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• W4306316631 abstract "The purpose of this study was to examine the impact of socioeconomic factors on fruit and vegetable consumption in the households of Pakistan. Secondary data were used from a national-level survey, i.e. Household Integrated Income and Consumption Survey (HIICS) 2015-2016 published by the Pakistan Bureau of Statistics. A total of 11,187 households were included in the final analysis. Quantile regression models were applied to investigate the association between socioeconomic factors and the consumption of fruit and vegetable. More than half of the households in the sample did not meet the World Health Organization's (WHO) recommended criteria for fruit and vegetable consumption, which is 400 g/day/capita. According to the quantile regression model, household income is an important factor in increasing fruit and vegetable intake because an increase in income leads to a greater likelihood of spending on healthy and nutritious foods. The increased consumption of fruit and vegetable was caused by the household head's high education, which created multiple resources to increase income. Households in two provinces, i.e. Khyber Pakhtunkhwa and Baluchistan, have a greater impact on fruit and vegetable consumption than other provinces due to natural resource availability. Household size and dependency ratio hurt the consumption of fruit and vegetable because women and children are not able to do work. These results are very useful because a better understanding of the socioeconomic characteristics associated with fruit and vegetable intake could improve the effectiveness of policies aimed at increasing fruit and vegetable consumption and reducing the risk of chronic diseases." @default.
• W4306316631 created "2022-10-16" @default.
• W4306316631 creator A5050907617 @default.
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• W4306316631 date "2022-10-15" @default.
• W4306316631 modified "2023-10-04" @default.
• W4306316631 title "The Association between Fruit and Vegetable Intake and Socioeconomic Factors in the Households of Pakistan Using Quantile Regression Model" @default.
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• W4306316631 doi "https://doi.org/10.1080/19371918.2022.2134249" @default.
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