TY - JOUR AU - Asifa Kamal , AU - Aqsa Asghar Ali, AU - Sameena Irfan, PY - 2021/11/01 Y2 - 2024/03/29 TI - Socio demographic determinants of BMI of Pakistani women: an evidence from PDHS (2017-18) using quantile regression analysis JF - Journal of the Pakistan Medical Association JA - J Pak Med Assoc VL - 71 IS - 4 SE - Original Article DO - 10.47391/JPMA.1459 UR - https://ojs.jpma.org.pk/index.php/public_html/article/view/443 SP - 1069-1075 AB - <p><strong>Objective:</strong> To explore the socio demographic determinants of nutritional status of Pakistani women.</p><p><strong>Methods:</strong> Secondary data from recent Pakistan Demographic and Health Survey (PDHS 2017-18) is taken. Data collection period is from 22 November 2017 to 30 April 2018. Ordinary least square (OLS) and quantile regression (QR) models are used for analysis.</p><p><strong>Results:</strong> QR model is found appropriate for BMI data to capture effect at different level of distribution of BMI. Less than 5% women are under nutrition for some categories of factors. Age of women, women’s education, frequency of watching TV, wealth index, husband’s education and region (KPK, Balochistan) showed a positive effect on women’s BMI in Pakistan across all conditional distribution of BMI. In contrary, age of women at first birth, women’s agriculture or manual working status, gender of household head (female) and region (Sindh) showed negative effect on women’s BMI in Pakistan.</p><p><strong>Conclusion:</strong> It is concluded that overweight/obesity is becoming serious problem as compared to undernutrition in Pakistani women. Percentage of deprived women is little and level of under nutrition is also not alarming.  Privileged women (with respect to education, economic status, urbanization, sedentary life style) have more chances to have higher BMI (overweight or obese). Women of KPK and Balochistan are at higher risk of overweight/ obesity as compared to Punjabi women.</p><p><strong>Keywords:</strong> PDHS 2017-18, Ordinary Least Square (OLS), Quantile Regression (QR) Model, <strong>Continuous....</strong></p> ER -