Factors associated with catch‐up growth in early infancy in rural Pakistan: A longitudinal analysis of the women's work and nutrition study

Abstract

The adverse health impacts of early infant stunting can be partially ameliorated by early catch‐up growth. Few studies have examined predictors of and barriers to catch‐up growth to identify intervention points for improving linear growth during infancy. This study aimed to estimate the prevalence of, and factors associated with, catch‐up growth among infants in Pakistan. A longitudinal study of mother–infant dyads ( n = 1,161) was conducted in rural Sindh province, with enrolment between December 2015 and February 2016 (infants aged 0.5–3 months), and follow‐up ( n = 1035) between November 2016 and January 2017 (infants aged 9–15 months). The outcome was catch‐up growth (change in conditional length‐for‐age z‐scores >0.67 between baseline and endline). Associated factors were examined using multivariable logistic regression analyses. The prevalence of stunting was 45.3% at baseline and 60.7% at follow‐up. 22.8% of infants exhibited catch‐up growth over this period. Factors positively associated with catch‐up growth included maternal height (odds ratio (OR) = 1.08 [1.05–1.11]), household wealth (OR = 3.61 [1.90–6.84]), maternal (OR = 2.43 [1.30–4.56]) or paternal (OR = 1.46 [1.05–2.03]) education, and households with two or more adult females (OR = 1.91 [1.26–2.88]). Factors negatively associated with catch‐up growth were two (OR = 0.64 [0.45–0.89]) or three or more (OR = 0.44 [0.29–0.66]) preschool children in the household and the infant being currently breastfed (OR = 0.59 [0.41–0.88]). Catch‐up growth was exhibited among approximately a quarter of infants despite living in challenging environments associated with extremely high rates of early infant stunting. Several modifiable factors were identified that might represent suitable programme intervention points to off‐set early infant stunting in rural Pakistan.

The prevalence of stunting among children and adolescents living in the Middle East and North Africa region (MENA): A systematic review and meta-analysis

“…Of the 58 included studies, 56 were cross-sectional and 2 were cohort studies [ 18 , 42 ]. The study population was 2- to 18-year-olds.…”

“…Of the 58 observational studies included in the systematic review, eight (13.8%) were conducted in countries involved in a civil war [ 15 , 29 , 30 , 39 , 40 , 45 , 50 , 61 ], 42 (72.4%) were reported from both urban and rural areas, 8 (13.8%) were conducted in urban areas only [ 14 , 24 , 26 , 35 , 36 , 53 , 62 , 67 ], and 8 (13.8%) in rural areas only [ 18 , 32 , 37 , 39 , 42 , 43 , 66 , 70 ]. Also, 7 (12.1%) studies involved refugees [ 15 , 16 , 20 , 22 , 27 , 34 , 65 ].…”

“…Of the 58 studies, 39 (67.2%%) [ 13 , 15 , 18 , 19 , 22 - 24 , 27 - 30 , 32 , 36 , 37 , 40 - 42 , 44 , 45 , 48 - 51 , 53 - 57 , 61 , 62 , 66 ] had data on stunting in children aged 2-5 years, and 19 (32.8%) on children aged 6 years and older [ 14 , 16 , 17 , 20 , 21 , 26 , 31 , 33 , 34 , 38 , 39 , 46 , 52 , 58 , 59 , 63 , 64 , 67 , 70 ].…”

The relationship between wasting and stunting in Cambodian children: Secondary analysis of longitudinal data of children below 24 months of age followed up until the age of 59 months

“…We also created the binary variables 'Ever had acute malnutrition and 'Ever had an episode of accelerated linear growth' to distinguish those who experienced the condition at any data collection point. Accelerated linear growth was defined using the cut-off proposed in the literature of +0.67 HAZ increase between two data collection points [30][31][32]. Based on this cut-off, a child who had an HAZd � 0.67 was considered as having experienced an accelerated linear growth [30].…”

Nutrition status and morbidity of Ethiopian children after recovery from severe acute malnutrition: Prospective matched cohort study

“…Both HAZ and HAD were used to characterize linear growth better. Based on the literature, accelerated linear growth (ALG) was diagnosed when HAZ increased by ≥0.67 between two assessments [ 23 – 26 ]. The covariant for outcome of wasting and morbidity was assessed by fitting mixed-effects models to include independent variables as covariates.…”