Version Changes
Revised. Amendments from Version 1
Below are the key changes in the update version; 1. Additional information on median weight increments under statistical analysis and results sections of the publication 2. Additional sentences under the statistical analysis sub-heading clarifying the applied gamma frailty model 3. Added in Figure 1 which is a flow chart diagram of study participants from recruitment to 12 months follow-up 4. Additional statistics on infant co-morbidity and maternal demographics on existing Table 1 Reviewers have been provided with a point-by-point response to their comments
Abstract
Background: Wasting and underweight in infancy is an increasingly recognised problem but consensus on optimum assessment is lacking. In particular, there is uncertainty on how to interpret anthropometry among low birth weight (LBW) infants who may be growing normally. This research aimed to determine growth of infants from birth to two months (around age of vaccination) and the mortality risk of underweight LBW infants compared to normal birth weight (NBW) infants at two and six months age.
Methods: A secondary analysis of a birth cohort of 1103 infants in Burkina Faso was conducted. Anthropometry was performed monthly from 0 to 12 months. We assessed associations with mortality using Cox proportional hazards models and assessed discriminatory values using area under receiver operating characteristics curves.
Results: Eighty-six (7.8%) children died by age one year, 26/86 (30%) and 51/86 (59%) within two and six months, respectively. At age two months, weight gain since birth did not better discriminate mortality risk than current weight-for-age (P=0.72) or mid-upper arm circumference (P=0.21). In total, 227 (21%) LBW infants had increased risk of mortality: adjusted hazards ratio (aHR) 3.30 (95%CI 2.09 to 4.90). Among infants who were underweight at two and six months, LBW infants (64% and 49%, respectively) were not at reduced risk of death compared to NBW infants (aHR 2.63 (95%CI 0.76 to 9.15) and 2.43 (95%CI 0.74 to 7.98), respectively).
Conclusion: Assessing weight gain since birth does not offer advantages over immediate anthropometry for discriminating mortality risk. LBW infants who are later identified as underweight require care to help prevent mortality.
Keywords: Low birthweight, Infants, Anthropometry, Growth, Mortality
Abbreviations
LAZ – Length-for-age Z score; LBW – Low birth weight; LMIC -Low- and middle-income countries; MUAC – Mid-upper arm circumference; NBW – Normal birth weight; SAM – Severe acute malnutrition; u6m – Under 6 months; WLZ – Weight-for-length Z score; WAZ – Weight-for-age Z score; MAMI – Management of At risk Mothers and Infants
Introduction
Infancy is the period of fastest relative growth; on average, a normally growing infant more than doubles their birth weight in the first six months of life :The WHO Child Growth Standards 2006: [Available from: http://www.who.int/childgrowth/standards/en/]. In early infancy, apparent wasting or underweight may occur due to growth faltering and/or as a result of having been born preterm, low birth weight (LBW) or small for gestational age 1 .
The global prevalence of LBW is 15%, representing more than 20 million infants, 91% of whom are in low and middle-income countries (LMICs) 2 . In LMICs, birthweight and subsequent anthropometry in infancy and childhood are predictive of both short and longer-term mortality 3 . Studies report ongoing extra-uterine weight and length restriction and reduced physical strength through to adolescence following LBW compared to normal birth weight (NBW) infants 4 . The rate of infant growth irrespective of birth weight is influenced by nutritional intake, absorption and assimilation of nutrients, nutrient losses due to infection, other acute or chronic diseases, and genetic or epigenetic predisposition 5 .
High-quality growth references standards exist for preterm infants from the INTERGROWTH-21 st study 6 . However, information on gestational age is often unavailable or unreliable in LMICs, hence WHO growth standards are usually applied to all infants irrespective of birth size. In growth monitoring or nutrition programs, LBW babies may be classified as underweight or wasted whilst having a normal (preterm) growth velocity tracking lower percentiles or ‘catching up’. Anthropometric indicators such as weight-for-length Z score (WLZ) and mid-upper arm circumference (MUAC) identify a large proportion of infants with a history of LBW. For example, in Kenya, 43% of ill hospitalized malnourished infants under six months old had a history of small size at birth 1 , while in India, LBW was a strong predictor of severe wasting among infants below the age of six months 7 .
Thus, there is uncertainty among health workers regarding interpretation of anthropometry in LBW infants, with low expectations of growth in LBW infants common and “slow” and potentially “poor” growth in infants (underweight) regarded as acceptable 8 . Additionally, practitioners target catch up growth of LBW infants to that of peers and assume that mortality risk is resolved among LBW infants who are no longer classified as undernourished 9 . There are also concerns for potential future health risks associated with accelerated weight gain 10, 11 . Whether and how to intervene on undernourished LBW infants has implications for health system workload and costs. Consequently, to evaluate risks associated with anthropometry and types of interventions needed, birthweight may need to be considered during anthropometric assessment in infancy. In practice, vaccination at around two months of age is an established opportunity to assess growth and to intervene.
Given this background, we examined data from a birth cohort to compare the discriminatory value for mortality for anthropometry at birth and changes from birth to the following timepoints in infancy: i) at two months of age which is around the time of infant immunisations; and ii) at six months of age. We also investigated overall mortality risk among LBW infants and whether among infants with low anthropometric values measured at two and six months of age, LBW was associated with lower risk of subsequent mortality.
Methods
Study site
Data utilized for this secondary analysis was from a birth cohort within Barsalogho Health District, part of the Kaya Health Region in Central North Burkina Faso. It was collected between 1 st April and 31 st December of 2004 in four health centers, including Barsalogho, Basma, Dablo, and Foubé. Though old, the dataset is valuable because it contains follow-up data of an untreated infant cohort, which would be difficult to generate at the present time. An untreated cohort offer a more natural experience of growth patterns within the study population. Additional details on the study site can be found in a previous publication 3 .
Study population and design
The study cohort recruited pregnant women in their third trimester attending scheduled antenatal care visits. The objective of the original study was to compare survival in infancy of full-term LBW infants to that of full-term NBW infants 12, 13 . In this secondary analysis, we included data from all live births within the cohort. Follow-up was from birth to 12 months of age through scheduled monthly clinic visits.
Variables
The main outcome of interest was mortality confirmed through hospital records or burial permits/death certificates. Deaths were included in this analysis if they occurred within the first year of life. Anthropometry was collected monthly from birth up to 12 months of age. Exposures examined were anthropometry at birth and ages two and six months (weight (kg), MUAC (cm) and length (cm)) and demographic factors.
Data source/measurements
Both the caregiver and infant demographics and anthropometric measurements were collected at birth, usually within two hours for health facility birth and 48 hours for births in the community by a trained community health worker. MUAC in centimetres were measured with a non-stretch measuring tape to the nearest one mm. An electronic scale (Seca 825 Birmingham, UK) was used to measure weight in kilograms. Length in centimetres were measured using an infantometer (Seca 416, Birmingham, UK). Anthropometric z-scores were calculated using WHO (2006) reference WHO; WHO growth standards STATA macro 2011 [Available from: http://www.who.int/childgrowth/software/en/]. Underweight was defined as weight-for-age z-score (WAZ) <-2. LBW was defined as <2.5kg.
Study size
The parent birth cohort recruited 1103 infants. This secondary analysis included all the 1103 infants. With 1103 infants provide a 7.8% probability of death in one year, a two-sided alpha level of 0.05, the study had power >90% to estimate adjusted hazard ratio of ≥2.25 of LBW associated with death and with power >80% to estimate similar hazard ratio from month two.
Even though the secondary analysis included all the 1103 infants, analyses at different time points used varying number of infants who were alive and in follow-up at the respective time-points.
Statistical analysis
Infant anthropometric measurements were summarised as means and standard deviation. Maternal age was reported as median and interquartile range. We calculated Medians (IQR) weight increments from birth to month 2 and 6 and compared with the WHO 2009 weight increment standards using Wilcoxon single sign rant test [Available from https://www.who.int/childgrowth/standards/velocity/tr3_velocity_report.pdf]. To estimate hazards ratios for death associated with anthropometry, we used Cox proportional hazards regression adjusted for features presumed to have biological association with anthropometry such as sex, birth weight, prematurity and being a twin. We assessed and found evidence of unobserved heterogeneity between the four sites using likelihood ratio test (P=0.002) in a regression model assessing effects of LBW on one year mortality. To account for this unobserved heterogeneity, we used shared gamma frailty model. We assessed and found no evidence of violation of proportional hazard assumption using the Schoenfeld residuals.
To test the discrimination of mortality risks from month two to twelve months of age by anthropometric measurements at birth, at month two and the change between birth and month two measurements, we estimated the area under receiver operating characteristics curves (AUC). We used the STATA version 15.1 “ roccomp” command to test the hypothesis that the AUCs were equal by comparing AUCs from a single time-point (month 2) with the change from birth to month two.
We examined differences in WAZ, and proportion of infants underweight at month two stratified by birth weight and used an independent t-test to test for differences in WAZ between infants born NBW and LBW.
Ethical considerations
The original birth cohort was approved by the Ministry of Health of Burkina Faso (approval number: 1014) in 2003 in accordance with national procedure. All study participants provided written consent to take part in the original study. All data were anonymized before being shared for this analysis.
Results
Cohort characteristics
The parent birth cohort recruited 1103 infants (( Figure 1), 570 (52%) males and 533 (48%) females. A total 492 (45%) of the infants were born in a health facility, 432 (39%) at home assisted by a community birth attendant and 179 (16%) not assisted by a community birth attendant. The median (IQR) gestation age was 39 (38 to 40) weeks, and 62 (5.6%) were born premature. The mean (sd) birth weight and WAZ were 2.8 (0.5) kg and -1.4 (1.6) Z, respectively ( Table 1). Of the 1103 infants, 227 (21% (95% CI 18 to 23%)) were born LBW. During the one year follow-up, 507 (46%), 777 (70%) and 544 (49%) infants had at least one episode of diarrhoea, fever and cough respectively. Fifty two (4.7%) infants were not exclusively breastfed, while 278 (25%), 773 (70%) were exclusively breastfed for three and six months respectively (Table 1).
Figure 1. Flow diagram of study participants from recruitment to 12 months follow-up.
Table 1. Participants characteristics at birth.
| All infants
(N=1,103) |
NBW
infants (N=876) |
LBW
infants (N=227) |
|
|---|---|---|---|
| Demographics | |||
| Sex, N (%) | |||
| Male | 570 (52) | 477 (54) | 93 (41) |
| Birthplace, N (%) | |||
| Health facility | 492 (45) | 399 (46) | 93 (41) |
| Home with CBA | 432 (39) | 349 (40) | 83 (37) |
| Home with no CBA | 179 (16) | 128 (14) | 51 (22) |
| Recruitment health centre, N (%) | |||
| Basma | 416 (38) | 323 (37) | 93 (41) |
| CMA | 320 (29) | 261 (30) | 59 (26) |
| Dablo | 286 (26) | 227 (26) | 59 (26) |
| Foube | 81 (7.3) | 65 (7.4) | 16 (7.1) |
| Born premature (gestation age
<37 weeks) N (%) |
62 (5.6) | 14 (1.6) | 48 (21) |
| Anthropometry | |||
| Weight (Kg); mean ±sd | 2.8 ± 0.5 | 3.0 ± 0.3 | 2.2 ± 0.3 |
| MUAC in cm; mean ±sd | 10.2 ± 1.1 | 10.5 ± 0.9 | 9.2 ± 0.9 |
| Length (cm), mean ±sd | 48.9 ± 2.6 | 49.5 ± 2.1 | 46.4 ± 2.8 |
| WLZ; mean ±sd | -1.4 ± 1.6 | -1.1 ± 1.5 | -2.8 ± 1.2 |
| WAZ; mean ±sd | -1.1 ± 1.1 | -0.8 ± 0.7 | -2.7 ± 0.8 |
| LAZ; mean ±sd | -0.3 ± 1.4 | -0.01 ± 1.1 | -1.6 ± 1.5 |
| ZHC; mean ±sd | -1.3 ± 1.5 | -1.0 ± 1.4 | -2.5 ± 1.6 |
| Infant co-morbidity | |||
| Any diarrhea episode | 507(46) | 396(45) | 111(49) |
| Any fever episode | 777 (70) | 614 (70) | 163 (72) |
| Any cough episode | 544 (49) | 424 (48) | 120 (53) |
| Exclusive breastfeeding | |||
| None | 52 (4.7) | 38 (4.3) | 14 (6.2) |
| First 3 months | 278 (25) | 226 (26) | 52 (23) |
| Four to six months | 773 (70) | 612 (70) | 161 (71) |
| Maternal demographics | |||
| Mother age; median (IQR) years | 25 (20-30) | 25 (20-30) | 22 (19-30) |
| Illiterate N (%) | 854 (77) | 666 (76) | 188 (83) |
| Height (cm); median (IQR) | 163 (159–168) | 164 (160–168) | 163 (157–167) |
| ANC visits | |||
| None | 60 (5.4) | 38 (4.3) | 22 (9.7) |
| 1 to 3 | 1024 (93) | 822 (94) | 202 (89) |
| ≥4 | 19 (1.7) | 16 (1.8) | 3 (1.3) |
| Distance to nearest health facility
(kilometres); median (IQR) |
7 (2–15) | 7 (2–15) | 8 (2–15) |
CBA-community based assistant, MUAC-mid-upper arm circumference, WLZ-Weight-for-length z-score, WAZ-Weight-for-age z-score, LAZ-Length-for-age z-score, ZHC-Head circumference z-score, sd-Standard deviation, NBW-Normal birth weight (≥2.5kg), LBW-Low birth weight (<2.5kg).
At two months of age, of 927 infants who had anthropometry assessed, 148 (16%) were underweight; 94 (64%) of whom were LBW. At six months of age, of the 968 infants who had anthropometry assessed, 236 (24%) were underweight; 92 (39%) of whom were LBW. Anthropometry by month and LBW is shown in Table 2.
Table 2. Monthly weight-for-age z-score and proportion of children underweight stratified by birth weight.
| Birth | Month 1 | Month 2 | Month 3 | Month 4 | Month 5 | Month 6 | Month 7 | Month 8 | Month 9 | Month 10 | Month 11 | Month 12 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| All infants | |||||||||||||
| N | 1103 | 938 | 927 | 986 | 940 | 916 | 968 | 917 | 917 | 938 | 899 | 887 | 941 |
| Mean WAZ ±sd | -1.1 ±1.1 | -0.7 ±1.3 | -0.8 ±1.3 | -0.9 ±1.3 | -1.0 ±1.3 | -1.1 ±1.2 | -1.3 ±1.2 | -1.4 ±1.2 | -1.5 ±1.2 | -1.5 ±1.2 | -1.6 ±1.2 | -1.6 ±1.2 | -1.6 ±1.2 |
| WAZ<-2 (%) | 185 (17) | 134 (14) | 148 (16) | 159 (16) | 171 (18) | 176 (19) | 236 (24) | 278 (30) | 304 (33) | 304 (32) | 310 (34) | 298 (34) | 307.(33) |
| Normal birth weight | |||||||||||||
| N (%) # | 876 (79) | 745 (79) | 740 (80) | 786 (80) | 756 (80) | 739 (81) | 780 (81) | 741 (81) | 739 (81) | 762 (81) | 730 (81) | 724 (82) | 762 (81) |
| Mean WAZ ±sd | -0.7 ±0.7 | -0.3 ±1.0 | -0.5 ±1.1 | -0.6 ±1.1 | -0.7 ±1.1 | -0.9 ±1.1 | -1.1 ±1.1 | -1.2 ±1.1 | -1.3 ±1.2 | -1.4 ±1.2 | -1.4 ±1.1 | -1.4 ±1.1 | -1.4 ±1.1 |
| WAZ<-2 (%) | 0 | 35 (4.7) | 54 (7.3) | 73 (9.3) | 95 (13) | 109 (15) | 144 (18) | 183 (25) | 208 (28) | 209 (27) | 209 (29) | 203 (28) | 205 (27) |
| Low birth weight | |||||||||||||
| N (%) # | 227 (21) | 193 (21) | 187 (20) | 200 (20) | 184 (20) | 177 (19) | 188 (19) | 176 (19) | 178 (19) | 176 (19) | 169 (19) | 163 (18) | 179 (19) |
| Mean WAZ ±sd | -2.7 ±0.8 | -2.2 ±1.4 | -2.2 ±1.4 | -2.0 ±1.5 | -2.0 ±1.4 | -2.0 ±1.3 | -2.2 ±1.3 | -2.2 ±1.3 | -2.2 ±1.2 | -2.3 ±1.2 | -2.3 ±1.1 | -2.3 ±1.2 | -2.3 ±1.2 |
| WAZ<-2 (%) | 185 (82) | 99 (51) | 94 (50) | 86 (43) | 76 (41) | 67 (38) | 92 (49) | 95 (54) | 96 (54) | 95 (54) | 101 (60) | 95 (58) | 102 (57) |
| P-value * | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 | <0.001 |
WAZ-Weight-for-age z-score, *the P-value is from the comparison of the means of WAZ between infants born LBW and normal birth weight using independent t-test, #-percentage of all infants in that follow-up visit.
During twelve months of follow-up, 86 (7.8%) infants died and 76 (6.9%) were lost to follow up after a median of 154 (IQR 91 to 247) days. The total period of observation was 1015 child/years ( Figure 1). Twenty-six (30%) deaths occurred before two months of age. LBW was associated with an increased risk of death during the first year of life: hazards ratio (HR) 3.3 (95% CI 2.09 to 4.90) and P<0.001.
Anthropometric changes from birth and their association with mortality
At two months of age, MUAC had increased by mean (sd) of 2.37 (1.3) cm to 12.3 (sd 1.3); WAZ had increased by 0.27 (sd 1.1) to -0.81 (sd 1.3) Z; and WLZ had increased by mean (sd) of 1.19 (2.2) to -0.19 (2.0) Z; however length-for-age Z score (LAZ) had declined by 0.35 (sd 1.6) to 0.67 (1.7) Z ( Figure 2). The median (IQR) weight increment in Kg from birth to month 2 was 2.2 (1.785 to 2.53) and 1.95 (1.55 to 2.26) for boys and girls respectively which was not significantly different from the WHO increment standards: median (IQR) 2216 (1890 to 2552) grams, P=0.08 for boys and 1897 (1604 to 2210) grams, P=0.13 for girls. The median (IQR) weight (Kg) increment from birth to month 6 was 3.98 (3.4 to 4.5) and 3.6 (3.12 to 4.09) Kg for boys and girls respectively which were significantly lower than the WHO increment standards: median (IQR) 4580 (4072 to 5114) grams P<0.001 for boys and 4079 (3620 to 4597) grams P<0.001 for girls. Anthropometric changes from birth were no better at discriminating mortality compared to single timepoint anthropometric measure taken at month 2, P=0.72, 0.21, 0.28 and 0.80 for WAZ, MUAC, WLZ and LAZ respectively ( Table 3). Results were similar when the regression models of month two measures were adjusted for LBW ( Table 3).
Figure 2.
Month two medians of: A) WAZ, B) WLZ, C) MUAC and D) birth to month two weight difference. The red bars are the medians, for panel A and B, the dashed line is the cut-off of -2 and panel C MUAC=11 cm.
Table 3. Comparison in AUCs of single time measurements (month 2) and change between two-time points (birth & month 2).
| Month two
measurement |
Change between
birth & month two |
P-value: Changes
compared to month two only |
Month two
measurement adjusted for LBW |
P-value: Adjusted
compared to unadjusted month two only |
|
|---|---|---|---|---|---|
| AUCs (95% CI) | AUC (95% CI) | AUCs (95% CI) | |||
| Weight-for-age z-score | 0.65 (0.55, 0.74) | 0.66 (0.57, 0.75) | 0.72 | 0.66 (0.57, 0.76) | 0.44 |
| Mid-upper arm
circumference |
0.63 (0.53, 0.73) | 0.61 (0.51, 0.71) | 0.21 | 0.65 (0.55, 0.76 | 0.44 |
| Weight-for-length
z-score |
0.55 (0.44, 0.65) | 0.59 (0.50, 0.69) | 0.28 | 0.65 (0.56, 0.75) | 0.11 |
| Length-for-age z-score | 0.64 (0.54, 0.73) | 0.53 (0.55, 0.) | 0.80 | 0.66 (0.56, 0.75) | 0.40 |
P-value from the roccomp command comparing the values to month 2.
LBW and its association with underweight and mortality at two and six months of age
LBW infants were persistently more underweight through the first 12 months of life; the proportion was highest at birth at 82% and lowest at age month 4 at 38% ( Table 2).
At two months, of the 148 (16%) underweight infants, 94 (64%) were LBW ( Table 2). Being underweight was associated with mortality; adjusted hazard ratio (aHR) 1.75 (95% CI 1.04 to 2.79). Among underweight infants at two months, having been born LBW compared to NBW was associated with lower risk of mortality; aHR 2.63 (95% CI 0.76 to 9.15) ( Figure 3).
Figure 3. Cumulative hazard of death from two months of age among underweight infants by low birth weight.
At six months, 236 (24%) infants were underweight, of whom 92 (49%) had been born LBW ( Table 2). Being underweight was associated with mortality; aHR 2.20 (95% CI 1.06 to 4.55); AUC 0.67 (95% CI 0.57 to 0.77). Among underweight infants at six months, having been born LBW compared to NBW was not associated with lower risk of mortality; aHR 2.43 (95% CI 0.74 to 7.98); AUC 0.66 (95% CI 0.56 to 0.75).
At six months, of the 968 infants assessed, 173 (18%) and 101 (10%) met WHO criteria for moderate acute malnutrition (MAM) and severe acute malnutrition (SAM) respectively. LBW was present in 38/173 (22%) MAM and 39/101 (39%) SAM cases.
Discussion
We set out to determine if LBW infants who are underweight at month two, which coincides with the age of first vaccination, could be assumed to be growing normally and at a lower risk of mortality than non-LBW underweight infants. We found that being underweight at two or six months of age is associated with a significantly increased risk of mortality irrespective of birth weight. Anthropometric changes observed since birth to two months were no better than a single measure at the point of vaccination in discriminating mortality risk.
LBW was associated with an increased risk of death during the first year of life. This risk is well-documented 14 . WHO guidelines recommend feeding LBW infants mother’s breastmilk for the first six months of life 15 , as this is associated with lower incidence of infections and necrotizing enterocolitis than those fed with infant formula 16 . There is also strong evidence for supplementation with Vitamin D, Iron and Calcium among very low-birth weight (VLBW) infants who are fed on mother’s breastmilk within the first six months of life 16 . In VLBW and LBW infants, vitamin D supplementation resulted in increases in height, weight, and MUAC in two randomised control trials 17, 18 . These specific interventions for LBW infants need to be implemented; however, the impact of growth monitoring programmes on growth and mortality of LBW infants is less clear 15 . A LBW infant tracking below the reference line may be inappropriately considered as “protected” from the risk of mortality because they were born small and apparently growing ‘normally’. Mothers of LBW infants where poor growth is recognised may be advised or initiate supplemental feeds before six months in an attempt to facilitate accelerated growth 19 , which may actually increase risk. There may also be maternal nutritional and health factors that influence feeding and care, which may usually be missed if the wellbeing of the mother is not assessed with that of her infant. Growth monitoring does present an opportunity if accompanied by informed assessment and appropriate action.
Emerging evidence suggests that proactive peer support to mothers of LBW infants improves compliance to exclusive breastfeeding in and out of the hospital environment 20 . Our results indicate that at two months, infants identified to be underweight, irrespective of birth weight, are at increased risk of mortality and should receive targeted support. Underweight infants with a history of LBW should receive micronutrient supplementation as currently recommended, noting the existing gaps in guidance 16 .
We found that a single MUAC, WAZ or LAZ measure taken at two months of age discriminate mortality risk better than WLZ and that a single measure was better than change from birth. This is an important finding given that WLZ is the currently recommended criterion for intervention and among LBW infants, health worker may consider change in anthropometry more important than the single measure taken at growth monitoring. Our results concur with studies of community infants in The Gambia 21 and BukinaFaso 3 , and from hospital infant cohort in Kenya 22 and India 23 where among infants u6m, WLZ is not reliably measured 24, 25 possibly partly explaining its poor prediction of subsequently mortality. Although using growth velocity may be better at identifying risk, in practice repeated measures may be more complex to implement 26 . Current evidence suggests that using a MUAC cut-off of <11.0cm and WAZ<-3 when applied at two months (vaccination point) will effectively identify infants with a high risk of subsequent mortality 21, 22 . As a simple tool, MUAC may be applied at home by either community health workers or family members to help identify at risk infants early 27 .
In many LMIC settings it is not feasible to distinguish LBW and NBW infants and so there is a need to identify early growth failure in infancy and determine intervention strategies appreciating that LBW infants will comprise a considerable proportion of these. To guide interventions, the Management of At risk Mothers and Infants (MAMI) care pathway approach leverages and connects existing services with active case identification and holistic management of the mother-infant dyad (Emergency Nutrition Network MAMI Tool 2018 available from: https://www.ennonline.net/c-mami).
A strength of this study was the large birth cohort with systematically collected monthly infant anthropometry up to one year of age and their vital status. However, the analysis at month two and six only used data from children with a measured anthropometry. Those excluded because of missing anthropometry could have differed with those included in this analysis. In the absence of ultrasound in pregnancy we were not able to distinguish risks from being preterm versus small for gestational age. A further limitation is that risks and care provision may now differ from those when the data were collected. The data used in this analysis was collected in 2004 and may not reflect improvements in infants care practices in the health system such as improvements in vaccination, community management of acute malnutrition.
Conclusions
In the first year of life, LBW infants are more likely to be underweight and continue to be at higher risk of mortality than NBW infants. To reduce risk of mortality among infants, research should focus on interventions to prevent LBW and on effective comprehensive interventions to reduce risks of mortality and promote neurodevelopment. Since LBW infants who are underweight have at least the mortality risk of non-LBW infants, all underweight infants need identification during screening or growth monitoring, to have individual nutritional, health and family assessment, and actions to address the risks associated with being underweight.
Data availability
Underlying data
Study data were obtained through a data sharing agreement with the School of Public Health, Center of Research in Epidemiology Biostatistics and Clinical Research, Université Libre de Bruxelles, Brussels, Belgium and KEMRI Wellcome Trust Research Programme. The data sharing agreement prohibits sharing of child-level data beyond the research team.
Request to access the de-identified data should be sent to Dr. Paluku Bahwere of School of Public Health, Center of Research in Epidemiology Biostatistics and Clinical Research, Université Libre de Bruxelles, Brussels, Belgium, paluku.bahwere@gmail.com. The request should include: a) full names, designation and affiliation of the person requesting data, b) a copy of study protocol approved by ethics review board showing the methods and statistical analyses plan, and c) proposed period that data will be accessed.
Acknowledgements
We acknowledge Dr Marko Kerac for his scientific insights and input during data analysis and write up.
Funding Statement
Irish Aid funding grant number HQPU/2018/ENN, Health Systems Research Initiative- Foundation grant number MR/R002738/1 and Bill and Melinda Gates grant number (OPP1131320).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
[version 2; peer review: 1 approved, 2 approved with reservations]
References
- 1. Mwangome M, Murunga S, Kahindi J, et al. : Individualized breastfeeding support for acutely ill, malnourished infants under 6 months old. Matern Child Nutr. 2020;16(1):e12868. 10.1111/mcn.12868 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2. Blencowe H, Krasevec J, de Onis M, et al. : National, regional, and worldwide estimates of low birthweight in 2015, with trends from 2000: a systematic analysis. Lancet Glob Health. 2019;7(7):e849–e860. 10.1016/S2214-109X(18)30565-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3. Mwangome M, Ngari M, Bwahere P, et al. : Anthropometry at birth and at age of routine vaccination to predict mortality in the first year of life: A birth cohort study in BukinaFaso. PLoS One. 2019;14(3):e0213523. 10.1371/journal.pone.0213523 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4. Raaijmakers A, Jacobs L, Rayyan M, et al. : Catch-up growth in the first two years of life in Extremely Low Birth Weight (ELBW) infants is associated with lower body fat in young adolescence. PLoS One. 2017;12(3):e0173349. 10.1371/journal.pone.0173349 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5. Lahiri A, Chakraborty A: Predictors of Weight Velocity in the First 6 Months of Life in a Rural Block of West Bengal: A Longitudinal Study. Indian J Community Med. 2019;44(2):92–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6. Villar J, Papageorghiou AT, Pang R, et al. : The likeness of fetal growth and newborn size across non-isolated populations in the INTERGROWTH-21st Project: the Fetal Growth Longitudinal Study and Newborn Cross-Sectional Study. Lancet Diabetes Endocrinol. 2014;2(10):781–92. 10.1016/S2213-8587(14)70121-4 [DOI] [PubMed] [Google Scholar]
- 7. Choudhary TS, Srivastava A, Chowdhury R, et al. : Severe wasting among Indian infants <6 months: Findings from the National Family Health Survey 4. Matern Child Nutr. 2019;15(4):e12866. 10.1111/mcn.12866 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8. Koenraads M, Phuka J, Maleta K, et al. : Understanding the challenges to caring for low birthweight babies in rural southern Malawi: a qualitative study exploring caregiver and health worker perceptions and experiences. BMJ Glob Health. 2017;2(3):e000301. 10.1136/bmjgh-2017-000301 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9. Martin A, Connelly A, Bland RM, et al. : Health impact of catch-up growth in low-birth weight infants: systematic review, evidence appraisal, and meta-analysis. Matern Child Nutr. 2017;13(1):10. 10.1111/mcn.12297 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10. Lule SA, Namara B, Akurut H, et al. : Are birthweight and postnatal weight gain in childhood associated with blood pressure in early adolescence? Results from a Ugandan birth cohort. Int J Epidemiol. 2019;48(1):148–156. 10.1093/ije/dyy118 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11. Dulloo AG: Regulation of body composition during weight recovery: integrating the control of energy partitioning and thermogenesis. Clin Nutr. 1997;16 Suppl 1:25–35. 10.1016/s0261-5614(97)80046-5 [DOI] [PubMed] [Google Scholar]
- 12. Kabore P, Potvliege C, Sanou H, et al. : [Growth velocity and survival of full-term low birth weight infants in an African rural area (Burkina Faso)]. Arch Pediatr. 2004;11(7):807–814. 10.1016/j.arcped.2004.03.034 [DOI] [PubMed] [Google Scholar]
- 13. Kabore P, Donnen P, Dramaix-Wilmet M: Impact du petit poids de naissance à terme sur la morbidité et la mortalité infantile en milieu rural sahélien. Journal de pédiatrie et de puériculture. 2009;22(3):121–127. 10.1016/j.jpp.2009.02.003 [DOI] [Google Scholar]
- 14. Watkins WJ, Kotecha SJ, Kotecha S: All-Cause Mortality of Low Birthweight Infants in Infancy, Childhood, and Adolescence: Population Study of England and Wales. PLoS Med. 2016;13(5):e1002018. 10.1371/journal.pmed.1002018 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15. World Health Organisation: Optimal feeding of low-birth-weight infants: technical review.2006. Reference Source [Google Scholar]
- 16. World Health Organisation: Guidelines on Optimal feeding of low-birth-weight infants in low- and-middle-income countries.2011. Reference Source [PubMed] [Google Scholar]
- 17. Kumar GT, Sachdev HS, Chellani H, et al. : Effect of weekly vitamin D supplements on mortality, morbidity, and growth of low birthweight term infants in India up to age 6 months: randomised controlled trial. BMJ. 2011;342:d2975. 10.1136/bmj.d2975 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18. Mathur NB, Saini A, Mishra TK: Assessment of Adequacy of Supplementation of Vitamin D in Very Low Birth Weight Preterm Neonates: A Randomized Controlled Trial. J Trop Pediatr. 2016;62(6):429–35. 10.1093/tropej/fmv110 [DOI] [PubMed] [Google Scholar]
- 19. Adair LS, Popkin BM: Low birth weight reduces the likelihood of breast-feeding among Filipino infants. J Nutr. 1996;126(1):103–12. 10.1093/jn/126.1.103 [DOI] [PubMed] [Google Scholar]
- 20. Agrasada GV, Gustafsson J, Kylberg E, et al. : Postnatal peer counselling on exclusive breastfeeding of low-birthweight infants: a randomized, controlled trial. Acta Paediatr. 2005;94(8):1109–1115. 10.1111/j.1651-2227.2005.tb02053.x [DOI] [PubMed] [Google Scholar]
- 21. Mwangome MK, Fegan G, Fulford T, et al. : Mid-upper arm circumference at age of routine infant vaccination to identify infants at elevated risk of death: a retrospective cohort study in the Gambia. Bull World Health Organ. 2012;90(12):887–94. 10.2471/BLT.12.109009 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22. Mwangome M, Ngari M, Fegan G, et al. : Diagnostic criteria for severe acute malnutrition among infants aged under 6 mo. Am J Clin Nutr. 2017;105(6):1415–1423. 10.3945/ajcn.116.149815 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23. Kumar P, Meiyappan Y, Rogers E, et al. : Outcomes of Hospitalized Infants Aged One to Six Months in Relation to Different Anthropometric Indices - An Observational Cohort Study. Indian J Pediatr. 2020;87(9):699–705. 10.1007/s12098-020-03236-9 [DOI] [PubMed] [Google Scholar]
- 24. Mwangome MK, Berkley JA: The reliability of weight-for-length/height Z scores in children. Matern Child Nutr. 2014;10(4):474–480. 10.1111/mcn.12124 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25. Mwangome MK, Fegan G, Mbunya R, et al. : Reliability and accuracy of anthropometry performed by community health workers among infants under 6 months in rural Kenya. Trop Med Int Health. 2012;17(5):622–9. 10.1111/j.1365-3156.2012.02959.x [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26. Schwinger C, Fadnes LT, Van den Broeck J: Using growth velocity to predict child mortality. Am J Clin Nutr. 2016;103(3):801–807. 10.3945/ajcn.115.118679 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27. Ale FG, Phelan KP, Issa H, et al. : Mothers screening for malnutrition by mid-upper arm circumference is non-inferior to community health workers: results from a large-scale pragmatic trial in rural Niger. Arch Public Health. 2016;74(1):38. 10.1186/s13690-016-0149-5 [DOI] [PMC free article] [PubMed] [Google Scholar]