dataesr/scientific-paragraphs-categorization huggingface dataset:
29, 64 An updated Kantar food price database created in 2007 was merged with the INCA2 nutrient composition data to generate the illustrative tables and figures for the present review. The US Department of Agriculture's national food price database,  68  which corresponds to the National Health and Nutrition Examination Survey (NHANES) 2001-2002 data, was released in 2008. Other than one update for 2003-2004 NHANES, no further updates have been released. However, that database was sufficient to link national food prices in the United States with the NHANES food recalls  35  and with food frequency questionnaires. 33, 35 Other researchers obtained prices from the National Food Survey in the United Kingdom  26  or the Department of Trade and Tourism in Spain. 31, 32 ood prices were also obtained from supermarkets and the Internet,  37, 65, 67  or directly from a distributor.
66 hen the prices were from supermarkets or the Internet, researchers developed pricing protocols, selecting multiple items or lower-cost foods in familysized packaging. 65, 67 When the prices were drawn from surveys on food expenditures, the average price paid (mean price weighted by the quantities bought) was calculated. 68, 69 For example, the average price of green beans reflected the price of fresh, canned, and frozen green beans of several brands, weighted by the quantities consumed by the population. 29 Supermarket data have been used to assess the energy cost and nutrient content of a freely chosen market basket of foods. 70 n all cases, fresh foods as purchased were then transformed into edible foods as consumed, adjusting for preparation and waste. Since cooked pasta absorbs water, 100 g of cooked pasta costs 3 times less than 100 g of dry pasta.
Lower cost of energy-dense foods Comparative metrics of food cost have been based on energy and nutrient content expressed per 100 g of edible portion. 69 Table  2  shows the energy density (in kcal/ 100 g), the price (E/100g), and the energy cost (E/ 100 kcal) of several foods, as based on the INCA2 nutrient composition food database and 2007 prices of current food items, ranked by decreasing order of energy cost. Potato chips, sweets, and biscuits (cookies) were more expensive in terms of euros per kilogram than were low-energy-density apples, tomatoes, and carrots but were cheaper in terms of euros per 100 kcal. This is because apples, tomatoes, and carrots have a higher water content than do sweets and fats. As noted by Atwater in 1896,  79  water in foods drives energy density because it provides bulk but no energy or nutrients.
These two nutrient profiling models have served as the basis of several quantitative studies on the relation between the nutritional quality of foods and their cost. 30, 42, 69, 83 ultiple studies based on French food databases have shown that higher nutrient density scores were directly linked to higher per-calorie cost. 30, 69 In general, the more nutrient-rich foods were more expensive per kilocalorie than were foods of lower nutritional value. 86 owever, there was a wide variability in nutrient density and cost within food groups. 30 For example, not all vegetables were equally expensive. The question of concern was which vegetables, including juices and soups, provided the most nutrients per unit cost. 87 Nutrient density was measured using the Nutrient Rich Foods Index, and food cost was calculated per 100 g, per 100 kcal, and per serving.
Estimating monetary cost of individual-level diets The ability to estimate diet costs is a prerequisite for studies on the relation between socioeconomic status, diet quality, and health. 88 The collection of such data, however, has been hampered by methodological problems. First, dietary and economic data at the national level tend to be collected by different agencies. In the United States, the NHANES, the basis for many policy decisions, provides no data on food prices or food expenditures. 35 Conversely, surveys of food expenditures collected by the US Bureau of Labor Statistics lack data on food consumption or health and are collected at the level of the household. Studies in nutritional epidemiology that use individual health outcomes require that all dietary variables be at the individual level.
Data collected at different levels of aggregation pose problems for any analyses of diets and health. In particular, household food expenditures are difficult to link to individual health outcomes. One approach to studying the relation between diet quality and diet cost, adopted early by the present authors, was to attach standard food prices or individual food expenditures to dietary intake data. 88 ollowing the design of classic intermethod validity studies,  [89] [90] [91]  Monsivais et al. 92  compared three alternative methods of estimating individual diet costs. First, 4-day food diaries were linked with actual food expenditures as documented by store receipts. Second, 4-day food diaries were linked to local supermarket food prices. Third, food frequency questionnaire data were linked to local food prices.
Attaching supermarket prices to dietary intake data allowed the calculation of the intrinsic monetary cost of each study participant's diet. 92 All three estimates of diet cost were significantly linked to household incomes. 92 Another study also compared the estimated diet cost with either true expenditures (grocery receipts) or a vector of standard food prices. 78 The results showed that the mean diet cost estimates for the whole population were similar with the two methods, but that at the individual level, estimates differed by as much as £3. 00/d. 78 Another study also found relatively weak agreement between diet costs estimated with standard prices and with prices actually paid by individuals. 93 lthough the expenditure-based method might be thought to be the true indication of diet cost, it had no parallel in nutritional epidemiology studies.
Dietary surveys do not estimate the nutrient value of each food as consumed by each study participant. Instead, researchers have estimated individual energy and nutrient intakes on the basis of standard nutrient composition tables. For example, the vitamin C content of fresh, raw apples, as consumed by NHANES participants, has been set at 4. 6 mg per 100 g of edible portion in the Food and Nutrient Database for Dietary Studies nutrient composition database. 94 That estimate may or may not accurately reflect the vitamin C content of the apples that the NHANES participants had actually consumed. In the studies described below, a cost vector (i. e. , standard food prices) was inserted into the nutrient composition database. The key to modeling individual diet costs is to treat the monetary cost variable as analogous to a nutrient variable.
However, the exact same caveats apply to the average content of calories, fiber, vitamins, and minerals of foods. Nutrient composition data sets have been based on many of the same assumptions. In general, researchers have treated food prices per 100 g of edible portion as equivalent to a nutrient composition vector and have integrated food prices into the nutrient composition database. That nutrient composition database was then used to estimate individual-level energy and nutrient intakes and individual-level diet cost on the basis of dietary intake data obtained using different methods. 8] 103  However, standard prices were also attached to data from diet records,  29, 64  dietary history,  27, 28  or 24-hour recalls. 15, 66  healthier diets cost more? The earliest studies on the higher cost of healthier diets were conducted in Australia and the United Kingdom.
37, 95 ietary energy density  27  has also been used as a measure of diet quality. Attaching 50 food prices to the dietary survey data from the Val de Marne study  27, 28  showed that energy-dense diets were associated with lower diet costs. 27 By contrast, higher-quality diets were associated with higher energy-adjusted diet costs. Similar results were obtained using the nationally representative INCA1 dietary survey and the retail prices for over 800 foods in France, based on purchase data from Secodip, a nationally representative panel of consumers. 29, 64 Importantly, lower dietary energy density and higher nutrient content were each independently associated with higher energy-adjusted diet costs. 64 When dietary intakes data from two different populations in Spain were examined, the same direct correlation between diet quality and cost was found.
27, 28 These food groups account for a far smaller proportion of energy intakes. By contrast, starches, added fats, and fatty, sweet, and/or salty products provided a large proportion of energy, while contributing relatively little to the total cost of food. 30 The consumption of milk and milk products was cost neutral. 28 On the basis of the French INCA1 survey, the food group of meat/eggs/fish contributed only 18% of daily calories, while accounting for 35% of diet cost. Fruits and vegetables provided 8% of calories, while accounting for 17% of cost. Starches and added fats contributed 23% and 10% of the total energy intake but only 9% and 2% of the total cost, respectively. The energy contribution of dairy products was equivalent to their cost, around 11%. 30  Figure  3 , based on the French INCA2 nutrient composition food database and 2007 food prices, indicates the mean and median cost of energy provided by foods in each of the major food groups (E/100 kcal).
123 In Australia, adhering to the national food guidelines would take 40% of income of the poor, defined as those receiving a living allowance from the state, even with a market basket of cheapest or private-label products. 124 A 25% budget share was considered acceptable,  124  with fruit and vegetables accounting for the largest share of the food budget. 124, 126 However, in many studies, the definition of a balanced diet was based on experts' guidelines, and the methodology for defining a basket was not described clearly enough to be reproduced. This constitutes a significant limitation. In France, linear programming modeling was used to generate low-cost nutritionally adequate diets. 113 tarting with a dietary intakes database, nutrient adequacy standards, and a list of foods of known nutritional composition and prices, linear programming modeling created food patterns that met nutritional guidelines as well as social acceptability constraints, all at the lowest possible price.
The linear programming model was first applied to the Val de Marne data, using 50 food items,  127  and later to the INCA1 database, using 614 food items. 113 The same theoretical minimal price for a nutritionally adequate diet was obtained from both sets of data, i. e. , E3. 20/d for women and E3. 40/d for men. This amount was about 2 times lower than the average daily food expenditure in France (E6. 5/d) but was close to the food budget of people living in poverty. 46 ased on the above analyses, the minimum food budget for a nutritionally adequate diet was estimated at E3. 5/d. However, that amount imposed significant practical limitations. Even on E3. 5/d, the theoretical access to an adequate diet meant zero food waste, drinking water from the tap, knowing how to cook, and not eating out at all.
Nutrient quality was Figure  4  Foods with a good quality-to-price ratio in the French food database (n 5 1117). Abbreviations: SAIN/LIM, Score of Nutritional Adequacy of Individual foods / score of nutrients to be LIMited significantly higher for the national brands, which were also considerably more expensive (by a factor of 2. 5). Additional studies in France conducted by the Observatoire de la Qualitede l'Alimentation used food labels and price data to examine differences in nutrient composition by sale price. 145 They confirmed that there was no systematic association between sale price and the fat, salt, or sugar content of food products of a particular type (e. g. , corn flakes). Such price variability within the same narrow food category may explain why weak agreement was found between diets cost estimated with standard prices and those estimated with actual purchase prices.
112 The second condition was to select foods with the highest nutrient-to-price ratio. 42 Those population subgroups whose higher-quality diets were not associated with higher diet costs  33, 106  may well have made those selections. For example, Hispanic adults in the United States had higher-quality but lower-cost diets as compared with non-Hispanic adults. 35, 106 This was likely due to higher consumption of foods with a good ratio of nutritional quality to price. 42, 83, 154 nking food prices and diet quality with socioeconomic status The literature shows that more-expensive and higherquality diets were consumed by higher-socioeconomic status groups with better health outcomes. In the United States, food-insecure households had lower estimated diet costs, expressed in dollars per day and in dollars per 2000 kcal, based on analyses of the 2001-2002 NHANES database.
Median (in gray) and mean (in black) cost of energy provided by foods in each main food group in the French food database 2. 5 2. 27 2. 25 2 €/100 kcal 1. 5 1. 75 1. 61 Energy cost, 0. 75 1 1. 25 0. 82 0. 64 0. 51 0. 5 0. 38 0. 32 0. 36 0. 32 0. 25 0. 22 0. 14 0. 19 0. 06 0. 07 0 Fruit & Meat/eggs/fish Mixed dishes Dairy products High sugar/fat Starches (n=81) Fat (n=67) Vegetables (n=234) (n=157) (n=172) products (n=203) (n=203) Figure 3.
Search strategy As our review is focused on health benchmarking, we operated our search from the PubMed database only and did not expand it to databases containing non-health journals. We initially searched for articles using the following keywords: [benchmark*] AND ([health facilit*] OR [Hospital*]). We undertook a subsequent search using the following keywords [Benchmark*] AND [Europe*] OR [international] in order to include European and International Benchmarking projects. Through snowballing, we included relevant articles found in the references. After enlisting the projects mentioned in the articles, we searched for grey literature related to those projects using the website of the benchmarking organisations listed or through a general Internet search.
Data collection For each benchmarking project, we collected general information about the project, analytical data and indicators used in those projects. The general information was extracted in order to be able to draw a general picture of the benchmarking projects and be able to describe them. It included the following: The data extraction for each study was carried out by one author (F. T. ) after the data extraction methodology was tested among a sample of six studies by two authors (F. T. , M. S. ). The data extraction forms can be found in Appendix 1 in supplementary material.
Indicators used in benchmarking projects According to Donabedian,  58  indicators can be classified into three categories: structure indicators (measuring all factors that affect the context in which the health care is delivered), process indicators (the sum of actions that make health care) and outcome indicators (effects of health care on patients or population). Most of the projects use a mix of process, structure and outcome indicators (N = 6) or a mix of process and outcomes (N = 9). Four projects use process indicators only, two used a mix of process and structure indicators and two used outcome indicators only. Two projects used unusual indicators: one about Hospital Information System and one about clinical productivity. One benchmarking project (National Oncology Practice) used two levels of indicators with 'core' data and 'additional' data.
Success factors and threats linked to indicators or data collection One recurring issue from the benchmarking projects concerns the crucial importance of the complexity and amount of data. In the NCDAH project, while most participants agreed that the feedback report contained the right amount of information, some participants felt that the data were too complex and the reports contained too much information. Participants in the BELIEVE benchmarking project felt the burden of participating in the project was too heavy or not properly evaluated beforehand. An evaluation of the PATH project reported that there was a major agreement that the burden of data collection was too important for the following indicators: prophylactic antibiotic use, surgical theatre use, training expenditure and absenteeism.
In addition to a data collection burden, definition and methodology is of crucial importance. The feedback of the PATH project reported major disagreements regarding the definition of three indicators: training expenditure, health promotion budget and patient-centredness. Those indicators were later abandoned for the project. 31, 30 Participants in the BELIEVE project also reported difficulties in interpreting the questions (that were resolved during training sessions). Adjusting indicators for diagnostic-related groups was mentioned as one success factor of the Benchmarking project in Lombardy. 16 Indeed, this adjustment allowed for fairer comparison and enabled to identify the areas that need improvement the most. Using a combination of process and outcome indicators, rather than outcome only measures was considered as beneficial.
The advantage of process indicators over outcome indicators is that they reflect true variations in care delivery, while outcome indicators can be influenced by other factors. 57 Including process indicators in the benchmarking projects allows us to identify the remedial actions. This finding is similar to one of the conclusions related to the CALNOC project: outcome measures include near-misses, which allows us to correct the system. 43 ifferent projects had different policies regarding public release of data. Two projects (the National Practice Benchmark project and the NCDAH project) released only anonymised data or average results, or no data at all, even for the project participants. Two projects disclosed nominative data but only for the benchmarking participants while releasing only anonymised data to the public (CALNOC project and Australia's mental health project).
Background: Negative-pressure wound therapy (NPWT) has been marketed for about 20 years and remains popular. The only real obstacle to NPWT is the cost; therefore, we designed an inexpensive NPWT connected to a wall vacuum. Here, we report the feasibility and safety of this product, which we call PROVACUUM (Z-Biotech, Saint-Avertin, France). Methods: As a first step, the constraints imposed on the manufacturer were equipment quality similar to that of commercial NPWT systems, with an average treatment cost of $15/d. Then, we conducted a prospective study of patients with indications for NPWT from September 2013 to January 2015. Data collected included ease of use, quality of materials, and occurrence of complications during treatment. Results: We enrolled 23 patients with a mean age of 50.
com or available at http://links. lww. com/PRSGO/A106). The dressings were changed every 3-4 days. Data collected included ease of use, quality of materials, and occurrence of complications during treatment. Pain was evaluated at each stage using a visual analogue scale. To monitor bleeding and infection, blood count and C-reactive protein were measured twice weekly. All of the patients consented to participate in this study, which had institutional review board approval.
Slurry explosives and nonel detonators were used for blasting. The spacing and burden in overburden/ore benches varied from 5 m to 6 m. The study mines have the same occupational, safety, and health practices. The study protocol included: (1) face-to-face interview using a questionnaire to record personal factors including operator's age, weight, height, and drilling experience; (2) collection of drill machine-related data (manufacturer, age of drill machine, height of seat and seat rest, and thickness of seat pad) from mine office; (3) collection of rock samples from drilling sites and laboratory test for the hardness, uniaxial compressive strength, and density of rock; and (4) measurement of WBV exposure.
Background: The tempos framework provides GPs with a fl exible and practical guide to refl ect on their organization and practices in the analysis of adverse events and supplement existing classifi cation systems. The tempos framework specifi es fi ve timescales that need to be managed by physicians: the disease ' s tempo (unexpected rapid changes, slow reaction to treatment); the offi ce ' s tempo (day-to-day agenda and interruptions); the patient ' s tempo (time to express symptoms, compliance, and emotion); the system ' s tempo (time for appointments, exams, and feedback); and the time to access to knowledge. Objective: This paper reviews the tempos framework and two studies that underpin its conceptual development. Methods: Two databases were used. Results: The use of the framework as a mechanism for analysing insurance claims is described.
Database of malpractice claims Two studies have so far been conducted on the emerging concept of ' tempos '. The material used for the two studies are malpractice claims from one major French liability insurer (MACSF). This insurance company provides cover to about 60% of all French GPs. The authors used synthesis reports written by the medical staff of this insurance company. These reports are two-to-six pages long. They provide a detailed update of the medical history, exams, medical strategies, evolutions, and complications. A fi nal section summarizes the results of external expertise by independent doctors (one or two, depending on the case) and indicates legal directions for the defence.
Position of the tempos framework It is obvious that the tempos framework needs to be elaborated to optimize its potential. The framework of tempos was only tested in two studies in France, and further studies are needed for further development and validation. However, the analysis of adverse events on the basis of the tempos framework appears to provide immediate feedback to the practice, whereas other classifi cation systems may be more adapted for aggregating all causes of adverse events for the purpose of a regional or national database leading to regional or national analysis and policies.
For example, although France adopted the principle in 1994, the ANRS-PRI 2 DE survey (2009-2010), which measured the level of adherence to national and WHO/ UNODC guidelines, revealed a gap between the official recommendations and their application in French prisons  [6]. Data on health policy implementation in general in the prison setting are sparse, and conducting research to explore related issues is difficult  [7] [8] [9]. The main objective of this international survey was to compare the levels of environmental infectious risk in prisoners between 4 different countries, by measuring to what extent prisons in these countries adhere to international recommendations for HIV prevention. This measurement was achieved by computing an adherence score which was modified to take into account the latest international recommendations.
Data collection A nationwide survey targeting all prisons in each of the following 4 European countries was conducted between June 2013 and April 2014: Belgium  (35 prisons) , Austria (28 prisons), Denmark (50 prisons) and Italy (205 prisons). A previous survey in France was conducted between November 2009 and May 2010 and targeted all French prisons (170 prisons). In order to estimate "environmental" infectious risk, an electronic/postal survey questionnaire focusing primarily on the availability of HIV-HCV preventive measures and HBV vaccination for prisoners was sent to the heads of medical units in all the prisons in all 5 countries (in 2009-2010 for France, in 2013-2014 for the other 4 countries). More specifically, the questionnaire collected data about the structural characteristics of the prisons and assessed the availability of different specific prevention measures.