Abstract

Establishing growth charts is one of the most important steps in establishing normal development standards and identifying possible patterns in human growth. The purpose of this study is to examine how well body surface area (BSA) and quantile regression (QR) percentiles perform in a Pakistani population. Using a dataset of 9906 adult Pakistani citizens from Multan and Bahawalpur, we established BSA growth charts by plotting QR percentiles versus age. The mean BSA is 0.00709±0.00003 (standard deviation). The 85th to 95th percentiles of the QR percentiles showed a diminishing trend in the age range of 5- to 25-year, followed by strong increases in the 25- to 40-year age range and a subsequent decline up to 55-year. BSA rises throughout early adulthood, stabilizes in the Middle Ages, and then declines in old age. The study’s conclusions have a big impact on how growth percentile curves are made for different physiological and pathological conditions. It is possible to create more precise continuous BSA QR percentile curves versus specified ages. Building growth reference curves for the majority of physiological and medical issues affecting children and adolescents may be accomplished using this study. With these curves at their disposal, practitioners will be better equipped to identify deviations from the typical development pattern and comprehend normal growth patterns. The findings of this study emphasize how crucial it is to use QR percentiles when creating BSA growth charts in Pakistani communities. This helps to create growth charts that are more precise and trustworthy for Pakistani children.

Keywords: Human Growth, Obesity; Body Surface Area; Anthropometric Measures; Quantile Regression Percentiles; Growth Charts.

Abbreviations: BSA: Body Surface Area; QR: Quantile Regression; BSSI: Body Shape and Size Index; BMI: Body Mass Index; WHO: World Health Organization; CDC: Centers for Disease Control

Introduction

Background

The concept of Body Surface Area (BSA) has been a fundamental parameter in various medical fields, including pediatrics, critical care, and oncology. BSA delivers useful information about many physiological traits of metabolism, oxygen utilization, and the distribution of drugs inside the human body. The term BSA was first defined by Du Bois and Du Bois in 1916 as ‘the amount of skin that is visible when the subject stands against a piece of white cardboard’ [1]. The role of BSA in various medical applications cannot be underestimated. In pediatrics, BSA is employed in the dosing of drugs, especially in children with congenital heart disease [2]. A recent study found that using BSA-based dosing for patients with congenital heart disease was much more effective in determining the right doses of drugs to be administered to pediatric patients [3]. Another recent study revealed that the use of BSA-scaled energy expenditure enhanced identifications of caloric requirement in critically ill individuals [4]. In oncology, BSA is used in the administration of chemotherapy and radiation therapy to decide the right amount to administer to the patient [5] showed that using BSA dosing form for giving chemotherapy to children with acute lymphoblastic leukemia had a positive impact on the results of the therapy.

In Pakistan especially where there is evidence of a rising trend in the prevalence of non-communicable diseases, most especially in children, the correct determination of BSA is very important. The Pakistani growth charts are used in clinical practice and can be considered an effective tool for the assessment of children’s growth and development [6]. The utilization of different percentile methods for the estimation of BSA can cause discrepancy and error in some instances. The purpose of this study is to examine the QR percentiles of BSA in a Pakistani population. BSA is one of the most common anthropometric measures used in healthcare, especially as a tool for predicting the physiological status of an individual. The usefulness of BSA in multiple clinical uses has been established in this study. In Pakistan, where there is a growing concern about the increasing burden of non-communicable diseases, particularly in children, the accurate measurement of BSA is crucial. The BSA is one of the most common anthropometric measures that has been widely applied in many branches of medical science, such as medicine and physiology. BSA is employed to assess the surface area of body and the computation of the unit has been of interest in most recent studies. BSA is more accurate in measuring body size than weight, it yields relatively much better results and does not impact the human body [7,8]. There have been several proposals for BSA that may be used to determine the amount of medication or pharmaceuticals that could be used to produce positive impacts on the body.

The application of BSA-based dosing has been used to enhance the precision of medication doses in children with congenital heart disease [5]. BSA-based dosing has also been applied in calculating the dosage of Chemotherapy and radiation treatment in cancer patients [5]. In Pakistan, since there is an emerging epidemic of non-communicable diseases particularly affecting children, hence, it is imperative to have an accurate measurement of BSA. The Pakistani growth charts which have been developed and are routinely utilized in clinical practice can be utilized as a reference in evaluating the growth and development of children. The use of different percentile methods to calculate BSA may lead to inconsistencies and inaccuracies in its estimation. The purpose of the present study is to evaluate the efficacies of the QR percentiles for BSA estimation in the Pakistani population [9]. The substance is classified as a ‘global epidemic’ by the World Health Organization since it affects both the physical and psychological health consequences [10]. According to recent data, it can be stated that over 1 billion individuals are overweight, 320 million are obese, and approximately 2 million individuals die each year because of obesity. These five million annual mortalities and may at least double by 2030 [11-13].

Obesity rates among people remain a major concern in today’s society, with over one billion people being overweight and a hundred and thirty million being obese across the world [10]. Obesity leads to short- and long-term adverse effects on physical and mental health, the rate is expected to rise in the future. A recent study revealed that the frequency of obesity in Iran has been rising in particular through the years that range from 2005 to 2015 with an increased rate of 25%. In the same way, another study observed that the prevalence of obesity in Pakistan had risen by 15% in 2015. BSA is a commonly used anthropometric measure that helps in the determination of the psychological characteristics of an individual. BSA has significant clinical uses in pediatrics as well as oncology fields which make it important to have its measurements accurately determined. The use of QR percentiles for BSA estimation has been proposed as a potential method for improving the accuracy of BSA measurement.

Centile charts demonstrate the growth of an individual in terms of centile at a specific age in contrast to height-for-age growth charts which show the height of an individual at a given age and about the age centile [14]. CDC and WHO, have provided the growth charts for child growth and development to be employed in the screening of underweight, overweight, and obese children and adults [15]. These charts help in detecting cases of early growth, and assessing the progress of a child. These charts also determine whether a child has any development issues that may be a result of growth, and attending to a particular child with health complications. These growth charts can also be utilized by the clinician to identify initial signs of anomalous growth and development, or poor nutrition and feed intake [16-18].

Objective of Study

The objective of this research study is to investigate the estimated quantiles of BSA through quantile regression, with a specific focus on examining the BSA gap for the adult population in Pakistan. This study aims to contribute to the existing literature by exploring the BSA quantiles, which has been largely overlooked in previous studies that have focused on BMI or other obesity indices.

Methodology

Study Design and Sampling Technique

A cross-sectional research approach was employed in this study to choose 9906 individuals, ranging in age from 2 to 60, from various public locations in Pakistan. Convenience sampling was the method used for the study, and participants were drawn from parks, marketplaces, hospitals, and transportation hubs [19]. In the study, data collection for preschoolers aged 0-3 is accomplished by simple sampling, whereas data collection for school-age children aged 4-19 is accomplished using laborintensive and convenient sampling. The chosen schools were both public and private, and permission to gather the data was obtained from the relevant school administrations. In the study, convenience sampling was employed to collect samples of persons in public settings, ranging in age from 20 to 60. Two male and one female enumerator teams were hired to visit households and gather data by asking standardized questions [20]. One important factor that contributed to the study’s findings was gender difference. For respondents who identified as male and female, separate questionnaires were consequently prepared. The gathered information offers valuable insights into the socioeconomic, health, and demographic characteristics of the population.

Setting and Participants

The study’s target population consisted of people who were at least two years old. Pregnant women were not involved in the study [21]. The sample included both males and females, and great effort was taken to gather information from the Pakistani regions of Multan and Bahawalpur.

Study Variables

The study sought to establish numerous variables with the view of fulfilling the stated objectives of the research. The main dependent variable was BSA which is an anthropometric measure. Exploratory variables involved age with six powers as used by Chen and co-authors in their study [22], and categorical variables involved gender, marital status, monthly income, and residential area [23]. These variables included both the numerical and nonnumerical data derived from a wide group of participants.

Data Collection

Each of the two groups of three people gathered the data with the assistance of nutritionists and doctors in the area. The research for the study was carried out over six months, from July to December 2023. A customized, two-part questionnaire that was self-administered was used to gather primary data. Basic biographical information, such as gender and age (rounded to the closest year), was gathered in the first part from school enrollment records or, in the case of children under five, with parental consent. The details of the anthropometric measures were given in the second part.

Patient and Public involvement

Notwithstanding the research team’s best efforts, several difficulties occurred throughout the study’s execution and data collection, as the analysis below shows. By using self-completion to measure the participant’s height and weight, the data was gathered objectively.

Reliability of Data

Regarding internal consistency, the reliability of the collected data was determined by Cronbach’s Alpha, which had a value of 0.7891 that was found within the normal range of 0.70-0.90 [24]. This means that the collected data is correct and valid for statistical analysis since it conforms with the hypothesized distribution.

Bias

During the data editing and data cleaning process, the observations that did not fit the nature of the research question or were considered outliers were not included to minimize the bias [25]. It was necessary to ensure that the collected data was correct and did not contain any errors to identify the findings of the study as true.

Informed Consent

Every responder provided written, informed permission.

Study Size

We determine the sample size for our study by using the following formula: [26]:

where n is the Sample Size, N is the size of the population, e = Precision Level Now, N= 1872000

As a result, 9906 people and children are removed from Multan and Bahawalpur, two Pakistani cities.

Statistical Methods

The BSA formula is as follows:

QR Model and its Percentiles

The QR is a sophisticated statistical technique suitable in situations when the distribution is non-normal, was used in this study. If the covariate has been considered, QR also yields an estimate of the response variable’s density. It is best suited for usage with non-normal distributions since it can easily manage extreme values and outliers. QR provides more details on the response variable’s center and spread [27-29]. Compared to the traditional regression approaches that were previously employed, it offers the following advantages: Estimating distributional shapes is not necessary, and the presence of outliers has no bearing on the method’s effectiveness. As was previously said in this study, the models’ comparison revealed that QR fits the association between BSSI, BSA, and variables better [30,31]. The QR model allows for the incorporation of age, and its six powers enable us to take age-related differences in BSSI and BSA into account [5,32]. Let’s look at a real-valued random variable with the following distribution function:

subsequently, the inverse function of the distribution function mentioned above is the 𝜏−𝑡ℏ quantile of the real valued random variable Y as given below:

where the 𝜏 is between 0 and 1. To be more precise, the median is Q(1/ 2) . The estimated τ -th sample quantile is ξ(τ), which is an analogue of Q(τ), may be formulated as the solution of the optimization problem

The response variable is represented in terms of covariates using a linear equation, per the linear quantile model (Liu, 2018). This model was employed in our study to analyze the Body Shape and Size Index (BSSI). Especially, the median regression approach was used where an idea is to find the scenario ξ (τ ) = 0.5 that results in the smallest sum of the absolute residuals [33]. We built a QR model with the natural logarithm of BSA (log BSA) as the outcome variable in order to create BSA growth charts. The study has demonstrated that this approach involves six powers of age as variables [34]. A strategy of τ = 0.05 to 0.95 was used to choose the values of τ, resulting in BSA levels at the 5th, 10th, 25th, 50th (mid-point), 75th, 85th, 90th, and 95th percentiles [35]. The E-VIEWS 7.0 software was utilized to conduct analysis of the theoretical model.

The BSSI growth charts could then be produced where the above-calculated percentiles were done against the age of the respondents. It also helped in achieving the goal of presenting the distribution of BSA values by age and the growth patterns due to visualization. Concerning the shape of the growth curves, six powers of ‘age’ were included as covariates in the model [36]. Growth charts are preferably used in medical practice when assessing the growth of patients and some characteristics that are associated with the process of abnormal growth might be identified [37]. The developed BSA growth charts are useful for tracking changes in body size in health-related places. From the percentile of the BSA values at various ages, the clinician will be in a position to diagnose a problem that is causing a deviation from normal growth patterns at a very early stage. QR helped in confirming the presence of outliers and thus non-normality of the data while at the same time giving a more holistic look at the growth of BSA. We used the natural logarithm of BSA as the dependent variable for which the six powers of age were used as covariates to analyze the adequate relationship between the two variables. The result of the study is consequent growth charts for children of different ages and sizes that will be of great benefit to doctors and other professionals in the provision of health care.

Results

Participants

In this study, we have taken a sample of 9906 participants of which the majority are of male gender although women are also included. Among them the breakdown is as follows: 5524 are men, which account for 55 percent, and 4382 are women which is equivalent to 44 percent. The gender distribution is almost equal in the study sample; however, men form a slightly larger proportion as compared to women. It is necessary to recognize the fact that gender distribution might influence the results of the research as well as the conclusions that are drawn from the results. Male and female people differ in terms of body size, which might have an impact on the validity and applicability of our research. It is also important to recognize that, as a consequence of the limited sample size, the proportion of female participants is significantly lower than that of male participants. As a result, it is not possible to extrapolate the findings to a large number of female participants. Therefore, to ensure that the samples are more representative, future research may need to include a larger number of individuals, particularly women. The existing database can serve as a valuable foundation for describing the relationships between individuals in this community in terms of size, shape, and age.

Descriptive Analysis

This study aims to quantify human growth using QR percentiles for BSA in Pakistan. As a preliminary step, we conducted a descriptive analysis of BSA values across different demographic variables, including age, gender, marital status, residential area, and monthly income or wage distribution. The results of the descriptive analysis are presented in (Table 1). which shows the mean, median, minimum, maximum, standard error (S.E), variance (Var), standard deviation (S.D), and p-value for each category. The overall mean BSA value is 0.00709, with a median value of 0.00570. The minimum and maximum values range from 0.0011 to 0.0149. The analysis revealed significant differences in BSA values across various demographic variables. For instance, the p-value for gender-based comparison is <0.001, indicating that there is a significant difference between the BSA of males and females.

The mean BSA value for males is 0.00475, whereas for females it is 0.01004. Similarly, the p-value for residential area comparison is <0.001, indicating that there is a significant difference between the BSA of urban and rural inhabitants. The mean BSA value for urban residents is 0.00708, whereas for rural residents it is 0.00709. The analysis also revealed significant differences in BSA values across different marital status categories. The p-value for marital status comparison is <0.001, indicating that there is a significant difference between the BSA of single and married individuals. The mean BSA value for singles is 0.00604, whereas for married individuals it is 0.00864. The analysis showed significant differences in BSA values across different income or wage distribution categories. The p-value for income or wage distribution comparison is <0.001, indicating that there is a significant difference between the BSA of individuals with different income levels. These findings suggest that BSA values are significantly influenced by demographic variables such as gender, residential area, marital status, and income or wage distribution in Pakistan. Descriptive statistics of BSA were calculated for the 2-5 age group and compared across gender and residential areas. The results, presented in (Table 2), indicate that there is a significant difference between the BSA of males and females (p-value < 0.001). The mean BSA value for males is 0.00182, whereas for females it is 0.00368. Similarly, there is a significant difference between the BSA of urban and rural inhabitants (p-value < 0.001). The mean BSA value for urban residents is 0.00283, whereas for rural residents it is 0.00273. These findings are consistent with previous studies that have reported significant differences in BSA values across gender and residential areas [38,39]. The results highlight the importance of considering these factors when assessing human growth in Pakistani children.

Descriptive statistics of BSA were calculated for the 5-14 age group and compared across gender, residential area, and family income or wage distribution. The results, presented in (Table 3), indicate that there is a significant difference between the BSA of males and females (p-value < 0.001). The mean BSA value for males is 0.00346, whereas for females it is 0.00712. There is a significant difference between the BSA of urban and rural inhabitants (p-value < 0.001), with the mean BSA value for urban residents being 0.00499 and for rural residents being 0.00489. The analysis also revealed significant differences in BSA values across various levels of income or wage distribution (p-value < 0.001). The mean BSA value for individuals with a monthly income of <9999 is 0.00376, whereas for those with an income of >50000 it is 0.00613. These findings are consistent with previous studies that have reported significant differences in BSA values across gender, residential area, and socioeconomic status [40,41]. The results highlight the importance of considering these factors when assessing human growth in Pakistani children. BSA is a widely used indicator of human growth, and it is essential to assess BSA values in different populations to identify trends and patterns. In this study, we used descriptive statistics to calculate BSA values for the 14 and above age group and compared them across gender, marital status, residential area, and monthly income or wage distribution.

The results, presented in (Table 4), indicate that there is a significant difference between the BSA of males and females (p-value < 0.05). The mean BSA value for males is 0.00530, whereas for females it is 0.01126. This finding suggests that females have a significantly larger BSA than males, which is consistent with previous studies that have reported differences in body composition and growth patterns between males and females. Our analysis revealed significant differences in BSA values between married and unmarried individuals (p-value < 0.05). The mean BSA value for married individuals is 0.00865, whereas for unmarried individuals it is 0.00717. This finding suggests that married individuals have a larger BSA than unmarried individuals, which may be due to differences in lifestyle and socioeconomic status. The analysis also revealed significant differences in BSA values across residential areas (p-value < 0.05). The mean BSA value for urban residents is 0.00797, whereas for rural residents it is 0.00796. This finding suggests that there are differences in body composition and growth patterns between urban and rural residents, which may be due to differences in diet, lifestyle, and socioeconomic status.

Our study found significant differences in BSA values across various levels of income or wage distribution (p-value < 0.05). The mean BSA value for individuals with a monthly income of <9999 is 0.00783, whereas for those with an income of >50000 it is 0.00809. This finding suggests that there are differences in body composition and growth patterns between individuals with different socioeconomic statuses. Our findings are consistent with previous studies that have reported significant differences in BSA values across gender, marital status, residential area, and socioeconomic status. The results highlight the importance of considering these factors when assessing human growth in Pakistani individuals. In conclusion, this study demonstrates the use of QR percentiles for BSA in Pakistani individuals aged 14 and above. The results highlight the significance of considering various demographic and socioeconomic factors when assessing human growth. The implications of our study are significant for healthcare policymakers and practitioners in Pakistan. Firstly, it highlights the need to address health disparities across different socioeconomic groups, particularly in urban and rural areas. Secondly, it emphasizes the importance of considering gender and marital status when developing healthcare programs and policies. Our study demonstrates the importance of considering demographic and socioeconomic factors when assessing human growth in Pakistani individuals. We hope that our findings will contribute to the development of more effective healthcare programs and policies that address the needs of diverse populations.

Inferential Analysis QR Percentiles and Growth Charts of BSA

The assessment of human growth is a crucial aspect of pediatric medicine, particularly in developing countries like Pakistan, where population growth rates are high and the prevalence of malnutrition and stunting is a significant public health concern. In this study, we aimed to quantify human growth using QR percentiles for BSA in Pakistani individuals. The results are as follows:

QR Analysis of BSA for Complete Data

The QR approach was used to analyze the data and estimate the BSA values at different quantiles. The QR estimates were obtained using EViews 7.0, and the results are presented in (Table 5). The below table shows the QR estimates of BSA for complete data at the 50th percentile (τ=0.5). The results indicate that the BSA is significantly influenced by the six powers of age, with the first two powers having a positive impact on BSA, while the other four having a negative impact. The median BSA value for both genders at an average age of 30 years is estimated to be 0.005689. The p-values for all variables are highly significant at the 0.05 level of significance, indicating that the BSA is significantly affected by these variables. Our findings are consistent with previous studies that have reported significant associations between age and BSA. The results suggest that age is a significant predictor of BSA, with older individuals having a larger BSA. The QR approach has been widely used in various fields, including medicine and economics. In this study, we applied the QR approach to analyze the data and estimate the BSA values at different quantiles. The results of this study have important implications for healthcare policymakers and practitioners in Pakistan. Firstly, they highlight the importance of considering age as a significant predictor of BSA when developing healthcare programs and policies. Secondly, they suggest that older individuals may require more resources and attention to ensure their health and well-being. This study demonstrates the use of QR percentiles for BSA in Pakistani individuals. The results highlight the significance of considering age as a predictor of BSA when developing healthcare programs and policies.

 Growth Chart

The growth charts of BSA were created using the QR technique, with six powers of age in the analysis. The various BSA percentile curves for each individual were obtained, and the results are presented in (Figure 1). As shown in the figure, the BSA rapidly increases between the ages of 2 and 25 for all percentiles, then BSA slightly decreases between the ages of 25 and 38 years, after which it rapidly increases until age 50, after which BSA decreases till the age of 60 years. This pattern is consistent with previous studies that have reported a rapid increase in BSA during childhood and adolescence, followed by a plateau in adulthood. The BSA also rapidly increases between the ages of 2 and 4 years for all quantiles, which is consistent with previous studies that have reported a rapid increase in BSA during early childhood. The growth charts for eight percentiles (fifth, tenth, twentyfifth, fiftieth, seventy-fifth, eighty-fifth, ninetieth, and ninetyfifth) are displayed in (Figure 1). These charts provide a visual representation of the BSA values at different percentiles and can be used to monitor the growth of individuals over time.

QR Analysis of BSA for Gender

This study aimed to quantify human growth using QR percentiles for BSA in Pakistani individuals. In this section, we present the findings of the QR analysis of BSA for gender, specifically for males.

 Male Data

The QR method was used to determine the significance of age on BSA in males, with the data analyzed at the Median Percentile (τ=0.5). The results are presented in (Table 6). The below table shows the QR estimates of BSA for male data at the 50th percentile, with the first column listing the variables, the second column listing the QRM coefficients, and the third column listing the standard errors. The t-value and p-value are also presented in columns 4 and 5, respectively. The results indicate that the P-value for all variables is highly significant at the 0.05 level of significance, suggesting that age is a significant predictor of BSA in males. The combined data’s 50th quantile (Median) BSA is estimated to be 0.00 499, which demonstrates that the BSA is significantly influenced by these six powers of age. The coefficients suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact. This suggests that age has a non-linear effect on BSA in males, with older ages having a more pronounced impact on BSA. These estimates provide a comprehensive picture of the relationship between age and BSA in males and can be used to monitor growth and development in this population.

 Growth Chart

The growth charts of BSA were created using the QR technique with six powers of age in the analysis. The various BSA percentile curves for each individual were obtained, and the results are presented below. As shown in (Figure 2), the BSA growth charts demonstrate a complex pattern of growth and development in males. The BSA rapidly increases between the ages of 2 and 22 for all percentiles, indicating a rapid growth phase during early childhood and adolescence. This is followed by a slight decrease in BSA between the ages of 23 and 35 years, suggesting a plateau or stabilization of growth during young adulthood. However, this decline is short-lived, as the BSA rapidly increases again until age 50, indicating a significant growth spurt during middle adulthood. Finally, the BSA decreases until age 60 years, indicating a decline in growth during older adulthood. The BSA also rapidly increases between the ages of 2 and 4 years for all quantiles, suggesting a rapid growth phase during early childhood. This is consistent with previous studies that have reported rapid growth during early childhood. The growth charts for eight percentiles (fifth, tenth, twenty-fifth, fiftieth, seventy-fifth, eighty-fifth, ninetieth, and ninety-fifth) are displayed in (Figure 2). These charts provide a visual representation of the BSA values at different percentiles and can be used to monitor growth and development in males. The analyses were performed using both combined and separate data to determine the effect of variables on BSA. The median (50th quantile) regression model was fitted to the data using EViews 7.0, and the results are presented in (Table 7). The below table shows the QR estimates of BSA for female data at the 50th percentile, with the first column listing the variables, the second column listing the QRM coefficients, and the third column listing the standard errors.

The t-value and p-value are also presented in columns 4 and 5, respectively. The results indicate that all variables are highly significant at the 0.05 level of significance, suggesting that age is a significant predictor of BSA in females. The combined data’s 50th quantile (Median) BSA is estimated to be 0.010653, which demonstrates that the BSA is significantly influenced by these six powers of age. The coefficients suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact. This suggests that age has a non-linear effect on BSA in females, with older ages having a more pronounced impact on BSA. These estimates provide a comprehensive picture of the relationship between age and BSA in females and can be used to monitor growth and development in this population. This study demonstrates the use of QR percentiles for BSA in Pakistani females. The results highlight the importance of considering age as a significant predictor of BSA when developing healthcare programs and policies.

 Growth Chart

In this section, we present the growth charts of BSA obtained using the QR technique with six powers of age in the analysis. The growth charts provide a visual representation of the BSA values at different percentiles for each individual. The below (Figure 3) demonstrates the growth charts of BSA for female data. The chart shows that the BSA rapidly increases between the ages of 2 and 26 for all percentiles, indicating a rapid growth phase during early childhood and adolescence. This is followed by a slight decrease in BSA between the ages of 27 and 36 years, suggesting a plateau or stabilization of growth during young adulthood. However, this decline is short-lived, as the BSA rapidly increases again until age 52, indicating a significant growth spurt during middle adulthood. Finally, the BSA decreases until age 60 years, indicating a decline in growth during older adulthood. The BSA also rapidly increases between the ages of 2 and 4 years for all quantiles, suggesting a rapid growth phase during early childhood. This is consistent with previous studies that have reported rapid growth during early childhood. The growth charts for eight percentiles (fifth, tenth, twenty-fifth, fiftieth, seventy-fifth, eighty-fifth, ninetieth, and ninety-fifth) are displayed in (Figure 3). These charts provide a visual representation of the BSA values at different percentiles and can be used to monitor growth and development in females. This study demonstrates the use of QR percentiles for BSA in Pakistani females. The results highlight the complex pattern of growth and development in females, with significant increases in BSA during early childhood, adolescence, and middle adulthood.

 Gender Comparison

In this section, we present the results of the QR analysis of BSA for males and females in Pakistan. The QR method was used to determine the significance of age on BSA, with the data analyzed at the Median Percentile (τ=0.5). The results are presented in (Tables 6,7), which show the QR estimates of BSA for male and female data, respectively. The QR estimates for male data (Table 6) suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact. This suggests that age has a non-linear effect on BSA in males, with older ages having a more pronounced impact on BSA [42]. The combined data’s 50th quantile (Median) BSA is estimated to be 0.00 499, which demonstrates that the BSA is significantly influenced by these six powers of age. The growth charts for male data (Figure 2) demonstrate a complex pattern of growth and development, with significant increases in BSA during early childhood, adolescence, and middle adulthood. The BSA rapidly increases between the ages of 2 and 22 for all percentiles, indicating a rapid growth phase during early childhood and adolescence. This is followed by a slight decrease in BSA between the ages of 23 and 35 years, suggesting a plateau or stabilization of growth during young adulthood.

The QR estimates for female data (Table 7) suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact. This suggests that age has a non-linear effect on BSA in females, with older ages having a more pronounced impact on BSA [43]. The combined data’s 50th quantile (Median) BSA is estimated to be 0.010653, which demonstrates that the BSA is significantly influenced by these six powers of age. The growth charts for female data (Figure 3) also demonstrate a complex pattern of growth and development, with significant increases in BSA during early childhood, adolescence, and middle adulthood. The BSA rapidly increases between the ages of 2 and 26 for all percentiles, indicating a rapid growth phase during early childhood and adolescence. This is followed by a slight decrease in BSA between the ages of 27 and 36 years, suggesting a plateau or stabilization of growth during young adulthood.

Comparison of male and female data reveals significant differences in the pattern of growth and development. Males have a more rapid growth phase during early childhood and adolescence, whereas females have a more gradual growth phase during early childhood and adolescence. Additionally, females have a more pronounced growth spurt during middle adulthood compared to males. This study demonstrates the use of QR percentiles for BSA in Pakistani males and females. The results highlight the complex pattern of growth and development in both sexes, with significant increases in BSA during early childhood, adolescence, and middle adulthood. The comparison of male and female data reveals significant differences in the pattern of growth and development, highlighting the importance of considering sex-specific differences when developing healthcare programs and policies.

QR Analysis of BSA for Residential Area

In this study, we investigated the association between BSA and residential area, specifically comparing rural and urban areas. We used QR to examine the impact of age on BSA at different quantiles, including the median.

 Rural Data

Our results show that the BSA in rural areas is significantly influenced by age, with a highly significant p-value for all variables at the 0.05 level of significance. The median BSA of rural areas at an average age of 30 years is estimated to be 0.00569, indicating a significant impact of age on BSA. The QR estimates for rural data are presented in (Table 8). The below table shows the coefficients, standard errors, t-values, and p-values for each variable. The results indicate that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact. The highly significant p-values suggest that these six powers of age are significant predictors of BSA in rural areas. The combined data’s 50th quantile (Median) BSA is estimated to be 0.00569, indicating a significant influence of age on BSA. These estimates provide a comprehensive picture of the relationship between age and BSA in rural areas and can be used to monitor growth and development in this population. This study demonstrates the use of QR percentiles for BSA in rural Pakistan. The results highlight the importance of considering age as a significant predictor of BSA when developing healthcare programs and policies.

 Growth Chart

In this section, we present the growth charts of BSA for rural Pakistan, obtained using the QR technique with six powers of age in the analysis. The growth charts provide a visual representation of the BSA values at different percentiles for each individual. The below (Figure 4) demonstrates the growth charts of BSA for rural data. The chart shows that the BSA rapidly increases between the ages of 2 and 26 for all percentiles, indicating a rapid growth phase during early childhood and adolescence. This is followed by a slight decrease in BSA between the ages of 27 and 36 years, suggesting a plateau or stabilization of growth during young adulthood. However, this decline is short-lived, as the BSA rapidly increases again until age 52, indicating a significant growth spurt during middle adulthood.

Finally, the BSA decreases till the age of 60 years, indicating a decline in growth during older adulthood. The BSA also rapidly increases between the ages of 2 and 4 years for all quantiles, suggesting a rapid growth phase during early childhood. This is consistent with previous studies that have reported rapid growth during early childhood. The growth charts for eight percentiles (fifth, tenth, twenty-fifth, fiftieth, seventy-fifth, eighty-fifth, ninetieth, and ninety-fifth) are displayed in (Figure 4). These charts provide a visual representation of the BSA values at different percentiles and can be used to monitor growth and development in rural Pakistani populations. This study demonstrates the use of QR percentiles for BSA in rural Pakistan. The results highlight the complex pattern of growth and development in rural Pakistani populations, with significant increases in BSA during early childhood, adolescence, and middle adulthood.

 Urban Data

In this section, we examined the relationship between BSA and age in urban Pakistan using QR analysis. The median (50th quantile) regression model was fitted on the data to determine the effect of variables on BSA. The results are presented below. The analysis of urban data is presented in (Table 9). The below table shows the coefficients, standard errors, t-values, and p-values for each variable. The results indicate that all variables are highly significant at the 0.05 level of significance, with the combined data’s 50th quantile (Median) BSA estimated to be 0.00569. The QR estimates for urban data suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact. The constant term is also significant, indicating that BSA is influenced by factors beyond age. The highly significant p-values for all variables suggest that these six powers of age are significant predictors of BSA in urban Pakistan. The median BSA of both locations at an average age of 30 years is anticipated based on certain parameters for male data and is presented in (Table 9). These estimates provide a comprehensive picture of the relationship between age and BSA in urban Pakistan and can be used to monitor growth and development in this population. This study demonstrates the use of QR percentiles for BSA in urban Pakistan. The results highlight the significant influence of age on BSA in urban populations, with positive effects from early childhood to adolescence and negative effects from young adulthood to older adulthood.

 Growth Chart

In this section, we present the growth charts of BSA for urban Pakistan, obtained using the QR technique with six powers of age in the analysis. The growth charts provide a visual representation of the BSA values at different percentiles for each individual. The below (Figure 5) demonstrates the growth charts of BSA for urban data. The chart shows that the BSA rapidly increases between the ages of 2 and 21 for all percentiles, indicating a rapid growth phase during early childhood and adolescence. This is followed by a slight decrease in BSA between the ages of 22 and 33 years, suggesting a plateau or stabilization of growth during young adulthood. However, this decline is short-lived, as the BSA rapidly increases again until age 49, indicating a significant growth spurt during middle adulthood. Finally, the BSA decreases till the age of 60 years, indicating a decline in growth during older adulthood. The BSA also rapidly increases between the ages of 2 and 4 years for all quantiles, suggesting a rapid growth phase during early childhood. This is consistent with previous studies that have reported rapid growth during early childhood. The growth charts for eight percentiles (fifth, tenth, twenty-fifth, fiftieth, seventyfifth, eighty-fifth, ninetieth, and ninety-fifth) are displayed in (Figure 5). These charts provide a visual representation of the BSA values at different percentiles and can be used to monitor growth and development in urban Pakistani populations. This study demonstrates the use of QR percentiles for BSA in urban Pakistan. The results highlight the complex pattern of growth and development in urban Pakistani populations, with significant increases in BSA during early childhood, adolescence, and middle adulthood.

 Residential Area Comparison

The study aimed to investigate the relationship between BSA and residential area in Pakistan, specifically comparing rural and urban areas. We used QR to examine the impact of age on BSA at different quantiles, including the median. The results of the QR analysis for rural data showed that BSA is significantly influenced by age, with a highly significant p-value for all variables at the 0.05 level of significance (Table 8). The median BSA of rural areas at an average age of 30 years is estimated to be 0.00569, indicating a significant impact of age on BSA. The QR estimates for rural data suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact (Table 8). The combined data’s 50th quantile (Median) BSA is estimated to be 0.00569, indicating a significant influence of age on BSA. The growth charts for rural data (Figure 4) demonstrate a complex pattern of growth and development, with significant increases in BSA during early childhood, adolescence, and middle adulthood. The rapid growth phase during early childhood and adolescence is consistent with previous studies that have reported rapid growth during this period [44].

The QR analysis for urban data showed that BSA is also significantly influenced by age, with all variables having a highly significant p-value at the 0.05 level of significance (Table 9). The median BSA of urban areas at an average age of 30 years is estimated to be 0.00569, indicating a significant impact of age on BSA. The QR estimates for urban data suggest that the first two powers of age have a positive impact on BSA, while the other four powers have a negative impact (Table 9). The combined data’s 50th quantile (Median) BSA is estimated to be 0.00569, indicating a significant influence of age on BSA. The growth charts for urban data (Figure 5) also demonstrate a complex pattern of growth and development, with significant increases in BSA during early childhood, adolescence, and middle adulthood. The growth charts for urban data show a more rapid growth phase during early childhood and adolescence compared to rural data. A comparison between urban and rural data reveals significant differences in the pattern of growth and development. Urban areas show a more rapid growth phase during early childhood and adolescence, while rural areas show a more gradual growth phase during early childhood and adolescence. Additionally, urban areas show a more pronounced growth spurt during middle adulthood compared to rural areas. This study demonstrates the use of QR percentiles for BSA in Pakistan. The results highlight the complex pattern of growth and development in both rural and urban populations, with significant increases in BSA during early childhood, adolescence, and middle adulthood. The comparison between urban and rural data reveals significant differences in the pattern of growth and development, highlighting the importance of considering residential area when developing healthcare programs and policies.

Discussionn

Human growth is a complex process that is influenced by a variety of factors, including genetics, nutrition, and environmental factors [45]. One important aspect of human growth is the measurement of BSA, which is a widely used indicator of body size [46]. BSA is a critical factor in many medical and surgical applications, including the calculation of drug dosages and the estimation of organ sizes [47]. Despite its importance, there is limited research on the quantification of human growth using BSA in Pakistani individuals. This study aimed to address this gap by examining the relationship between BSA and various demographic and socioeconomic factors in a sample of 9906 individuals from Pakistan. The sample consisted of both men and women aged 2-60 years, recruited from urban and rural areas of Pakistan. BSA was measured using the advanced formula, which has been widely used in previous studies [46-48]. Demographic and socioeconomic data were collected using standardized questionnaires, including information on age, gender, residential area, marital status, and monthly income or wage distribution. The objective of this study is to outline the correlation of BSA with various demographic factors and to use the QR percentiles and their growth charts. These findings of the present investigation provide useful information on the multiple interactions between BSA and demographic factors and indicate the importance of considering the curvilinear trends in the change of body composition across the life span. The analysis of the descriptive statistics showed that there is a significant relationship between BSA and age, gender, area of residence, marital status, and the level of monthly family income. These findings are in line with the past studies that have pointed out the relation between BSSI and several demographic variables [5]. The studies carried out in the Obesity journals showed that, the type of region an individual lives in and the income level affects body mass and body size [11,50-52]. The results of the study showed that BSA values were significantly influenced by age, with older individuals having a larger BSA. This finding is consistent with previous studies that have reported a positive correlation between age and BSA in adults [45,47, 53]. The study also found significant differences in BSA values across different gender, residential area, marital status, and socioeconomic groups. For example, males had a larger BSA than females, while urban residents had a larger BSA than rural residents. These findings are consistent with previous studies that have reported differences in BSA values across different population groups [45,53]. To further examine the relationship between BSA and demographic and socioeconomic factors, QR analysis was used to estimate BSA values at different percentiles. The results showed that BSA values were significantly influenced by age at all percentiles (p < 0.001), with older individuals having larger BSA values at all percentiles. The study also found significant differences in BSA values across different gender and residential area groups at all percentiles. The implications of the study are significant for healthcare policymakers and practitioners in Pakistan. The study highlights the importance of considering age as a significant predictor of BSA when developing healthcare programs and policies. The study suggests that older individuals may require more resources and attention to ensure their health and well-being. The study’s findings also have important policy implications for Pakistan. The country faces significant challenges in terms of health care delivery, particularly in rural areas where access to healthcare is limited. The study’s findings suggest that healthcare policymakers should prioritize the development of healthcare programs and policies that address the needs of older individuals, particularly those living in rural areas. This study provides valuable insights into the relationship between BSA and demographic and socioeconomic factors in Pakistani individuals. The study’s findings are consistent with previous studies that have reported a positive correlation between age and BSA in adults. The study’s findings also highlight the importance of considering age as a significant predictor of BSA when developing healthcare programs and policies.

Conclusions

The present study investigated the relationship between BSA and different demographic variables. The findings can shed light on the relationship between BSA and demographic characteristics and reveal the need for a deeper understanding of nonlinear trends in body composition changes during the human life stages. The cross-sectional analysis showed that there were statistically meaningful variations in BSA based on age, gender, residential area, marital status, and monthly income. These results support the literature review findings that BSA depends on several demographical factors. It is also found from the results that boys and girls have differences in body shape and size changes in the childhood and adolescent stage, unmarried people also have differences compared to married people. The findings of this study provide valuable insights into the growth patterns of BSA in Pakistani adults, which can be used to construct growth reference curves for various physiological and pathological states. The study’s results showed that the mean BSA was 0.00709 ± 0.00003, with a declining pattern in the age range of 5-25 for the 85th to 95th percentiles, followed by sharp rises in the ages of 25-40, and then declining up to the age of 55.

The QR percentiles exhibited a smooth and continuous pattern against given ages, which is a significant advantage over traditional methods of constructing growth charts. This is because traditional methods often use linear or quadratic models to fit the data, which can result in abrupt changes in the growth curves. In contrast, the QR method provides a more accurate and flexible model that can capture non-linear relationships between BSA and age. The study’s findings also highlight the importance of considering individual differences in growth patterns when constructing growth reference curves. This is particularly important in Pakistani adults, who may have different nutritional and environmental factors that affect their growth patterns compared to other populations. The study’s results have significant implications for healthcare professionals and researchers working with Pakistani adults. For example, the QR percentiles can be used to identify individuals who are at risk of developing certain health conditions, such as obesity or diabetes, due to their below-average or above-average BSA. Additionally, the QR percentiles can be used to develop targeted interventions to improve health outcomes in Pakistani adults, such as nutrition and exercise programs that are tailored to individual needs and goals. Furthermore, the study’s findings highlight the need for further research on the relationships between BSA and other anthropometric measures in Pakistani adults, such as body mass index (BMI) and waist circumference. This study demonstrates the importance of using QR percentiles to quantify human growth in Pakistani adults. The study’s findings provide valuable insights into the growth patterns of BSA in Pakistani adults, which can be used to construct growth reference curves for various physiological and pathological states. The study’s results also highlight the importance of considering individual differences in growth patterns when constructing growth reference curves and identifying individuals.

Disclosure Statement Acknowledgments

The authors would like to thank respondents for their assistance in collection of data for this study.

Financial Support

This study was not financially supported by any organization.

Conflict of Interest

The authors have no conflict of interest.

Authorship

All the authors are contributed significantly to the design, data collection, analysis, and interpretation of the results. All authors have read and approved the final manuscript.

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