Growth charts of body surface area for adults in Pakistan using quantile regression
Abstract
Objective: To construct growth charts of body surface area (BSA) for men and women, separately using quantile regression approach.
Methodology: A cross-sectional data comprising of 2000 individuals aged 14 years or more, both males and females were taken from Multan city. Data about gender, weight, height, age, marital status and other demographic measures were recorded. Pregnant women were not included in the study. A latest set of formulas was used to compute BSA for men and women, separately. A brief statistical description was provided for the variables under study. Quantile regression approach was used to construct BSA growth charts taking BSA as dependent variable while age with its square and cube as covariates in the said regression.
Results: For our data set, the mean age of the respondents was 24.35 ± 6.96 years. The mean BSA (m2) is 1.68 ± 0.16 and 1.46 ± 0.15 for men and women, respectively with the respective 95% confidence intervals, (1.67, 1.69) and (1.45, 1.47). Married men and women have larger BSA as compared to unmarried ones. The percentile curves of BSA for men show a rapid drop followed by a quick growth until the age 20, showing greater concavity between the ages 20 to 30 and then stability at the ages of 30 and above. For women, on the other hand, no obvious concavity is found and the BSA growth increases slowly up to the age 40 and then becomes stable for older ages.
Conclusion: Since the use of empirical BSA percentiles with grouped age provide a discrete approximation for the population percentiles so it is more accurate to use continuous BSA percentile curves against given ages while using quantile regression approach. Furthermore, this approach can also be adopted to construct many other growth charts for physiological and medical sciences.
Methodology: A cross-sectional data comprising of 2000 individuals aged 14 years or more, both males and females were taken from Multan city. Data about gender, weight, height, age, marital status and other demographic measures were recorded. Pregnant women were not included in the study. A latest set of formulas was used to compute BSA for men and women, separately. A brief statistical description was provided for the variables under study. Quantile regression approach was used to construct BSA growth charts taking BSA as dependent variable while age with its square and cube as covariates in the said regression.
Results: For our data set, the mean age of the respondents was 24.35 ± 6.96 years. The mean BSA (m2) is 1.68 ± 0.16 and 1.46 ± 0.15 for men and women, respectively with the respective 95% confidence intervals, (1.67, 1.69) and (1.45, 1.47). Married men and women have larger BSA as compared to unmarried ones. The percentile curves of BSA for men show a rapid drop followed by a quick growth until the age 20, showing greater concavity between the ages 20 to 30 and then stability at the ages of 30 and above. For women, on the other hand, no obvious concavity is found and the BSA growth increases slowly up to the age 40 and then becomes stable for older ages.
Conclusion: Since the use of empirical BSA percentiles with grouped age provide a discrete approximation for the population percentiles so it is more accurate to use continuous BSA percentile curves against given ages while using quantile regression approach. Furthermore, this approach can also be adopted to construct many other growth charts for physiological and medical sciences.
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