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Money metric and ordinal utility of common beans consumption

For permission to use where not already granted under a licence please go to http: Design Studies reviewed independently and in duplicate were included if reporting mean retail price of foods or diet patterns stratified by healthfulness. Using random effects models, we quantified price differences of healthier versus less healthy options for specific food types, diet patterns and units of price serving, day and calorie. Statistical heterogeneity was quantified using I2 statistics.

Results 27 studies from 10 countries met the inclusion criteria. Comparing nutrient-based patterns, price per day was not significantly different top vs bottom quantile: Adjustment for intensity of differences in healthfulness yielded similar results.

The strengths include the systematic search; adjustment for inflation and purchasing power parity; separate analyses of food groups, diet patterns and units of price; and evaluation of heterogeneity by food type, intensity of contrast and unit of comparison. The study was limited by less available data on restaurant prices and prices from low-income and middle-income countries.

High statistical heterogeneity was evident, although the actual observed range of price differences was more modest. Introduction Consumption of a healthy diet is a priority for reducing chronic diseases including obesity, diabetes, cardiovascular diseases and several cancers. This is especially crucial for socioeconomically disadvantaged populations, who have less healthy diets and higher disease risk than higher socioeconomic groups.

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One of the most commonly described barriers is cost: In addition, little is known about the potential heterogeneity of this relationship. For example, price differences may vary by the foods or diets being compared.

Many studies compare healthier and less healthy versions of the same food ie, more vs less healthy grainswhile other studies examine the price differences of healthier vs less healthy overall diet patterns, containing very different foods.

Price differences may also depend on how healthfulness is defined, ranging from definitions based on single nutrients eg, fat or sugar content to those based on food types or more complex diet patterns. The intensity of the health contrast could also affect the price difference; for example, a fast food meal versus a healthier home-cooked meal is a more extreme comparison than a low-fat versus high-fat cookie. Finally, price differences may vary by the unit of comparison, for example, per serving, per calorie, or per day.

In particular, price differences per calorie may be limited by reverse causation, as healthier foods eg, fruits and vegetables often have fewer calories; and evaluation of price differences per serving may alter the conclusions. The protocol, which was not altered after beginning the study, is available from the authors on request. Because our focus was on contemporary price differences related to healthfulness, and because such price differences could vary in earlier decades, we focused our search on studies having collected price data in the year 2000 or later.

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Studies were included if they reported the mean retail prices of foods including beverages or diet patterns stratified by a specified measure of healthfulness, as well as sufficient or obtainable by direct contact data to derive or estimate the statistical uncertainty ie, SE of difference in means.

No foods or diet patterns were excluded. Studies reporting wholesale price or perceived rather than the actual price, as well as reviews, letters, editorials and commentaries, were excluded. One investigator screened all identified studies based on these inclusion and exclusion criteria by title and abstract. Any differences were resolved by discussion among all of the investigators. A list of excluded citations is available from the authors on request.

Data extraction and synthesis For each included study, two investigators extracted data independently and in duplicate using a standardised electronic spreadsheet.

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These ratings were based on growing evidence that different types of foods and food-based diet patterns predict chronic disease outcomes better than differences in single nutrients.

These ratings are available in the supporting information.

Statistical analysis Our primary endpoint was the difference in mean price between the healthier and less healthy foods or diet patterns. When data on the variance of the difference in means or information to directly calculate this variance were not reported, we calculated it based on the variance of the mean prices in each category, based on standard formulas 14: For nine studies in which mean prices were reported without their uncertainty, the SEs were imputed from the number of observations in each category, based on linear regression of studies with complete data, performed separately for market surveys 6 studies comparing samples of foods and individual dietary surveys 3 studies comparing diets across samples of participants; supporting figure 1.

We recognised that price comparisons within food groups ie, healthier vs less healthy options within the same category of food may vary from price comparisons across overall diet money metric and ordinal utility of common beans consumption. Furthermore, price differences may vary for diet patterns largely based on foods versus diet patterns largely based on one or a few isolated nutrients.

For analyses of diet patterns, we evaluated price differences for the extreme categories eg, the top vs bottom quartile or quintile of diet, to enable comparisons of the largest differences in diet quality. Because price differences could also vary by the unit of comparison, findings for foods were evaluated and standardised to one usual serving and to 200 kcal; and for diet patterns, standardised to 1 day 3 meals and to 2000 kcal.

All price differences were adjusted for inflation by country to reflect the prices in 2011. Inflation rates and purchasing power parity conversion factors were obtained from the World Bank; 2011 is the latest year for which these data are available.

  1. Studies reporting wholesale price or perceived rather than the actual price, as well as reviews, letters, editorials and commentaries, were excluded. Several studies reported prices for multiple food comparisons or from different types of stores and contributed more than one estimate to the analysis.
  2. As the subtitle of EIP suggests, the book focuses on design patterns for asynchronous messaging systems. URN is an acronym for uniform resource name.
  3. Here are some examples of the Camel-supported endpoint technologies. Summary estimates were quantified using inverse-variance weighted, random effects meta-analysis metan command in Stata.

Summary estimates were quantified using inverse-variance weighted, random effects meta-analysis metan command in Stata. Statistical heterogeneity was evaluated using the I2 statistic. Publication bias was assessed using the Egger test and visual inspection of funnel plots. Statistical analyses were performed using Stata V.

Results Search results and study characteristics Of 1010 articles identified by the MEDLINE search and screened for inclusion, 83 were selected for full-text review figure 1.

Of these, 19 articles met the inclusion criteria, and an additional 8 articles were identified from hand-searches of references lists, related citations in PubMed and expert consultations. Twelve studies were market surveys, and 15 were dietary surveys. The number of foods evaluated by the market surveys ranged from 2 to 133, with prices collected from between 1 and 1230 stores.

The number of participants evaluated by the dietary surveys ranged from 30 to 78 191. Several studies reported prices for multiple food comparisons or from different types of stores and contributed more than one estimate to the analysis.