https://orcid.org/0000-0001-7973-0388
Figures
Abstract
Background
This study characterized undernutrition among children (0–24 months) by age groups specified for Infant and Young Child-feeding (IYCF) and determined the association between child malnutrition and IYCF.
Methods
This cross-sectional survey recruited mother-children dyads (N = 1443). WHO standards were used to assess nutritional status and IYCF indicators. Multivariate analyses were performed to assess the association between IYCF and nutritional indicators.
Results
Stunting, underweight, wasting, overweight, and obesity were prevalent in 33.1%, 26%, 20.2%, 4.6%, and 2.9% of the children, respectively. Age-wise distribution of undernutrition identified severity of stunting and underweight at 10–24 months (median < -1.6 SD; < -1.2 SD; 25th percentile at -2.6 & -2.2 SD respectively) and wasting highest at 0–6 months (25th percentile close to -2SD). Boys manifested higher stunting (lower value -5.2 SD) and were more wasted (lower value -4.7 SD). IYCF prevalence recorded early initiation at 45.2%, exclusive breastfeeding at 23.1%, and prelacteal and bottle-feeding at 37.5 and 22.5% respectively. Child minimum diet diversity (MDD) ≥4 was not achieved by 84%. Minimum meal frequency and minimum acceptable diet were achieved by 75% and 14% respectively. Bottle-feeding increased the odds of wasting [AOR: 1.501 (95% CI: 1.062–2.121)], severe stunting [AOR: 1.595 (95% CI: 1.079–2.358)] and underweight [AOR: 1.519 (95% CI 1.102–2.094)]. Wasting according to BAZ scores was associated with delayed initiation of breastfeeding [AOR: 1.387 (95% CI: 1.018–1.889)] and bottle feeding [AOR: 1.538 (95% CI: 1.087–2.175)]. Delayed introduction of complementary feeding increased the odds of severe stunting [AOR: 2.189 (95% CI: 1.090–4.399)]. Formula feeding increased the odds of underweight [AOR: 1.738 (95% CI: 1.046–2.888)] and obesity [AOR: 4.664 (95% CI: 1.351–16.10)]. Prelacteal feeding increased the odds of severe forms of stunting and underweight by 56% and 79% respectively, and overweight by 96%.
Citation: Jeyakumar A, Babar P, Menon P, Nair R, Jungari S, Tamboli A, et al. (2022) Is Infant and Young Child-feeding (IYCF) a potential double-duty strategy to prevent the double burden of malnutrition among children at the critical age? Evidence of association from urban slums in Pune, Maharashtra, India. PLoS ONE 17(12): e0278152. https://doi.org/10.1371/journal.pone.0278152
Editor: Mohan Kumar, KMCH Institute of Health Sciences and Research, INDIA
Received: November 4, 2021; Accepted: November 10, 2022; Published: December 1, 2022
Copyright: © 2022 Jeyakumar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the paper and its Supporting Information files.
Funding: AJ received the funding. The project was funded by UNICEF. Code: (MAH/CDN/2019/178) The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
1. Introduction
The Global Burden of Disease (GBD) estimated child malnutrition as a significant contributor to the overall disease burden [1]. Malnutrition impedes physical growth, cognitive development, and productivity in children. The GBD study [1999–2017] highlighted a modest improvement in child nutrition indicators and the ensuing double burden of over and undernutrition across the developing nations [2–4]. Nations have the task of achieving the Sustainable Development Goals (SDG), where ending any form of malnutrition ideally referred to a reduction of malnutrition to less than 3% by 2025. This seems ambitious as the concentration of stunted children in the South-East Asian Region (SEAR), and Africa was over 85%, and wasting prevalence was more than the critical threshold in at least 15% of the low- and middle-income countries (LMICs). Prevalence of underweight, a composite index of stunting and wasting was reported at >30% in the SEAR [3, 4]. Infections combined with undernutrition in these regions are a fatal combination attributed to weaker immune systems [5]. The need for optimum nutrition during the critical period of development has been highly prioritized. Optimal IYCF practices including Early Initiation (EI), Exclusive Breast Feeding (EBF), age-appropriate complementary feeding, and improved diet diversity have proven to support growth and development [6–9]. Despite the proven benefits of breastfeeding, less than one-half of the countries with data report EBF among children under 6 months [10]. Such performances are unlikely to lead the LMICs to the SDGs target 2.2.
Among the LMICs, India, despite a significant improvement, houses a substantial proportion of undernourished children beyond critical levels [11, 12]. Although, the National Family Health Surveys (NFHS) 3 and 4 found improvements (23 to 41%) in the early initiation of breastfeeding and a moderate improvement (46 to 54%) in exclusive breastfeeding, India witnessed a decline in the age-appropriate complementary feeding practices [11, 12]. The combined effect of this and the fact that less than 10% of children under five received an adequate diet, places India well short of the SDGs. A similar trajectory was observed in Maharashtra, even lower compared to the national statistics [11, 12].
Setting and age-specific factors determine child-feeding practices. In Maharashtra, risk transition as an outcome of urbanization is evident from the health disparities in urban settings. The urban poor represents the migrant population living in overcrowded settlements with poor hygiene and sanitation. Restricted food environments affect age-appropriate child-feeding practices [13, 14]. The resource-constrained urban slums provide a vulnerable environment for poor maternal and child health outcomes. Consequently, a high concentration of undernutrition in urban slums is documented [15, 16]. The prevalence of child undernutrition in Maharashtra is extensively available in broad age groups such as those under five and two years. However, studying child undernutrition as per IYCF age categories is likely to provide better insights into the age-wise distribution of specific forms of malnutrition. Likewise, hospital-based studies dominate, whereas epidemiological studies that identify determinants of poor child-feeding practices in urban slums are few. The purpose of this study is (i) to characterize child undernutrition as per the IYCF age categories, (ii) to study the age-specific IYCF practices, and (iii) to determine the association between child malnutrition and IYCF practices in the urban slums of Pune city in Maharashtra.
2. Methods
2.1 Study design and setting
A community-based cross-sectional survey was conducted from December 2018 to April 2019 in the urban slums of Pune city in the state of Maharashtra, India. According to the Indian Census [17], the estimated population of Pune city is 31.2 lakhs. Of this, the slums constitute almost 40% of the population in Pune city.
2.2 Sample
Pune city is divided into 5 zones and subdivided into 15 administrative wards. From the list of wards on the Pune municipal corporation’s official website [18], ten administrative wards were randomly selected through computerized random numbers. The selected wards included Aundh, Bibwewadi, Dhankawadi, Dhole Patil Road, Hadapsar, Kasba Peth, Sahakar Nagar, Sangamwadi, Tilak Road, and Warje. Further, one registered slum with the highest population was selected from each administrative ward.
2.3 Sample size
Sample size was calculated using the formula, n = [Z2 *P (1-P)]/ d2, where n = sample size, Z = Z statistic for a level of confidence, P = expected prevalence or proportion, and d = precision. We estimated a sample size of N = 618, after considering a combined prevalence of acute and chronic malnutrition (i.e. wasting and stunting) among children under five (58%), [7] and at 5% precision, 10% non-response, 95% CI, and design effect of 1.5 respectively. After the pre-test identified a lack of representation of children 0–6 and 7–10 months, the sample size was increased 2.3 times and the final sample consisted of 1443 children [19].
2.4 Inclusion criteria
Mother-children dyads with the age of children between 0–24 months, representing both sexes, who consented to participate were included. Children with congenital anomalies or with a history of chronic illness and caretakers other than mothers were excluded.
2.5 Data collection
Using the simple random sampling technique, the youngest child from each household, who matched the age criteria was selected. Data was collected using a semi-structured, pretested questionnaire in the local language (Marathi). The questions were validated for appropriateness of terms and sequence of questions. To assess the nutritional status of the children, standard anthropometric instruments were used; the length was measured using Seca portable infantometer close to 0.1cm (Model no: 416). The weight of the mother, while she carried her child, was recorded using Omron digital scale (Model no: HN283). The mother’s weight was recorded and subtracted from the combined weight of the mother and child to obtain the child’s weight. Weight was measured at close to 0.1kg. Mid-upper-arm circumference (MUAC) of the upper left arm was measured, over bare skin, using Seca inextensible tape close to 0.1cm (Model No: 201). All anthropometric measurements were recorded in duplicates and their mean value was considered for the analysis. Technical errors of measurements were maintained within the standard limits (<0.1).
2.6 Variables
Data for IYCF indicators as defined by the World Health Organisation [20] was elicited from the mothers. Accordingly, children under 24 months who were put to the breast within the first hour of birth were considered to have practiced early initiation (EI); infants 0–5 months of age who were fed exclusively with breast milk were categorized as those who practiced exclusive breastfeeding (EBF); children 12–15 months of age, fed breast milk along with complementary foods were considered to have practiced continued breastfeeding at 1-year; children 6–8 months who were introduced to solid, semi-solid, or soft foods were considered to have initiated age-appropriate complementary feeding; children 6–23 months of age who received at least five out of eight defined were categorized to have met minimum dietary diversity (MDD), minimum meal frequency, and minimum acceptable diet: Proportion of children 6–23 months of age who received a minimum acceptable diet (apart from breast milk). It is further categorized as follows: Breastfed children 6–23 months of age who had at least the minimum dietary diversity and the minimum meal frequency during the previous day. Non-breastfed children 6–23 months of age who received at least 2 milk feedings and had at least the minimum dietary diversity not including milk feeds and the minimum meal frequency during the previous day. IYCF indicators were the independent variables for this study.
Dependent variables included nutritional status indicators such as stunting, wasting, underweight, overweight, and obesity. Nutritional status was assessed using WHO growth standards [21] Accordingly, Length for age Z scores (LAZ) less than -2SD indicated stunting, weight for age Z scores (WAZ) less than -2SD indicated underweight, and weight for length Z scores (WLZ) less than -2SD indicated wasting. BMI for age Z scores below -2 indicated underweight and > 1, > +2, and > +3 SD were categorized as at risk of being overweight and obese respectively [22].
2.7 Statistical methods
Z scores were computed from the anthropometric data using the WHO Anthro version 3.2.2, 2011 software [22]. SPSS, version-20 was used for data management and analysis. Summary statistics such as frequency distribution and percentage were used to describe the study variables. Multivariate analyses were performed to assess the association between IYCF practices and nutritional indicators. Multiple logistic regression was performed after adjusting for covariates. Covariates for the study included socio-demographic information such as family size and the color of ration cards as a proxy for economic status. Maternal characteristics such as age, marital status, age at marriage, parity, birth interval, and type of delivery were elicited. Child characteristics such as age, gender, term, and birth weight were recorded. Statistical significance was established at p-value < 0.05, at 95% confidence interval.
2.8 Ethical considerations
Ethics approval for this work was obtained from the institutional ethics committee (SPPU/IEC/2019/06). Permission to work in the community was obtained from Pune Municipal Corporation (PMC). Participants were informed about the purpose of the study before enrolment and their consent. Freedom to withdraw at any time during the study period and confidentiality of the data was ensured.
3. Results
3.1 Socio-demographic characteristics
A total of 1443 mother-children dyads provided complete information for the study. About half of the mothers represented the 21–25 years age group, and the next highest distribution (30%) was seen between 26–30 years. Nearly 80% of the mothers completed secondary to higher secondary education. While one-third belonged to the scheduled caste, another equal proportion chose not to disclose their caste. A majority were Hindus (67%) and a quarter was Muslims. Family size greater than four was reported by over 65%. As per the color of the ration card, close to 94% represented the lower and lower-middle-income groups (Table 1).
3.2 Maternal and child characteristics
Over 60% of the mothers reported having had a normal delivery, in a public facility. More than one-fourth of the mothers reported birth intervals of less than 33 months. Among the mothers, 40% did not receive information about IYCF practices. Out of those mothers who received IYCF counselling, 42.3% reportedly received it during postnatal care (Table 1). The mean age of children who participated in the study was 11.5 ± 6.54 months. Children of both sexes were equally represented. Over 90% reported full-term births. Almost 42% of children were reportedly low or very low birth weight (LBW). A birth order of less than two was a common observation (82%). Twenty-six children manifested edema during the survey. The mean length, weight, and mid-upper arm measurements were 69.53 ± 9.07 cm, 7.77 ± 2.12 kg, and 13.94 ± 1.52 cm respectively (Table 2).
3.3 IYCF practices
Results of IYCF practices identified a 45% prevalence of early initiation (EI) and 23% of exclusive breastfeeding (EBF). Inappropriate feeding practices such as the introduction of prelacteal feeds and bottle feeding were reported by 37 and 22% respectively. A majority (83.6%) could not achieve a diet diversity score greater than four. Minimum meal frequency was achieved by 76%. As reported by the mothers almost 15% of children received a minimum acceptable diet. Age-appropriate complementary feeding was reported by 42%. Almost 50% introduced complementary feeds before six months of age. Processed foods were introduced at least once a day by 50% of the respondents (Table 3).
3.4 Prevalence of undernutrition
Among the indicators for nutritional status, stunting was the highest prevalent form (33%). Severe stunting was prevalent among 16.5%. Underweight was the second most prevalent manifest (26%), and its severe form was prevalent among 7.3%. Wasting and its severe form were observed among 20% and 6.9% respectively (Table 4).
3.5 Prevalence of overweight and obesity
The double burden of malnutrition was evident with 11.7% of children being at risk of being overweight, 4.6% being overweight, and up to 3% being obese.
3.6 Age-wise distribution of undernutrition, overweight, and obesity as per Z scores
Age-wise distribution of nutrition indicators is shown in the series of box plots (Fig 1). As indicated by the interquartile range, minimum, and maximum values (not shown in the graph), the variation in the distribution was highest in 0–6 months for every form of manifestation. The mean and median for weight for length/ Z score were around -1 Standard Deviation (SD) while the 25th percentile was close to -2 SD indicating almost 25% of wasting in each age group, the highest being at 0–6 months (Fig 1A).
A. Weight for Height (Wasting) B. Height for Age (Stunting) C. Weight for Age (Underweight) D. BMI for Age (BMI Wasting) E. BMI for Age (Overweight and Obesity).
Regarding length for age (stunting), mean and median were placed close to -1 SD at 0–6 months and 7–9 months which shifted lower to -1.6 SD at 10–24 months. Similarly, the 25th percentile of height for age improved marginally from -2.2 SD at 0–6 months to -1.8 SD at 7–9 months, thereafter shifting significantly lower to -2.6. This indicates the severity of stunting at 10–24 months (Fig 1B).
There was no marked difference in the distribution of underweight between the age groups with mean and median being close to -1 SD and the lower quartile lying in the range of -2 to -4.8 SD, -1.8 to -4.2 SD, and -2.2 to -4.8 SD for 0–6 months, 7–9 months, and 10–24 months respectively. These results highlight that almost 25% underweight was constant across all three age groups with negligible improvement at 7–9 months and deterioration at 10–24 months (Fig 1C).
According to BMI for age Z scores (BAZ-wasting) the 25th percentile improved from -2.2 SD at 0–6 months to -1.8 SD at 7–9 months, significantly increasing to -1.4 SD at 10–24 months. Similarly, there was a marginal shift in the mean and median for BAZ-wasting from -1 SD at 0–6 months to -0.6 SD at 7–9 months, and to –0.4 SD at 10 to 24 months. This indicated a decrease in wasting (BAZ) which was highest (slightly more than 25%) at 0–6 months (Fig 1D). The mean and median BMI for age Z scores (BAZ-overweight and obese) marginally shifted positively from -1SD with increasing age. Furthermore, there is a significant increase in the 75th percentile from +0.4 SD at 6 months to +0.8SD at 10–24 months. Thus, an increase in the percentage of those who are at risk of being overweight (BMI for Age Z score >1) up to 25% at 10–24 months was observed. These results are indicative of the prevalent double burden of nutrition (Fig 1E).
3.7 Sex-wise distribution of undernutrition, overweight, and obesity as per Z scores
Sex-wise distribution of nutrition indicators is shown in the series of box plots (Fig 2). The median for WLZ scores (wasting) was lower by 0.2SD for boys with a wide interquartile range as compared to girls. Although the 25th percentile was almost equal for boys (-1.8SD) and girls (-1.6SD) indicating overall wasting close to 25%; more boys (lower value -5.2SD) were wasted compared to girls (lower value -4.7SD) (Fig 2A).
Gender wise distribution of nutritional indicators. A. Weight for Height (Wasting) B. Height for Age (Stunting) C. Weight for Age (Underweight) D. BMI for Age (BMI Wasting) E. BMI for Age (Overweight and Obesity).
A marginal difference in the median LAZ scores (stunting) for boys (-1.4SD) and girls (-1.2SD) was observed. However, as evident by the 25th percentile (boys: -2.6SD, girls: -2.2SD) and lower range (boys: -6, girls: -5.4), more boys were stunted as compared to girls (Fig 2B). Similarly, for WAZ scores (underweight), the median for boys is marginally lower compared to girls. However, as indicated by the 25th percentile (boys: -2.2, girls: -1.8) and lower range (boys: -4.8, girls: -4.4); more boys were underweight compared to girls (Fig 2C).
A small difference between BAZ (wasting) median for boys (-0.7SD) and girls (-0.5SD) was observed. Also, the 25th percentile was similar for boys (-1.8SD) and girls (-1.6SD). However, the minimum value for boys (-5.4SD) was lower than for girls (-4.5SD) indicating severe wasting in boys (Fig 2D).
With regards to overweight and obesity, the median (boys: -0.7SD, girls: -0.5SD) and 75th percentile (boys: 0.6, girls: 0.7) were closely placed for boys and girls (Fig 2E).
3.8 Results of multivariate analysis
Table 5 presents only the significant associations of IYCF practices with nutritional status after multivariate analysis. Other associations are given in S1–S4 Tables.
3.8.1 Wasting and IYCF.
In the adjusted model bottle-feeding increased the odds of wasting by 1.5 times [AOR: 1.501 (95% CI: 1.062–2.121)] as compared to not introducing bottle feeds. Not initiating age-appropriate complementary feeding increased the odds of severe wasting as per the crude model [COR: 4.790 (95% CI: 1.072–21.40)] but, did not show significance in the adjusted model (S1 Table).
3.8.2 Stunting and IYCF.
Even though breastfeeding initiation was delayed (> 1 hr after birth), it was associated with 29% decline in stunting of any form] [AOR: 0.719 (95% CI: 0.550–0.939)]. Introducing bottle feeds showed a weak association with severe stunting [COR 0.631 (95% CI: 0.435–0.915) in the crude model. However, when adjusted with other variables it increased the risk of severe stunting 1.5 times [AOR:1.595 (95% CI:1.079–2.358)] compared to not introducing bottle feeds. Introducing prelacteal feeds increased the odds of any form of stunting by 1.3 times [COR: 1.315 (1.000–1.730)], but was not significant in the adjusted model. Prelacteal feeds increased the odds of severe stunting by 1.5 times [AOR: 1.563(95% CI: 1.070–2.284)] as per the crude and adjusted models respectively.
Delayed introduction of complementary feeding for more than 8 months increased the odds of any form of stunting by 2.4 times as per the adjusted (AOR: 2.401 (95% CI: 1.323–4.360) model. It increased the odds of the severe form of stunting by 2.1 times as per the adjusted model [AOR: 2.189 (95% CI: 1.090–4.399)] (S2 Table).
3.8.3 Underweight and IYCF.
In this study, children introduced to prelacteal feeds showed 1.74 times [AOR: 1.789 (95% CI 1.047–3.056)] more odds of severe underweight and bottle feeding increased the odds of any form of underweight by 1.5 times as per the adjusted model [AOR: 1.519 (95% CI 1.102–2.094)]. Formula feeding increased the odds of all forms of underweight 1.7 times [AOR: 1.738 (95% CI 1.046–2.888)] after being adjusted with other variables (S3 Table).
3.8.4 BAZ and IYCF.
In our work, the late initiation of BF emerged as a determinant for dual indicators. It increased the odds of wasting as per BAZ scores by 1.3 times in both models [COR: 1.389 (95% CI: 1.033–1.868)], [AOR: 1.387 (95% CI: 1.018–1.889)]. While it decreased the odds of being overweight as per BAZ scores by up to 60% in both models. Prelacteal feeding increased the odds of being overweight 1.9 times [AOR: 1.960 (1.006–3.817)]. Bottle feeding increased the odds of wasting by 54% in the adjusted model [COR: 0.693 (95% CI: 0.498–0.966)]; [AOR: 1.538 (95% CI: 1.087–2.175)]. Formula feeding increased the odds of obesity 4.6 times compared to not introducing formula feeds [AOR: 4.664: 95% CI: 1.351–16.10] (S4 Table).
4. Discussion
4.1 Undernutrition in children under two years in urban slums
Characterizing undernutrition among children by settings, age, sex and age-appropriate feeding practices within the critical age provides valuable directions for policymakers to design prevention strategies [23]. Our work in the urban slums of Pune highlights the association between IYCF practices and nutritional outcomes of children in the critical age group. Urban slums in India are characterized by populations with less education, a lack of awareness of childcare and child-feeding practices, and living in resource-constrained environments. Studies report a higher prevalence of undernutrition in urban areas compared to rural settings [24, 25]. Age has been identified as an independent determinant of undernutrition. As feeding guidelines vary within the first two years, we studied the distribution of undernutrition according to age categories 0–6 months, 7–9 months, and 10–24 months as defined by IYCF guidelines. In our work, age-wise characterization identified acute undernutrition, i.e., wasting higher in the 0–6 months age and higher prevalence of stunting (chronic undernutrition) among 10–24 months. Almost 42% of children in our sample being LBW or VLBW draws our focus to pre and post-natal care and maternal nutrition. This combined with delayed initiation of breastfeeding and age-appropriate complementary feeding, not being exclusively breastfed, and frequent infections, are associated with wasting between 0–6 months. The cumulative effect of the undernourished state is most apparent in the higher age group 10–24 months. Almost 42% of our sample being LBW or VLBW draws our focus to pre and post-natal care and maternal nutrition. These results are in line with other studies from India and other developing countries [26–30]. The critical period thus provides a window of opportunity to prevent growth from faltering which impacts the nutritional status in the subsequent years.
We observed manifestations of undernutrition higher among boys as compared to girls. Such findings are commonly observed in studies where the prevalence of undernutrition varied with gender, culture, setting, and geography. Exposure to infections, preferential feeding, and differences in growth are some of the factors that contribute to differences in undernutrition between sexes [16, 31, 32].
4.2 Wasting and IYCF
Among feeding practices, prelacteal feeding in addition to delayed initiation of breastfeeding was significantly associated with undernutrition. Previous studies have reported that prelacteal feeding interferes with early initiation [33–36]. Cultural practices specific to informal settings determine such practices. The results of the present study are similar to the earlier observation in urban slums [37]. The increase in the prevalence of wasting is a national concern in India. In the 4th round of NFHS, Maharashtra showed a 10% increase in the prevalence of stunting from 16.5 to 25.6% and severe wasting from 5 to 9% [11, 12]. In the 5th round, there is negligible or no change in nutritional indicators. Despite the literature evidence on the benefits of breastfeeding and the thrust by the government only 55 to 60% are exclusively breastfed or had initiated early breastfeeding [38]. The results of our study regarding late initiation as a consequence of prelacteal feeding or lack of awareness are comparable with other studies from developing countries [39, 40]. In these settings improving awareness among the mothers and family members could be a major step towards improving EI and preventing prelacteal feeding. In an Indonesian study, prelacteal feeding was associated with smaller babies [41]. Other studies identified depriving colostrum increased the odds of infections and wasting [29, 42, 43].
4.3 Stunting and IYCF
Stunting in India as per the prevalence threshold is categorized as ‘very high’. Our study estimated a prevalence (33%) close to the national (38.7%) and Maharashtra state (34.4%) prevalence [11, 12]. In addition to prelacteal feeding, bottle feeding emerged as a significant risk factor for stunting in our population. Literature evidence supports the high prevalence of bottle feeding in Indian urban slums [44, 45]. Studies from other developing nations and from within India have found associations between bottle feeding and either wasting or any form of child undernutrition [44, 46–48]. Bottle feeding is known to increase the frequency of diarrhea, and have long-term effects on maternal feeding, and child-feeding behavior [42, 49]. Keeping the urban slums in context, mothers could be engaged in income-generating activities in the unorganized sector post-delivery. This probably could limit the time for child care, and the convenience of caretakers to introduce bottle feeds which could be either water, milk available in sachets, or commercial formulas. Convenience feeding through bottles may prevent the intake of semisolid and solid foods appropriate for age. It further limits the fruit and vegetable intake that affect diet diversity. Few studies have suggested age as a predictor for stunting, where age-inappropriate complementary feeding resulted in undernutrition [50, 51]. In impoverished settings, poor knowledge about the reconstitution of formulas leading to over-dilution could result in nutrient deficits that impede growth. Among the background characteristics, the mother’s education, age at marriage, type of delivery, and birth weight of the child were significantly associated with the nutritional status of the children. This draws attention to maternal characteristics that need strengthening in these environments (S1–S3 Tables).
4.4 Underweight and IYCF
Prelacteal and bottle feeding along with formula feed emerged as risk factors for any form of underweight. While formula feeding is associated with rapid weight gain in developed countries [52, 53], our study identified associations with being underweight as well as with increased odds of obesity according to BAZ scores. This mirrors the findings in Ethiopia and other developing countries [42, 54]. In the informal settings in LMICs, exposure to unhygienic conditions, poor and unsafe handling of bottles, and preparation of infant formulas are likely to impact nutritional status compared to households of better economic status in developed countries. Further, increased exposure to mass media in informal settings, combined with availability and affordability could probably be other reasons for introducing formula feeds [55, 56]. Being overweight among children was associated with the socio-demographic index (SDI) in the GBD India state-level disease burden study [2]. On the contrary, our work brings to light the gradual increase of overnutrition in slums among children. The WHO and UNICEF target to improve EBF by 70% in the first 6 months and reduce overweight among children to 3% by 2025 [57]. However, almost 50% did not practice EBF, and the prevalence of childhood overweight greater than 10% poses a huge challenge in meeting these targets. Our study also identified processed foods given to children at least once a day in 50% of the households. Addressing the setting specific determinants in addressing child malnutrition would probably improve the pace of reducing malnutrition.
4.5 Minimum diet diversity and nutritional status
In contrast to other studies, diet diversity did not show an association with nutritional outcomes in ours. This does not undermine the importance of diet diversity in child growth and development. Scores on diet diversity are less sensitive and our work in addition did not elicit the frequency of diverse foods included in the diet. Being cross-sectional, the study was further restricted to a one-day dietary intake recall which is less likely to represent the true intake. However, 83% did not achieve a score of four, and only 14% received a minimum acceptable diet that emphasizes a restrained environment for diet diversity and nutritional adequacy.
4.6 Strength and limitations
The data elicited from mothers were self-reported and not measured bringing in the probability of recall bias. However, the strength of the study is the representative sample including 0–6 months, often excluded in other studies, from the urban slum setting where previous studies have emphasized a high prevalence of undernutrition. This is one of the few studies that employed the comprehensive list of IYCF indicators recommended for the critical age group [18, 19]. Having a substantial representation from 0–6 months enabled us to make suitable inferences for this age group.
The uniqueness of this work is to advocate IYCF as a potential public health double-duty strategy for the prevention of the double burden of malnutrition. This work was carried out in 2018–2019 before the publication of the IYCF 2021 guidelines [20]. The definitions used in the guidelines (2010) were strictly adhered to, for the completion of the study. Further, questions on formula feed and intake of processed foods were elicited which reflect in the 2021 guidelines. A diet diversity score ≥ five is the new cut-off as compared to the earlier ≥ 4. However, the results of our work changed negligibly, where close to 85% percent did not meet the diet diversity as per both standards. Considering the above similarities in the measurements, the data were analyzed as per the 2010 guidelines, to prevent distortion of definitions, results, and deviation from the study context. While guidelines enable the identification and definition of variables, it also allows the flexibility for setting specific use. Although the use of old guidelines could limit comparisons with studies that used the new, the indicators from the 2021 guidelines align with the research context preserving the uniqueness of the work.
5. Conclusion
Our work highlights the critical need to address inappropriate practices such as prelacteal, bottle, and formula feeding to prevent malnutrition in urban slums among children in the critical age group. Improving maternal nutrition to prevent LBW in urban slums appears a promising strategy to hasten the pace to improve child nutrition indicators. These findings call for the attention of policymakers and service providers to emphasize pre and post-natal care and to ensure the continuum of care during every phase of the feeding process. Infiltration of processed foods in the diets of children under two years is evident. Behavior change communication should aim at improving family awareness of prelacteal feeds, achieving maximum diet diversity using low-cost foods, and limiting unhealthy food choices to address the rising double burden among young children.
Supporting information
S1 Table. Crude and adjusted odds ratio of socio-demographic, maternal, and child characteristics, with wasting.
https://doi.org/10.1371/journal.pone.0278152.s001
(PDF)
S2 Table. Crude and adjusted odds ratio of socio-demographic, maternal, and child characteristics, with stunting.
https://doi.org/10.1371/journal.pone.0278152.s002
(PDF)
S3 Table. Crude and adjusted odds ratio of socio-demographic, maternal, and child characteristics with underweight.
https://doi.org/10.1371/journal.pone.0278152.s003
(PDF)
S4 Table. Crude and adjusted odds ratio of maternal and child characteristics, IYCF practices with BMI.
https://doi.org/10.1371/journal.pone.0278152.s004
(PDF)
Acknowledgments
The authors wish to acknowledge the authorities for their permission to conduct the study in PMC. We sincerely acknowledge the cooperation of the study participants.
References
- 1. Health Effects of Overweight and Obesity in 195 Countries over 25 Years. New England Journal of Medicine [Internet]. 2017 Jul 6 [cited 2022 Jul 7];377(1):13–27. Available from: https://www.nejm.org/doi/10.1056/NEJMoa1614362
- 2. Swaminathan S, Hemalatha R, Pandey A, Kassebaum NJ, Laxmaiah A, Longvah T, et al. The burden of child and maternal malnutrition and trends in its indicators in the states of India: the Global Burden of Disease Study 1990–2017. The Lancet Child and Adolescent Health [Internet]. 2019 Dec 1 [cited 2022 Jul 7];3(12):855–70. Available from: http://www.thelancet.com/article/S2352464219302731/fulltext pmid:31542357
- 3. Kinyoki DK, Osgood-Zimmerman AE, Pickering B v., Schaeffer LE, Marczak LB, Lazzar-Atwood A, et al. Mapping child growth failure across low- and middle-income countries. Nature 2020 577:7789 [Internet]. 2020 Jan 8 [cited 2022 Jul 7];577(7789):231–4. Available from: https://www.nature.com/articles/s41586-019-1878-8 pmid:31915393
- 4. Abbafati C, Abbas KM, Abbasi-Kangevari M, Abd-Allah F, Abdelalim A, Abdollahi M, et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. The Lancet [Internet]. 2020 Oct 17 [cited 2022 Jul 7];396(10258):1223–49. Available from: http://www.thelancet.com/article/S0140673620307522/fulltext pmid:33069327
- 5. Walson JL, Berkley JA. The impact of malnutrition on childhood infections. Current Opinion in Infectious Diseases [Internet]. 2018 Jun 1 [cited 2022 Jul 7];31(3):231–6. Available from: https://journals.lww.com/co-infectiousdiseases/Fulltext/2018/06000/The_impact_of_malnutrition_on_childhood_infections.5.aspx pmid:29570495
- 6. Menon P, Bamezai A, Subandoro A, Ayoya MA, Aguayo V. Age-appropriate infant and young child-feeding practices are associated with child nutrition in India: Insights from nationally representative data. Maternal and Child Nutrition. 2015 Jan 1;11(1):73–87. pmid:23557463
- 7. Black MM, Walker SP, Fernald LCH, Andersen CT, DiGirolamo AM, Lu C, et al. Early childhood development coming of age: science through the life course. The Lancet [Internet]. 2017 Jan 7 [cited 2022 Jul 7];389(10064):77–90. Available from: http://www.thelancet.com/article/S0140673616313897/fulltext pmid:27717614
- 8. Britto PR, Lye SJ, Proulx K, Yousafzai AK, Matthews SG, Vaivada T, et al. Nurturing care: promoting early childhood development. The Lancet [Internet]. 2017 Jan 7 [cited 2022 Jul 7];389(10064):91–102. Available from: pmid:27717615
- 9. Richter LM, Daelmans B, Lombardi J, Heymann J, Boo FL, Behrman JR, et al. Investing in the foundation of sustainable development: pathways to scale up for early childhood development. The Lancet [Internet]. 2017 Jan 7 [cited 2022 Jul 7];389(10064):103–18. Available from: http://www.thelancet.com/article/S0140673616316981/fulltext pmid:27717610
- 10. Richter LM, Cappa C, Issa G, Lu C, Petrowski N, Naicker SN. Data for action on early childhood development. The Lancet [Internet]. 2020 Dec 5 [cited 2022 Jul 7];396(10265):1784–6. Available from: http://www.thelancet.com/article/S014067362032482X/fulltext pmid:33220852
- 11.
National Family Health Survey [Internet]. [cited 2022 Jul 8]. Available from: http://rchiips.org/nfhs/nfhs3.shtml
- 12.
National Family Health Survey [Internet]. [cited 2022 Jul 7]. Available from: http://rchiips.org/nfhs/factsheet_nfhs-4.shtml
- 13. Jeyakumar A, Ghugre P. Assessment of personal hygiene and sanitation using a composite index among adolescent girls and their households in urban slums of Pune, Maharashtra. Journal of Water, Sanitation and Hygiene for Development. 2017 Jun 1;7(2):323–30.
- 14.
Why urban health matters [Internet]. [cited 2022 Jul 8]. Available from: https://apps.who.int/iris/handle/10665/70230
- 15. Rasheed W, Jeyakumar A. Magnitude and severity of anthropometric failure among children under two years using Composite Index of Anthropometric Failure (CIAF) and WHO standards. International Journal of Pediatrics and Adolescent Medicine. 2018 Mar 1;5(1):24–7. pmid:30805528
- 16. Huey SL, Finkelstein JL, Venkatramanan S, Udipi SA, Ghugre P, Thakker VM, et al. Prevalence and covariates of undernutrition in young children living in urban slums of Mumbai, India: A cross sectional study. Frontiers in Public Health. 2019;7(JUN):191.
- 17.
Home | Government of India [Internet]. [cited 2022 Jul 8]. Available from: https://censusindia.gov.in/census.website/
- 18.
All Ward Office | Pune Municipal Corporation [Internet]. [cited 2022 Jul 7]. Available from: https://www.pmc.gov.in/en/all-ward-office
- 19. Asiamah N, Kofi Mensah H, Fosu Oteng-Abayie E. Do Larger Samples Really Lead to More Precise Estimates? A Simulation Study. American Journal of Educational Research [Internet]. 2017 [cited 2022 Jul 7];5(1):9–17. Available from: http://pubs.sciepub.com/education/5/
- 20.
Indicators for assessing infant and young child-feeding practices: definitions and measurement methods [Internet]. [cited 2022 Jul 8]. Available from: https://www.who.int/publications/i/item/9789240018389
- 21. M de O, C G, CG V, AW O, EA F, J M. The WHO Multicentre Growth Reference Study: planning, study design, and methodology. Food Nutr Bull [Internet]. 2004 Jan [cited 2022 Jul 8];25(1 Suppl):S13–4. Available from: https://pubmed.ncbi.nlm.nih.gov/15069916/
- 22.
WHO Anthro Survey Analyser and other tools [Internet]. [cited 2022 Jul 8]. Available from: https://www.who.int/tools/child-growth-standards/software
- 23.
Impact of feeding and breastfeeding practices on the nutritional status of infants in a district of Andhra Pradesh, India—PubMed [Internet]. [cited 2022 Jul 8]. Available from: https://pubmed.ncbi.nlm.nih.gov/23278776/
- 24.
Health status in India: A study of urban slum and non-slum population [Internet]. [cited 2022 Jul 8]. Available from: https://www.pulsus.com/scholarly-articles/phealth-status-in-india-a-study-of-urban-slum-and-nonslum-populationp-4137.html
- 25. Murarkar S, Gothankar J, Doke P, Pore P, Lalwani S, Dhumale G, et al. Prevalence and determinants of undernutrition among under-five children residing in urban slums and rural area, Maharashtra, India: a community-based cross-sectional study. BMC Public Health [Internet]. 2020 Dec 1 [cited 2022 Jul 8];20(1):1–9. Available from: https://bmcpublichealth.biomedcentral.com/articles/10.1186/s12889-020-09642-0
- 26. Titaley CR, Ariawan I, Hapsari D, Muasyaroh A, Dibley MJ. Determinants of the Stunting of Children Under Two Years Old in Indonesia: A Multilevel Analysis of the 2013 Indonesia Basic Health Survey. Nutrients 2019, Vol 11, Page 1106 [Internet]. 2019 May 18 [cited 2022 Jul 8];11(5):1106. Available from: https://www.mdpi.com/2072-6643/11/5/1106/htm pmid:31109058
- 27. Derso T, Tariku A, Biks GA, Wassie MM. Stunting, wasting and associated factors among children aged 6–24 months in Dabat health and demographic surveillance system site: A community based cross-sectional study in Ethiopia. BMC Pediatr [Internet]. 2017 Apr 4 [cited 2022 Jul 8];17(1). Available from: https://pubmed.ncbi.nlm.nih.gov/28376746/
- 28. Goudet S, Griffiths P, Bogin B, Madise N. Interventions to tackle malnutrition and its risk factors in children living in slums: a scoping review. [Internet]. 2016 Jan 2 [cited 2022 Jul 8];44(1):1–10. Available from: https://www.tandfonline.com/doi/abs/10.1080/03014460.2016.1205660 pmid:27356853
- 29. Jeyakumar A, Nikam S, Nayak S. Prevalence and Risk Factors of Undernutrition among Children Less than 2 Years in Urban Slums of Pune, Maharashtra, India. https://doi.org/101080/0367024420191613985 [Internet]. 2019 Sep 3 [cited 2022 Jul 8];58(5):456–69. Available from: https://www.tandfonline.com/doi/abs/10.1080/03670244.2019.1613985 pmid:31082268
- 30. Patwari AK, Kumar S, Beard J. Undernutrition among infants less than 6 months of age: An underestimated public health problem in India. Maternal and Child Nutrition. 2015 Jan 1;11(1):119–26. pmid:23551459
- 31. Thurstans S, Opondo C, Seal A, Wells J, Khara T, Dolan C, et al. Boys are more likely to be undernourished than girls: a systematic review and meta-analysis of sex differences in undernutrition. BMJ Global Health [Internet]. 2020 Dec 1 [cited 2022 Jul 8];5(12):e004030. Available from: https://gh.bmj.com/content/5/12/e004030 pmid:33328202
- 32. Kumar D, Goel NK, Kalia M, Mahajan V. Socio-demographic Factors Affecting the Nutritional Status of the Under Three Children in Chandigarh, UT. Original Article Healthline Journal. 46.
- 33. Ogah A., Ajayi A.M., Akib S. and Okolo S.N. (2012) Pre-Lacteal Feeding Practice. A Cross-Sectional Study Kampala International University Teaching Hospital Maternal and Child Health Clinic, Bushenyi, Western Uganda. Asian Journal of Medical Sciences, 4, 79–85.—References—Scientific Research Publishing [Internet]. [cited 2022 Jul 8]. Available from: https://www.scirp.org/%28S%28vtj3fa45qm1ean45vvffcz55%29%29/reference/referencespapers.aspx?referenceid=1237708
- 34. Das A, Mala GS, Singh RS, Majumdar A, Chatterjee R, Chaudhuri I, et al. Prelacteal feeding practice and maintenance of exclusive breast feeding in Bihar, India–identifying key demographic sections for childhood nutrition interventions: a cross-sectional study. Gates Open Research [Internet]. 2019 [cited 2022 Jul 8];3. Available from: /pmc/articles/PMC6584739/ pmid:31259312
- 35. Legesse M, Demena M, Mesfin F, Haile D. Prelacteal feeding practices and associated factors among mothers of children aged less than 24 months in Raya Kobo district, North Eastern Ethiopia: A cross-sectional study. International Breastfeeding Journal [Internet]. 2014 Dec 14 [cited 2022 Jul 8];9(1):1–8. Available from: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-014-0025-2
- 36. Bekele Y, Mengistie B, Mesfine F, Bekele Y, Mengistie B, Mesfine F. Prelacteal Feeding Practice and Associated Factors among Mothers Attending Immunization Clinic in Harari Region Public Health Facilities, Eastern Ethiopia. Open Journal of Preventive Medicine [Internet]. 2014 Jul 11 [cited 2022 Jul 8];4(7):529–34. Available from: http://www.scirp.org/Html/3-1340298_47955.htm
- 37. Jeyakumar A, Jungari S, Nair R, Menon P, Babar P, Bhushan B, et al. Prevalence and Determinants of Early Initiation (EI), Exclusive Breastfeeding (EBF), and Prelacteal Feeding among Children Aged 0–24 Months in Slums of Pune City, in Maharashtra. https://doi.org/101080/0367024420201858407 [Internet]. 2020 [cited 2022 Jul 8];60(3):377–93. Available from: https://www.tandfonline.com/doi/abs/10.1080/03670244.2020.1858407 pmid:33334182
- 38.
National Family Health Survey (NFHS-5) [Internet]. [cited 2022 Jul 8]. Available from: http://rchiips.org/nfhs/factsheet_NFHS-5.shtml
- 39. Tewabe T. Prelacteal Feeding Practices among Mothers in Motta Town, Northwest Ethiopia: A Cross-sectional Study. Ethiopian Journal of Health Sciences [Internet]. 2019 May 7 [cited 2022 Jul 8];28(4):393–402. Available from: https://www.ajol.info/index.php/ejhs/article/view/174742
- 40. Asim M, Asim M, Asim M, Ahmed ZH, Hayward MD, Widen EM. Prelacteal feeding practices in Pakistan: A mixed-methods study. International Breastfeeding Journal [Internet]. 2020 Jun 8 [cited 2022 Jul 8];15(1):1–11. Available from: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-020-00295-8 pmid:32513203
- 41. Rahmartani LD, Carson C, Quigley MA. Prevalence of prelacteal feeding and associated risk factors in Indonesia: Evidence from the 2017 Indonesia Demographic Health Survey. PLOS ONE [Internet]. 2020 Dec 1 [cited 2022 Jul 8];15(12):e0243097. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0243097 pmid:33270720
- 42. Neves PAR, Vaz JS, Maia FS, Baker P, Gatica-Domínguez G, Piwoz E, et al. Rates and time trends in the consumption of breastmilk, formula, and animal milk by children younger than 2 years from 2000 to 2019: analysis of 113 countries. The Lancet Child and Adolescent Health [Internet]. 2021 Sep 1 [cited 2022 Jul 8];5(9):619–30. Available from: http://www.thelancet.com/article/S2352464221001632/fulltext pmid:34245677
- 43. Sorrie MB, Amaje E, Gebremeskel F. Pre-lacteal feeding practices and associated factors among mothers of children aged less than 12 months in Jinka Town, South Ethiopia, 2018/19. PLOS ONE [Internet]. 2020 Oct 1 [cited 2022 Jul 8];15(10):e0240583. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0240583 pmid:33048981
- 44. Rathaur V, Pathania M, Pannu C, Jain A, Dhar M, Pathania N, et al. Prevalent infant feeding practices among the mothers presenting at a tertiary care hospital in Garhwal Himalayan region, Uttarakhand, India. J Family Med Prim Care [Internet]. 2018 [cited 2022 Jul 8];7(1):45. Available from: https://pubmed.ncbi.nlm.nih.gov/29915732/ pmid:29915732
- 45. Tiwari R, Mahajan PC, Lahariya C. The Determinants of Exclusive Breast Feeding in Urban Slums: A Community Based Study. Journal of Tropical Pediatrics [Internet]. 2009 Feb 1 [cited 2022 Jul 8];55(1):49–54. Available from: https://academic.oup.com/tropej/article/55/1/49/1646766 pmid:18499736
- 46. Khatun H, Comins CA, Shah R, Munirul Islam M, Choudhury N, Ahmed T. Uncovering the barriers to exclusive breastfeeding for mothers living in Dhaka’s slums: A mixed method study. International Breastfeeding Journal [Internet]. 2018 Sep 26 [cited 2022 Jul 8];13(1):1–11. Available from: https://internationalbreastfeedingjournal.biomedcentral.com/articles/10.1186/s13006-018-0186-5
- 47. Bavdekar SB, Bavdekar MS, Kasla RR, Raghunandana KJ, Joshi SY, Hathi GS. INFANT FEEDING PRACTICES IN BOMBAY SLUMS. pmid:7883366
- 48. Lokare L, Hippargi A. Qualitative exploration of bottle feeding practices among mothers of Dharwad district, Karnataka: a focus group discussion study. International Journal Of Community Medicine And Public Health [Internet]. 2017 Jan 31 [cited 2022 Jul 8];3(1):90–3. Available from: https://ijcmph.com/index.php/ijcmph/article/view/651
- 49. Avila WM, Pordeus IA, Paiva SM, Martins CC. Breast and Bottle Feeding as Risk Factors for Dental Caries: A Systematic Review and Meta-Analysis. PLOS ONE [Internet]. 2015 Nov 1 [cited 2022 Jul 8];10(11):e0142922. Available from: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0142922 pmid:26579710
- 50. Garg A, Chadha R. Index for Measuring the Quality of Complementary Feeding Practices in Rural India. Journal of Health, Population and Nutrition [Internet]. 2009 Dec [cited 2022 Jul 8];27(6):763–71. Available from: https://www.banglajol.info/index.php/JHPN/article/view/4328 pmid:20099760
- 51. Roche ML, Gyorkos TW, Blouin B, Marquis GS, Sarsoza J, Kuhnlein H v. Infant and young child-feeding practices and stunting in two highland provinces in Ecuador. Maternal and Child Nutrition. 2017 Apr 1;13(2). pmid:27265847
- 52. Yang S, Mei H, Mei H, Yang Y, Li N, Tan Y, et al. Risks of maternal prepregnancy overweight/obesity, excessive gestational weight gain, and bottle-feeding in infancy rapid weight gain: evidence from a cohort study in China. Science China Life Sciences 2019 62:12 [Internet]. 2019 Nov 18 [cited 2022 Jul 8];62(12):1580–9. Available from: https://link.springer.com/article/10.1007/s11427-018-9831-5 pmid:31745693
- 53. Flores-Barrantes P, Iguacel I, Iglesia-Altaba I, Moreno LA, Rodríguez G. Rapid Weight Gain, Infant Feeding Practices, and Subsequent Body Mass Index Trajectories: The CALINA Study. Nutrients 2020, Vol 12, Page 3178 [Internet]. 2020 Oct 17 [cited 2022 Jul 8];12(10):3178. Available from: https://www.mdpi.com/2072-6643/12/10/3178/htm pmid:33080922
- 54. Dinku AM, Mekonnen TC, Adilu GS. Child dietary diversity and food (in)security as a potential correlate of child anthropometric indices in the context of urban food system in the cases of north-central Ethiopia. Journal of Health, Population and Nutrition [Internet]. 2020 Dec 1 [cited 2022 Jul 8];39(1):1–11. Available from: https://jhpn.biomedcentral.com/articles/10.1186/s41043-020-00219-6
- 55.
Infant and Young Child-feeding Behaviors among Working Mothers in India: Implications for Global Health Policy and Practice—PubMed [Internet]. [cited 2022 Jul 8]. Available from: https://pubmed.ncbi.nlm.nih.gov/27621981/
- 56. Hans G. Campaign for Promotion of Breastfeeding: Evolution, Experience and Future Directions. The Indian Journal of Social Work. 1998;59(2):581–98.
- 57.
WHO/UNICEF discussion paper: The extension of the 2025 maternal, infant and young child nutrition targets to 2030—UNICEF DATA [Internet]. [cited 2022 Jul 8]. Available from: https://data.unicef.org/resources/who-unicef-discussion-paper-nutrition-targets/