

 

 Meat, fish or insects

Yes

n (%)

104 (28)

57 (62)

4 (3)

43 (36)

< 0.01

No

 

267 (72)

35 (38)

155 (97)

77 (64)

 

 Milk and dairy

Yes

n (%)

284 (77)

21 (23)

143 (90)

120 (100)

< 0.01

No

 

87 (23)

71 (77)

16 (10)

0 (0)

 

Young Child Feeding Index components

Modified Minimum Dietary

Yes

n (%)

216 (58)

66 (72)

72 (45)

78 (65)

< 0.01

 Diversity

No

 

155 (42)

26 (28)

87 (55)

42 (35)

 

Modified Minimum Meal

Yes

n (%)

297 (80)

55 (60)

147 (92)

95 (79)

< 0.01

 Frequency

No

 

74 (20)

37 (40)

12 (8)

25 (21)

 

Modified Minimum Acceptable

Yes

n (%)

174 (47)

43 (47)

66 (42)

65 (54)

0.11

 Diet

No

 

197 (53)

49 (53)

93 (58)

55 (46)

 

Child growth at 36 months

 Height Z-score

 

N

354

86

158

110

 
 

Mean (SD)

−2.36 (1.19)

−2.34 (1.60)

−2.31 (0.95)

−2.44 (1.12)

0.56

 

Median

−2.28

−2.38

−2.24

−2.37

 
 

Min, Max

−5.69, 0.84

−5.69, 0.84

−5.19, −0.18

−5.31, 0.42

 

 Weight Z-score

 

N

363

91

159

113

 
 

Mean (SD)

−1.54 (1.13)

−0.62 (0.99)

−1.87 (1.01)

−1.81 (1.00)

< 0.01

 

Median

−1.55

−0.81

−1.80

−1.69

 
 

Min, Max

−5.05, 2.33

−3.01, 2.33

−5.05, 0.61

−4.60, 0.61

 

 Weight-for-Height Z-score

 

N

354

86

158

110

 
 

Mean (SD)

−0.29 (1.50)

1.00 (1.53)

−0.78 (1.22)

− 0.58 (1.26)

< 0.01

 

Median

−0.40

0.97

−0.93

−0.37

 
 

Min, Max

−4.56, 4.05

−3.02, 4.05

− 3.72, 2.70

− 4.56, 2.13

 

 Head Circumference Z-sore

 

N

359

91

156

112

 
 

Mean (SD)

−0.86 (1.39)

0.42 (1.11)

−1.64 (0.96)

−0.79 (1.31)

< 0.01

 

Median

−1.03

0.35

−1.78

−0.74

 
 

Min, Max

−4.66, 3.18

−2.09, 3.18

− 4.63, 1.00

− 4.66, 1.79

 

 Stunting

Yes

n (%)

218 (59)

48 (52)

96 (60)

74 (62)

0.33

No

 

153 (41)

44 (48)

63 (40)

46 (38)

 

 Wasting

Yes

n (%)

44 (12)

4 (4)

25 (16)

15 (13)

0.03

No

 

327 (88)

88 (96)

134 (84)

105 (88)

 

*P-values test difference between sites by chi-square, Fisher’s exact and Kruskal Wallis tests

All children in the cohort were said to be consuming grains, roots and tubers with the majority in each country also consuming fruits and vegetables (Table 1). Children from Zambia were more likely to have diets high in legumes or nuts (91%) and meat, fish or insects (62%) than children in India (41% and 3%, respectively) or Pakistan (5% and 36%, respectively) (p < 0.01). However, milk and other dairy were consumed less frequently by children in Zambia (23%) compared to children in India (90%) and Pakistan (100%) (p < 0.01). A significantly smaller proportion of children from India (45%) received the mMDD compared to Pakistan (65%) and Zambia (72%) (p < 0.01). On the other hand, a significantly larger proportion of children from India (92%) met the mMMF compared to Pakistan (79%) and Zambia (60%) (p < 0.01). About half (47%) of the cohort met the mMAD. Overall, children from Zambia tended to have higher WAZ-, WHZ-, and HCZ-scores (p < 0.01) and were less likely to experience wasting (p = 0.03) whereas children from India had the lowest HCZ-scores (p < 0.01) (Table 1).

MDI and PDI scores were measured in 369 of 371 (99%) children studied. Two children scored within normal limits for the 12- and 24-month evaluations but failed to complete the 36-month BSID-II evaluation. Among the 369 children evaluated, no significant associations were found between MDI and feeding practices or specific food groups. However, consumption of legumes or nuts was significantly associated with lower PDI scores in Pakistan. Although differences were not statistically significant, higher adjusted mean MDI and PDI scores were observed in those children consuming animal-source foods (eggs, dairy, and meat, fish or insects) (Table 2).
Table 2

Unadjusted and adjusted means for Bayley Scales of Infant Development measures at 36 months

 

Mental Developmental Index (MDI)

Psychomotor Developmental Index (PDI)

Measures

 

N a

Mean ± SD (unadjusted)

Mean (adjusted)b

P valueb

Mean ± SD (unadjusted)

Mean (adjusted)b

P valueb

Exclusively breastfed first 6 months

Yes

230

94.25 ± 14.18

97.47

0.27

101.73 ± 15.07

101.80

0.61

No

139

96.39 ± 13.96

95.43

 

100.40 ± 17.58

100.57

 

Legumes and nuts

Yes

155

99.00 ± 12.05

95.62

0.35

105.02 ± 15.42

No

214

92.20 ± 14.83

97.32

 

98.48 ± 15.97

 

Zambia

Yes

84

105.08 ± 13.00

101.99

0.48

No

8

  

101.88 ± 6.36

97.98

 

India

Yes

65

106.60 ± 15.42

101.25

0.61

No

92

  

95.63 ± 13.95

102.74

 

Pakistan

Yes

6

87.00 ± 32.47

85.05

0.01

No

114

  

100.54 ± 17.60

100.44

 

Eggs

Yes

101

97.08 ± 14.92

96.98

0.62

102.87 ± 15.48

102.19

0.46

No

268

94.29 ± 13.76

96.28

 

100.60 ± 16.25

100.85

 

Meat, fish or insects

Yes

104

100.19 ± 12.21

98.04

0.13

104.39 ± 15.23

103.54

0.08

No

265

93.04 ± 14.33

95.67

 

99.98 ± 16.22

100.03

 

Milk and dairy

Yes

282

94.53 ± 14.45

96.74

0.57

100.97 ± 16.84

101.67

0.47

No

87

96.77 ± 12.93

95.58

 

102.06 ± 13.23

99.74

 

Modified Minimum Dietary Diversity

Yes

216

98.57 ± 13.10

96.87

0.38

103.97 ± 16.00

101.43

0.69

No

153

90.09 ± 14.06

95.73

 

97.35 ± 15.36

100.76

 

Modified Minimum Meal Frequency

Yes

295

94.79 ± 14.39

96.51

0.87

101.61 ± 15.86

101.57

0.44

No

74

96.14 ± 13.03

96.27

 

99.70 ± 16.81

100.05

 

Modified Minimum Acceptable Diet

Yes

174

98.41 ± 13.42

96.55

0.87

104.24 ± 15.93

101.21

0.98

No

195

92.07 ± 14.09

96.36

 

98.54 ± 15.72

101.16

 

aNumbers are among the 369 children with non-missing BSID-II scores. BSID-II scores were missing for 2/371 (0.5%) children [India: 2/159 (1.3%)]

bBSID-II means were estimated using linear models fit to each Bayley index score and adjusted for SES, site, intervention group, resuscitation status, evaluator, sex, exclusively breastfed first 6 months, birth weight, preterm status, maternal age, and maternal education level. Statistical significance for a difference in means for those with and without each food consumption measure was determined by the F test. Tests of whether the association between measure and outcome differed by site were conducted. Interactions involving milk and dairy could not be tested as all participants from Pakistan received milk and dairy on an average day. Interactions with site were significant for legume/nut consumption (p = 0.01); PDI score means are shown by site for this measure

Children who received the mMMF had significantly higher WHZ-scores, but not WAZ- or HAZ-scores, than those who did not (adjusted mean WHZ: 0.16 vs − 0.28, p = 0.02) (Table 3). Likewise, children who received the mMAD had significantly higher WHZ-scores, but not WAZ- or HAZ-scores (adjusted mean WHZ: 0.20 vs − 0.08, p = 0.05) (Table 3). HCZ-scores did not differ significantly between those who did and did not receive mMAD (Table 4). However, the association between flesh food consumption and HCZ-scores differed significantly by country [egg consumption and HCZ-score interaction (p = 0.01); meat/fish/insect consumption and HCZ-score interaction (p = 0.05)] (Table 4). Children from Pakistan who consumed eggs showed significantly higher HCZ-scores than those who did not (adjusted mean: − 0.31 vs − 1.24, p < 0.01), yet these same children were the only group with significantly lower HCZ-scores associated with meat, fish or insect consumption (adjusted mean: − 1.30 vs − 0.68, p < 0.01).
Table 3

Unadjusted and adjusted means for 36 month anthropometric measures

 

Height Z-Scorea

Weight Z-Scorea

Weight-for-Height Z-Scorea

Measures

 

N b

Mean ± SD (unadjusted)

Mean (adjusted)c

P valuec

Mean ± SD (unadjusted)

Mean (adjusted)c

P valuec

Mean ± SD (unadjusted)

Mean (adjusted)c

P valuec

Exclusively breastfed first 6 months

Yes

223

−2.30 ± 1.22

− 2.30

0.19

−1.39 ± 1.22

−1.32

0.72

−0.15 ± 1.61

− 0.04

0.41

No

131

− 2.47 ± 1.12

−2.56

 

−1.80 ± 0.90

−1.38

 

− 0.52 ± 1.26

0.15

 

Legumes and nuts

Yes

148

−2.41 ± 1.35

−2.58

0.07

− 1.23 ± 1.27

− 1.41

0.44

0.20 ± 1.63

0.13

0.41

No

206

−2.32 ± 1.06

−2.28

 

−1.77 ± 0.96

−1.30

 

−0.64 ± 1.29

−0.02

 

Eggs

Yes

93

−2.46 ± 1.07

−2.46

0.78

−1.57 ± 1.03

−1.29

0.49

−0.24 ± 1.32

0.20

0.26

No

261

−2.33 ± 1.23

−2.42

 

−1.52 ± 1.16

−1.38

 

−0.31 ± 1.56

0.01

 

Meat, fish or insects

Yes

95

−2.39 ± 1.51

−2.42

0.92

−1.12 ± 1.19

−1.32

0.77

0.25 ± 1.81

0.04

0.92

No

259

−2.35 ± 1.05

−2.44

 

−1.70 ± 1.07

−1.36

 

−0.48 ± 1.32

0.06

 

Milk and dairy

Yes

270

−2.38 ± 1.11

−2.46

0.61

−1.73 ± 1.10

−1.34

0.81

−0.55 ± 1.37

0.09

0.66

No

84

−2.28 ± 1.40

−2.34

 

−0.91 ± 0.99

−1.39

 

0.56 ± 1.59

−0.03

 

Modified Minimum Dietary Diversity

Yes

203

−2.41 ± 1.25

−2.48

0.36

−1.44 ± 1.20

−1.33

0.66

−0.09 ± 1.53

0.15

0.12

No

151

−2.30 ± 1.10

−2.36

 

−1.68 ± 1.01

−1.38

 

−0.55 ± 1.42

−0.09

 

Modified Minimum Meal Frequency

Yes

284

−2.41 ± 1.15

−2.50

0.12

−1.59 ± 1.15

−1.32

0.35

− 0.31 ± 1.52

0.16

0.02

No

70

−2.16 ± 1.32

−2.23

 

−1.31 ± 1.03

−1.45

 

− 0.21 ± 1.43

− 0.28

 

Modified Minimum Acceptable Diet

Yes

164

−2.47 ± 1.22

−2.52

0.18

−1.51 ± 1.23

−1.33

0.65

− 0.12 ± 1.60

0.20

0.05

No

190

−2.27 ± 1.15

−2.35

 

−1.56 ± 1.04

−1.38

 

−0.43 ± 1.40

−0.08

 

aZ-scores deemed implausible according to WHO criteria were set to missing. This includes: WAZ < -6SD or > 5SD; HAZ < -6SD or > 6SD; WHZ < -5SD or > 5SD

b N includes those with non-missing height and weight-for-height measurements (N = 354). There were additional weight measurements available (N = 363)

cAdjusted means were estimated using linear models fitting each anthropometric outcome and adjusted for SES, site, intervention group, resuscitation status, sex, exclusively breastfed first 6 months, birth weight, preterm status, maternal age, and maternal education level. Statistical significance for a difference in means for those with and without each food consumption measure was determined by the F test. Tests of whether the association between measure and outcome differed by site were conducted with none found to be significant. Interactions involving milk and dairy could not be tested as all participants from Pakistan received milk and dairy on an average day

Table 4

Unadjusted and adjusted means for head circumference Z-scores at 36 months

 

Head Circumference Z-Scorea

Measures

 

N b

Mean ± SD (unadjusted)

Mean (adjusted)c

P valuec

Exclusively breastfed first 6 months

Yes

228

−0.82 ± 1.45

−0.81

0.92

No

131

−0.92 ± 1.27

−0.79

 

Legumes and nuts

Yes

153

−0.55 ± 1.51

−0.92

0.15

No

206

− 1.08 ± 1.25

−0.69

 

Eggs

Yes

95

−0.56 ± 1.28

No

264

−0.96 ± 1.41

  

Zambia

Yes

18

0.32 ± 1.29

0.20

0.94

No

73

0.44 ± 1.07

0.18

 

India

Yes

28

−1.62 ± 0.92

− 1.68

0.74

No

128

−1.65 ± 0.97

−1.75

 

Pakistan

Yes

49

−0.28 ± 1.05

−0.31

< 0.01

No

63

−1.19 ± 1.35

− 1.24

 

Meat, fish or insects

Yes

101

−0.33 ± 1.32

No

258

−1.06 ± 1.36

  

Zambia

Yes

57

0.27 ± 0.97

0.07

0.13

No

34

0.66 ± 1.29

0.44

 

India

Yes

4

−0.77 ± 1.24

− 0.91

0.12

No

152

−1.67 ± 0.94

−1.78

 

Pakistan

Yes

40

−1.14 ± 1.34

−1.30

< 0.01

No

72

−0.60 ± 1.26

− 0.68

 

Milk and dairy

Yes

273

−1.16 ± 1.28

−0.87

0.22

No

86

0.11 ± 1.29

−0.61

 

Modified Minimum Dietary Diversity

Yes

209

−0.74 ± 1.42

−0.86

0.27

No

150

−1.01 ± 1.33

−0.72

 

Modified Minimum Meal Frequency

Yes

287

−0.97 ± 1.32

−0.77

0.32

No

72

−0.41 ± 1.54

−0.92

 

Modified Minimum Acceptable Diet

Yes

168

−0.79 ± 1.36

−0.78

0.65

No

191

−0.91 ± 1.41

−0.83

 

aZ-scores deemed implausible (<-5SD or > 5SD) according to WHO criteria were set to missing

b N includes those with non-missing head circumference measurements

cAdjusted means were estimated using linear models fitting head circumference Z score and adjusted for SES, site, intervention group, resuscitation status, sex, exclusively breastfed first 6 months, birth weight, preterm status, maternal age, and maternal education level. Statistical significance for a difference in means for those with and without each food consumption measure was determined by the F test. Tests of whether the association between each measure and the outcome differed by site were conducted. Interactions with site were significant for egg consumption (p = 0.01) and for meat, fish, or insects (p = 0.05); head circumference Z score means are shown by site for these measures

The prevalence of children with wasting was lower in those who received mMAD compared to those who did not (9% vs. 14%), but the difference was not statistically significant (Table 5). The only food group significantly associated with lower risk of wasting was egg consumption [adjusted RR (95% CI): 0.37 (0.15, 0.89), p = 0.03]. Yet, none of the YCF indicators nor any food groups were found to be associated with stunting.
Table 5

Unadjusted and adjusted relative risks of stunting and wasting for 36 month children

  

Stuntinga

Wastingb

Characteristics/Measures

 

Unadjusted

Adjustedc

 

Unadjusted

Adjustedc

n (%)d

Relative Risk (95% CI)

P valuee

Relative Risk (95% CI)

P valuee

n (%)d

Relative Risk (95% CI)

P valuee

Relative Risk (95% CI)

P valuee

Sex

Male

134 (61)

1.07 (0.81,1.40)

0.63

1.14 (0.96,1.36)

0.12

31 (14)

1.60 (0.84,3.05)

0.16

1.61 (0.88,2.93)

0.12

Female

84 (58)

REF

 

REF

 

13 (9)

REF

 

REF

 

SES tercile

Low

110 (63)

1.72 (0.95,3.12)

0.07

1.42 (0.87,2.31)

0.16

25 (14)

1.56 (0.47,5.18)

0.47

1.17 (0.35,3.96)

0.80

Medium

96 (59)

1.63 (0.89,2.97)

0.11

1.48 (0.92,2.39)

0.11

16 (10)

1.09 (0.32,3.73)

0.90

0.79 (0.23,2.68)

0.71

High

12 (36)

REF

 

REF

 

3 (9)

REF

 

REF

 

Legumes and nuts

Yes

89 (57)

0.96 (0.73,1.26)

0.78

1.10 (0.89,1.35)

0.37

14 (9)

0.65 (0.34,1.23)

0.18

0.95 (0.46,1.99)

0.90

No

129 (60)

REF

 

REF

 

30 (14)

REF

 

REF

 

Eggs

Yes

61 (60)

1.04 (0.77,1.40)

0.80

1.01 (0.84,1.22)

0.90

5 (5)

0.34 (0.14,0.87)

0.02

0.37 (0.15,0.89)

0.03

No

157 (58)

REF

 

REF

 

39 (14)

REF

 

REF

 

Meat, fish or insects

Yes

55 (53)

0.87 (0.64,1.18)

0.36

0.89 (0.71,1.13)

0.34

10 (10)

0.76 (0.37,1.53)

0.43

1.17 (0.55,2.48)

0.68

No

163 (61)

REF

 

REF

 

34 (13)

REF

 

REF

 

Milk and dairy

Yes

171 (60)

1.11 (0.81,1.54)

0.51

0.96 (0.70,1.31)

0.78

39 (14)

2.39 (0.94,6.06)

0.07

1.31 (0.56,3.06)

0.54

No

47 (54)

REF

 

REF

 

5 (6)

REF

 

REF

 

Modified Minimum Dietary Diversity

Yes

125 (58)

0.96 (0.74,1.26)

0.79

0.99 (0.84,1.17)

0.91

18 (8)

0.50 (0.27,0.91)

0.02

0.63 (0.35,1.15)

0.13

No

93 (60)

REF

 

REF

 

26 (17)

REF

 

REF

 

Modified Minimum Meal Frequency

Yes

179 (60)

1.14 (0.81,1.62)

0.45

1.12 (0.89,1.41)

0.32

36 (12)

1.12 (0.52,2.41)

0.77

0.88 (0.43,1.80)

0.73

No

39 (53)

REF

 

REF

 

8 (11)

REF

 

REF

 

Modified Minimum Acceptable Diet

Yes

103 (59)

1.01 (0.78,1.32)

0.92

1.01 (0.86,1.19)

0.89

16 (9)

0.65 (0.35,1.20)

0.16

0.74 (0.42,1.33)

0.32

No

115 (58)

REF

 

REF

 

28 (14)

REF

 

REF

 

aStunting was defined as having −6 ≤ height Z-score < −2

bWasting was defined as having −5 ≤ weight-for-height Z-score < −2

cRelative risks were estimated using Poisson regression models with robust variance estimators and adjusted for SES, site, intervention group, resuscitation status, sex, exclusively breastfed first 6 months, birth weight, preterm status, maternal age, and maternal education level

dNumber and percent who were determined to have stunting or wasting, respectively

eStatistical significance was determined using the Wald chi-square test

Discussion

In a cohort of 371 children aged 36 months of age from rural communities in India, Pakistan, and Zambia, we found 47% of the children met the mMAD. Mean WHZ-scores were significantly higher for children who received either the mMMF or mMAD compared to those who did not and notably, egg consumption was associated with a significant decreased risk of wasting. However, adjusted mean HAZ-, WAZ- and HCZ-scores were not statistically different between those who did and did not receive mMAD. Likewise, there was no significant association between these dietary practices and MDI and PDI.

While the majority of children (80%) met the mMMF, inclusion of either legumes/nuts, dairy, eggs, or meat/fish/insects was required to meet the mMDD, and thus the mMAD. As expected, due to the majority of the participants in India being lacto-vegetarians, very few children in India consumed animal-flesh foods (3%) and only a minority consumed eggs (18%) and legumes or nuts (41%) resulting in a smaller percentage reaching the mMAD. Indian children also had the lowest WHZ-scores and the highest prevalence of wasting. While encouraging a diet higher in animal-flesh foods or eggs may not be culturally appropriate for this group, increasing consumption of legumes and/or nuts could help them meet the mMDD and, therefore, mMAD. In contrast, Zambian children were more likely to be consuming meat, fish or insects (62%) and legumes or nuts (91%), had the highest WHZ-scores and the lowest prevalence of wasting. While causal inferences cannot be made, the significant positive overall associations reported here between WHZ-scores and mMAD highlight the importance of providing optimal feeding practices for young children, including achieving the required number of meals as well as a variety of foods in the diet.

Of interest was the association between egg consumption and decreased risk of wasting found here. Eggs are rich in amino acids, especially leucine which stimulates muscle protein synthesis and may potentially explain this protective effect. In addition, consumption of eggs and other animal source foods was found in children with higher neurodevelopment scores, although not statistically significant. These foods provide a concentrated source of dietary macro- and micronutrients essential for optimal growth, immune response and cognitive function, including protein, iron, zinc, and vitamin B12 [9, 19, 20]. Iron and zinc are critical micronutrients for appropriate development of the hippocampus and prefrontal cortex in the first 1000 days of life [21, 22] and vitamin B12, a key factor for normal brain and nervous system function, is only found in animal-source foods.

However, mixed findings were observed between micronutrient-rich food groups and HCZ-scores, specifically in Pakistan. In these children, better HCZ-scores were significantly associated with egg consumption, in contrast to a negative association with meat, fish, or insect consumption. While these results were unexpected, differences in demography and feeding habits may have contributed to these conflicting results, as well as potential inconsistencies in head circumference measurement.

The relationship between diet and child growth is complex and the lack of specificity and sensitivity of this qualitative dietary tool may also account for the variability found here, as well as the inability to identify other meaningful associations, which is in line with findings from a systematic review conducted by Jones and colleagues [23]. For example, a study from Senegal showed that HAZ was positively associated with the WHO indicators up to one year of age but less strongly as children became older, which may explain why we saw no significant results at 36 months of age between diet and linear growth [24]. Further, our results are in line with another study from Ghana which showed that these indicators better explain WHZ-scores than HAZ-scores for young children because the tool reflects current diet rather than habitual intake [25].

There was a significant association between legume/nut consumption and lower PDI scores in Pakistan (p = 0.01). However, given that only six children at this site were reportedly consuming legumes or nuts on a typical day, these findings may not be reliable. It is possible we found no other significant associations between diet and MDI and PDI scores because of the difficulty of assessing neurodevelopment in young children. Yet, to our knowledge, this is the first study to explore the relationship between YCF indicators and BSID-II MDI and PDI scores at 36 months and a longitudinal examination from an earlier age using a quantitative dietary approach may provide more meaningful results.

We acknowledge the WHO YCF indicators were designed for younger children, but suggest our modifications were age-appropriate and consistent with guidelines for MMF and MDD. Long term growth is influenced by feeding practices beginning from birth. A limitation of this study is that data on dietary practices were only collected at the final 36-month visit. As a result, this cross-sectional design, prohibited us from assessing the impact of earlier feeding practices, such as the introduction of complementary foods, on growth and development. However, data on nutrition may not change over time as the food types in these settings are typically limited. Lastly, another limitation of our study was that different sites used different measurement techniques (i.e. type of scale utilized, and standing height versus recumbent length) and measurement bias and inter-observer bias can not be ruled out.

Conclusion

This study demonstrated positive associations between meeting either the mMMF or mMAD and favorable WHZ-scores, and more specifically a decreased risk of wasting with egg consumption. For cohorts such as our Indian site who are primarily vegans and vegetarians, increased consumption of legumes and nuts may help these populations meet the mMDD and, thus the mMAD. While the WHO YCF indicators may be valuable tools on a population level, additional specific longitudinal measures of dietary intake are needed to fully assess feeding practices and their relationship with linear growth and neurodevelopment in children.

Abbreviations

BRAIN-HIT: 

Brain Research to Ameliorate Impaired Neurodevelopment Home-based Intervention Trial

WHO

World Health Organization

BSID-II

Bayley Scores of Infant Development – Second Edition

MDI

Mental Development Index

PDI

Psychomotor Development Index

EDI

Early developmental intervention

mMDD

modified Minimum Dietary Diversity

mMMF

modified Minimum Meal Frequency

mMAD

modified Minimum Acceptable Diet

LMIC

low- and low-middle-income countries

Declarations

Funding

This work was supported by Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Global Network for Women’s and Children’s Health Research (HD034216), the National Institute of Neurological Disorders and Stroke (HD43464, HD42372, HD40607, and HD40636), the Fogarty International Center (TW006703), the Perinatal Health and Human Development Research Program, and the Children’s of Alabama Centennial Scholar Fund of the University of Alabama at Birmingham. W.C. is on the Mednax Board of Directors.

Availability of data and materials

Datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

As the BRAIN-HIT principal investigator, WC led the study conception and design, with support from the study investigators (EC, SSG, RD, SMD, OP, RG), child development specialists (JW, FB), and the data coordinating center (VRT). EC, SSG, OP, SMD supervised the collection of study data. BTD, NIH, CB, and RLL led the secondary analysis conception and design, and were supported by WC, JW, FB and VRT. BTD, NIH, CB, RLL, VRT completed the analysis and interpretation of data. BTD, NIH, CB and VRT had access to all the data in the study. BTD drafted the initial manuscript and NIH, CB, and RLL critically revised the text. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

BRAIN-HIT was reviewed and approved by the Institutional Review Boards at University of Alabama at Birmingham, Birmingham, AL; Centre for Infectious Disease Research in Zambia, Lusaka, Zambia; University of Zambia, Lusaka, Zambia; Jawaharlal Nehru Medical College, Belgaum, India; Aga Kahn University Medical College, Karachi, Pakistan; and, RTI International, Research Triangle Park, NC. The study was also reviewed and approved by the NICHD Global Network for Women’s and Children’s Health Research data monitoring committee. Mothers of participants provided written informed consent before randomization.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

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Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
RTI International, Research Triangle Park, USA
(2)
University of Colorado School of Medicine, Aurora, USA
(3)
KLE Jawaharlal Nehru Medical College, Belgaum, India
(4)
Department of Population, Family & Reproductive Health, John Hopkins University Bloomberg School of Public Health, Baltimore, USA
(5)
University of Zambia, Lusaka, Zambia
(6)
University of California, Merced, Merced, USA
(7)
Department of Pediatrics/Division of Neonatology, University of Alabama at Birmingham, Birmingham, USA
(8)
Thomas Jefferson University, Philadelphia, USA
(9)
Columbia University, New York, USA

References

  1. World Health Organization, Children: reducing mortality. http://www.who.int/mediacentre/factsheets/fs178/en/. Accessed 03 Dec 2016.
  2. Odunayo IS, Oyewole OA. Risk factors for malnutrition among rural Nigerian children. Asia Pac J Clin Nutr. 2006;15(14):491–5.PubMedGoogle Scholar
  3. Hien NN, Kam S. Nutritional status and the characteristics related to malnutrition in children under five years of age in Nghean, Vietnam. J Prev Med Public Health. 2008;41(4):232–40.View ArticlePubMedGoogle Scholar
  4. As O, Oyekale TO. Do mothers’ educational levels matter in child malnutrition and health outcomes in Gambia and Niger? Soc Sci. 2009;4(1):118–27.Google Scholar
  5. Black RE, Allen LH, Bhutta ZA, Caulfield LE, de Onis M, Ezzati M, et al. Maternal and child undernutrition: global and regional exposures and health consequences. Lancet. 2008;371(9608):243–60.View ArticlePubMedGoogle Scholar
  6. Wachs TD. The nature and nurture of child development. Food Nutr Bull. 1999;20:7–22.View ArticleGoogle Scholar
  7. Edris M. Assessment of nutritional status of preschool children of Gumbrit, north West Ethiopia. Ethiop J Health Dev. 2007;21(2):125–9.View ArticleGoogle Scholar
  8. Ali SS. A brief review of risk-factors for growth and developmental delay among preschool children in developing countries. Adv Biomed Res. 2013;2:91.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Allen LH. Global dietary patterns and diets in childhood: implications for health outcomes. Ann Nutr Metab. 2012;61(Suppl 1):29–37.View ArticlePubMedGoogle Scholar
  10. World Health Organization. UNICEF, USAID, AED, UCDAVIS, IFPRI. In: Indicators for assessing infant and child feeding indicators: part, vol. 1. Switzerland: Definitions, World Health Organization; 2008.Google Scholar
  11. Carlo WA, Goudar SS, Jehan I, Chomba E, Tshefu A, Garces A, et al. Newborn-care training and perinatal mortality in developing countries. N Engl J Med. 2010;362(7):614–23.View ArticlePubMedPubMed CentralGoogle Scholar
  12. World Health Organization. Department of Child and Adolescent Health and development, UNICEF, handbook: IMCI integrated management of childhood illness. Switzerland: World Health Organization; 2005.Google Scholar
  13. Wallander JL, McClure E, Biasini F, Goudar SS, Pasha O, Chomba E, et al. BRAIN research to ameliorate impaired neurodevelopment-home-based intervention trial (BRAIN-HIT). BMC Pediatr. 2010;10:27.View ArticlePubMedPubMed CentralGoogle Scholar
  14. Carlo WA, Goudar SS, Pasha O, Chomba E, Wallander JL, Biasini FJ, et al. Randomized trial of early developmental intervention on outcomes in children after birth asphyxia in developing countries. J Pediatr. 2013;162(4):705–12.View ArticlePubMedGoogle Scholar
  15. World Health Organization, United Nations Children’s Fund. WHO child growth standards and the identification of severe acute malnutrition in infants and children. Switzerland: World Health Organization; 2009.Google Scholar
  16. WHO Multicentre Growth Reference Study Group. WHO Child Growth Standards: Length/height-for-age, weight-for-age, weight-for-length, weight-for-height and body mass index-for-age: methods and development. Switzerland: World Health Organization; 2006.Google Scholar
  17. Bayley N. Bayley scales of infant development: manual. New York: Psychological Corporation; 1993.Google Scholar
  18. Zou G. A modified Poisson regression approach to prospective studies with binary data. Am J Epidemiol. 2004;159(7):702–6.View ArticlePubMedGoogle Scholar
  19. Black RE, Sazawal S. Zinc and childhood infectious disease morbidity and mortality. Br J Nutr. 2001;85:S125–9.View ArticlePubMedGoogle Scholar
  20. Wintergerst ES, Maggini S, Hornig DH. Contribution of selected vitamins and trace elements to immune function. Ann Nutr Metab. 2007;51:301–23.View ArticlePubMedGoogle Scholar
  21. Nyaradi A, Li J, Hickling S, Foster J, Oddy WH. The role of nutrition in children’s neurocognitive development, from pregnancy through childhood. Front Hum Neurosci. 2013;7:97.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Wachs TD, Georgieff M, Cusick S, McEwen BS. Issues in the timing of integrated early interventions: contributions from nutrition, neuroscience, and psychological research. Ann N Y Acad Sci. 2014;1308:89–106.View ArticlePubMedGoogle Scholar
  23. Jones AD, Ickes SB, Smith LE, Mbuya MNN, Chasekwa B, Heidkamp RA, Menon P, Zongrone AA, Stoltzfus RJ. World Health Organization infant and young child feeding indicators and their associations with child anthropometry: a synthesis of recent findings. Maternal and Child Nutr. 2014;10:1–17.View ArticleGoogle Scholar
  24. Bork K, Cames C, Barigou S, Cournil A, Diallo A. A summary index of feeding practices is positively associated with height-for-age, but only marginally with linear growth, in rural Senegalese infants and toddlers. J Nutr. 2012;142(6):1116–22.View ArticlePubMedGoogle Scholar
  25. Saaka M, Wemakor A, Abizari AR, Aryee P. How well do WHO complementary feeding indicators relate to nutritional status of children aged 6-23 months in rural northern Ghana? BMC Public Health. 2015;15:1157.View ArticlePubMedPubMed CentralGoogle Scholar

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© The Author(s). 2018

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