ABSTRACT.
India has a substantial burden of undernutrition coupled with overweight and obesity at the other end of the spectrum of malnutrition. Nuh district, in the Haryana State in northern India, is an impoverished district in India. With an aim to investigate the problem of malnutrition in the community, a cross-sectional study was conducted in four villages of the Nuh district. Height/length, weight, and age data of children under 5 years were used to calculate three indices: weight-for-age, height-for-age, and weight-for-height. The body mass index was calculated for individuals older than 6 years. Associations between malnutrition and other factors were assessed using simple and multiple logistic regression to get adjusted coefficients. The total surveyed population comprised 11,496 individuals. Over 51% were female, and 13.2% of the surveyed population were children under 5 years. Almost half of the population was illiterate and unemployed. The prevalences of underweight, stunting, and wasting in children under 5 years were 37%, 53%, and 21%, respectively. The prevalences of underweight and stunting in the 6- to 19-year-old age group were 29% and 38%, respectively. The prevalence of overweight was 36% in the 20- to 40-year-old and > 60-year-old age groups, and 44% in the 41- to 60-year-old age group. Our findings reveal a considerable burden of undernutrition among children under 5 years and a dual burden of undernutrition and overnutrition in adults, highlighting the need to map these areas and sharpen our responses to mitigate the overwhelming and long-term consequences of malnutrition in the Nuh district.
INTRODUCTION
Malnutrition is the term used to describe any deficits, excesses, or imbalances in a person’s intake of energy and/or nutrients. The term encompasses undernutrition, overnutrition, and micronutrient-related imbalances. According for World Health Organization (WHO) (2021), 1.9 billion adults are overweight and 462 million are underweight.1 In the category of children under 5 years old, 149 million suffer from undernutrition, and 38.9 million are overweight. Approximately, 45% of deaths among children under 5 years have been found to be associated with undernutrition, most of them occurring in low- and middle-income countries (LMIC).1 On the other hand, nearly 40% of the world’s adult population was found to be overweight or obese in 2014, a figure that has doubled since 1975.
India contributes a third to the global burden of undernutrition.2 The most recent National Family Health Survey (NFHS-5), the largest national survey of India , which was carried out from 2019 to 2021, revealed that 36% of children under 5 years are stunted, 19% are wasted, 32% are underweight, and 3% are overweight, whereas 19% of women and 16% of men are underweight.3 We have previously analyzed the co-occurrence of malaria and malnutrition across India and found significant overlap.4 Nationally representative data on nutritional indicators and malaria prevalence were correlated using an innovative digital dashboard developed by the authors.5 The present survey was part of an elaborate study with the primary aim of assessing the feasibility of using field-based molecular tests in assessing malaria endemicity and the utility of information technology via phone services and mobile malaria clinics for bridging the gaps in health care delivery in communities where malaria is endemic, particularly in underserved populations. Strengthening surveillance through the use of molecular tools and developing novel methods of improving health care access have been recognized as significant interventions to achieve malaria elimination in India.6,7 Here, we report the findings of our nutritional survey carried out in four villages in the Nuh district of the Haryana state in northern India.
The National Institution for Transforming India Aayog launched an Aspirational Districts Programme in 2018, which was aimed at a quick and effective transformation of the underdeveloped districts across the nation. Nuh is the only district in Haryana to be placed on the list of aspirational districts. According to the 2011–2012 census, Nuh had the lowest per capita income among all the districts of Haryana.8 It also fared poorer on other health indicators, like the maternal mortality rate, infant mortality rate, and mortality rate of children under 5 years vis-à-vis Haryana.9 The overall literacy rate in the district was 54%, with 70% males and 37% females in the last census (2011).9
MATERIALS AND METHODS
A cross-sectional, community-based study was conducted from May 2022 to June 2022 in the Nuh district (Haryana, India). Four villages, namely, Bibipur, Bhopawali, Naushera, and Sangel, in the catchment area of Ujina Primary Health Center, were selected for the purpose of the study. All residents living in the study villages of the Nuh district for 6 months or more were included in this study, and the survey was carried out by duly obtaining the consent of the individuals and assent in the case of minors. A flow chart in Supplemental Figure 1 summarizes the study design of the present work.
The residents of the four villages of Nuh were involved as part of the study. An individual-wise interview, in simple, easy-to-understand language, was carried out with informed written consent and a preplanned, pretested schedule. Information pertaining to sociodemographic details, such as age, sex, educational status, occupation, marital status, and family size, was elicited from the respondents. To assess nutritional status, clinical examination and anthropometric measurements such as weight and height/length were carried out in accordance with standard operating procedures.10 Those found undernourished or overnourished were given appropriate nutritional counseling and referred to the nearest health care facility for further management. Mothers were educated on the importance of exclusive breastfeeding, timely complementary feeding, and measures for preventing infection.
Tools and techniques.
Using a standardized steel anthropometric rod with a parallel bar, the standing height of each child was measured (accuracy, 0.1 cm). A Seca 417 infantometer was used to measure the recumbent length of children younger than 24 months. A digital scale with 10-g precision was used to measure weight by standard procedures.11 Each observer was assigned to take one measurement for the entire study to reduce interobserver bias.
Three indices, weight-for-age, height-for-age, and weight-for-height, were generated based on children’s height/length, weight, and age data. WHO Child Growth Standards population medians were used as a reference standard. A mean of −2 SD was taken as the cutoff point to define wasting, stunting, underweight, and thinness. The following operational definitions were used:
Underweight = a weight-for-age Z-score (WAZ) < −2 SD indicates underweight, and a WAZ < −3 SD is considered an indicator of severe underweight.12
Stunting (chronic malnutrition) = a height-for-age Z-score (HAZ) < −2 SD indicates stunting, and a HAZ < −3 SD indicates severe stunting.12
Wasting (acute malnutrition) = a weight-for-height Z-score (WHZ) < −2 SD is considered an indicator of wasting, and a WHZ < −3 SD indicates severe wasting.12
Thinness = body mass index (BMI)-for-age Z-score (BAZ) < −2.13
Body weight (in kilograms)/height (in meters squared) was used to determine body mass index (kg/m2). Age- and sex-specific WHO recommended cutoff points for BMI were used.14,15 Overweight is characterized by a BMI of over 25 kg/m2, and obesity is characterized by a BMI of over 30 kg/m2. The following BMI ranges are recommended by the WHO for Asian people owing to their higher risk of diabetes and cardiovascular disease: <18.5 kg/m2, underweight; 18.5–22.9 kg/m2, normal weight; 23–24.9 kg/m2, overweight; and ≥25 kg/m2, obese.16–21
Data collection and management.
The data were collected in electronic format using the Open Data Kit (ODK) toolkit (www.getodk.org) and uploaded to a server at the host institute, i.e., the Indian Council of Medical Research-National Institute of Malaria Research. Android-based tablets were used in the study and installed with the “ODK Collect App” from Google Play. These platforms have been found useful in supplementing the surveillance procedures.22 Data were downloaded from the server and exported in comma-separated value format for further analysis in Microsoft Excel and other statistical software.
Data analysis.
The primary outcome variables in this study were underweight, stunting, wasting, and thinness. Using the WHO Anthro software, v. 3.2.2, the values for height, weight, and BMI were transformed into Z-scores of the indices HAZ, WAZ, and BAZ. Categorical variables were summarized using frequency and percentage, whereas continuous variables were summarized using mean and SD. The association between malnutrition parameters and several baseline indicators was assessed using simple logistic regression, followed by multivariable logistic regression to get adjusted coefficients. The magnitude of the association was represented in terms of the odds ratio (OR) and corresponding 95% CI. All analyses were done using Stata (Version 15.0; StataCorp, College Station, TX), and a P-value of less than 0.05 (P < 0.05) was considered statistically significant.
RESULTS
Sociodemographic characteristics.
The four villages (namely, Bibipur, Bhopawali, Naushera, and Sangel) of the Nuh district, Haryana, had a total population of 13,934 according to the health center record. The individual population of each village were as follows: Bibipur, 3,734; Bhopawali, 1,643; Naushera, 4,438; and Sangel, 4,119. Of the total of 13,934 individuals, 11,496 (82.5%) were available for the study, and the rest worked outside the villages. The sociodemographic findings from the village are presented in Supplemental Table 1 and highlight the differences in sex, population age group, literacy rate, and employment status in the study villages.
Nutritional status.
The population was stratified into three age strata, under 5 years, 6–19 years, and > 20 years, to study their nutritional indicators. Different sets of analysis were performed for each of these strata according to our study objectives. First, we present results for children under 5 years, followed by younger children and adults. The findings are summarized in the respective tables.
Children under 5 years.
The under-5-year-old study group was divided into subgroups of < 6 months, 6–12 months, 13–24 months, and 25–59 months, as depicted in Table 1.
Table 1.
Prevalence of underweight, stunting, and wasting among children under 5 years old in four study villages
| Background characteristics | Weight-for-age (underweight) | Height-for-age (stunting) | Weight-for-height (wasting) | |||
|---|---|---|---|---|---|---|
| Below –3 SD | Below –2 SD | Below –3 SD | Below –2 SD | Below –3 SD | Below –2 SD | |
| n/N (%) | n/N (%) | n/N (%) | n/N (%) | n/N (%) | n/N (%) | |
| [95% CI] | [95% CI] | [95% CI] | [95% CI] | [95% CI] | [95% CI] | |
| Overall | 288/1,518 (19.0) | 565/1,518 (37.2) | 476/1,518 (31.4) | 806/1,518 (53.1) | 187/1,518 (12.3) | 323/1,518 (21.3) |
| [17.0–20.9] | [34.7–39.6] | [29.0-33.7] | [50.6–55.6] | [10.7–14.0] | [19.2–23.3] | |
| Age (months) | ||||||
| < 6 | 27/144 (18.7) | 69/144 (47.9) | 37/144 (25.7) | 58/144 (40.3) | 36/144 (25.0) | 49/144 (34.0) |
| [12.3–25.2] | [39.6–56.1] | [18.5–32.9] | [32.2–48.4] | [17.8–32.2] | [26.2–41.9] | |
| 6–12 | 55/254 (21.6) | 97/254 (38.2) | 73/254 (28.7) | 110/254 (43.3) | 46/254 (18.1) | 74/254 (29.1) |
| [16.5–26.7] | [32.2–44.2] | [23.1–34.3] | [37.2–49.4] | [13.3–22.9] | [23.5–34.8] | |
| 13–24 | 70/375 (18.7) | 121/375 (32.3) | 130/375 (34.7) | 218/375 (58.1) | 51/375 (13.6) | 76/375 (20.3) |
| [14.7–22.6] | [27.5–37.0] | [29.8–39.5] | [53.1–63.1] | [10.1–17.1] | [16.2–24.4] | |
| 25–59 | 136/745 (18.3) | 278/745 (37.3) | 236/745 (31.7) | 420/745 (56.4) | 54/745 (7.3) | 124/745 (16.6) |
| [15.4–21.0] | [33.8–40.8] | [28.3–35.0] | [52.8–59.9] | [5.4–9.1] | [14.0–19.3] | |
| Sex | ||||||
| Females | 119/714 (16.7) | 266/714 (37.3) | 222/714 (31.1) | 360/714 (50.4) | 77/714 (10.8) | 131/714 (18.4) |
| [13.9–19.4] | [33.7–40.8] | [27.7–34.5] | [46.7–54.1] | [8.5–13.1] | [15.5–21.2] | |
| Males | 169/804 (21.0) | 299/804 (37.2) | 254/804 (31.6) | 446/804 (55.5) | 110/804 (13.7) | 192/804 (23.9) |
| [18.2–23.8] | [33.8–40.5] | [28.4–34.8] | [52.0–58.9] | [11.3–16.1] | [20.9–26.8] | |
| Village | ||||||
| Bibipur | 82/427 (19.2) | 176/427 (41.2) | 138/427 (32.3) | 240/427 (56.2) | 50/427 (11.7) | 87/427 (20.4) |
| [15.5–22.9] | [36.5–45.9] | [27.9–36.8] | [51.5–60.9] | [86.5–14.8] | [16.5–24.2] | |
| Bhopawali | 50/219 (22.8) | 88/219 (40.2) | 80/219 (36.5) | 126/219 (57.5) | 27/219 (12.3) | 47/219 (21.5) |
| [17.2–28.4] | [33.6–46.7] | [30.1–42.9] | [50.9–64.1] | [79.4–16.7] | [16.0–26.9] | |
| Naushera | 132/618 (21.4) | 252/618 (40.8) | 201/618 (32.5) | 340/618 (55.0) | 83/618 (13.4) | 144/618 (23.3) |
| [18.1–24.6] | [36.9–44.7] | [28.8–36.2] | [51.1–58.9] | [10.7–16.1] | [19.9–26.6] | |
| Sangel | 24/254 (9.4) | 49/254 (19.3) | 57/254 (22.4) | 100/254 (39.4) | 27/254 (10.6) | 45/254 (17.7) |
| [58.3–13.1] | [14.4–24.2] | [17.3–27.6] | [33.3–45.4] | [68.1–14.4] | [12.9–22.4] | |
Prevalence of underweight (weight-for-age) and its association with different background characteristics in children under 5 years.
Of 1,518 children under 5 years, 565, i.e., 37.2% (95% CI, 34.7–39.6), and 288, i.e., 19% (95% CI, 17–20.9), had a Z-score for weight-for-age lower than −2 SD (moderate to severe) and −3 SD (severe), respectively. Detailed prevalence estimates (both < −2 SD and < −3 SD) in different age categories (< 6 months, 6–12 months, 13–24 months, and 25–59 months), sex (male, female), and villages (Bibipur, Bhopawali, Naushera and Sangel), are given in Table 1.
To assess the association of underweight with different background characteristics, the WAZ was categorized into two values, ≥ −2 SD and < −2 SD, and different characteristics (i.e., age categories, sex, village, maternal education, and paternal occupation) were assessed in these two categories of outcome variables using a contingency table (Supplemental Table 2). Among these five characteristics, three (i.e., age categories, villages, and paternal occupation) were found to be predictors of underweight (≥ −2 SD) in multivariate logistic regression. The distribution of underweight (< −2 SD) across the four age categories varied statistically. The odds of being underweight were lower in older children than in infants < 6 months. The children belonging to the age groups of 13–24 months and 25–59 months had 0.51 and 0.65 lower odds of being underweight than children belonging to < 6-month categories, whereas children belonging to the 6- to 12-month age group had a distribution comparable to that of the reference category. Village was the second underlying characteristic that showed association with underweight status; the village Sangel had the lowest proportion of underweight children (19%) relative to that of the other three villages, where it was ∼40%, and the odds of being underweight were approximately 3 times higher if a person belonged to any of the three villages other than Sangel. The parental occupation was the third important predictor of underweight; children whose parents were involved in activities other than agriculture were more likely to be underweight. There was no significant difference between males and females in their underweight status.
Prevalence of stunting (height-for-age) and its association with different background characteristics in children under 5 years.
The HAZ was categorized into < −2 SD and < −3 SD, representing moderate to severe stunting and severe stunting, respectively. It was observed that 53.1% (806) and 31.4% (476) of all the children under 5 years were found to be moderately to severely and severely stunted, respectively. Table 1 gives detailed prevalence estimates of stunting in children under 5 years. Considering the age group of < 6 months as the reference category, the children above 13 months of age were almost twice as likely to be stunted as those < 6 months of age at higher statistically significant values (P < 0.001).
We noted that Bibipur, Bhopawali, and Naushera had similar distribution patterns of stunting, with a 55% to 60% prevalence rate. However, as with the prevalence of low numbers of underweight children, Sangel fared better in nutritional status, with 39.4% of stunted children under 5 years. It was found that with respect to the Sangel village, the other three villages had twice the odds of preponderance of stunted children (P < 0.001).
The education and occupation of the parents were also correlated with the nutritional status of these children. It was observed that 53.4% of stunted children had illiterate parent(s), whereas only 29.2% of these children had parents with at least a primary education, and these children were 0.37 times less likely to be stunted than the children of illiterate parents (P = 0.024). However, stunting was seen in 50% to 55% of children, irrespective of the occupation of their parents (Supplemental Table 3). The education and occupation data were missing for the parents of 55 children (3.6%).
Prevalence of wasting (weight-for-height) and its association with different background characteristics in children under 5 years.
We found that 21.3% (95% CI, 19.2–23.3) of children under 5 years were wasted, of which more than half (12.3%) at a 95% CI of 10.7–14.0 were categorized as severely wasted (< −3 SD). By use of multivariate logistic regression analysis, it was observed that age and sex categories were the only predictors of outcome variable, as seen in a contingency table (Supplemental Table 4).
Children less than 6 months of age were the most affected, with 34% of them having low weight-for-height. However, prevalence of wasting was observed in 29% (OR, 0.80), 20% (OR, 0.49), and 16.6% (OR, 0.39) of the children in the age categories of 6–12 months, 13–24 months, and 25–59 months, respectively. Though the wasting distribution for the 6- to 12-month-old age group was statistically insignificant relative to that of the < 6-month-old age group, the 13- to 24-month age group and the 25- to 59-month age group had 0.49 and 0.39 lower odds of being wasted, with these values being statistically significant at a P-value of 0.001 and lower.
Furthermore, for the children under 5 years, wasting was more prevalent in males (23.9%) than in females (18.3%). The former was ∼0.4 times more likely to be wasted than the latter (P = 0.009). According to the data, the highest number of wasted children belonged to the village of Naushera (23.30%), and the lowest number of wasted children were from the village of Sangel (17.7%), although the difference was not statistically significant. The education and occupation of the parents did not seem to be correlated to wasting status.
Figure 1A is a line graph that represents the age-wise distribution of malnutrition among children under 5 years, while the Venn diagram in Figure 1B shows a combination of the three determinants of malnutrition in the under-5-year-old population. It was seen that among the total number of children under 5 years, 7.64% suffered from all three forms of malnutrition (i.e., underweight, stunting, and wasting).
Figure 1.
(A) Age-wise distribution of malnutrition among study subjects under 5 years old. (B) Venn diagram showing combination of various forms of malnutrition.
Children and adolescents: 6–19 years of age.
As above, WAZ, WHZ, and HAZ were categorized into two categories: < −2 SD and ≥ −2 SD. The prevalence of underweight (WAZ < −2 SD), stunting (HAZ < −2 SD), and wasting (WHZ < −2 SD) were calculated overall and for different subgroups of age, sex, and village. Owing to the inherent differences in food consumption pattern and hence nutritional status pattern, we divided the 6- to 19-year-old age group into two classes: 6–10 years (young children) and 11–19 years (adolescents). Table 2 outlines the prevalence rates of different malnutrition parameters for children aged 6–19 years in different groups of baseline characteristics, which are also discussed below.
Table 2.
Percentage of children and adolescents (6–19 years old) classified as undernourished according to three anthropometric indices of nutritional status, i.e., weight-for-age, height-for-age, and BMI-for-age, in different subgroups
| Background characteristics | Weight-for-age (underweight; n %) | Height-for-age (stunting; n %) | BMI-for-age (thinness; n %) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Number of children | Percent below –3 SD | Percent below –2 SD | Number of children | Percent below –3 SD | Percent below –2 SD | Number of children | Percent below –3 SD | Percent below –2 SD | |
| Age (years) | |||||||||
| 6–10 | 2,307 | 272 (11.8) | 668 (29.0) | 2,307 | 407 (17.6) | 873 (37.8) | 2,307 | 127 (5.5) | 330 (14.3) |
| [0.10–0.13] | [0.27–30.8] | [0.16–0.19] | [0.36–0.40] | [0.05–0.06] | [0.13–0.16] | ||||
| 11–19 | 2,586 | NA | NA | 2,586 | 354 (13.7) | 990 (38.3) | 2,586 | 72 (2.8) | 315 (12.2) |
| [0.12–0.15] | [0.36–0.40] | [0.02–0.03] | [0.11–0.13] | ||||||
| Sex | |||||||||
| Females | 1,113 | 124 (11.1) | 340 (30.6) | 2,447 | 381 (15.6) | 933 (38.1) | 2,447 | 80 (3.3) | 277 (11.3) |
| [0.09–0.13] | [0.28–0.33] | [0.14–0.17] | [0.36–0.40] | [0.03–0.04] | [0.10–0.13] | ||||
| Males | 1,194 | 148 (12.4) | 328 (27.5) | 2,446 | 380 (15.5) | 930 (38.0) | 2,446 | 119 (4.9) | 368 (15.0) |
| [0.10–0.14] | [0.25–0.30] | [0.14–0.17] | [0.36–0.40] | [0.04–0.06] | [0.14–0.16] | ||||
| Village | |||||||||
| Bibipur | 689 | 92 (13.4) | 200 (29.0) | 1,412 | 223 (15.8) | 533 (37.8) | 1,412 | 58 (4.1) | 216 (15.3) |
| [0.11–0.16] | [0.26–0.32] | [0.14–0.18] | [0.35–0.40] | [0.03–0.05] | [0.13–0.17] | ||||
| Bhopawali | 320 | 34 (10.6) | 89 (27.8) | 657 | 106 (16.1) | 252 (38.4) | 657 | 17 (2.6) | 69 (10.5) |
| [0.07–0.14] | [0.23–0.33] | [0.13–0.19] | [0.35–0.42] | [0.014–0.04] | [0.08–0.13] | ||||
| Naushera | 886 | 104 (11.7) | 291 (32.8) | 1,847 | 308 (16.7) | 766 (41.5) | 1,847 | 74 (4.0) | 234 (12.7) |
| [0.09–0.14] | [0.30–0.36] | [0.15–0.18] | [0.39–0.44] | [0.03–0.05] | [0.11–0.14] | ||||
| Sangel | 412 | 42 (10.2) | 88 (21.4) | 976 | 124 (12.7) | 312 (32.0) | 976 | 49 (5.0) | 125 (12.8) |
| [0.07–0.13] | [0.17–0.25] | [0.11–0.15] | [0.29–0.35] | [0.04–0.06] | [0.11–0.15] | ||||
| Parent education | |||||||||
| Illiterate | 1,010 | 137 (13.6) | 333 (33.0) | 1,587 | 321 (20.2) | 683 (43.0) | 1,587 | 61 (3.8) | 200 (12.6) |
| Primary (5th grade) | 1,041 | 112 (10.8) | 278 (26.7) | 1,554 | 245 (15.8) | 607 (39.1) | 1,554 | 93 (6.0) | 237 (15.3) |
| Middle (6th–8th grade) | 141 | 15 (10.6) | 31 (22.0) | 1,460 | 159 (10.9) | 475 (32.5) | 1,460 | 36 (2.5) | 171 (11.7) |
| Higher/graduate | 1 | 0 | 0 | 33 | 0 | 6 (18.2) | 33 | 1 (3.0) | 3 (9.1) |
| Parent occupation | |||||||||
| Agriculture | 75 | 9 (12.0) | 19 (25.3) | 138 | 24 (17.4) | 44 (31.9) | 138 | 4 (2.9) | 17 (12.3) |
| Other | 903 | 120 (13.3) | 286 (31.7) | 1,876 | 301 (16.0) | 706 (37.6) | 1,876 | 91 (4.9) | 272 (14.5) |
| Unemployed | 1,329 | 143 (10.8) | 363 (27.3) | 2,879 | 436 (15.1) | 1,113 (38.7) | 2,879 | 104 (3.6) | 356 (12.4) |
BMI = body mass index. 95% CI values are shown in brackets.
Prevalence of underweight (weight-for-age).
In the 6- to 10-year-old category, 29% (668 children; 95% CI, 0.27–30.8] of the 2,307 children were found to be underweight, of which 30.6% (340 children; 95% CI, 0.28–0.33) were girls and 27.5% (328 children; 95% CI, 0.25–0.30) were boys. Sangel had the lowest percentage of underweight children, i.e., 21%, whereas Naushera harbored the highest percentage (32.8%). There was a decline in the percentage prevalence of underweight with the increase in parental education. It was observed that 33% of children whose parents were illiterate were underweight, in comparison with 22% children whose parents were educated up to middle school. Likewise, 25% children with parents in an agricultural occupation and 27% children with unemployed parents were found to be underweight. Almost 32% children of children with parents of any other occupation were underweight with weight-for-age values below −2 SD.
Prevalence of stunting (height-for-age).
Children of both age groups (namely, 6–10 years and 11–19 years) as well as both sexes showed similar stunting patterns, with nearly 38% (95% CI, 0.36–0.40) prevalence in each case. The proportion of children with stunting was lowest in Sangel village (32%) (Supplemental Figure 2). The children of educated parents had marginally better nourishment status (< 40%) than children with illiterate parents (43%). The lowest prevalence was among the children who had parents with a high school education and above (18.2%). Again, the children with parents in an agriculture occupation showed relatively lower stunting rates, at 31.9%, than those with parents in other occupations.
Prevalence of thinness (BMI-for-age).
Weight-for-age does not differentiate between relative height and body mass in children 11 years of age and older, when children and adolescents are going through pubertal growth spurts and may appear to be overweight even though they are simply tall. Therefore, BMI-for-age and height-for-age better reflect nutritional status than does weight-for-age alone. In this study, 14.3% and 12.2% children between 5 and 10 years and between 11 and 19 years of age, respectively, were categorized as thin. More boys (15%) than girls (11.3%) had low BMI-for-age. Bhopawali had the lowest percentage of thin children, with 10.5%, followed by Naushera and Sangel (each ∼13%) and then Bibipur (15%) (Supplemental Figure 2).
Adults (≥ 20 years).
Table 3 is a compilation of sociodemographic correlates and malnutrition prevalence in the adult population with specific BMI levels. The highest proportion of study subjects in this category was in the 20- to 40-year-old age group, at 62%, whereas 1,321 (25.8%) individuals were from the 41 to 60 year-old age group and 636 (12.5%) individuals were above the age of 60 years.
Table 3.
Sociodemographic correlates and nutritional status of adult population (> 20 years old)
| Background characteristics | Underweight (N = 566) | Normal weight (N = 2,600) | Overweight (N = 985) | Obesity (N = 935) | P-value |
|---|---|---|---|---|---|
| Age (years) | |||||
| 20–40 | 286 (9.1) | 1,739 (55.4) | 604 (19.3) | 509 (16.2) | <0.001 |
| 41–60 | 155 (11.8) | 580 (44.2) | 278 (21.2) | 299 (22.8) | |
| > 60 | 125 (19.7) | 281 (44.2) | 103 (16.2) | 127 (20.0) | |
| Sex | |||||
| Female | 326 (11.9) | 1,369 (49.8) | 490 (17.8) | 565 (20.5) | <0.001 |
| Male | 240 (10.3) | 1,231 (52.7) | 495 (21.2) | 370 (15.8) | |
| Village | |||||
| Bibipur | 191 (15.9) | 658 (54.7) | 161 (13.4) | 192 (16.0) | <0.001 |
| Bhopawali | 65 (12.4) | 309 (58.8) | 83 (15.8) | 69 (13.1) | |
| Naushera | 200 (11.9) | 933 (55.3) | 292 (17.3) | 261 (15.5) | |
| Sangel | 110 (6.6) | 700 (41.9) | 449 (26.9) | 413 (24.7) | |
| Education | |||||
| Illiterate | 355 (12.8) | 1,401 (50.3) | 488 (17.5) | 540 (19.4) | <0.001 |
| Primary | 54 (10.9) | 241 (48.6) | 129 (26.0) | 73 (14.7) | |
| Upper primary | 48 (8.8) | 285 (52.2) | 98 (18.0) | 115 (21.1) | |
| Secondary/high school | 61 (9.7) | 331 (52.4) | 128 (20.3) | 112 (17.7) | |
| Graduate | 11 (4.7) | 131 (55.7) | 46 (19.6) | 47 (20.0) | |
| Occupation | |||||
| Agriculture | 117 (12.9) | 449 (49.6) | 172 (19.0) | 168 (18.5) | <0.001 |
| Job | 20 (5.4) | 180 (48.5) | 82 (22.1) | 89 (24.0) | |
| Other | 152 (9.5) | 834 (51.9) | 325 (20.2) | 295 (18.4) | |
| Unemployed | 238 (13.2) | 922 (51.2) | 308 (17.1) | 332 (18.4) | |
BMI = body mass index. Underweight if BMI < 18.5; normal weight if BMI ≥ 18.5 and BMI ≤ 22.9; overweight if BMI ≥ 23 and BMI ≤ 24.9; obesity if BMI ≥ 25; percentages were calculated column-wise; numbers are expressed as n (%).
In this study, an upward trend in the percentage of underweight category was evident from the youngest to the oldest age groups, with approximately 9%, 12%, and 20% underweight in the 20- to 40-, 41- to 60-, and >60-year-old age groups, respectively (Supplemental Figure 3A). However, the percentage prevalence of elderly in the overweight-obese group was found to be ∼36%, as in the 20- to 40-year-old age group. Besides, the prevalence of the dual burden of overweight and obesity was much higher, i.e., 44%, in the 41- to 60-year-old age group (Supplemental Figure 3A). Of the villages, Sangel had > 50% population in the overweight-obese group and only 6.6% in the underweight group (Supplemental Figure 3B).
DISCUSSION
This is a large, community-based study that profiles nutritional indicators in all age groups, including children under 5 years, in four villages in the Nuh district of Haryana State. We compared the sociodemographic profile of the study population of the Nuh district to that of the state of Haryana from the National Family Health Survey-5 (NFHS-5). The distribution of the male and female populations in the study villages were found to be 48% and 52%, respectively. According to the NFHS-5 data, Haryana State has 52% males and 48% females.3 More importantly, the proportion of illiterates was 17.7% for Haryana State according to NFHS-5 data, but our study in the Nuh district showed that half of the population (50.5%) had no schooling.
Children under the age of 5 years are most at risk for malnutrition. The number of underweight children in the under-5-year-old category is 37%, based on our study as well as the NFHS-5 data for the Nuh district, but for Haryana State, relatively fewer children (21.5%) are underweight (Figure 2).
Figure 2.
Prevalence of malnutrition in children under 5 years old from the present study in four villages in comparison to NFHS-5 data of Nuh, Haryana, and India.
Stunting is a sign of chronic undernutrition throughout a child’s formative years, when growth and development are most important.23 In our study, it was observed that the overall prevalence of stunting among all children under 5 years old in the four study villages was 53.1%. Our findings corroborate with the findings of NFHS-5, which showed 44.4% prevalence of stunting in the Nuh district. But Haryana as a state had a prevalence of only 27.5% of children under 5 years with stunting, and 35.3% represented children under 5 years with stunting in all of India (Figure 2). Globally, one-third of the overall total population of stunted children under 5 years of age are from India.24
The most apparent manifestation of malnutrition is wasting, which is defined as low weight-for-height. Its most lethal form is severe wasting, commonly referred to as severe acute malnutrition. The prevalence of wasting in the four villages was found to be higher (21.28%) than the overall wasting statistics for Nuh (14.2%), and the difference was more marked when Nuh wasting statistics were compared with the wasting statistics of Haryana (4.4%). But the data for Nuh are comparable to the prevalence of wasting in India at a country level (19.3%) (Figure 2).
Similar studies in children under 5 years in Haryana have also revealed the burden of malnutrition. In a study by Gupta et al. in 2019 from a rural area of Haryana, the prevalences of stunting, underweight, and wasting among the children under 5 years were 41.3%, 38.3%, and 18.4%, respectively.25 Similar to this, Prinja et al. in 2017 conducted a community-based cross-sectional survey in four Haryana districts: Ambala, Karnal, Panchkula, and Yamunanagar. According to their findings, the prevalences of stunting, underweight, and wasting were 38.2%, 37.4%, and 16.4%, respectively, in these four districts of Haryana.26 In another community-based study of children aged 1–5 years conducted in 2020, Samdarshi et al. found that 21.5% of the children were underweight, 30.2% were stunted, and 8.9% were wasting.27 Pooja et al. in 2019 did a community-based, cross-sectional study in an urban slum of Faridabad to examine the prevalence of malnutrition and its relationship to prevalent pediatric illnesses. According to the report, among children aged 1–5 years, there are high rates of underweight (29.2%), stunting (66.8%), and wasting (12.9%).28
Next, in the case of 6- to 19-year-old children attending school, ∼38% of the participants were stunted, 29% were underweight, and those with wasting ranged from 12% to 15%. The malnutrition data for this age group is scanty for Haryana State. However, a study in the Makrauli village of the Rohtak district showed a much lower prevalence of stunting (16.8%), a higher prevalence of wasting (23.7%), and a comparable proportion of underweight (27.5%).29
Adults in LMICs like India face the dual burden of malnutrition, i.e., the coexistence of underweight and prevalence of overweight/obesity in the population. Our study among the > 20-year-old population revealed that 11.1% were underweight, 19.4% were overweight, and 18.4% were obese. As per the NFHS-5 survey report for Haryana, more men (28.3%) and women (33.1%) are in the obese category than in the undernourished category (14.5% men and 15.1% women), which supports our results showing more adults in the overweight-obese category. There has been a rise in the prevalence of overnutrition from 2015–2016 to 2019–2020.3,30 The prevalence of overweight and obesity among women has increased significantly over the years (21% in 2015–33% in 2021) (Figure 3).
Figure 3.
Underweight (BMI < 18.5 kg/m2) and obesity (BMI > 25 kg/m2) trends in adult women from the present study population with respect to NFHS-4 and NFHS-5 data.
The decreased frequency of underweight (9%) among those aged 21–40 years could potentially be attributed to improved social and economic status and better nutrition. However, a greater burden of overweight and obesity is a matter of concern. It has been shown that obesity is more prevalent among women than men.31–33 The sex differences in BMI are notable, with women respondents being more affected by overweight/obesity and its consequences.32,34,35
Limitations and implications.
We did not do a dietary survey of the study population, which could have been correlated with the malnutrition levels in the different age groups. In addition, as this was a cross-sectional study, establishing cause-and-effect relationships was not possible. Another limitation is that the study was conducted in a small geographical region, and hence, generalizability to other areas would be difficult.
The current study also has certain implications. The findings of this study will assist policymakers in developing strategies for the inclusive engagement of primary care professionals in identifying and managing cases of undernutrition among children under the age of 5 years.
CONCLUSION
Malnutrition has been identified as one of the most severe challenges hindering global development. It moves from childhood to adolescence and finally adulthood, thereby impeding physical and cognitive abilities and curtailing an individual’s productivity. It thus maintains the cycle of poverty and malnutrition.
Our data on children who are under 5 years old revealed that 37% were underweight, 53% were stunted, and 21% were wasted. Among the 6- to 19-year-old age group, 29% were underweight, and 38% were stunted. The prevalences of overweight were 36% in the 20- to 40-year-old and > 60-year-old age groups and 44% in the 41- to 60-year-old age group.
Ongoing public health initiatives have led to an improvement in the development status of the Nuh district, as can be seen in advances made by the district in recent years. The overall ranking of Nuh on the list of aspirational districts has improved from 30th position to 2nd position.36 Specially curated programmes targeted to address the issue of malnutrition in the study villages will be highly beneficial in sustaining the development status.
Supplemental Materials
ACKNOWLEDGMENTS
A. Sharma is a recipient of the J. C. Bose fellowship. We thank the staff of National Institute of Malaria Research for assisting in surveillance work. We also especially thank the participants of this study for their cooperation in conducting the survey in a systematic manner. The American Society of Tropical Medicine and Hygiene (ASTMH) assisted with publication expenses.
Note: Supplemental material appears at www.ajtmh.org.
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