WIC Participation and Relative Quality of Household Food Purchases: Evidence from FoodAPS

2019 ◽  
Author(s):  
Di Fang ◽  
Michael R. Thomsen ◽  
Rodolfo M. Nayga ◽  
Aaron M. Novotny
Keyword(s):  
Food Purchases ◽  
Household Food ◽  
Wic Participation ◽  
Relative Quality

scholarly journals WIC Participation and Relative Quality of Household Food Purchases: Evidence from FoodAPS

2018 ◽  
Author(s):  
Di Fang ◽  
Michael Thomsen ◽  
Rodolfo Nayga ◽  
Aaron Novotny
Keyword(s):  
Food Purchases ◽  
Household Food ◽  
Wic Participation ◽  
Relative Quality

scholarly journals Development of the Healthy Purchase Index (HPI): a scoring system to assess the nutritional quality of household food purchases

2018 ◽  
Vol 22 (5) ◽  
pp. 765-775 ◽  
Author(s):  
Marion Tharrey ◽  
Christophe Dubois ◽  
Matthieu Maillot ◽  
Florent Vieux ◽  
Caroline Méjean ◽  
...  
Keyword(s):  
Low Income ◽  
Nutritional Quality ◽  
Dietary Guidelines ◽  
Convenience Sample ◽  
Food Purchases ◽  
Deprived Neighbourhoods ◽  
Sugary Drinks ◽  
Household Food ◽  
The Mean

AbstractObjectiveTo develop an index to assess the nutritional quality of household food purchases based on food expenditures only.DesignA database of monthly food purchases of a convenience sample of low-income households was used to develop the Healthy Purchase Index (HPI). The HPI is the sum of two sub-scores based on expenditure shares of food categories in total household food expenditure: the purchase diversity sub-score and the purchase quality sub-score. The first was adapted from an existing diversity score. The second integrated those food categories identified as the best predictors of the nutritional quality of purchases based on associations between expenditure shares of food categories and two nutritional quality indicators: the mean adequacy ratio (MAR) and the mean excess ratio (MER). Correlation between the HPI and a score assessing adherence to French dietary guidelines (PNNS-GSmod) was performed as a first validation.Setting/ParticpantsFood purchases of 112 households from deprived neighbourhoods of Marseille (France), participating in the Opticourses and Jassur projects (2012–2015).ResultsThe purchase diversity sub-score reflects the presence in food purchases of five food categories: fruits, vegetables, starches, dairy products, and meat, fish & eggs. The purchase quality sub-score is based on expenditure shares for fruit & vegetables, added fats & seasonings, sweet snacks, cheese, sugary drinks, refined grains and fish, as these were identified as predictors of the nutritional quality of purchases. The HPI was positively associated with the PNNS-GSmod (rs=0·378; P<0·001).ConclusionsThe HPI helps assess the healthiness of household food purchases.

Household food security: Perceptions, behavior and nutritional quality of food purchases

2016 ◽  
Vol 55 ◽  
pp. 139-148 ◽  
Author(s):  
Andrés Silva ◽  
Juan Carlos Caro ◽  
David Magaña-Lemus
Keyword(s):  
Food Security ◽  
Nutritional Quality ◽  
Household Food Security ◽  
Food Purchases ◽  
Household Food ◽  
Quality Of Food

scholarly journals Impact of Fear of Contracting COVID-19 and Complying with the Rules of Isolation on Nutritional Behaviors of Polish Adults

2021 ◽  
Vol 18 (4) ◽  
pp. 1631 ◽  
Author(s):  
Iwona Kowalczuk ◽  
Jerzy Gębski
Keyword(s):  
Eating Behaviors ◽  
Eating Habits ◽  
Food Product ◽  
Alcoholic Beverages ◽  
Online Study ◽  
Food Purchases ◽  
Product Consumption ◽  
Close Relationship ◽  
Two Factors

The aim of the study was to examine whether, and to what extent, fear of contracting Covid-19 and compliance with the mandatory rules of isolation affected Polish adults’ nutritional behaviors. The online study was carried out during the first wave of the pandemic on a sample of 926 adults. Through cluster analysis, three groups of respondents were isolated: 1. People who fear a Covid-19 infection and follow the isolation rules (FFR), 2. People moderately afraid of the disease and following the rules loosely (MFFR), 3. People who are not afraid of the infection and do not follow the rules of isolation. (NFFR). The clusters were profiled with consideration of different aspects of eating behaviors as well as socio-demographic and economic features. The results of the study show a close relationship between the level of fear of contracting Covid-19 and the degree to which isolation rules are followed. These two factors were found to have a significant impact on eating behaviors, such as food purchases, eating patterns, and levels of consumption. It was stated that the FFR group changed their eating behaviors the most in terms of food purchasing, eating habits (excluding diversity and quality of diet), and food product consumption. The greatest stability in the majority of the analyzed areas of nutritional behaviors was observed in the MFFR cluster. The NFFR group shown the greatest decrease both in regularity and quality of their meals. This group also exhibited a significant increase in the consumption of alcoholic beverages. The results of the study can be useful in the decision making process when introducing restrictions or managing information. They also point to the need for extensive nutritional education focused on explaining the relationship between nutrition and health during a pandemic.

An Evaluation of Volatility Forecasting Techniques

Metamorphosis ◽  
2005 ◽  
Vol 4 (2) ◽  
pp. 113-124
Author(s):  
Madhusudan Karmakar
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Relative Ability ◽  
Conflicting Evidence ◽  
Competing Models ◽  
Relative Quality ◽  
Indian Data ◽  
Naive Model

The existing literature contains conflicting evidence regarding the relative quality of stock market volatility forecasts. This paper employs daily Indian data to examine the relative ability of various models to forecast monthly stock market volatility. The forecasting models which were selected range from naive model to relatively complex GARCH model. While it is difficult to claim superiority of any one model under all measures used to assess the accuracy of the forecast, the overall results clearly identify two competing models i.e., the RWM and GARCH (1, 1).

Sign in / Sign up

Export Citation Format

Share Document