Temporal Trends in Apparent Energy and Macronutrient Intakes in the Diet in Bangladesh: A Joinpoint Regression Analysis of the FAO's Food Balance Sheet Data from 1961 to 2017

Nutrients. 2020 Aug 2;12(8):2319. doi: 10.3390/nu12082319.

Abstract

We analyzed the temporal trends and significant changes in apparent energy and macronutrient intakes in the Bangladeshi diet from 1961 to 2017. Due to the lack of a long-running national dietary intake dataset, this study used the Food and Agriculture Organization (FAO)'s old and new food balance sheet dataset. We used the joinpoint regression model and jump model to analyze the temporal trends in apparent energy and macronutrient intakes. The annual percentage change (APC) was computed for each segment of the trends. Bangladesh has experienced a late energy revolution in their dietary history. During the 1960s, 1970s, 1980s, and 1990s, Bangladesh was suffering from substantive calorie deficits, where in apparent energy intake was less than 2200 kcal/day/person. Since the late 1990s, Bangladesh has made significant progress in raising the apparent energy consumption in the diet. Since the late 1970s, apparent fat intake started to increase significantly at a marked rate (APC = 2.16), whereas since the early 1990s, protein intake increased significantly by 1.33% per year. Plant sources have mostly governed the protein and fat intake trends in the Bangladeshi diet since 1960, whereas animal sources began to contribute significantly in protein intake since 1990 (APC = 3.43) and in fat intake since 2000 (APC = 2.88). Bangladesh overcame the substantive calorie deficit condition in the diet from the late 1990s. Excessive carbohydrate intake along with imbalanced and low-quality protein and fat intakes have been the central features in the diet in Bangladesh.

Keywords: apparent energy intake; apparent macronutrient intake; food balance sheet; joinpoint regression analysis; jump model.

MeSH terms

  • Bangladesh
  • Diet / trends*
  • Diet Surveys
  • Eating
  • Energy Intake*
  • Financial Statements*
  • Food
  • Humans
  • Nutrients*
  • Nutrition Surveys
  • Regression Analysis