Introduction

Nutritional status is one of the major indicators of health, wealth and development as well. Good nutrition is required for physical growth, cognitive and mental development, health, and well-being.¹ It represents several indicators and aspects such as health, economic, socio-demographic, and growth and development and determines future life among school-age children. School-age children go through a journey of rapid physical growth and cognitive development.² The Constitution of Nepal (CoN) 2015 has declared that food sovereignty is one of the citizen’s fundamental rights and that the state is responsible for ensuring it.³ However, recent national health surveys of Nepal show that more than one-third of children under five years of age are suffering from malnutrition,⁴ and almost the same scenario persists in other age groups.

Inadequate nutrition claims the lives of one-third of children globally, accounting for 2.6 million deaths.⁵  The latest United Nations report shows that 10% of the world’s population suffers from hunger, and one in three people have regular access to adequate food. In central and southern Asia, 29.8% and 13.6% of children are stunted and wasted, respectively, and the COVID-19 pandemic has worsened the situation.⁶ The Global Hunger Index 2023 report revealed that one in three people worldwide has insufficient calories, resulting in undernourishment. One in six children is stunted, which means low height-for-age, resulting from chronic undernutrition. A proportion of these children also have low weight-for-height, known as wasted, caused by acute undernutrition.⁷ Malnutrition poses a severe public health threat to Nepalese children.^8,9 Malnutrition among young children in Nepal is considered a complex issue by the National Planning Commission/Second Multi-sectoral Nutritional Plan (NPC/MSNP, 2018-2022). The 15^th development plan, ZERO HUNGER, Sustainable Development Goals (SDG), economic growth, employment promotion, poverty alleviation, post-conflict reconstruction, social transformation, and human resource development are the government’s development priorities, which are also clearly outlined in the framework of the Constitution of Nepal 2072.³ These high-priority categories are closely related to the nutritional status of the people.

The school lunch program was first started in France in 1885, and since then it has been gradually introduced in various countries: the US (1946), UK (1945), Japan (1947), China (1964-69), Australia (1950), Switzerland (1946), Singapore (1975), Indonesia (1967), Thailand (1970), Korea (1973), and India (1995).¹⁰ Similarly, the Government of Nepal (GoN) has initiated a midday meal program for all students in lower basic community schools throughout Nepal since 2020.¹¹ The recent studies provide strong evidence of the positive impact of school feeding programs on children’s nutritional status and overall well-being, reinforcing the importance of such initiatives in promoting health and education in low-income and food-insecure regions.^12,13

A study conducted in a landlocked country in southern Africa, which shares similar geopolitical and socio-economic conditions with Nepal, demonstrated a complex malnutrition landscape. Despite a more significant proportion of learners having adequate nutrition knowledge and exhibiting healthy eating practices, overweight was the leading form of malnutrition, coexisting with stunting and wasting. Although evidence is limited regarding the current state of knowledge about the nutritional status of school-age children in Nepal, national policies and programs such as the National School Health Nutrition Strategy, Multi-Sectoral Nutrition Plans, and the School Education Sector Plan aim to improve the nutritional status of school children through nutrition education.

Most previous studies have focused on children under five years of age or teenagers, but the middle age group needs to be addressed. However, all age groups, especially children aged 6-12, are equally important as other life stages. The previous studies have laid the gap in nationally representative data on the nutritional status of school-aged children and their associated factors in Nepal.  Additionally, national benchmarks still need to be created for those who study at the primary school level, particularly in lower basic schools. Given the gap, the present study aimed to determine the nutritional status and their underlying co-variates of socio-demographic determinants among community school children in Nepal.

Materials and Methods

Study Design

The study used a quantitative observational cross-sectional survey design.¹⁴ Information was collected retrospectively to assess the nutritional status of students who completed the 5^th grade and enrolled in schools.

Population and Study Setting

The study’s population included all students who completed the 5th grade (completed lower basic) and enrolled in the 6th grade in community schools in 2023. The study was conducted in selected community schools from different districts covering all geographic areas, including rural/urban areas and both primary/secondary schools in various provinces.

Study Size

The sample size was determined using the following formula.¹⁵

n = [DEFF * Np(1-p)] / [(d² / Z²_1-α/2 * (N-1)) + p(1-p)]

Where:

The ‘n’ represents the sample size, and ‘N’ denotes the population size (for the finite population correction factor or FPC). The total number of students enrolled in grade five was 455,409, but according to the Flash I Report 2078 Field,¹⁶ 134,471 students were enrolled in grade five in the study area. The ‘p’ signifies the hypothesized percentage frequency of the outcome factor in the population (p), estimated at 50% +/- 5. Similarly, the ‘d’ indicates the confidence limits as a percentage of 100 (absolute +/- %), set at 5%. DEFF stands for the design effect, which was assigned a value of 2. Initial calculations yielded a sample size of 767. Subsequently, this value was adjusted for three eco-belts multiplied by three and an assumed 90% response rate, resulting in a final sample size of 2556. Therefore, the study proceeded with a sample size 2556 to ensure adequate statistical power and precision in the findings.

Sampling Techniques

A multi-stage sampling design was employed to select students for the survey. We first selected all provinces and then randomly selected three districts from the Mountain, Hill, and Inner-terai/Terai regions as applicable and one district from the Kathmandu valley. Then, we selected two municipalities at the local level that covered both urban and rural areas. Similarly, we randomly assigned each local municipality’s primary and secondary schools. Therefore, there were 88 schools in 22 urban and 22 rural municipalities and 46 basic and 52 secondary schools in 22 districts. The sample was distributed by probability proportional to size (PPS) using the Flash Report by the Centre for Education and Human Resource Development.¹⁶ At the school level, we took all students who completed the fifth grade and enrolled in the sixth grade as a sample. In case the targeted sample was lacking, a proximal school was visited. Therefore, the sample exceeded the target by 107%, representing 2,727 students from 46 basic and 52 secondary schools. There was no non-response since there was a mandatory provision in the KoBoTool box for proceeding with the online application form. Field enumerators were experienced in rapport building and data collection. During data collection, no one refused to participate; therefore, all the respondents/participants participated in nutritional measurement and other responses.

Inclusion and Exclusion Criteria

The students who came under the sampling frame wanted to participate, provided assent/consent, completed 5^th grade and, enrolled in 6th grade, and consumed the midday meal. As per the ethical norms of research, the students who did not want to participate in the anthropometric assessment were excluded from the study. Similarly, the students who completed their 5th grade from institutional (private) school, were sick during data collection, failed the final exam of 6th grade, were unavailable during data collection, or did not consume the midday meal in school were excluded.

Research Tools and Instruments

The research tools and instruments used in the study included a survey questionnaire, a digital weighing scale, and a stadiometer. The questionnaire was validated through the test-retest method, which also involved Delphi techniques. Additionally, the instruments were calibrated using a reference from the Nepal Bureau of Standards and Metrology [MCC:102/1036/1085, CCLMI: 172/1037/1100].

Data Collection

Though all the field enumerators were experienced enough, we conducted a three-day orientation program for field enumerators along with the team leader and supervisor to ensure data quality and exact information. Moreover, a day pre-test program was conducted in real field situations in three different schools. Ten geographic clusters were created for data collection. According to the sample size, each group had two to four field researchers and a team leader. An online survey tool called KoBoTool box was used to collect the data. Day-to-day monitoring was conducted to ensure the data standard and quality. Data collection for quantitative information started on May 10, 2023, and was completed on May 31, 2023. All the data were kept confidential and were only handled by the KoBoTool expert and principal investigator. After completion of the data collection, it was only handled by the principal investigator to ensure the quality and confidentiality of the issues.

Variables Consideration

Socio-demographic variables such as age, sex, caste, parents’ education and occupation, family size and type, wealth status, and residence setting were the independent variables. At the same time, weight-for-age, height-for-age, and body mass index-for-age were considered as dependent variables. Age, family number, and educational status were recorded for better understanding.

Data Analysis and Statistical Methods

WHO Anthro Plus v1.0.4  was used to calculate weight-for-age, height-for-age, and body mass index-for-age z scores.¹⁷ For the calculation of weight-for-age and individuals above ten years of age, LMS (Lambda Mu and Sigma) parameters were used for girls/boys: weight for age; National Health and Nutrition Examination Survey (NHANES) by the CDC/National Center for Health Statistics.^18,19 Descriptive analysis, such as frequencies and percentages, was performed as a univariate analysis. The chi-square test measured the association as a bivariate analysis to assess the association between the variables as bivariate analysis. If there was an association between independent and dependent variables and it was statistically significant, then logistic regression was performed as a multivariate analysis.²⁰ Before performing the logistic regression, the variables were assessed to determine whether there was an issue of multicollinearity.²¹ We found that there was no issue of multicollinearity.  All these statistical analyses were performed using IBM SPSS Statistics version 25.²²

Ethical Consideration

All administrative procedures were followed—national ethical guidelines.²³ , and ethical compliance checklists²⁴ were followed throughout the research process. Written consent/assent was obtained from participants and guardians. No personal identity was disclosed in the study to ensure participant confidentiality, and participants were informed that their data would be securely used solely for the study.

Results

This section presents an analysis of the nutritional assessment and explores the association between general characteristics and nutritional status of students.

Univariate Analysis

Of the 2,727 participants, the majority were from Madhesh province, accounting for 28.2%, followed by Bagmati province (14.7%) and Karnali province (13.4%). More than half (51.3%) of the participants were from urban municipalities. More than two-thirds (69.5%) of them studied in secondary schools, almost two-thirds (74.9%) were more than 11 years old, and 24.5% of the participants were 11 years old and below. Similarly, the majority (53.6%) were girls and 28.9% were from Adibasi/Janajati. More than half of the participants had a family of up to five members, and more than two-thirds (69.2%) were from a nuclear family. Almost one-fourth of the participants stated that their family’s main income was labor, and 86.4% lived in their homes. Nearly all (90.6%) participants had eaten mid-day meals during school hours, and 59.4% brought money to school. Only eight percent of the fathers and 19% of the students’ mothers reported they could not read and write (illiterate). Most of the parents were involved in the informal sector, including labor. More than half (56%) of the participants belonged to a poor wealth status, followed by the middle (27%) and rich (17%). Most of the participants (51.6%) were from the Terai eco-belt, followed by the Hill (34.7%) and the Mountain (13.6%).

Of the total participants, 28%, 24.8% and 18.2% had non-normal weight-for-age [WAZ] z scores, low/high height-for-age[HAZ] z scores, and low/high body mass index [BMI]-for-age [BAZ] z scores, respectively, accounting for below and above ±2 sigma. In this study, z scores within ±2 sigma of WAZ, HAZ, and BAZ are considered normal nutritional status. In contrast, z scores outside of (more than) ±2 sigma of WAZ, HAZ, and BAZ are considered abnormal or indicative of malnutrition. Less than -2 sigma z scores of WAZ, HAZ, and BAZ are considered undernutrition, referred to as underweight, stunted, and thinness, which represent 27.5%, 23%, and 16.6%, respectively (Table 1).

Table 1: Background characteristics and nutritional status of the students.

Bivariate Analysis

The highest proportion (38.1%) of underweight students was observed in Madhesh province, followed by Lumbini (29.8%) and Sudurpashchim (23.4%). The same scenario persists for thinness, which accounts for 30% of total participants from Madhesh, followed by Sudurpashchim (19.8%) and Koshi, Lumbini, and Karnali province (10%,  p=<0.001). More than one-third (36.2%) of the participants from Lumbini province observed a low height for age (stunted) compared to Madhesh (26.4%) and Bagmati (23.6%) (p=<0.001). The students in rural areas were more vulnerable to malnutrition than those in urban areas (p=<0.001). The older the participants, the higher the proportion of malnutrition in all three forms (p=<0.001). Interestingly, boys were more vulnerable to malnutrition than their counterparts (p=0.01) [Table 2].

The proportion of wasted, stunted, and thinness was found among participants of Muslim and other castes, which accounted for 36.8%, 27.4%, and 28.3%, respectively (p=0.003). The size of the participants’ families was significantly associated with undernutrition. The larger the family size (more than ten members), the higher the underweight and thinness, which accounted for 33.3% and 20.8%, respectively, compared to those households that had less than ten family members (p=<0.001). The same results were observed for joint families, which had a higher proportion of malnutrition, mainly in the underweight, stunted, and thinness rates, compared to nuclear families. Interestingly, participants who lived in their own homes noticed a higher prevalence of undernutrition compared to those who lived in rented/other homes (p=0.010), and participants who had temporary or mud-built homes appeared to have all three types of undernutrition compared to those who had a concrete or semi-concrete house (p=<0.001).  The WAZ and BAZ scores (less than -2 s)  were higher among the participants who had agriculture as the primary source of household income, while the BAZ score was noticed to be higher in labor as the primary source of income (p=0.009) [Table 2].

Surprisingly, the participants who had taken mid-day meals at school had a higher proportion of underweight, stunted, and thinness rates than those who had not taken day meals at school and the sufficiency of food available during day meals. Mother’s education was significantly associated with malnutrition in children. The higher the mother’s education, the lower the malnutrition rate (p=<0.021). Wealth status is significantly related to nutritional status. The poorest participants had a higher proportion of malnutrition rates than the wealthiest participants (p=<0.001). In the case of eco-belts, the highest proportion of wasted, thinness, and malnutrition appeared in terai areas, while the most stunted were found in mountain areas (p=<0.001) [Table 2].

Multivariate Analysis

Multivariate analysis shows that although provinces, residence settings, age groups, castes, homestay status/types of homes, primary sources of income, mother’s education, wealth quintiles, and eco-belts were significant variables in determining the nutritional status of school children, province and ecological belts, residence setting such as home status, age group, gender, and wealth status were significant predictors of nutritional status. The participants living in Madhesh province were noticed to be 2.01, 1.15, and 2.14 times more likely to be underweight, stunted and thin, respectively, compared to the participants who lived in the Koshi province (AOR = 2.01, 95% CI: 1.32-3.05; AOR = 1.59, 95% CI: 1.05-2.39; AOR = 2.14, 95% CI: 1.29-3.56). Participants from Terai and Hill regions observed that 45% and 44% were less likely to be stunted compared to those who lived in mountain areas (AOR = 0.55, 95%CI: 0.38-0.79; AOR = 0.56, 95%CI: 0.40-0.78). However, participants who lived in urban areas were 25% to 42% less likely to be underweight and stunted compared to those who lived in rural areas, respectively (AOR = 0.75, 95% CI: 0.60-0.94; AOR = 0.58, 95% CI: 0.47-0.72). The age of the participants was a significant predictor of malnutrition, indicating that the older the age, the higher the chances of malnutrition.

Similarly, the participants over 11 years of age were 8.66, 6.65 and 4.20 times more likely to be underweight, stunted, and thin compared to those who were less than 11 years respectively (AOR = 8.66, 95% CI: 5.29-14.16; AOR = 6.65, 95% CI: 4.16-10.64; AOR = 4.20, 95% CI: 2.52-7.02).

In the same way, boys were 1.52 and 1.35 times more likely to be underweight and thin compared to girls, respectively (AOR = 1.52, 95% CI: 1.25-1.85; AOR = 1.35, 95% CI: 1.08-1.70). Interestingly, participants who did not have their own homes or stayed in rented or other homes appeared less likely to be underweight than those who lived in their own homes (AOR = 0.64, 95% CI: 0.45-0.91). On the other hand, it was observed that participants with better socio-economic status appeared to be less likely to have malnutrition [Table 3].

Table 2: Association of  background characteristics and weight for age[WAZ], height for age[HAZ], and BMI for age [BAZ]. Click here to view Figure

Table 3: Logistic regression on  background characteristics and weight for age, height for age and BMI for age.

Note: The bold face indicates that the values are statistically significant, and ‘Ref.’ refers to the reference category.

Discussion

The present study found that 27.5%, 23%, and 16.6% of the school children were underweight, stunted, and thin, respectively, which was almost similar (underweight- 25.1%; stunted- 23%; thinness-12.5%) to the evidence laid by a systematic review-based study in developing countries conducted by Khan et al., (2022).²⁵  A longitudinal observational study conducted among basic-level school children in Nepal revealed that 16.7 %, 23.4 %, and 9.1%  of school children were underweight, stunted, and thin, respectively,¹¹ which seems almost similar to the present study.  A similar proportion of stunted (24.5%), underweight (14.9%), and wasted (9.7%) was observed in  Kenya.²⁶  A school-based cross-sectional study conducted among school children aged 6–12 in Ghana also revealed that 3.8%, 10.4%, and 12.1% were underweight, stunted, and thin, respectively,²⁷ which seems to be slightly lower than the present study. Similarly, in Cameroon, the prevalence of stunting was 27%, wasting 23%, thinness 22% and underweight 20% among the school children who were internally displaced, which is almost similar to this study.²⁸

A study in Benue State, Nigeria, showed that the proportions of stunted, underweight, and wasting were 32%, 20%, and 13 8.7±5.3 years old.²⁹ The magnitude of undernutrition seems to vary in time, place, and person. A review from Eastern and Southern Africa shows that the prevalence of thinness ranged from three to 36.8%, stunting from 6.6 to 57%, and underweight from 5.8 to 27.1% among school-age children and adolescents, which is almost similar to this study. Similarly, a scooping review following the evidence on the burden of malnutrition for children and adolescents aged 5–19 years in South Asia, revealed that the prevalence of thinness was 1.9 to  88.8%, wasting 3 to 48%, underweight  9.5 to 84.4%, and stunting 3.7 to 71.7% among school children and adolescent.¹ The above evidence suggest that malnutriotn in the forms of underweight, stunted and thinness remain a significant public health concern among school-going children and adolescnets of Nepal. A wide array of causes may be responsible for thetion, which may be low intake of foods and nutrients, communicable and non-communicable diseases, work infestation, and internal metabolic discorders.  Behaviour factors and other hygience factors are also equally important for the nutritional status including good health.⁵

Socio-Demographic Characteristics and Nutritional Status

Socioeconomic status (SES) has been a key and trending parameter for understanding malnutrition.³⁰ The present study revealed that the age of the participants remained a significant predictor of malnutrition, indicating that participants over 11 years of age were more likely to be underweight, stunted, and thin compared to those under 11 years of age. Similarly, boys were more likely to be underweight and thin compared to girls, respectively. Interestingly, participants who did not have their own homes or stayed in rented or other types of homes appeared to be less likely to be underweight than those who lived in their own homes. Furthermore, the results revealed that participants with a better socio-economic status appeared less likely to have malnutrition.

The previous study conducted in India revealed that the prevalence of underweight, wasted, and stunted was 38%, 33%, and 20%, respectively, in India.³¹ The study also found that the sex of the child, the type of family, the education, and the occupation of the parents were significantly associated with the nutritional status of the children, which is almost similar to this study. The Ghana study shows that 50% and 19% of school-age children aged 10-19 years (average 13.4) suffered from stunting and thinness, respectively. The study also observed that residence area, age, sex, and school-feeding program were associated with malnutrition and a higher proportion of stunted and thinness among school-feeding students than among non-school-feeding students.³²  Another study from Bangladesh conducted among grades 4 and 5 of age 10.83±1.03 years recorded an average weight of 32.4±7.21 kg and a height of 141.22±8.52. Of them, 91.3% had an average HAZ score, and 89.1% had a WAZ score. The study further explored that socio-demographic variables (sex, family size) were associated with nutritional status.³³ The rate of undernutrition was 51% with 45% in girls and 57% in boys among school children in Pakistan³⁴  while in Kenya boys were more stunted compared to girls²⁶ which is similar to this study.

Another study from Pakistan shows that 40% of school-age children suffer from malnutrition. Among them, 35% of boys and 22.5% of girls were stunted, and 25% of boys and 17.5% of girls were underweight. The severity of malnutrition was higher in boys than in girls, which is similar to this study. Interestingly, a systematic review shows that the median wasted, stunted, and underweight rates were 11%, 51%, and 32%, respectively in Papua New Guinea.³⁵ Similarly, a synthesis of evidence shows that the proportion of underweight was 25.1%, stunting 23%, wasting 24%, and thinness 12.5% among children and early adolescents (5-15 years) in developing countries.²⁵ The evidence suggests that participants above 11 years of age, male participants, those who live in their own homes, and participants with poor socio-economic status are more likely to experience malnutrition compared to their counterpart.

Geographic Location and Nutritional Status

The study showed that variables such as geographical locations (provinces and ecological belts), residence settings (homestay status/types of home), caste, primary source of income, mother’s education, and wealth quintiles were significantly associated with underweight, stunted, and thinness. However, geographical locations, residence conditions, age group, sex, and wealth status significantly influenced the nutritional status of children studying in Nepal’s community schools. The geography of any nation matters in terms of nutritional status. The present study also revealed that geographical locations such as the province and the ecological belt were significant covariates of nutritional status in schoolchildren. A higher proportion of underweight and thinness was found in Madhesh province, while stunts were found in Lumbini province. It is questionable why a higher proportion of malnutrition was found among children living in Madhesh Province since it is considered fertile land for food production and storage.

The Nepal Demographic Health Survey (NDHS) 2022 showed that the rates of stunted, underweight, and thinness in Terai were 24.8%, 18.7%, and 7.7%, respectively among the children under five years of age.⁴ The NDHS 2022 also showed that a higher proportion of malnutrition persisted in Madhesh Province. Children living in the Madhesh provinces appeared more vulnerable to malnutrition. Tentatively, children five or under five years old were assessed in 2016 and were more likely to be selected in this study. This means that the children who were already suffering from malnutrition are more likely to persist in malnutrition in the present situation. The study conducted in India ³⁰ highlights the significant association between geographical location and the nutritional status of children, focusing on district-level variations in stunting, wasting, and underweight. This suggests that geographical variation plays a crucial role in determining the nutritional status of children. This evidence underscores the importance of considering place-specific factors when addressing child nutrition issues.

School Feeding and Nutritional Status

Indeed, school feeding programs play a crucial role in enhancing children’s nutritional status and overall well-being.³⁶  The evidence strongly supports the efficacy of school feeding programs in enhancing the nutritional status of children. By addressing immediate nutritional needs and promoting long-term health and developmental benefits, these programs play a crucial role in improving the well-being of children, particularly in food-insecure and low-income regions. Although, previous studies have revealed that school feeding has a positive association with the nutritional status of school-going children^11,13,36, the present study found that school feeding was not positively associated with good nutritional status among school children. This indicates that students who had taken midday meals at school had a higher proportion of underweight, stunting and thinness compared to those who did not have midday meals from school. Additionally, the sufficiency of food available during the midday meal was not positively associated with good nutritional status. This association was not also supported by multivariate analysis. The previous study also showed similar results with a higher prevalence of thinness among children who had a midday meal at school compared to those who did not have a school day meal.³⁷

These results further explore whether the food provided to the students covers dietary diversity, adequate nutritional content, and healthy cooking and serving practices that meet their nutritional requirements. This remains a question for the mid-day meal program and requires further monitoring. It is also important that intake of food alone is not sufficient for better nutritional status; rather, it should be safe, fresh, nutritious, and diverse in terms of ingredients and nutritive vaule.³⁸ The above evidence indicates that school feeding programs can have a positive impact on children’s nutritional status, especially when the meals served are enriched with the necessary nutrients.

Strengths and Limitations of the Study

First, the study has covered a wide range of samples, ensuring diversity and increasing the likelihood that the results are representative of  Nepal. Second, the evidence of the study is strong since three layers of statistical analyses were performed: univariate, bivariate, and multivariate. Third, the findings serve as a benchmark for policymakers to design and implement targeted nutritional interventions and policies aimed at improving the health and well-being of school-age children.

However, there are some potential limitations to consider. The study was limited by the variables included and provided only snapshots of data mining, lacking previous data from the children. The results also relied on self-reported data, which may introduce social desirability bias. Lastly, since this study was a cross-sectional observational study, it could not establish causal relationships, highlighting the need for future interventional studies.

Conclusion

The present study suggests that being underweight, stunted, and thin are common malnutrition problems among basic-level children of public schools. The socio-demographic factors such as geographic location, residence setting, age group, gender, and wealth status were noted as significant predictors of the nutritional status of school children. Unlike the previous studies results, the present study found that school feeding was not positively associated with good nutritional status among school children. The findings of the study question the quality of midday meals being served to school children to reduce persistent malnutrition among them. Existing nutritional interventions must be reevaluated and re-considered since they have yet to reduce malnutrition among school children. More attention needs to be paid to the areas where the severity persists, such as children from Madhesh province, residents in rural settings, those aged more than 10 years, boy students, and children belonging to the poorest socio-economic status. The study recommends promoting food security across its four dimensions in the local context, where severe malnutrition persits: ensuring consistent food supply through robust agriculture and supply chains (availability); facilitating economic, physical, and social access to nutritious food (access); promoting the selection of locally produced or available foods for balanced diets (utilization); and maintaining secure food access despite external challenges (stability). Although the present study has established the relationship between covariates and the nutritional status of school children, further experimental study is nevertheless essential to determine their causal relationship.

Acknowledgments

Authors would like to express thier gratitude to the Research Coordination and Development Council (RCDC), Office of the Rector, Tribhuvan University, for providing financial support for this research. We also thank all the research participants for their valuable time and support.

Funding Sources

The research was supported by RCDC, Office of the Rector, Tribhuvan University [Project # TU-NPAR-078/79-2-02]

Conflict of Interest 

The authors declare that no conflicts of interest exist.

Authors’ Contribution

DRA, TRB, KBT,  and SG conceptualized the study, analyzed the data, and developed the manuscript. YRU, BD, SSB and KPT edited the manuscript rigorously with their critical feedback and inputs. All authors read and approved the final manuscript for publication and authorship.

Data Availability Statement

The data used in this study are available from the corresponding author upon request.

Ethics Approval Statement

The Ethical Review Board [ERB] of Tribhuvan University reviewed and approved the study proposal on April 17, 2023 [Ref# 384-079/80: ERBTU-079-001].

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