Peak expiratory flow rate (PEFR) is an important tool for assessing lung function, which can be affected by environmental and physical factors such as altitude, nutrition, genetics, age, height, and weight. Conducting a study to assess the correlation between peak expiratory flow rate and anthropometric measurements in Tanzanian schoolchildren is crucial to derive a population-specific prediction formula and further simplify respiratory health assessment.
This cross-sectional study was conducted in a single center private primary and secondary school in Dar es Salaam, Tanzania using data from an asthma screening camp. Variables of interest were height, weight, Body Mass Index (BMI) and PEFR. Independent t-test was performed to identify any differences in mean flow rate values between different ethnicities and genders. Correlation coefficients (r) were used to observe the relationship between PEFR and anthropometric measurements. A prediction equation by gender was generated using linear regression analysis. Statistical significance was set at the 5% level. All statistical data was analyzed using SPSS version 25.0.
The study involved 260 participants with a mean age of 9.5 years. Males were 51.2% and 65% of participants were of Asian ethnicity. PEFR was not observed to differ across the different ethnic groups and genders. Height was found to have the strongest correlation coefficient of 0.745, while BMI had the weakest correlation coefficient of 0.366. The strongest correlation was found with height for females (r = 0.787), while the weakest was with body mass index for boys (r = 0.203). The derived prediction equation for males was PEFR = 279.169 (Height of Student in meters) -134.12, while the predictive equation for females was PEFR = 318.32 (Height of Student in meters) -195.69.
This study found a strong correlation between PEFR and anthropometric characteristics in school children from Dar es Salaam, Tanzania. A prediction equation by gender for PEFR was developed based on anthropometric characteristics. This equation may be applied in population-based studies or situations where peak flow meters are not readily available. Further research is needed to explore how well this prediction formula performs in other Tanzanian settings and to determine other factors that may affect lung function in this population.

© 2024. The Author(s).