Undernutrition in children under five years of age continues to be a public-health issue because early childhood is a period of rapid physical growth, brain development, immune system maturation and future health. It is usually measured by stunting, wasting and underweight, and these represent different patterns of growth failure. Stunting is typically linked to chronic and long-term exposure to poor nutrition and environmental adversity, wasting to more recent or acute nutritional deficit, and underweight to a combination of both chronic and acute influences. The impact of early-life undernutrition is not limited to immediate illness and mortality and can also affect cognitive development, educational performance, productivity, and adult health [1,2]. Undernutrition in children is concentrated largely in low- and middle-income countries. India continues to bear a significant share of this burden, and there are marked regional and population-group disparities. A range of maternal, household, behavioural, environmental and healthcare factors are involved in the nutritional status of children. Subsequent growth could be affected by maternal nutritional status, birth weight, socioeconomic factors, maternal education, feeding practices, sanitation, recurrent infections, and access to preventive and curative health services [1,4]. World Health Organization Child Growth Standards are a standardized tool for clinical and epidemiological assessment of weight-for-age, height-for-age, and weight-for-height [3]. The period of infancy and complementary feeding is particularly critical because nutritional needs increase rapidly and children are at the same time exposed to changing dietary and infectious risks. Growth faltering during this period may be related to inadequate breastfeeding practices, delayed or inappropriate introduction of complementary foods, poor dietary diversity, and insufficient feeding frequency [5]. Indian studies have also demonstrated that the knowledge of caregivers, family practices, household resources, and capacity to adhere to recommended infant and young-child feeding practices can affect nutritional outcomes [6,7]. Another important dimension is environmental conditions. Recurrent diarrhoeal illness and other infections may decrease appetite, limit nutrient absorption, and increase metabolic requirements, which may further compromise nutritional status. Substandard water, sanitation and hygiene conditions can also increase the likelihood of repeated infection and indirectly affect growth [4,8]. These pathways are especially important in rural, economically disadvantaged and socially vulnerable populations where several nutritional risks are likely to coexist. This is especially pertinent in areas with a substantial tribal population. Childhood undernutrition has been reported to be high in vulnerable rural and tribal settings in western India and has been linked to maternal, household and socioeconomic factors [9]. Assessment in a hospital setting can be useful for providing complementary information, as it can detect nutritional deficits in children who seek healthcare, but these results should be interpreted in the context of the clinical population and not as direct estimates of community prevalence [10]. Against this background, the present study was conducted among children aged 0–59 completed months attending the paediatric services of Zydus Medical College and Hospital, Dahod, Gujarat. The study was designed to evaluate underweight, stunting and wasting using WHO-based anthropometric indices and to explore their relationship with demographic, maternal, feeding, environmental and morbidity factors.

Study design and setting An observational cross-sectional study was conducted in the pediatric services of Zydus Medical College and Hospital, Dahod, Gujarat, to determine the prevalence of malnutrition and associated risk factors in children aged 0–59 completed months attending the pediatric services of the hospital. The study period was 12 months and the planned sample size was 400 children. Study population Children aged 0–59 completed months who attended pediatric outpatient or inpatient services and had complete anthropometric and relevant socio-demographic and clinical data were included. Children with congenital anomalies, major genetic or chromosomal disorders, chronic diseases known to significantly affect growth, critical illness that precluded anthropometric assessment, severe edema, or other conditions that interfered with accurate weight or length/height measurement were excluded. One observation per child was taken into account. Data collection and study variables Data collected included age, sex, place of residence, tribal status, family characteristics, socioeconomic status, parental education and occupation, household income, overcrowding, drinking-water source and sanitation. Maternal and birth-related variables were maternal age, parity, birth order, birth interval, place and mode of delivery, birth weight, prematurity, antenatal-care adequacy and history of maternal anemia. The infant and young-child feeding variables were timely initiation of breastfeeding, prelacteal feeding, exclusive breastfeeding, timing of complementary feeding, minimum meal frequency, minimum dietary diversity, bottle feeding, vitamin A supplementation, and immunization status. Recent morbidity variables were diarrhea, acute respiratory infection, fever, recurrent illness, worm infestation, and hospitalization in the last six months. Age-specific feeding indicators were evaluated only for children for whom the respective indicators were applicable. Anthropometric assessment and definition of malnutrition Anthropometric measurements were weight, recumbent length or standing height according to age, and mid-upper arm circumference (MUAC) where applicable. Weight-for-age, height-for-age and weight-for-height Z-scores were used to assess nutritional status based on WHO Child Growth Standards. Underweight was classified as weight-for-age Z-score <−2 SD; stunting as height-for-age Z-score <−2 SD; and wasting as weight-for-height Z-score <−2 SD. Values from <−2 SD to ≥−3 SD were considered moderate, and those <−3 SD were considered severe. Any malnutrition was defined as the presence of underweight, stunting and/or wasting. Severe acute malnutrition was defined using severe wasting and/or age-appropriate MUAC criteria. Statistical analysis Data were analyzed using descriptive and inferential statistical methods. Continuous variables were summarized as mean ± standard deviation or median with interquartile range, as appropriate, while categorical variables were presented as frequency and percentage, with counts reported before percentages as n (%). The prevalence of underweight, stunting, wasting, and any malnutrition was estimated with 95% confidence intervals. Nutritional status was additionally examined according to age group and sex. For bivariate analysis, children with and without any malnutrition were compared across socio-demographic, maternal, birth-related, feeding, environmental, and morbidity-related factors. Associations between categorical variables were assessed using the Pearson chi-square test or Fisher’s exact test, where appropriate. Crude odds ratios with 95% confidence intervals were calculated for selected exposures. Multivariable binary logistic regression was performed with any malnutrition as the dependent variable. Variables considered clinically relevant and/or associated with malnutrition on bivariate analysis were included in the multivariable model while avoiding inclusion of closely correlated or redundant predictors. Adjusted odds ratios with 95% confidence intervals were reported. Statistical significance was assessed using a two-sided p-value <0.05. Relevant test statistics, including Pearson chi-square and Wald chi-square values, were reported alongside p-values where applicable.

Participant characteristics A total of 400 children aged 0–59 months were included. The mean age was 24.3 ± 13.7 months, and the median age was 24 months (IQR 13–34). Male children accounted for 222 (55.5%), while 317 (79.2%) were from rural areas and 231 (57.8%) belonged to tribal communities. The socio-demographic profile is summarized in Table 1. Table 1. Socio-demographic characteristics of study participants (N=400) Characteristic Category n (%) Age group 0–5 months 37 (9.2) 6–11 months 47 (11.8) 12–23 months 115 (28.8) 24–35 months 115 (28.8) 36–47 months 61 (15.2) 48–59 months 25 (6.2) Sex Male 222 (55.5) Female 178 (44.5) Residence Rural 317 (79.2) Urban 83 (20.8) Tribal status Tribal 231 (57.8) Non-tribal 169 (42.2) Service OPD 318 (79.5) IPD 82 (20.5) Family type Nuclear 223 (55.8) Joint 123 (30.8) Three-generation 54 (13.5) Socioeconomic class Upper 9 (2.2) Upper-middle 28 (7.0) Middle 73 (18.2) Lower-middle 101 (25.2) Lower 189 (47.2) Mother education Illiterate 67 (16.8) Primary 121 (30.2) Secondary 108 (27.0) Higher-secondary 70 (17.5) Graduate_or_above 34 (8.5) Overcrowding Yes 103 (25.8) No 297 (74.2) Safe drinking water Yes 329 (82.2) No 71 (17.8) Sanitary toilet Yes 300 (75.0) No 100 (25.0) Maternal, birth, feeding and morbidity characteristics Low birth weight was present in 97 (24.2%) children and maternal anemia history in 169 (42.2%). Inadequate age-appropriate feeding was identified in 288 (72.0%), while recent diarrhea was reported in 77 (19.2%). Detailed maternal, birth, feeding and morbidity characteristics are presented in Tables 2 and 3. Table 2. Maternal, birth and feeding characteristics (N=400) Characteristic Category n (%) Low birth weight Yes 97 (24.2) No 303 (75.8) Preterm birth Yes 58 (14.5) No 342 (85.5) Place of delivery Institutional 354 (88.5) Home 46 (11.5) Mode of delivery Vaginal 334 (83.5) Caesarean 66 (16.5) Adequate ANC Yes 302 (75.5) No 98 (24.5) Maternal anemia history Yes 169 (42.2) No 231 (57.8) Breastfeeding within 1 hour Yes 255 (63.8) No 145 (36.2) Prelacteal feed Yes 114 (28.5) No 286 (71.5) Exclusive breastfeeding for 6 months Yes 190 (47.5) No 173 (43.2) Not applicable 37 (9.2) Complementary feeding at 6 months Yes 224 (56.0) No 139 (34.8) Not applicable 37 (9.2) Minimum meal frequency Yes 243 (60.8) No 120 (30.0) Not applicable 37 (9.2) Minimum dietary diversity Yes 219 (54.8) No 144 (36.0) Not applicable 37 (9.2) Bottle feeding Yes 102 (25.5) No 298 (74.5) Immunization status Complete for age 306 (76.5) Partial 42 (10.5) Unimmunized 52 (13.0) Table 3. Morbidity characteristics (N=400) Characteristic Category n (%) Recent diarrhea Yes 77 (19.2) No 323 (80.8) Recent ARI Yes 90 (22.5) No 310 (77.5) Recent fever Yes 76 (19.0) No 324 (81.0) Recurrent illness Yes 72 (18.0) No 328 (82.0) Worm infestation Yes 46 (11.5) No 270 (67.5) Not applicable 84 (21.0) Hospitalization in previous 6 months Yes 50 (12.5) No 350 (87.5) Anthropometry and prevalence of malnutrition Underweight was observed in 135 (33.8%; 95% CI 29.3–38.5%), stunting in 121 (30.2%; 95% CI 26.0–34.9%), and wasting in 88 (22.0%; 95% CI 18.2–26.3%). Overall, 206 (51.5%; 95% CI 46.6–56.4%) had at least one form of malnutrition. The anthropometric distribution and severity categories are shown in Table 4 and Figure 1. Table 4. Anthropometric measurements and nutritional-status distribution Measure/index Category/statistic Value Weight (kg) Mean ± SD 9.72 ± 2.80 Median (IQR) 9.66 (7.70–11.58) Range 2.98–17.58 Length/height (cm) Mean ± SD 80.01 ± 12.54 Median (IQR) 80.90 (71.90–89.33) Range 47.20–102.60 WAZ Mean ± SD -1.60 ± 0.92 Median (IQR) -1.62 (-2.22–-0.93) Range -4.15–1.12 HAZ Mean ± SD -1.57 ± 0.96 Median (IQR) -1.56 (-2.19–-0.96) Range -4.54–1.13 WHZ Mean ± SD -1.30 ± 0.93 Median (IQR) -1.33 (-1.89–-0.61) Range -4.26–1.20 Underweight Normal 265 (66.2) Moderate 108 (27.0) Severe 27 (6.8) Stunting Normal 279 (69.8) Moderate 95 (23.8) Severe 26 (6.5) Wasting Normal 312 (78.0) Moderate 72 (18.0) Severe 16 (4.0) Any underweight Yes 135 (33.8) Any stunting Yes 121 (30.2) Any wasting Yes 88 (22.0) Any malnutrition Yes 206 (51.5) Severe acute malnutrition Yes 16 (4.0) Nutritional status by age group and sex The prevalence of individual anthropometric deficits and any malnutrition varied across age groups, but none of the age-group comparisons reached statistical significance. Similarly, no significant sex differences were observed for underweight, stunting, wasting or any malnutrition (Table 5). Table 5. Nutritional status stratified by age group and sex Stratum Category N Underweight n (%) Stunting n (%) Wasting n (%) Any malnutrition n (%) Age group 0–5 months 37 14 (37.8) 13 (35.1) 10 (27.0) 20 (54.1) 6–11 months 47 15 (31.9) 17 (36.2) 8 (17.0) 26 (55.3) 12–23 months 115 42 (36.5) 43 (37.4) 31 (27.0) 64 (55.7) 24–35 months 115 36 (31.3) 31 (27.0) 25 (21.7) 58 (50.4) 36–47 months 61 23 (37.7) 13 (21.3) 9 (14.8) 29 (47.5) 48–59 months 25 5 (20.0) 4 (16.0) 5 (20.0) 9 (36.0) Sex Male 222 75 (33.8) 64 (28.8) 45 (20.3) 115 (51.8) Female 178 60 (33.7) 57 (32.0) 43 (24.2) 91 (51.1) Overall Pearson χ² tests: Age group—underweight χ²=3.59 (df=5), p=0.610; stunting χ²=9.29 (df=5), p=0.098; wasting χ²=4.80 (df=5), p=0.441; any malnutrition χ²=4.00 (df=5), p=0.549. Sex—underweight χ²=0.00 (df=1), p=1.000; stunting χ²=0.34 (df=1), p=0.561; wasting χ²=0.66 (df=1), p=0.417; any malnutrition χ²=0.00 (df=1), p=0.973. Factors associated with any malnutrition On bivariate analysis, any malnutrition was significantly associated with rural residence, tribal background, lower/lower-middle socioeconomic status, low maternal education, maternal anemia history, low birth weight, delayed initiation of breastfeeding, inadequate age-appropriate feeding, unsafe drinking water, absence of a sanitary toilet, recent diarrhea and recurrent illness. The strongest crude associations were observed for low maternal education, tribal background, lower socioeconomic status and inadequate age-appropriate feeding (Table 6). Table 6. Bivariate associations between selected risk factors and any malnutrition Exposure Malnourished n (%) Not malnourished n (%) Reference malnourished n (%) Reference not malnourished n (%) Crude OR 95% CI χ² (df) p-value Rural residence 177 (55.8) 140 (44.2) 29 (34.9) 54 (65.1) 2.35 1.42–3.89 11.50 (1) <0.001 Tribal background 146 (63.2) 85 (36.8) 60 (35.5) 109 (64.5) 3.12 2.06–4.72 29.98 (1) <0.001 Lower/lower-middle SES 171 (59.0) 119 (41.0) 35 (31.8) 75 (68.2) 3.08 1.94–4.90 23.53 (1) <0.001 Maternal education ≤ primary 125 (66.5) 63 (33.5) 81 (38.2) 131 (61.8) 3.21 2.13–4.84 31.91 (1) <0.001 Maternal anemia history 97 (57.4) 72 (42.6) 109 (47.2) 122 (52.8) 1.51 1.01–2.25 4.07 (1) 0.044 Birth order ≥3 55 (59.1) 38 (40.9) 151 (49.2) 156 (50.8) 1.50 0.93–2.39 2.83 (1) 0.092 Low birth weight 62 (63.9) 35 (36.1) 144 (47.5) 159 (52.5) 1.96 1.22–3.14 7.91 (1) 0.005 Preterm birth 34 (58.6) 24 (41.4) 172 (50.3) 170 (49.7) 1.40 0.80–2.46 1.38 (1) 0.241 Delayed breastfeeding initiation 89 (61.4) 56 (38.6) 117 (45.9) 138 (54.1) 1.87 1.24–2.84 8.89 (1) 0.003 Inadequate age-appropriate feeding 169 (58.7) 119 (41.3) 37 (33.0) 75 (67.0) 2.88 1.82–4.55 21.23 (1) <0.001 Incomplete immunization 56 (59.6) 38 (40.4) 150 (49.0) 156 (51.0) 1.53 0.96–2.45 3.21 (1) 0.073 Unsafe drinking water 46 (64.8) 25 (35.2) 160 (48.6) 169 (51.4) 1.94 1.14–3.31 6.10 (1) 0.013 No sanitary toilet 61 (61.0) 39 (39.0) 145 (48.3) 155 (51.7) 1.67 1.05–2.65 4.82 (1) 0.028 Recent diarrhea 54 (70.1) 23 (29.9) 152 (47.1) 171 (52.9) 2.64 1.55–4.51 13.25 (1) <0.001 Recurrent illness 45 (62.5) 27 (37.5) 161 (49.1) 167 (50.9) 1.73 1.02–2.92 4.25 (1) 0.039 Percentages are row percentages within exposed and reference groups. Pearson χ² test used for all comparisons. Multivariable predictors of any malnutrition In multivariable logistic regression, tribal background, lower/lower-middle socioeconomic status, maternal education up to primary level, low birth weight, inadequate age-appropriate feeding and recent diarrhea remained independently associated with any malnutrition. Other included covariates did not retain statistically significant independent associations after adjustment (Table 7 and Figure 2). Table 7. Multivariable logistic regression for predictors of any malnutrition Predictor Adjusted OR 95% CI Wald χ² p-value Age (per month) 0.99 0.97–1.00 2.34 0.126 Male sex 0.97 0.62–1.53 0.01 0.913 Tribal background 2.01 1.25–3.22 8.44 0.004 Lower/lower-middle SES 1.82 1.05–3.15 4.55 0.033 Maternal education ≤ primary 1.92 1.19–3.09 7.11 0.008 Low birth weight 2.28 1.33–3.93 8.89 0.003 Maternal anemia history 1.02 0.64–1.62 0.01 0.932 Birth order ≥3 1.25 0.74–2.11 0.70 0.402 Inadequate age-appropriate feeding 1.90 1.12–3.23 5.69 0.017 Incomplete immunization 1.31 0.77–2.21 0.98 0.322 Poor WASH 1.04 0.65–1.67 0.03 0.873 Recent diarrhea 2.31 1.27–4.19 7.53 0.006 Recurrent illness 1.42 0.79–2.57 1.38 0.240 Model N=400; likelihood-ratio χ²=86.61 (df=13), p=<0.001; McFadden pseudo-R²=0.156.

The present study revealed a substantial level of undernutrition among children less than five years of age who visited a tertiary-care hospital in Dahod. The prevalence of underweight, stunting, wasting and having at least one anthropometric deficit were 33.8%, 30.2%, 22.0% and 51.5%, respectively. The multivariable results show that malnutrition in this population was associated mainly with social disadvantage, low birth weight, suboptimal feeding, and recent morbidity, whereas age and sex were not. The overall burden seen here was less than in some highly disadvantaged or inpatient populations. Undernutrition was observed in 68% of 550 children from urban slums in Gwalior, and maternal illiteracy was associated with markedly higher odds of undernutrition (OR 5.70), while low birth weight was associated with around twofold greater odds of undernutrition (OR 2.00) [10]. These results are in the same direction as our results, where low maternal education (AOR 1.92) and low birth weight (AOR 2.28) were found to be independently associated with malnutrition. Variation in prevalence is likely to be due to differences in setting, case mix and outcome definitions. The apparent magnitude of malnutrition is also affected by hospital selection. In a tertiary-care study conducted at Anand, Gujarat, the rates of stunting, underweight and wasting were much higher in admitted children, and almost one-third of children had severe wasting [11]. In contrast, 79.5% of children in the present study were recruited from outpatient services. This difference may have been a factor in the lower prevalence found here and highlights the need to be cautious in interpreting hospital-based estimates as direct measures of community prevalence. This is especially important in the context of Dahod. The adjusted odds of malnutrition were about two times greater for tribal children in our study. Meshram et al. also reported a high prevalence of underweight, stunting and wasting among preschool tribal children in Maharashtra and found that maternal illiteracy, morbidity and lower household wealth were important correlates [12]. In Wayanad, Philip et al. found that 39% of tribal preschool children were underweight, 38% were stunted and 20.5% were wasted, and that maternal educational disadvantage and recent diarrhea were independently associated with undernutrition [13]. The observations are similar to those for low maternal education (AOR 1.92), tribal status (AOR 2.01) and recent diarrhea (AOR 2.31) and indicate that social and infection-related risks tend to co-occur in vulnerable groups. Nutritional status was also strongly associated with feeding practices. Lack of age-appropriate feeding was observed in 72.0% of the participants and was independently associated with malnutrition (AOR 1.90). In a hospital-based case-control study from Rajkot, Solanki et al. reported that delayed or inappropriate complementary feeding, inadequate feeding frequency and poor enrichment of complementary foods were associated with severe acute malnutrition [14]. National data from Dhami et al. also revealed significant regional gaps in complementary-feeding indicators, such as low dietary diversity and minimum acceptable diet, especially in western and central India [15]. In the same way, Jeyakumar et al. found low levels of timely complementary feeding and dietary diversity among children in slums of Pune, with maternal education affecting various feeding indicators [16]. These studies collectively suggest that feeding inadequacy is influenced by education, supportive health services, caregiver behaviour, and household resources. One of the strongest predictors in the current analysis was low birth weight (AOR 2.28). This association suggests that vulnerability to undernutrition may start before birth and may reflect the influence of maternal nutrition, fetal growth, prematurity and other antenatal factors. Similar relationships were observed in the Gwalior and Wayanad studies [10,13], which further highlight the need to connect antenatal risk identification with postnatal growth monitoring. Recent diarrhea was also independently associated with malnutrition (AOR 2.31). This is in line with the known relationship between infection and nutritional deterioration, where enteric illness can decrease food intake and nutrient absorption, while undernutrition can make an individual more vulnerable to subsequent infection. The same morbidity-related associations were reported in the tribal populations of Wayanad and Maharashtra [12,13]. The composite poor-WASH variable was not significant after adjustment, whereas unsafe drinking water and absence of a sanitary toilet were significant in bivariate analyses in the present study. This attenuation could be due to overlap with socioeconomic status, tribal background, maternal education, and diarrheal morbidity and does not indicate the absence of an environmental contribution. Neither age nor sex was significantly associated with malnutrition. Nutritional deficits differed among age groups, but these differences were not statistically significant. The absence of a sex effect also indicates that socioeconomic, maternal, birth-related, feeding and morbidity factors were more important than biological sex in this hospital-attending population. The lack of age-related associations could also be due to small sample sizes in each age group and the clinical nature of the sample. The study has a number of strengths: the use of WHO-based anthropometric indices, the simultaneous assessment of stunting, wasting and underweight, and the evaluation of a wide range of socioeconomic, maternal, feeding, environmental and morbidity-related factors. Multivariable analysis was also used to identify factors that remained independently associated after adjustment. The cross-sectional design, however, does not allow for inferences about temporality and causation. Recruitment in the hospital may lead to selection bias and may not be representative of the broader under-five population in Dahod. Several exposures were reported by caregivers and may be subject to recall or classification error. Other potential sources of residual confounding include food security, maternal anthropometry, micronutrient status, dietary intake, and access to healthcare, and the sample size may have restricted age-specific and interaction analyses

A substantial proportion of under-five children in this tertiary-care sample were malnourished, with underweight, stunting and wasting frequently overlapping. The independent associations with tribal background, socioeconomic disadvantage, low maternal education, low birth weight, inadequate age-appropriate feeding and recent diarrhea suggest that malnutrition in this setting reflects interacting social, prenatal, dietary and infectious determinants. These findings suggest the need for integrated nutritional assessment, including growth monitoring along with attention to maternal education, birth outcomes, complementary feeding, and prevention and early management of childhood infections.

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