Chronic Illness, Nutritional Status, and Factors Associated with Malnutrition among Various Age Groups Residing in Urban Areas of Telangana and Rural Areas of Andhra Pradesh

Malnutrition includes both under-nutrition and over-nutrition, which have negative health impacts and social consequences. The present study aims to understand the demographic dynamics, burden of chronic illnesses, and risk factors associated with malnutrition (stunting, thinness, and obesity) among different age groups in urban and rural areas. Data were collected through a cross-sectional study conducted in an urban area in Hyderabad and four rural villages in Andhra Pradesh. A multivariable mixed-effect logistic regression was used to assess the risk factors associated with malnutrition among different age groups. The final analysis included the data of 10,350 individuals, consisting of 8317 (80.4%) from urban areas and 2033 (19.6%) from rural areas. The number of known cases of hypertension in the urban area was 926 (11.1%) and 114 (5.6%) in the rural areas, and that of diabetes was 511 (6.1%) in the urban area and 104 (5.1%) in the rural areas. The burden of stunting among under-five children and obesity among adults was 33.7% (95% CI; 29.7–37.9) and 47.4% (95% CI; 45.8–49.1), respectively. Adults aged 40–59 years (AOR 1.91; 1.59–2.28) and belonging to a clerical/skilled (AOR 1.32; 1.03–1.71) occupation were at higher odds of obesity compared to their counterparts. Policymakers and health practitioners should consider the insights from our findings to tailor effective interventions to address malnutrition.


Introduction
Malnutrition is a global public health problem affecting people of all ages and in all parts of the world [1].Malnutrition includes both under-nutrition and over-nutrition, which have negative health impacts and social consequences [2].According to the WHO, malnutrition accounts for nearly 45% of all deaths among under-five children globally [1].According to the Global Nutrition Report 2020, roughly one in every three people worldwide suffers from at least one form of malnutrition, including under-nutrition, micronutrient deficiencies, and overweight/obesity [3].Moreover, malnutrition affects a person's quality of life and productivity throughout their lifetime through having long lasting impacts on their physical and cognitive development [2].
Underweight is more common than overweight and obesity in developing countries compared to developed countries, although it is predicted that soon they will overtake the rate of obesity in developed countries [4,5].Because developing countries are seeing significant changes in terms of activity and diet patterns, a move from active to sedentary behaviours [5,6], they are facing a "double burden of malnutrition", meaning both underweight and overweight [7].Among older individuals, whereas being underweight causes exhaustion and persistent weakness, an increased chance of infection, and mortality, being overweight causes more chronic non-communicable illnesses such as diabetes, coronary artery disease, hypertension, weakened functioning, disability, and mortality [8].In low-and middle-income countries, the coexistence of under nutrition with an increasing incidence of overweight/obesity is a serious health concern [9][10][11].
Obesity and overweight pose a significant burden on both adults and adolescents, affecting their physical, emotional, and social well-being.In adults, these conditions have reached epidemic proportions and are associated with a plethora of health issues [12].A study conducted by the Global Burden of Disease (GBD) 2017 Diet Collaborators reported that high body mass index (BMI) was the fourth leading risk factor for mortality globally [13].Moreover, obesity-related healthcare costs have been escalating, straining healthcare systems and economies [14].Similarly, the prevalence of obesity and overweight among adolescents has risen dramatically in recent years.This trend poses serious immediate and long-term health consequences, such as insulin resistance, metabolic syndrome, and mental health problems such as depression and anxiety [15].Obese or overweight adolescents are more likely to carry these conditions into adulthood, further increasing their risk of developing chronic diseases [16].
Even though the country continues to see an increase in overweight/obesity, India has the largest percentage of underweight adults globally [17,18].According to the National Family Health Survey (NFHS-5) conducted in 2019-21, among children under five years of age, 35.5% were stunted, 32.1% were underweight, and 19.3% had wasting.The same survey showed that 19% of women and 16% of men aged 15-49 years were undernourished, while 24% of women and 23% of men aged 15-49 years were obese [19].Overweight and obesity are well-established risk factors for general mortality [20], chronic illnesses, including cardiovascular disease [21], diabetes [22], multimorbidity [23,24], and impairments [25].Furthermore, being underweight is highly connected with early mortality, disability, and poor self-evaluation of health and wellness, and this relationship is especially strong in developing countries [26,27].Studies have revealed that socioeconomic variables such as poverty, poor household income, and inadequate access to high-quality healthcare might affect the incidence of malnutrition differently in rural and urban parts of India [1,28].Additionally, dietary patterns [1], sanitation and hygiene [28], and maternal and child health [1,3] are also important to maintain a good nutritional status.
The goal is to develop and implement targeted interventions and strategies that will effectively address malnutrition and its associated health issues, with a focus on improving the overall health and well-being of individuals in urban and rural communities.The data contribution to the existing knowledge on malnutrition assists in the formulation of evidence-based public health policies both in India and potentially in other regions facing similar challenges.The present study aims to understand the demographic dynamics, burden of non-communicable diseases, and risk factors associated with malnutrition (stunting, thinness, and obesity) among different age groups in the urban and rural areas.

Study Design and Setting
A cross-sectional study was conducted in Addagutta (17.4506  residing in the selected area were recruited for the study.Data were collected from all the residents of the households available at the time of the survey. Data were collected using electronic tablets in the form of electronic forms.The questionnaire in the electronic forms was available in English and the local language (Telugu).Most of the responses were selected through a drop-down menu and entered in English.The tools used for the anthropometry included digital weighing scale and a stadiometer.Heights were measured using a stadiometer, with participants positioned against a flat surface to ensure heels, shoulders, and head were in contact with the wall.The stadiometer movable headpiece was gently lowered to record centimetre-accurate height measurements.Weights were taken on digital scales after removing footwear and socks to ensure precision.Trained investigators meticulously adhered to defined protocols throughout the data collection.Height measurements were obtained using a SECA adult portable stadiometer with 0.1 cm accuracy, while weight assessment utilized calibrated SECA digital weighing scales, documenting weights in kilograms with a precision of 0.1 kg.

Study Variables
We utilized a comprehensive set of demographic and socioeconomic variables to understand the intricate dynamics of the population.These variables include gender (male, female, transgender), religion (Hindu, Muslim, Christianity, others), house ownership (own, rented/leased/others), family type (nuclear, extended/three-generation family, joint), type of house (Pucca, mixed, Kutcha), overcrowding status (not overcrowded, overcrowded), place of cooking (separate kitchen, no separate kitchen), cooking fuel source (gas, firewood/others), water source (piped water, public tap/well/bore), defecation method (home toilet, public/shared toilet, open field), garbage disposal method (garbage services, drainage, no designated place), occupation (professional, skilled, unskilled worker, unemployed, student), literacy (age >7 years) (illiterate, read, read and write, preschool/Balwadi school), and wealth index (lowest, middle, highest).Overcrowding is defined as the number of persons living in the house divided by the number of rooms in the house, excluding the kitchen.It was categorized as overcrowded if there were more than two people per room and not overcrowded when there were ≤ two persons per room [29].The wealth index was calculated by categorizing respondents into tertiles, i.e., lowest, middle, and highest wealth, based on their ownership of 28 assets.Data were collected on the burden of chronic illness using a questionnaire.The WHO Child Growth Standards were used for the determination of nutritional status of under-5 children.The standard deviation of scores (Z-scores) for height-for-age, weight-for-height, and weight-for-age were calculated using WHO Anthro software (version 3.2.2).Furthermore, the malnutrition cutoffs are categorized as stunting when height-for-age < −2 SD, wasting when weight-forheight < −2 SD, overweight when weight-for-height > +2 SD, and underweight when weight-for-age < −2 standard deviations (SD) of the WHO Child Growth Standards median [30].Z-Scores for height-for-age and BMI-for-age were calculated for the 5-19-year group using WHO Anthro Plussoftware (version 1.0.4).In addition, the cut-offs of malnutrition status were categorised as height-for-age < −2 SD, considered stunting and BMI-for-age (BAZ) < −2 SD, BAZ > 1 SD, and BAZ > 2 SD, defined as thinness, overweight, and obesity, respectively [31].The age group greater than 19 and less than 60 was considered the adult category.Furthermore, the age group 60 and above was considered the elderly (or geriatric) group [32].Body mass index (BMI) was calculated for all the available heights and weights, and the calculation is stated as BMI (Kg/m 2 ) = Weight (Kg)/Height (m 2 ).These BMI values are categorised as underweight when BMI < 18.5 Kg/m 2 , normal when BMI is 18.5-22.9Kg/m 2 , overweight when BMI is 23-24.9Kg/m 2 , and obese when BMI ≥ 25 Kg/m 2 [33].

Ethics Approval and Consent
The study protocol for this research was approved by the ICMR-National Institute of Nutrition Ethics Committee (reference number: 3/II/2022; dated 16 March 2022).Fol-lowing the approval, data collection commenced, and consent forms were distributed to potential study participants in the local language (Telugu).Only individuals who willingly provided written informed consent were included in the study, while those who declined were excluded.

Statistical Analysis
Variables were summarized as frequencies and proportions with 95% confidence intervals.A univariate mixed-effect logistic regression was applied to assess stunting in under-5 children, thinness in the 5-19-year age group, and obesity in adult age groups, and the results were presented as an unadjusted odds ratio with a 95% confidence interval.The variables that had p value less than 0.25 in the univariate analysis and variables of clinical or contextual importance were included in the multivariable mixed-effect logistic regression to assess risk factors associated with malnutrition status.The results were presented as an adjusted odds ratios with a 95% confidence interval.The significance of the odds ratio was tested by Wald test.All the statistical analysis was carried out using STATA 14.1 version.

Mixed-Effect Logistic Regression
The data exhibit a clustered structure, with multiple individuals nested within each household.Neglecting this interdependency may introduce biased standard errors and lead to incorrect inferences, as it violates the assumption of independence in a simple logistic regression [34].In order to address the clustered structure of the data and to properly account for the correlation among individuals within households, we employed a mixed-effects logistic regression (MELR).This approach allowed us to model random effects associated with households, capturing the unobserved heterogeneity at the household level.We adjusted the analysis at the family level by utilizing a unique family identification number as a random effect in the MELR model, which accounted for unobserved factors that may influence the outcome variables but were not directly measured as predictor variables.To quantify the extent of clustering within households, we calculated the intraclass correlation coefficient (ICC), which revealed values greater than 0.4 for all outcome variables, indicating substantial clustering within households.This finding further supported the use of a MELR to properly account for the correlated nature of the data.Additionally, we assessed the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) for both simple logistic regression and MELR models.Notably, the MELR models exhibited smaller information criteria values compared to simple logistic regression, indicating improved model fit [34].The AIC and BIC were utilized to compare models, and the mixed-effect logistic regression model demonstrated the best fit for the data as it had the lowest values among the considered models (Supplementary Table S1).In the univariate analysis, variables with p value less than 0.25 and variables that are of clinical or contextual importance were included in a multivariable mixed-effect logistic regression (Supplementary Table S2).The variables place of residence, age group, gender, religion, house ownership, type of family, type of house, overcrowding, place of cooking, cooking fuel, source of water, method of defecation, occupation, and wealth index were included.

General Characteristics of the Respondents
The final analysis included the data of 10,350 individuals, consisting of 8317 (80.4%) from urban areas and 2033 (19.6%) from rural areas.Among the 3258 households that were surveyed, 484 (14.9%) had temporary door locks, while 33 (1.0%) households refused to give their consent, and the final analysis included the data of 2741 (84.1%) households.The gender distribution was almost equal, with males accounting for 5126 (49.6%) and females for 5221 (50.4%).In terms of religion, Hindus comprised the majority at 8567 (82.8%), followed by Muslims at 1193 (11.5%) and Christians at 574 (5.5%).In total, 6839 (66.1%) individuals belonged to nuclear families, 4651 (55.9%) individuals were residing in overcrowded households in urban areas, and 9302 (89.9%) individuals used piped water for drinking and cooking.The respondents wealth index categories were "Lowest", 4469 (43.2%), "Middle", 2434 (23.5%), and "Highest", 3447 (33.3%) (Table 1).The sample encompassed individuals ranging from 0 to 99 years of age.In the younger age groups (0-4 years, 5-9 years, 10-14 years, and 15-19 years), the number of males and females was relatively similar in both the urban and rural areas.The 20-24-year age group showed nearly equal representation of males and females, accounting for 9.4% in the urban population and 7.8% in the rural population.Similar patterns were observed in subsequent age groups, with slight variations in the proportions (Figure 1 The sample encompassed individuals ranging from 0 to 99 years of age.In the younger age groups (0-4 years, 5-9 years, 10-14 years, and 15-19 years), the number of males and females was relatively similar in both the urban and rural areas.The 20-24-year age group showed nearly equal representation of males and females, accounting for 9.4% in the urban population and 7.8% in the rural population.Similar patterns were observed in subsequent age groups, with slight variations in the proportions (Figure 1) (Section 2).2).

Factors Associated with Stunting among Under-Five Children
It was found that 0-2-year-old children were at higher odds of being stunted compared to 3-5-year-old children (AOR 2.32, 95% CI: 1.29-4.18).Children living in mixed house were at higher odds of being stunted compared to children living in Pucca houses (AOR 2.48, 95% CI: 1.12-5.47).Children from the middle (AOR 3.47, 95% CI: 1.27-9.45)and lower wealth index tertiles (AOR 3.46, 95% CI: 1.32-9.06)were at higher odds of being stunted compared to children from the high wealth index tertiles (Table 3).

Factors Associated with Malnutrition among Adults
It was found that 40-59-year-old adults were at higher odds of being obese compared to 20-39-year-old adults (AOR 1.91, 95% CI: 1.59-2.28).Males were at decreased odds of being obese compared to females (AOR 0.79, 95% CI: 0.66-0.93).Individuals from muslim religion were at higher odds of being obese compared to individuals from hindu religion (AOR 1.73, 95% CI: 1.28-2.34).Individuals with an occupation as a clerk/skilled worker/semi-skilled worker had higher odds of being obese compared to unskilled workers (AOR 1.32, 95% CI: 1.02-1.71).Individuals from the low wealth index tertiles were at decreased odds of developing obesity compared to individuals from the high wealth index tertiles (AOR 0.59, 95% CI: 0.46-0.74)(Table 3).

Discussion
The findings highlight the recent demographic composition, burden of chronic illnesses, and malnutrition status among different age groups residing in the urban and rural areas of South India.Among children under five years of age, the prevalence of stunting and underweight remains high, despite some improvements over the years.The study reveals a high prevalence of obesity among adults in both urban and rural areas.Among under-five children, living in mixed houses and belonging to middle or lower wealth index tertiles were associated with higher odds of stunting.Among the 5-19-year age group, residing in urban areas, being male, and living in overcrowded conditions were associated with higher odds of thinness.In adults in the 40-59 age groups, belonging to the Muslim community and having a clerk or skilled worker occupation were associated with higher odds of obesity.
Stunting remains a significant public health concern, affecting approximately one-third of children in both urban and rural areas, almost similar to the NFHS 5 data of Telangana and Andhra Pradesh [35,36].According to the Comprehensive National Nutrition Survey report (2016-2018), the proportion stunting in under-five children is 37% in rural and 27% in urban areas, which is in contrast to our findings of 33% in rural and 34% in urban areas [37].The reason for these differences is that our study was conducted in selected areas in two states with a small sample size compared to the CNNS study.Children from the lowest wealth quintile have a higher likelihood of being stunted, similar to the findings of the Comprehensive National Nutrition Survey report (2016-2018) [37].The agespecific analysis indicates that children between 0 and 2 years are particularly vulnerable to stunting.
Overweight and obesity are emerging as major health challenges in both urban and rural settings.This study reported the prevalence of overweight and obesity in adults as 14.8% and 47.7%, respectively, in contrast to the findings from the studies conducted in Karnataka [38] and Assam [39].In the urban geriatric population, more than 50% of the population were found to be obese.This shift towards over-nutrition could be attributed to changes in lifestyle, including dietary patterns and sedentary behaviour, as well as the lack of comprehensive strategies to promote healthy eating habits and physical activity.The prevalence of obesity is highest in the 40-59 age group, indicating that middle-aged adults are at the greatest risk.Gender disparities are also observed, with females having higher odds of thinness and males having higher odds of obesity.These findings highlight the importance of gender-sensitive interventions to address malnutrition in different age groups.
The high prevalence of overweight and obesity leads to related morbidities, notably hypertension and diabetes mellitus [40], which are also found in our study population.These findings are consistent with the epidemiological transition occurring in many developing regions, where non-communicable diseases are on the rise, accompanied by the persistent challenges of infectious diseases and malnutrition [41].The prevalence of chronic illness underscores the importance of adopting a holistic approach to healthcare that encompasses the prevention, early detection, and effective management of these conditions.
The study identified several risk factors associated with malnutrition and chronic illnesses.Socio-demographic factors such as place of residence, house ownership, and family type were found to be significant predictors of malnutrition.Overcrowding and poor housing conditions in rural areas were associated with increased odds of malnutrition, indicating the need for improved living conditions and access to basic amenities.Age, gender, religion, house ownership, occupation, and wealth index were significantly associated with obesity in adults.
This study utilized a rigorous methodology with a large sample size, enhancing the generalizability of the findings.The use of electronic data collection tools facilitated efficient data collection and minimized the errors associated with paper-based surveys.This study establishes the fact of the double burden of malnutrition in different age groups in India, along with the associated risk factors from more recent data.However, some limitations should be acknowledged.The cross-sectional design of this study limits its ability to establish causal relationships between risk factors and health outcomes; self-reported data on chronic illnesses may introduce reporting bias; and the prevalence of chronic illnesses could be underestimated.
The findings of this study have significant implications for public health policy and interventions.Targeted nutrition programs aimed at reducing malnutrition should prioritize interventions during the critical periods of early childhood and adolescence.These efforts should include nutrition education for caregivers and communities to promote optimal feeding practices.To tackle the rising burden of chronic illnesses, comprehensive prevention and management strategies are essential.Public health initiatives should focus on promoting awareness about risk factors for chronic illnesses.Integrating noncommunicable disease management into the primary healthcare system can improve early detection and access to essential services.Following the population over a period by establishing the health and demographic surveillance system will help us understand the modifiable risk factors for planning appropriate interventions in this population.

Conclusions
Our cross-sectional study included a diverse population across various age groups residing in both urban and rural areas: under-five children, adolescents (10-19 years), adults (20-59 years), and geriatric individuals (60 years and above).We found significant associations between socio-demographic factors and malnutrition indicators among the different age groups.Particularly, there was a considerable prevalence of stunting among under-five children, especially in urban areas.In the 5-19-year age group, both urban residents and males showed higher susceptibility to thinness.In adults, obesity was notably prevalent, especially among middle-aged individuals, males, and those with specific household characteristics.Our findings highlight the importance of targeted interventions to address malnutrition across diverse populations.Public health strategies should focus on improving living conditions, promoting healthier cooking practices, and fostering awareness about balanced nutrition.Policymakers and health practitioners must consider these insights to tailor effective interventions that address the multifaceted factors contributing to malnutrition within distinct age groups and residential settings.

Supplementary Materials:
The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/nu15204470/s1.Table S1: Information criterion values for the logistic and mixed-effect logistic regression analyses for factors associated with stunting among underfive children, thinness among the 5-19-year group, and obesity among adults (19-59 years) using the data from urban and rural areas in South India.Table S2: Factors associated with stunting among under-five children, thinness among the 5-19 year-group, and obesity among adults (19-59 years) through a univariate mixed-effect logistic regression analysis using the data from urban and rural areas in South India.
Author Contributions: N.S.R. was involved in designing the study, securing the funding, conducting the study, and reviewing and finalizing the manuscript.N.M., K.R. and H.K. were involved in data cleaning, data analysis, and writing the first draft of the manuscript.G.V.R.R., U.V.P. and R.S. were involved in executing the study and data collection.H.R. and J.B.G. were involved in reviewing and finalizing the manuscript.All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement:
The ICMR-National Institute of Nutrition-ethics committee approved this study with the reference number 3/II/2022 on 16 March 2022.Written informed consent, assent, and parental consent were taken from all the study subjects.
Informed Consent Statement: Written informed consent was taken from all the study participants.Assent and parental consent were taken from the study participants below 17 years of age.

Figure 1 .
Figure 1.Age pyramid of the study population using the data from urban (n = 8317) area in Telangana and rural areas (n = 2033) in Andhra Pradesh.Three transgender people were excluded from building age pyramid.

Figure 1 .
Figure 1.Age pyramid of the study population using the data from urban (n = 8317) area in Telangana and rural areas (n = 2033) in Andhra Pradesh.Three transgender people were excluded from building age pyramid.

Figure 2 .
Figure 2. Burden of chronic illness using the data from urban and rural areas in South India.

Figure 2 .
Figure 2. Burden of chronic illness using the data from urban and rural areas in South India.

Table 1 .
Socio-demographic, socio-economic, and individual-level characteristics of study participants using the data from urban (n = 8317) and rural areas (n = 2033) in South India.
* Data were not collected for below 7 years; therefore, it does not sum up to N.
* Data were not collected for below 7 years; therefore, it does not sum up to N.

Table 2 .
Malnutrition status of study participants using the data from urban and rural areas in South India.

Table 3 .
Factors associated with stunting among under-five children, thinness among the 5-19-yearold group, and obesity among adults (19-59 years) through a multivariable mixed-effect logistic regression using the data from urban and rural areas in South India.