Food Supplementation for Improving the Physical and Psychosocial Health of Socio-economically Disadvantaged Children Aged Three Months to Five Years: A Systematic Review

3.1.4.1 Primary outcomes

Physical health

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Growth (weight, height, weight-for-age, height-for-age, weight-for-height).

Psychosocial health

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Psychomotor development (the progressive attainment of skills that involve both mental and muscular activity; e.g. the ability to turn over, crawl, and walk).

Adverse effects

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Substitution or leakage (the family cuts home rations for the child who has been fed in order to spread food to other family members, or shares the child's supplementary rations with other family members).

We used primary outcomes in physical health and psychosocial health to populate the ‘Summary of findings’ tables.

3.1.4.2 Secondary outcomes

Physical health

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Biochemical markers of nutrition (Vitamin A, haemoglobin, hematocrit).

Psychosocial outcomes

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Stigmatisation (adverse effect, involves being shamed).

Where possible, we extracted data on cost and resource use.

We excluded reduction of dental caries and increased nutritional knowledge (although the latter was included in the data extraction form to help elucidate findings). We also excluded intermediate health outcomes such as reduction of hunger and nutrient intake.

For cognitive and behavioural outcomes, we accepted reliable and valid psychometric measures (e.g. Wechsler Intelligence Scale for Children (WISC), Raven's Progressive Matrices (RPM)). For physical outcomes, we accepted clinical measures of growth (e.g. length or height boards, digital or balance beam weighing scales, skinfold thickness, mid-upper-arm circumference (MUAC)), biochemical nutritional status (e.g. blood tests), and morbidity (diagnosis by physician).

Equity outcomes

To assess equity, we conducted subgroup analyses, examining results that compared boys to girls and poor (or more undernourished) to less poor.

Searches last updated 3 May 2012 (Appendix 2)

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EMBASE Classic and EMBASE (OVID) 1947 to 1 May 2012.

Searches last updated 5 July 2011 (Appendix 3)

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Social Services Abstracts (CSA).

3.3.2.1 Process evaluation

We assessed the following process elements (list modified from Arblaster 1996 and Kristjansson 2007, and based on our knowledge of the literature and our conceptual model).

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Type of meal.

Nutritional adequacy

A nutritionist (SL) assessed the nutritional adequacy of the meals provided to the children. Two other nutritionists (DF and MB) helped to develop the approach.

Methodology for calculating energy content, protein content, % DRI:

Energy: when the total kilocalories or % DRI of energy were provided in the text of the study, we used this figure. When this information was not provided but the descriptions of food were sufficient (quantity and type of food), we estimated energy content (kilocalories) of the meal or snack using the Food and Agriculture Organization (FAO) international food composition table.

Calculating % DRI for energy: we calculated the % DRI for energy by dividing the given or estimated average kilocalorie content of the meal or snack by the DRI for the age- or sex-specific target group in each study. For children aged three years and older, we identified the estimated energy requirement assuming an active physical activity level. When the intervention group comprised different age and sex groups, and outcomes were given for the entire group only, we used a weighted average of the % DRI for each group to calculate the overall % DRI. When the number of boys and girls was not reported, we assumed that equal proportions took part in the study, and estimated an average DRI for both sexes.

Categorisation of the level of energy in the supplementary food: we categorised % DRI for energy into three levels: low (0 to 29%), moderate (30 to 59%), and high (60% and above). We used this categorisation in subgroup analyses. When different levels of energy were provided in one study, we used the highest level of energy to categorise the level of energy provided. When the same amount of food was provided to different age groups, we based calculations on the oldest age groups, as these had the highest energy requirements.

Protein: when the total protein or % DRI of protein was provided in the text of the study, we used this figure. When the amount of protein was not provided but the descriptions of food were sufficient (quantity and type of food), we estimated the protein content of the meal or snack using the FAO international food composition table.

Calculating % DRI for protein: we calculated the % DRI for protein by dividing the average protein content of the meal or snack by the DRI for the age- or sex-specific target group in each study (DRI from Health Canada). DRI for protein is given in g/kg/d, and weight provided in the study was used to calculate DRI. When weight was not provided in the study, we considered World Health Organization (WHO) weight (average of boys and girls) to estimate the DRI.

Assessing leakage

Where possible, we used information on the energy content in the supplement as well as information on the reported energy intakes of the experimental and control children to calculate the net benefit that the children actually received from the supplement. We calculated this as follows: (Difference in energy intake between experimental and control at end of study) / total energy content of the supplement.

Level of supervision

We divided the studies into strict versus moderate versus low supervision (i.e. monitoring) of the supplementary feeding intake in the following manner.

Strict supervision. To be categorised as strictly supervised, the feeding had to be:

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In day cares, preschools or feeding centres; OR

Moderate supervision. We characterised studies as moderately supervised if they:

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Provided monthly home visits; OR

Low supervision. We characterised studies as low supervision if they provided fewer than monthly home visits.

Organization of process findings

We created an EXCEL file that contained process elements for all studies. The studies were in rows, and the columns contained: type of study, cluster or not, whether it was corrected for clustering, setting, country, feeding duration, the final ‘n’ rate of attrition, whether the intervention was single or multiple, the type of food and energy provided, programme delivery site, level of supervision, and the outcome measures covered.

We performed subgroup analyses by factors that could impact on effectiveness, including child's age, sex and income level, nutritional adequacy of supplement, level of supervision, location of feeding, and single versus multiple interventions.

3.3.4.1 Continuous data

If continuous outcomes were measured identically across studies, we calculated an overall mean difference (MD) and 95% confidence interval (CI). If the same continuous outcome was measured differently across studies, we calculated an overall standardised mean difference (SMD) and 95% CI (Higgins 2011a).

We analysed continuous data from means and standard deviations wherever possible. When means and standard deviations were not reported, we used other available data (for example, confidence intervals, T values, P values) and appropriate methods as described in the Cochrane Handbook for Systematic Reviews of Interventions (Cochrane Handbook, Section 9.4.5, Higgins 2011b) to calculate the means and standard deviations, in consultation with our statistician. Where other available data were not sufficient to calculate standard deviations, we contacted the trial authors.

3.3.4.2 Change data

We used change data in all analyses. Data were either taken directly from the papers or calculated from other information presented. When we calculated change scores, we used means and standard deviation from baseline and end-of-study according to the methods described in section 16.1 of the Cochrane Handbook (Higgins 2011b). We used before-and-after correlations of 0.9 for height, weight, height-for-age z-score (HAZ), weight-for-age z-score (WAZ), and weight-for-height z-score (WHZ). These correlations for growth are based on those provided by Zhang 2006 [pers comm]. For mental and psychomotor development, we used correlations of 0.71 and 0.69. We took these correlations from a publication on test-retest reliability of the Bayley Scales of Infant Development (BSID, Cook 1989).

When studies provided insufficient data to calculate an effect estimate, we selected regression analyses, multilevel analyses, or analyses of variance (ANOVA) as providing the better estimate of effect because: (a) multilevel analyses account for clustering, and (b) other ANOVAs and regressions provided results that were corrected for important covariates.

We conducted separate meta-analyses for RCTS and CBAs and, within those, for each outcome. We also separated low- and middle-income countries and high-income countries, as the two settings are very different in terms of the prevalence and severity of undernutrition; they also differ in many other ways, including political climate, traditions, and food delivery mechanisms.

Within each outcome, we assessed whether the tests used to assess that income were conceptually similar; in cases where the tasks covered by the test were too different, we did not combine them in a meta-analysis.

3.3.5.1 Methods of analysis for cluster-randomised trials

Studies allocated by village, neighbourhood, or day care could have unit of analysis errors if they did not adjust for between-cluster correlations. Where trials used clustered allocation, we determined whether or not they had controlled appropriately for clustering (e.g. variance-inflated standard errors, hierarchical linear models). If they had used appropriate methods, we used these data in our analyses. If they had not, we corrected for clustering where possible. Table 2 provides a summary of clustered studies.

Methods used to correct for design effect in clustered trials or CBAs that were not adjusted for clustering

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When we used a standardised mean difference (SMD) as the pooled estimate (because of varying metrics), we applied the methods outlined in Section 16.3 of the Cochrane Handbook (Higgins 2011b) to inflate the standard error. First, we calculated the unadjusted SMD and 95% confidence interval. We entered the unadjusted SMD as the effect estimate in the generic inverse variance method, and then we inflated the standard error of the effect estimate by multiplying by the square root of the variance inflation factor, calculated as: 1 + ((M - 1) multiplied by ICC), where M is the average cluster size. We calculated the standard error as the confidence interval divided by 3.92.

Calculating the variance inflation factor

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First, we calculated cluster size. When the number of participants in each analysis was provided, we divided this by the number of clusters to calculate cluster size. Otherwise, we used the number of participants provided in the Methods sections of the primary studies divided by the number of clusters.

1+ ((M - 1) multiplied by ICC), where M is the average cluster size (Ukoumunne 1999). We then multiplied the original standard deviation by the square root of the VIF for experimental and control groups separately. We then entered these adjusted standard deviations into the Review Manager 5 data tables, combining them with estimates from individual level trials.

3.3.9.1 Randomised controlled trials (RCTs), cluster-randomised controlled trials (c-RCTs), controlled before-and-after studies (CBAs)

For continuous data, we incorporated data on means, standard deviations, and the number of participants for each outcome in the two groups. While we did not adjust these means and standard deviations for confounders, we adjusted them for clustering when needed.

In performing our meta-analyses, we used the inverse-variance random-effects model. We calculated SMDs using Hedges g, taking the direction of effect into account. Following the Cochrane Handbook (Section 9.2.3.2), we interpreted results using clinical as well as statistical significance.

We compared the most intensive intervention (e.g. highest energy, co-intervention) to a non-intervention control. We also entered comparisons between baseline and the end of the feeding.

3.3.9.2 Interrupted time series (ITS)

We did not have any ITS studies in this review. However, should we find any suitable ITS studies in future updates, we will analyse them according to the methods in Appendix 4 (see also Kristjansson 2007).

3.3.9.3 ‘Summary of findings’ tables

We constructed ‘Summary of findings’ tables and rated the quality of evidence using GRADE (Grades of Recommendation, Assessment, Development and Evaluation) (Guyatt 2011) for all primary outcomes in physical and psychological health. GRADE categorises the quality of the evidence as high, moderate, low, or very low. Randomised controlled trials start out at high or medium quality, and observational studies (including CBAs) are low or very low. Evidence from RCTs is downgraded if there is a high risk of bias across studies, if results are inconsistent or imprecise or in the presence of publication bias. Observational studies with no limitations can be upgraded if there is a large magnitude of effect, dose-response or if plausible confounders would have reduced the effect.

3.3.10.1 Assessing impact on socio-economic inequities in health and psychosocial outcomes

We assessed this potential for primary outcomes. Our assessment of the potential for reductions in socio-economic inequities in health was classified as: effective for reducing inequities in health; potentially effective for reducing inequities in health; ineffective for reducing inequities in health; or uncertain.

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Effective: we rated an intervention as effective if the intervention worked, and if improvements in health were greater for children in lower socioeconomic groups than in higher groups.

4.3.1.1 Weight gain

Low- and middle-income countries: RCTs

We included nine RCTs with 1057 children (Bhandari 2001; Grantham-McGregor 1991 (see Walker 1991); Heikens 1989; Kuusipalo 2006; Mangani 2014; Oelofse 2003; Pollitt 2000a; Simondon 1996; Thakwalakwa 2010) in a meta-analysis for weight. The average period of feeding in these studies was six months. Our meta-analysis showed a small significant effect of feeding. Children who were given supplementation gained an average of 0.12 kg more than those who were not supplemented (95% confidence interval (CI) 0.05 to 0.18; Analysis 1.1). There was no heterogeneity (Chi² = 3.92, df = 8, P value = 0.86, I² = 0%). Sensitivity analyses using an intraclass correlation coefficient (ICC) of 0.10 made little difference (Analysis 2.1).

The 14-month Obatolu 2003 RCT (60 children) found a large and significant effect of feeding on weight gain for boys (end-of-study difference of 3.91 kg statistically significant) and girls (end-of-study difference of 2.55 kg statistically significant).

The Fauveau 1992 study found that 48 children who received supplementary feeding gained an average of 39 grams more than the 43 controls (six-month intervention: not significant).

Low- and middle-income countries: CBAs

Seven CBAs in low- and middle-income countries (Devadas 1971; Gopalan 1973; Gershoff 1988; Lutter 2008; Manjrekar 1986; Mittal 1980; Santos 2005) with 1784 children were included in this meta-analysis. The average length of feeding was one year. There was a significant effect of feeding on weight. Children who were given supplementation gained an average of 0.24 kg more than those who were not supplemented (95% CI 0.09 to 0.39). There was moderate heterogeneity (Chi² = 30.07, df = 15, P value = 0.01, I² = 50%; Analysis 3.1). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 5.1).

High-income countries: RCTs

Only one RCT in high-income countries assessed weight gain (Ziegler 2009). Our analyses found that children who received supplementation in the form of an iron-fortified cereal gained slightly less weight than children who received no supplementation (mean difference (MD) -0.10, 95% CI -0.52 to 0.32, n = 45; Analysis 4.1).

High-income countries: CBAs

We analysed results from Coyne 1980; 116 Aboriginal children were included. There were significant effects of four months of supplementation on weight (MD 0.95, 95% CI 0.58 to 1.33; Analysis 7.1).

4.3.1.2 Height gain

Low- and middle-income countries: RCTs

Nine RCTS (Bhandari 2001; Grantham-McGregor 1991 (see Walker 1991); Heikens 1989; Kuusipalo 2006; Mangani 2014; Oelofse 2003; Rivera 2004; Simondon 1996; Thakwalakwa 2010), with 1463 children, contributed to this meta-analysis. The average period of supplementation was six months. This analysis demonstrated that children who were given supplementation grew an average of 0.27 cm (95% CI 0.07 to 0.48) more than those who were not supplemented. There was little heterogeneity (Chi² = 11.33, df = 8, P value = 0.18, I² = 29%; Analysis 1.2). We did not perform a sensitivity analyses because no adjustments for clustering were needed.

Pollitt 2000a studied effectiveness for two age cohorts, 12 and 18 months old. They found that supplementary feeding had a significant effect on height for the younger (12-month-old) cohort only (see Beckett 2000). Obatolu 2003 (60 children) found a significant effect of feeding on length for boys (difference between experimental and controls: 5.12 cm; end-of-study difference of 5.02 statistically significant) and girls (difference: 6.95 cm; end-of-study difference of 5.92 cm statistically significant).

Low- and middle-income countries: CBAs

We included seven CBAs in low- and middle-income countries (Devadas 1971; Gopalan 1973; Gershoff 1988; Lutter 2008; Manjrekar 1986; Mittal 1980; Santos 2005) with 1782 children in this meta-analysis. The average duration of feeding was one year. Overall, there was a non-significant effect of feeding on height (MD 0.52, 95% CI -0.07 to 1.10). Heterogeneity was high (Chi² = 97.02 df = 15, P < 0.00001, I² = 85%; Analysis 3.2). A sensitivity analysis with the ICCs at 0.10 showed significant positive effects (MD 0.57, 95% CI 0.06 to 1.07) (Analysis 5.2) for height.

High-income countries: RCTs

One RCT (Ziegler 2009) (45 children) studied height. Our analysis indicates that there was no significant difference between children who received iron-fortified cereal and those who received no supplementation (MD -1.00, 95% CI -2.12 to 0.12; Analysis 4.2).

High-income countries: CBAs

Our analysis of Coyne 1980 found no significant effects of supplementation on height (MD 0.61, 95% CI -0.31 to 1.54, n = 116; Analysis 7.2).

4.3.1.3 Change in Weight for Age z-score (WAZ)

Low- and middle-income countries: RCTs

We included eight RCTs (Husaini 1991; Iannotti 2014; Kuusipalo 2006; Mangani 2014; McKay 1978; Oelofse 2003; Rivera 2004; Thakwalakwa 2010) and 1565 children in the meta-analysis for WAZ. The average duration was six months. There were statistically significant differences between the supplemented and non-supplemented groups (MD 0.15, 95% CI 0.05 to 0.24; Analysis 1.3). Heterogeneity was moderate (Chi² = 14.68, df = 7, P value = 0.04, I² = 52%). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 2.2).

In a cluster-RCT with 282 children, Roy 2005 found significant effects of supplementation with maternal nutrition education. Those children in the intervention group gained 0.71 more in WAZ than the children who received no treatment (P < 0.001) and 0.26 more than the children who received only maternal nutrition education (not significant).

Low- and middle-income countries: CBAs

Four CBAs (Lutter 2008; Santos 2005; Schroeder 2002; Tomedi 2012) with 999 children contributed to the meta-analysis for WAZ. The average study period was eight months. There was no statistically significant difference between children who received supplementation and those who did not (MD 0.27; 95% CI -0.13 to 0.68). There was significant heterogeneity (Chi² = 87.47, df = 3, P < 0.00001, I² = 97%; Analysis 3.3). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 5.3).

High-income countries: RCTs

One RCT (Yeung 2000) in 103 children in a high-income country assessed WAZ; infants who received iron-fortified cereals had a z-score change of 0.02 (95% CI 0.01 to 0.03; Analysis 4.3).

High-income countries: CBA

No CBAs assessed the change in WAZ in a high-income country.

4.3.1.4 Change in height-for-age z-scores (HAZ)

Low- and middle-income countries: RCTs

We included nine RCTs (Husaini 1991; Iannotti 2014; Isanaka 2009; Kuusipalo 2006; Mangani 2014; McKay 1978; Oelofse 2003; Rivera 2004; Thakwalakwa 2010) with 4544 children in this analysis. The average study duration was six months. we found a significant effect of supplementation (MD 0.15, 95% CI 0.06 to 0.24). Heterogeneity was moderate (Chi² = 20.96, df = 8, P value = 0.007, I² = 62%; Analysis 1.4). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 2.3).

Low- and middle-income countries: CBAs

Four studies (Lutter 2008; Santos 2005; Schroeder 2002; Tomedi 2012) with 999 children contributed to this meta-analysis. The average study period was eight months. There was no effect (MD 0.01; 95% CI -0.10 to 0.12) and little heterogeneity (Chi² = 3.95 df = 3, P value = 0.27, I² = 24%; Analysis 3.4). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 5.4).

De Romana 2000 (n = 250) found no significant difference between the experimental and the control groups in change in prevalence of stunting (i.e. height-for-age z scores (HAZ)).

High-income countries: RCTS

One RCT (Yeung 2000) with 103 children assessed HAZ. Infants who received iron-fortified cereals had a z-score change of 0.04 (95% CI 0.04 to 0.05; Analysis 4.4).

High-income countries: CBAs

No CBAs assessed the change in HAZ scores in a high-income country.

4.3.1.5 Change in weight-for-height z-scores (WHZ)

Low- and middle-Income countries: RCTs

Seven RCTs (Grantham-McGregor 1991; Isanaka 2009; Kuusipalo 2006; Mangani 2014; Oelofse 2003; Rivera 2004; Thakwalakwa 2010) with 4073 children contributed to this meta-analysis. The average study duration was six months. There was no effect of supplementation (MD 0.10, 95% CI -0.02 to 0.22; Analysis 1.5). Heterogeneity was high (Chi² = 18.39, df = 6, P < 0.005, I² = 67%). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 2.4).

Low- and middle-income countries: CBAs

Four studies (Lutter 2008; Santos 2005; Schroeder 2002; Tomedi 2012) with 999 children contributed to this meta-analysis for WHZ. The average study period was eight months. We found a non-significant difference between children who received supplementation and those who did not (MD 0.29, 95% CI -0.11 to 0.69, Analysis 3.5). There was significant heterogeneity (Chi² = 67.31, df = 3, P < 0.00001, I² = 96%). Sensitivity analyses with ICCs at 0.10 made little difference (Analysis 5.5).

High-income countries: RCTS

One RCT (Yeung 2000) assessed WHZ. There was a very small, statistically significant effect: infants in the control group fared better than children who received supplementation (MD -0.06, 95% CI -0.07 to -0.05, n = 103; Analysis 4.5).

High-income countries: CBAs

No CBAs assessed change in WHZ scores in a high-income country.

Primary outcomes: Psychosocial

4.3.1.6 Psychomotor development

Low- and middle-income countries: RCTs

Four RCTs in low- and middle-income countries assessed the effect of supplementary feeding on psychomotor development.

Our meta-analysis of two studies (Husaini 1991; Grantham-McGregor 1991) found that children who received supplementary feeding had greater improvement on tests of psychomotor functioning than children who did not receive any supplementary food (SMD 0.41, 95% CI, 0.10 to 0.72; n = 178; Analysis 6.1). There was no heterogeneity (Chi² = 0.1, df = 1, P value = 0.75, I² = 0%).

Waber 1981 reported that children who received 2.5 years of supplementation (Group B; n = 60) beginning at six months of age had better overall scores at the end of the study on the Griffiths Mental Development Scales (GMDS) than those who received no supplementation (n = 54), but significance was not given.

Pollitt 2000a reported no main effect of supplementary feeding on children's psychomotor performance in a Repeated Measures ANOVA (experimental group: n = 53 in 12-month cohort; n = 83 in 18-month cohort), but did find significant differences in change over time contrasts.

None of the CBAs in low- and middle-income countries or RCTs and CBAs in high-income countries assessed psychomotor development.

Motor milestones

Findings concerning the effect of supplementation on achievement of motor milestones are equivocal. Pollitt 2000a found that significantly more of the supplemented children walked by 18 months (100% compared to 50%: P² (Kruskal-Wallace) = 11.4, df = 2, P < 0.01). Iannotti 2014 (n = 420) and Mangani 2014 (n = 840) found no significant effects.

None of the CBAs in low- and middle-income countries or RCTs and CBAs in high-income countries assessed motor milestones.

4.3.1.7 Cognitive development

Low- and middle-Income countries: RCTs

Three RCTs in low- and middle-income countries assessed change in cognitive development. The outcome measures in these studies were too different conceptually to be included in a meta-analysis.

For McKay 1978, we compared results for T4 children (supplemented with stimulation from 42 to 84 months of age) to those of T2 children (supplemented from 63 to 84 months of age) at 63 months. Our analysis (n = 99) found that cognitive ability of the supplemented children improved more than the children who were not yet supplemented (SMD 0.58, 95% CI 0.17 to 0.98; Analysis 6.2).

Our analysis of Husaini 1991 (n = 113) found a non-significant difference in change on the Bailey Scales of Mental Development (BSMD) (SMD -0.40, 95% CI -0.79 to -0.00); Analysis 6.3).

Pollitt 2000a found no main effects of supplementation on the BSMED (Bailey Scales of Mental Development). They reported positive effects in a contrast over time for the younger cohort but not for the older cohort (F₂, 48 = 4.58, P < 0.05; n = 53).

None of the CBAs in low- and middle-income countries or RCTs and CBAs in high-income countries assessed cognitive development.

4.3.1.8 Long-term follow-up of cognitive development

Low- and middle-Income countries: RCTs

Grantham-McGregor 1997 followed up 97% (n = 127) of the original cohort of stunted children (Grantham-McGregor 1991; n = 129) after four years and tested them on a battery of cognitive and perceptual tests. A multiple regression found effects on perceptual motor tasks, but not on general cognition or memory. Interestingly, stimulation had a significant effect on later perceptual motor skills for all children (P < 0.05), but supplementation only had a significant effect for children whose mothers had higher scores on a test of verbal intelligence. (P < 0.05). Grantham-McGregor 2007 also found that the supplemented children had higher average scores than the controls on 14 out of 15 cognitive tests (P value = 0.02).

Pollitt 1997 performed a seven-year follow-up of Husaini 1991. They found no differences between the experimental (n = 125) and control (n = 106) groups in the Peabody Picture Vocabulary Test (PPVT), emotionality, and maths. They did find small, (15-second difference) positive effects of supplementation on working memory performance, although these are unlikely to be clinically significant.

None of the CBAs in low- and middle-income countries or RCTs and CBAs in high-income countries assessed long-term follow-up of cognitive development.

4.3.1.9 General development

Low- and middle-Income countries: RCTs

Oelofse 2003 (n = 60) found no significant differences on the Denver Developmental Screening Test (DDST) between the group of South African infants (aged six months at baseline) given a micronutrient-fortified supplement for six months and control infants.

None of the CBAs in low- and middle-income countries or RCTs and CBAs in high-income countries assessed general development.

4.3.1.10 Attention, Language and Memory

We found no reports of effects on attention or memory. For language, Pollitt 2000a reported that supplemented children in the younger cohort (n = 53) had greater increases in vocalisations over time than those who were not given supplementary feeding.

Primary outcomes: Adverse effects

4.3.1.11 Substitution or leakage

We were able to calculate the net benefit from supplementary feeding for seven studies that provided home-delivered rations (RCTs: Bhandari 2001; De Romana 2000; Grantham-McGregor 1991 Rivera 2004; CBAs: Lutter 2008; Santos 2005; Tomedi 2012) and three of the day-care/feeding centre studies (RCTs: Husaini 1991; Pollitt 2000a; CBA: Devadas 1971). We found important differences in the number of calories provided by the supplementary food and the number of extra calories that the children actually consumed in addition to their regular food. In the take-home studies, we found that the net benefit to children was only 36% of the extra calories provide by the supplement. In the day-care and feeding centres, the net benefit was 85% of the extra calories provided by the supplement.

Secondary outcomes: Physical health

4.3.1.12 Biochemical markers of nutrition

Low- and middle-income countries: RCTs

Five RCTs (300 children) in low- and middle-income countries (Husaini 1991; Kuusipalo 2006; Oelofse 2003; Rivera 2004; Thakwalakwa 2010) contributed to the meta-analysis for haemoglobin. We found a significant effect of supplementation (SMD 0.49, 95% CI 0.07 to 0.91; Analysis 8.1); children who were supplemented showed positive change in haemoglobin status compared to controls. There was significant heterogeneity (Chi² = 10.78, df = 4, P value = 0.03; I² = 63%).

Low- and middle-income countries: CBAs

Among the CBAs, Lutter 2008 reported a significant effect of supplementation on the risk of anaemia (P value = 0.003; n = 110 at final survey); those who were supplemented had lower risk of being anaemic (OR 0.58, 95% CI 0.24 to 0.75). Similarly, De Romana 2000 (n = 250) reported that while the prevalence of anaemia decreased by 27% in the intervention group, it decreased by only 13% in the control group.

High-income countries: RCTs

Yeung 2000 (103 children) found no significant difference between the experimental and control group in change in haemoglobin.

High-income countries: CBAs

Coyne 1980 (116 children) reported an increase in the number of children who had low haemoglobin levels in the experimental group and a decrease in the corresponding number in the control group.

4.3.1.13 Physical activity

Low- and middle-income countries: RCTs

Pollitt 2000a (see Jahari 2000) reported a significant main effect of supplementation on motor activity in the youngest (12-month-old; n = 53) cohort (F₂,48 = 4.8, P < 0.05). Over the 12-month period of the supplementation, the supplemented group had significantly greater increases in high energy cost motor activity that began at 18 months of age and continued to the end of the study (24 months) (P < 0.05).

Grantham-McGregor 1991 found no significant effect of supplementation alone (n = 26) or supplementation plus stimulation (n = 26) on changes in motor activity in stunted children (see Meeks Gardner 1999).

No CBAs in low- and middle-income countries and no RCTs or CBAs in high-income countries assessed physical activity.

4.3.1.14 Morbidity

Six studies (four RCTs; two CBAs) reported on morbidity. Three RCTs (Bhandari 2001; Iannotti 2014; Isanaka 2009) and two CBAS (Gopalan 1973; Tomedi 2012) found few differences between the supplemented group and the control group in the prevalence of morbidity. Roy 2005 (a CBA) reported mixed results; the prevalence of diarrhoea and fever was higher in the children who received supplementation (n = 99), while the prevalence of respiratory infection was higher in the control group (n = 90).

4.3.1.15 Mortality

Low- and middle-income countries: RCTs

Isanaka 2009 reported that there was no significant difference in mortality between children supplemented with ready-to-use therapeutic feeding (RUTF; n = 1671) and those who were unsupplemented (adjusted hazard ratio (HR) 0.76, 95% CI 0.51 to 1.13; n = 1862).

No CBAs in low- and middle-income countries and no RCTs or CBAs in high-income countries assessed mortality.

4.3.1.16 Overweight or obesity

There were no reports of overweight or obesity in the included studies.

Secondary outcomes: Psychosocial outcomes

4.3.1.17 Stigmatisation and behaviour problems

There were no reports of stigmatisation or behaviour problems in the included studies.

4.3.1.18 Subgroup analyses

We conducted subgroup analyses across seven categories: age, sex, socio-economic disadvantage (poor versus less poor or undernourished versus well-nourished), nutritional adequacy, location of feeding, level of supervision (monitoring), and single intervention versus multiple interventions.

Age

We conducted subgroup analyses to explore the possible impact of age on weight and height. For the RCTs, we compared the following age groups: < 12 months, one to two years, and > 2 years. The age groups for the CBAs were: < 1 year, 1 year, 2 years, and > 2 years.

Weight

We found no significant differences in either the subgroup analyses of nine RCTs (Chi² = 1.95, df = 2, P value = 0.38, I² = 0%; n = 1057; Analysis 1.1) or that of seven CBAs (Chi² = 5.7, df = 3, P value = 0.13, I² = 47.4%; n = 1784; Analysis 3.1).

Height

This analysis showed significant subgroup differences (Chi² = 6.01, df = 2, P value = 0.05, I² = 66.7%). Supplementary feeding was effective for the youngest age groups (< 12 months: MD 0.22, 95% CI 0.05 to 0.39, 7 trials, n = 1316; and 1 to 2 years: MD 0.9, 95% CI 0.33 to 1.47, 1 trial, n = 65), while the height gains in the oldest age group (> 2 years old) were non-significant (1 trial, n = 82; Analysis 1.2).

Seven CBAs (n = 1782) in low- and middle-income countries contributed to this subgroup analysis. There were no significant differences among subgroups (Chi² = 0. 63, df = 3, P value = 0.89, I² = 0%) and no discernible pattern by age (Analysis 3.2).

4.3.1.19 Exploring heterogeneity

Analysis 3.3, Analysis 3.5, Analysis 5.3, and Analysis 9.10 were highly heterogeneous with I² values above 90%. We checked this in several ways. First, we examined any potential errors in data entry and found none. Second, we performed sensitivity analyses, taking out each study in these analyses one by one. We found that deleting Tomedi 2012 resulted in the largest drop in heterogeneity in analyses Analysis 3.3, Analysis 3.5, Analysis 5.3. We then compared Tomedi 2012 to the other studies and found that it had very good implementation procedures, including a provision of a high percentage of the recommended daily allowance, nutrition education, and take-home rations for other children in the family. For Analysis 9.10, we found the largest drop in heterogeneity when we deleted Gopalan 1973, but heterogeneity was still high at 70%.

Children who were younger at the start of the study may grow more in height/length than older children in response to supplementary feeding; results were mixed for weight

Our hypothesis that younger children would grow more in response to supplementary feeding was largely upheld. For example, our subgroup analysis for height (1463 children) was significant. Children in the two younger subgroups (< 12 months and ages one to two years; 1057 children; Analysis 1.2) gained significant amounts of weight, but those in the older age groups did not. There was no evidence of subgroup differences for weight, but the children in the two younger subgroups were the only ones who gained significant amounts of weight.

The meta-analysis of the CBAs showed no subgroup differences for either weight (1784 children) or height (1782 children). For weight, children who were two years old gained more weight than controls, while the older and younger groups did not.

Our findings are consistent with those of Beaton 1992 and Dewey 2008, who concluded that feeding interventions can have maximum impact on linear growth if they are started in infancy, as the period between six and 24 months is a period of rapid growth (Dewey 2008). It is important to note that feeding can also have an impact on linear growth in older children (Beaton 1992). In fact, our review of school meals found that linear growth in school-aged children increased by 0.25 to 1.47 cm per year (Galloway 2009; Kristjansson 2007). But it does mean that, for greatest impact on growth, and to help slow the rate of growth faltering, feeding should start when children are well below two years of age.

Only one of the studies (n = 53) reporting psychosocial outcomes assessed the impact of age; the authors report that feeding only benefited younger children (< 18 months).

Sex

The evidence is sparse but generally indicates few sex differences

Gender equity is an important consideration in low- and middle-income countries. In some contexts, there is a family preference for favouring male adults and children in the distribution of food within the family. This was found in a qualitative study in Guatemala (Engle 1992b), in surveys in Bangladesh and the Philippines (Haanga 1987), and was reported in one of our included studies (Roy 2005). Thus, the question of whether boys and girls benefit equally from feeding interventions is an important one.

Our subgroup analyses of two CBAS (840 children) found no difference in effectiveness by sex. However, two CBAs (211 children) reported stronger effects on growth for girls than for boys. This latter finding is consistent with analyses from the Oriente Longitudinal Study, which found that girls benefited from supplementation more than boys in terms of growth and cognition (Engle 1992b). We cannot draw firm conclusions from this data as only two studies were included in the analysis. This should be explored further, both quantitatively and qualitatively, in future research.

Socioeconomic status or initial nutritional status

Children who are poorer or more undernourished at baseline may grow more in response to supplementary food

Our hypothesis that feeding would be more effective for children who were poorer or more undernourished was generally supported. For example, our analysis of one study (196 children) found greater effectiveness for weight gain if children were undernourished at baseline. Analyses from two primary studies also found greater effectiveness for undernourished children: one for weight and another for skinfold thickness. Two other studies (863 children) found that young undernourished children had greater height and WAZ gain in response to feeding, but that older undernourished children did not. Further evidence comes from the fact that the only study in a high-income country that reported beneficial effects of feeding was performed among Aboriginal children, who are generally far more marginalised than non-Aboriginals. For example, Australian Aboriginal families are much more likely to be food-insecure (24% food-insecure) than non-Aboriginals (5% food-insecure) (Browne 2009; Rosier 2011).

In contrast, as mentioned above, one primary study (n = 247) found that children living in very poor socio-economic conditions did not respond as well to supplementary feeding as those living in better socio-economic conditions. The author suggested that poor environmental conditions may have reduced effectiveness. Furthermore, in the follow-up of the Jamaican study, supplemented children only experienced long-term cognitive benefits if their mothers had higher verbal ability at baseline. Others have found that maternal education and intelligence are important contributors to infants' dietary intake and nutritional status (Wachs 2005).

It makes biological sense that the children who are poorer or undernourished would benefit more from supplementary feeding. Our findings concur with those of Beaton 1982 and Kennedy 1987. We suggest that, in general, poorer children are more likely to benefit from feeding, but that feeding may not be all that is needed to overcome the effects of deprived environments.

It is important to point out that we were not able to assess whether or not the food actually reached those children who were most in need. Beaton 1982 and Rondo 1990 both noted that feeding programmes in developing countries often fail to do this.

Nutritional Adequacy

There is sparse evidence that programmes which provide more energy may more effective

Our hypothesis that higher nutritional adequacy would result in better outcomes was partially supported. Among the RCTs, there were few differences among subgroups for weight (eight trials, 975 children). For height, there was no evidence of subgroup differences in the RCTs, but the subgroup that provided high nutritional adequacy was the only group which found positive effects for feeding; the differences between high and low and moderate nutritional adequacy subgroups were 0.37 cm and 0.46 cm respectively. We believe that this subgroup analysis may have been non-significant*** because of the low number of trials in the high (two trials, n = 254) and low (one trial, n = 127) nutritional adequacy groups.

Among the CBAs, there were no significant subgroup differences, but programmes which provided moderate nutritional adequacy (four trials, 651 children) had significant positive gains in weight after supplementary feeding, while the group who received low nutritional adequacy (five trials, 961 children) did not, but differences between the two groups were small. The mean difference for the moderate adequacy group was also 0.32 kg higher than that of the high-energy group. It is important to note that the high-energy intervention group for the CBAs contained only the Santos 2005 trial (n = 191), which had substantial issues with unreliable delivery and leakage within the family. In this study, 50% of the caregivers reported ‘gaps in delivery’; 36% of caregivers reported that these gaps occurred more than twice. Furthermore, only 32.5% of the participating children received the full ration. For the remainder of the children, the ration was shared with one to three other children and one to two adults. Despite the fact that the ration should have provided a high amount of energy, the supplemented group actually took in fewer calories than the control group.

The CBA results for height found no subgroup differences and the mean difference for the low-energy group (five studies, n = 959) was higher than that for the moderate-energy (four studies, n = 651) and high-energy (one study, n = 172) groups.

Mode of delivery, amount of supervision of the supplementary feeding, leakage, and substitution

Location of feeding. There is some evidence that feeding given in day-care may be more effective than that given at home

There were not enough data to fully test this in the RCTs, as only one study provided feeding on the spot. Among the CBAs, there was no evidence of subgroup differences but children who were fed in day-care or feeding centres were the only ones who gained significant amounts of weight relative to controls (seven trials, 1784 children). For height, there was no evidence of an effect for any of the subgroups, but the subgroup who was fed ‘on-the spot’ had a mean that was 0.93 cm higher than those who were fed in day-cares. We believe that the lack of subgroup differences may have been due to other differences in implementation. An exploratory sensitivity analysis showed that when Manjrekar 1986 (whose results were markedly different from those of the other studies) was removed from the subgroup analyses for weight and height, heterogeneity was slightly lower, there was evidence of an effect for both subgroup analyses, and the effects in the day-care group were stronger. It is notable that this study had a very high drop-out rate.

Relatedly, our analysis in EXCEL found that when supplementary food was take-home or home-delivered, the children took in an average of only 36% of the energy provided by the supplement. In day-cares and feeding centres, however, the children benefited from an average of 85% of this energy. This is consistent with findings reported in a synthesis by Kennedy 1987; ‘on-site’ feeding resulted in higher intakes than did ‘take-home’ feeding.

It is likely that this reduction in energy benefits from the home-delivered food or poorly-supervised programmes was at least partially due to ‘leakage’ within the family. In interviews with mothers, Santos 2005 found that the target child only received the full ration one-third of the time; Tomedi 2012 reported that children in the experimental group received “at least” 50% of the supplement. This is an important issue for feeding programmes in developing countries (Patel 2005). With home-based delivery, some of the food provided for one child often gets redistributed within poor families or sold to augment the family's income; this is one type of ‘leakage’. When food is given at school or at day-care, families may give that child less at home so that other family members can have more; this is known as ‘substitution’.

Although, “this is understandable in the context of food-insecure families” (Patel 2005, p 4), the result of such leakage is that the targeted child gets less food, and therefore less impact on growth and development can be expected. However, other researchers have pointed out that supplementary feeding may be seen as a net benefit to the whole family, and not just to one child.

Level of supervision. Our analyses suggest that stricter supervision of feeding may produce better child outcomes

Our hypothesis that programmes with stricter supervision would be more effective was partially supported. There was no evidence of subgroup differences for RCTs. For height, the supplemented children in the subgroup with the strictest supervision (four trials, 762 children) were the only ones who grew more than controls, although differences in means between subgroups were small. This analysis only compared moderate to strict supervision. There was no evidence of an effect in the CBAs, but we did find that children in the studies with the strictest supervision (five trials, 1286 children) gained more in weight from feeding than did children in the studies with moderate or little supervision (0.24 kg and 0.29 kg respectively). The same was true for height (0.54 cm and 0.85 cm difference between high and moderate and low supervision respectively). We believe that the lack of evidence for an effect in the CBA subgroup analyses may have been due to other differences in study implementation. An exploratory sensitivity analysis showed that when Manjrekar 1986 (whose results were markedly different from those of the other studies) was removed from the subgroup analyses for weight and height, heterogeneity was slightly lower and there was evidence of a subgroup effect for weight. It is notable that Manjrekar 1986 had a very high drop-out rate.

Leakage in the supply chain

Two of our studies reported breakdowns in the supply chain; the supplements only reached the families part of the time. Such failures in delivery have been reported by others who have reviewed preschool feeding programmes (Kennedy 1987) and school feeding programmes (Galloway 2009).

Multiple interventions. There is little evidence to support our hypothesis that multiple interventions would be more effective for growth than single interventions

Our hypothesis that multiple interventions would be more effective for growth was unsupported. There were no subgroup differences. Among the RCTs, both single and multiple interventions were effective for weight gain but the effect size for multiple interventions was higher. For height, two RCTs that provided multiple interventions (495 children) did not show effects while the seven RCTs that provided single interventions (952 children) were effective for increasing height. Among the CBAs (1782 children), neither single nor multiple interventions were effective for increasing height.

For psychosocial outcomes, there was no evidence of subgroup differences, but the effect size for the supplementation + stimulation group in one study (n = 65) was twice as high as effects for feeding only in two studies (n =178). It is likely that stimulation combined with feeding is especially effective for psychosocial development.

Disruption of breastfeeding

When food is given to infants, it is important to ensure that it does not disrupt breastfeeding, as this could lead to a rise in morbidity (Dewey 2008). Only three studies in our review examined whether or not the feeding intervention interfered with breastfeeding, and they had contradictory results. Findings from a survey done in conjunction with the Ecuador study found that supplementary feeding did not interfere with breastfeeding practices (Lutter 2008; n = 110 at final survey). In Indonesia, supplementation did not interfere with breastfeeding boys (Pollitt 2000a; n = 47), but it did seem to decrease breast feeding of girls (n = 48). However, Bhandari 2001 found that the proportion of infants who were breastfed was lower in the food supplementation group (n = 96) compared with the visitation-only group (n = 96).