Modern lifestyles and time scarcity have contributed to an increase in food consumption away from home, and the increasing trend is likely to continueReference Kant and Graubard1–Reference Jabs and Devine7. The energy and nutrient intakes of individuals who frequently eat at locations other than the household premises (such as restaurants, canteens, cafeterias, fast-food restaurants and similar establishments) may differ from those of individuals who generally eat at homeReference Roos, Sarlio-Lahteenkorva and Lallukka8. There have been several studies in the USA and Australia focusing on changes in food and energy intakes related to eating locationsReference Kant and Graubard1–Reference Shrapnel10, but there is a paucity of such studies in European countriesReference Kearney, Hulshof and Gibney11.
An additional limitation of the available literature is the lack of a common definition of the eating out of home concept. In general, two main definitions have been used: (1) all food items sourced from external eating locations, irrespective of place of consumption; and (2) all food items consumed at external locations, regardless of whether they were prepared in or outside the home. The use of a common definition would allow direct comparisons of results and would facilitate the formulation of public health policies with the aim to encourage consumers in making healthier dietary choices when eating outReference Burns, Jackson, Gibbons and Stoney9.
The objectives of the present study were to assess and compare the average out-of-home (OH) consumption of major foods and beverages, as well as energy intake, among populations from 10 European countries participating in the European Prospective Investigation into Cancer and Nutrition (EPIC) study and to describe the characteristics of those frequently eating out of home in comparison to other individuals.
Subjects and methods
EPIC is a large prospective cohort study, encompassing about half a million individuals from 10 western European countries, aiming to elucidate the role of dietary, biological, lifestyle and environmental factors in the aetiology of cancer and other chronic diseases. All procedures have been in accordance with the Helsinki Declaration and all participants signed an informed consent form before enrolment. In most centres (but not in all, e.g. Norway, and with harmonised but not identical protocols), the baseline examination, on the day of enrolment, included the completion of detailed dietary, medical history and lifestyle questionnaires, the measurements of anthropometric characteristics and arterial blood pressure and the collection of blood samples. Details on the design and methods of the EPIC study have been presented elsewhereReference Riboli and Kaaks12.
In order to adjust for possible systematic over- or underestimation in dietary intake measurements and to correct for attenuation bias in relative risk estimates, a calibration process was utilised. Thus, a single 24-hour dietary recall (24-HDR) was collected from a random sample of 5–12% of each EPIC cohort, weighted according to the cumulative numbers of cancer cases expected per fixed age and sex stratumReference Slimani, Kaaks, Ferrari, Casagrnde, Clavel-Chapelon and Lotze13.
In total, 36 894 individuals from the participating countries provided one 24-HDR between 1995 (study initiated in France) and 2000 (study completed in Norway)Reference Slimani, Kaaks, Ferrari, Casagrnde, Clavel-Chapelon and Lotze13. There were several centres in some of the countries, but these were grouped together by country with a single exception: in the Oxford centre (UK), a group of individuals following vegetarian/vegan or other types of presumably healthy diets was evaluated separately (‘health-conscious’ as contrasted to the ‘general population’). In order to maintain the same age range in all the EPIC cohorts, subjects below 35 and over 74 years of age were excluded from the datasets (944 individuals). Of the remaining 35 950 participants, 1680 were excluded because of missing information on one or more of the variables of interest in the analyses. Thus, 34 270 eligible individuals from 10 European countries, 12 537 men and 21 733 women, were included in the present study. The French and Norwegian cohorts included women only and the sex ratio varied considerably among the remaining studied populations. Study participants by sex are shown in Table 1.
GP – general population; HC – health-conscious.
The consumption of foods and beverages was recorded by a single 24-HDR, using a highly standardised computerised software, named EPIC-SOFT, that was developed at the International Agency for Research on Cancer (IARC) in collaboration with the EPIC centres. EPIC-SOFT was administered by trained interviewers and included a series of functions and logical structures, in order to ensure the highest possible level of standardisation and lessen the difficulty of the respondents to remember what they had consumedReference Slimani, Deharveng, Charrondiere, van Kappel, Ocke and Welch14. Information was collected on all foods and beverages consumed by each individual during the time period between waking up on the day of recall and waking up on the following day (interview day).
For the calculation of energy and nutrient intakes the EPIC Nutrient Database (ENDB) was used. In the absence of an existing standardised European nutrient database and as a prerequisite for pooled analyses on an international scale, the ENDB was developed to harmonise nutrient databases across the countries participating in EPIC. Details on the development of ENDB have been published elsewhereReference Slimani, Deharveng, Unwin, Southgate, Vignat and Skeie15, Reference Charrondière, Vignat, Møller, Ireland, Becker and Church16.
Data on most of the non-dietary variables were collected at baseline and details on their collection have been published elsewhereReference Slimani, Kaaks, Ferrari, Casagrnde, Clavel-Chapelon and Lotze13, Reference Riboli, Hunt, Slimani, Ferrari, Norat and Fahey17, Reference Haftenberger, Schuit, Tormo, Boeing, Wareham and Bueno-de-Mesquita18.
Information on education and physical activity was obtained using a self- or interviewer-administered questionnaireReference Riboli, Hunt, Slimani, Ferrari, Norat and Fahey17. For the purpose of the present analysis, the level of education was classified into four categories: none or primary school completed; technical/vocational school completed; secondary school completed; and university degree. With respect to physical activity, IARC generated two variables: (1) physical activity at work, based on the physical demand of the participant’s current profession and classified into sedentary, standing, manual, heavy manual or none, the latter including all individuals who did not work or were retired (data on this variable were not collected in Norway); and (2) physical activity at leisure, expressed as a score, estimated by the sum of products of the time spent on each of several household and recreational activities and the energy cost coefficient of each activityReference James and Schofield19. Sex- and population group-specific tertiles of the estimated score for physical activity at leisure were then used at IARC, to label physical activity at leisure as minimum, moderate and intense.
With respect to smoking, subjects were classified as: never smokers; former smokers; current smokers of up to 1 pack (20 cigarettes); current smokers of more than 1 pack; and current smokers of unknown number of cigarettes per day.
Anthropometric data were collected both at baseline (measured in most instances) and at the day of the 24-HDR interviews (self-reported). These values, however, were highly correlated (overall Spearman correlation coefficient for height, r = +0.99; and for weight, r = +0.97). In the present analysis, body mass index (BMI), relying on weight and height values reported the day of the 24-HDR interview, was used. BMI was estimated as weight (kg) divided by the square of height (m) and participants were classified into four categories according to definitions of the World Health Organization: underweight (BMI < 18.5 kg m−2), normal (BMI ≥ 18.5 to <25 kg m−2), overweight (BMI ≥ 25 to <30 kg m−2) and obese (BMI ≥ 30 kg m−2)20. However, because of the small number of individuals in the group of underweight (551), the first two groups were merged (BMI < 25 kg m−2).
For each eating (drinking) occasion mentioned in the 24-HDRs, the place of consumption was reported. Locations, other than the household premises, included the following: restaurant, friend’s house, workplace, cafeteria, bar, fast-food establishment, street, car/boat and other out-of-home places. OH eating was defined to include consumption of all foods and beverages at any of the aforementioned locations, irrespective of the place of purchase or preparation. This definition has been used previouslyReference Kearney, Hulshof and Gibney11, Reference Gregory, Foster, Tyler and Wiseman21.
Substantial out-of-home eaters
To identify OH eaters of substantial quantities, the fraction of a particular food or the energy intake during OH eating occasions out of the corresponding total was calculated. Substantial OH eaters of particular food groups were then defined as those individuals receiving at least 25% of their daily intake of the corresponding food group through eating out. However, in the analysis exploring the determinants of substantial OH eating, the critical outcome variable was whether study participants consumed (or not) ≥ 25% of their daily energy intake through eating out.
The information available on substantial OH eaters concerns a single day, that of the 24-HDR. We have no information about the frequency of OH eating over time or about the correlation of the quantity of OH eating between different days of the same person. We have assumed that those who were substantial OH eaters, as operationally defined, on the particular day of the 24-HDR are more likely to be substantial OH eaters in general than those who were not. The correlation of OH eating among different days of the same person is likely to be positive but weak, thus entailing considerable misclassification and underestimation of the statistical significance of any association with this variable.
All statistical analyses were performed separately for men and women at the country level (except for Table 4), as well as overall, using the statistical package Intercooled Stata 7.0 for Windows 98/95/NT (Stata Corporation, 2002). The food categories included in this analysis were potatoes and other tubers (subsequently referred to as ‘potatoes’), vegetables/legumes (subsequently referred to as ‘vegetables’), fruits/nuts (subsequently referred to as ‘fruits’), dairy products (subsequently referred to as ‘dairies’), cereals/cereal products (subsequently referred to as ‘cereals’), meat/meat products (subsequently referred to as ‘meats’), fish/shellfish (subsequently referred to as ‘fish’), added lipids, sweets (including sugar, confectionery and cakes), non-alcoholic beverages (distinguished into ‘coffee/tea/waters’ and ‘other’, essentially soft drinks, which can be sweetened), alcoholic beverages and sauces/condiments (subsequently referred to as ‘sauces’). A detailed description of the food items/groups included in the aforementioned main food categories is given in Table 2. In order to maximise comparability between countries, population mean intakes (overall, at home and out of home) and corresponding standard errors were calculated adjusting for age and using a set of weights to control for the day (Monday to Thursday, Friday to Sunday) and season (spring, summer, autumn, winter) of the 24-HDRs. The detailed methodology has been described previouslyReference Slimani, Kaaks, Ferrari, Casagrnde, Clavel-Chapelon and Lotze13, Reference Agudo, Slimani, Ocké, Naska, Miller and Kroke22.
The odds of being a substantial OH eater (on the basis of total energy intake) in comparison to the odds of not being one were estimated separately for men and women through multiple logistic regression analyses using the following as mutually adjusted possible determinants: the aforementioned non-dietary variables (education, occupational and leisure physical activity, smoking habits and BMI; categorically as previously indicated), age (per 5-year increment; continuously), day of recall (Monday to Thursday, Friday to Sunday; categorically), season of recall (spring, summer, autumn, winter; categorically) and total energy intake (per standard deviation; continuously). Interactions were assessed, when necessary, using the likelihood ratio testReference Lutkepohl23.
Table 3 shows daily intakes of major food groups, as well as energy intake at home and out of home, among men and women by country. Means adjusted for age, season and day of the week differed little from the crude mean values.
M – males; F – females; GP – general population; HC – health-conscious.
*Sweets included sugar, confectionery and cakes.
† Means adjusted for age, season and day of the week.
‡ Intakes within the household premises.
§ Intakes at places other than the household (e.g. restaurant, the workplace, cafeteria).
On average, the contribution of OH eating to the total energy intake of the EPIC participants ranged among men from 12% in the health-conscious group in the UK to 28% in Denmark and among women from 11% in the UK health-conscious group to 28% again in Denmark. In general, the highest mean values of OH consumption were recorded in the Scandinavian populations for both men and women. Men reported consuming higher fractions of their total energy intake out of home than did women, except in Sweden and the health-conscious group in the UK where they were similar. Separate tables for men and women (including overall consumption and standard errors) are available online (www.nut.uoa.gr).
We have calculated the fraction (%) of (1) energy and (2) quantity contributed by the various food groups to respectively total energy and total quantity consumed out of home and at home, and then we divided these fractions to obtain a ratio. Table 4 presents the results. In this table, ratios below 1 indicate that the particular food is, in proportional terms, less frequently consumed out of home than in home. Clearly these ratios vary across centres, genders and age groups and the data shown are crude overall averages. In terms of both energy and quantity, sweets, coffee and tea (on account of the added sugar), alcoholic beverages and other non-alcoholic beverages tend to be over-consumed out of home.
SD – standard deviation.
* Intakes at places other than the household premises (e.g. restaurant, the workplace, cafeteria).
† Intakes within the household premises
‡ Sweets included sugar, confectionery and cakes.
We have operationally defined as substantial OH eaters those who consumed more than one-quarter of their respective food group (or total energy) out of home on the day of the recall. It is assumed that this variable correlates, although weakly, with OH eating in general. Table 5 shows, separately for men and women, the proportion of substantial OH eaters, as operationally defined, out of the total sample (including those who have not consumed the respective food group), by country. Focusing on total energy intake, the proportion of substantial OH eaters was generally relatively low in the Mediterranean countries and the health-conscious men in the UK and generally relatively high in the Scandinavian countries. Among the food groups, those selectively consumed out of home were coffee/tea/waters and sweets and, to a lesser extent, cereals, meats, added lipids and vegetables. There were, however, variable patterns among countries.
GP – general population; HC – health-conscious; M – males; F – females.
* Substantial out-of-home eaters were defined as those who consumed more than 25% of their respective food group or energy intake in out-of-home eating occasions on the day of the recall.
† Sweets included sugar, confectionery and cakes.
Table 6 shows, separately for men and women, the ratios of the odds of being a substantial OH eater with respect to total energy, as operationally defined, vs. the odds of not being one, by specified categories or increments of a series of potentially predictor variables. Odds ratios below the null value of one indicate that the proportion of substantial OH eaters is lower than that in the referent category and vice versa. Judging the patterns in both sexes together, it was evident that substantial OH eating tends to be more common in central and northern European countries and less common in Mediterranean countries and among the health-conscious UK residents, particularly men. Substantial OH eating, as operationally defined, declined consistently with age, among both men and women. Increased energy intake and reduced physical activity (both at work and at leisure) were associated with increased frequency of substantial OH eating, although the group that included retirees and individuals who for any other reason were not working, were rarely substantial OH eaters. No association was evident, however, between self-reported BMI and frequency of substantial OH eating, a weak positive trend among women balancing a weak inverse one among men (P for interaction = 0.193). No clear pattern emerged in relation to smoking among either men or women, whereas a tendency for less frequent OH eating during winter was more evident among women. More educated participants were more frequently OH eaters than less educated ones, but the pattern was not monotonic among men and was inconsistent across centres among women (the pattern being mostly driven by women in Spain and Greece and much less evident in other countries). Overall, the frequency of substantial OH eating, as operationally defined, was lower during weekends than during weekdays. However, there was significant heterogeneity among countries in that in Mediterranean countries the frequency of substantial OH eating was higher during the weekends than during weekdays. We repeated this analysis by excluding Friday from the weekend period. The results were qualitatively similar, although in most instances they tended to become more extreme, since eating-out occasions in Friday are for many countries part of the weekly routine, whereas for others (e.g. Mediterranean countries) an opportunity for eating out in the evenings.
SD – standard deviation; GP – general population; HC – health-conscious.
* Substantial out-of-home eaters were defined as those reporting more than 25% of their total energy intake in out-of-home eating occasions on the day of the recall.
† Variables are mutually adjusted.
‡ 1705 Norwegian women are excluded since there were no available data on physical activity at work.
In a large study, 34 270 adults of both sexes from 10 European countries provided a 24-HDR through a computerised and highly standardised interview. The analysis points to patterns of OH eating in Europe. Even though measurements refer to a single day and reflect overall patterns modestly at best, there was evidence that the fraction of energy intake during OH eating was generally higher in northern European countries and generally lower in the southern ones, as well as among the group of health-conscious UK participants. Food groups eaten out of home particularly frequently were coffee/tea/waters and sweets. We have considered as substantial OH eaters those individuals receiving at least 25% of their daily energy intake through eating out, under the assumption that individuals who consumed a small fraction of their daily food out of home on a particular day are less likely to be frequent and/or substantial OH eaters than individuals who reported consuming a relatively large fraction. More men than women belonged in this category. Moreover, we found that young age, sedentary lifestyle and increased energy intake were positive predictors of the probability of substantial OH eating, among both men and women throughout Europe. Substantial OH eating, as operationally defined, was less frequent during winter and among the less educated, who were also, as a rule, less well off24. Substantial OH eating was less common during the weekends than in weekdays in western and northern Europe, but more common in southern European countries.
Our results concerning the foods preferentially eaten away from home (including coffee/tea/waters and sweets) are generally similar to those reported previouslyReference Burns, Jackson, Gibbons and Stoney9, Reference Harnack, Jeffery and Boutelle25–Reference French, Harnack and Jeffery28, although in some studies in the USA and Australia (but not in our investigation) potatoes were also identified as foods preferred when eating outReference Burns, Jackson, Gibbons and Stoney9, Reference Harnack, Jeffery and Boutelle25, Reference French, Harnack and Jeffery28. The inverse association of substantial OH eating with age has also been reported beforeReference Kant and Graubard1, Reference Nielsen, Siega-Riz and Popkin5, Reference Burns, Jackson, Gibbons and Stoney9, Reference Kearney, Hulshof and Gibney11, Reference French, Harnack and Jeffery28, although the existing data in all studies, including ours, do not allow the distinction between an age-related and an evolving cohort-dependent phenomenon. With respect to OH eating and educational status, the data in our study, as well as in the literature at largeReference Kant and Graubard1, Reference Haines, Hungerford, Popkin and Guilkey29, are not consistent, possibly because changes in lifestyle have different velocities in different countries and sociodemographic groups.
Increased prevalence of obesity has been linked by several investigators to increased frequency of OH eating, under the assumption that diets consumed away from home are more energy-richReference Bell and Swinburn26, Reference Nielsen, Siega-Riz and Popkin30. Our findings and those of othersReference Kant and Graubard1, Reference Nielsen, Siega-Riz and Popkin5, Reference Burns, Jackson, Gibbons and Stoney9, Reference French, Harnack and Jeffery28, Reference Clemens, Slawson and Klesges31–Reference Jeffery and French33 that OH eating is associated with increased energy intake support this view. Also supportive is our finding of an inverse association between physical activity and substantial OH eating, as operationally defined, even though this finding is not consistent in the literatureReference French, Harnack and Jeffery28, Reference Jeffery and French33. However, we were unable to document an association between BMI and OH eating in our investigation, a finding that does not contradict the collective evidence from the literature, since positive associations have generally been found among adolescents and young adultsReference Kant and Graubard1, Reference Bell and Swinburn26, Reference Nielsen, Siega-Riz and Popkin30, Reference Jahns, Siega-Riz and Popkin34–Reference Taveras, Berkey, Rifas-Shiman, Ludwig, Rockett and Field37 and only rarely among older individualsReference French, Harnack and Jeffery28, Reference McCrory, Fuss, Hays, Vinken, Greenberg and Roberts32, Reference Binkley, Eales and Jekanowski38, Reference Ma, Bertone, Stanek, Reed, Hebert and Cohen39. It is possible that OH eating, as operationally defined, is poorly associated with general OH eating, but we are unable to correct for this misclassification because repeated daily measurements for the same individual were not available. It is also possible that weight and height were incorrectly reported in our investigation and the resulting misclassification attenuates a possible positive association. Another explanation is that OH eating and the possibly associated increased BMI is a developing phenomenon, which is not adequately captured in a cross-sectional investigation. Finally, it is not possible to exclude that overweight individuals selectively underreport OH snacking or eating, in an attempt to claim adherence to what are generally perceived as healthier dietary choicesReference Lafay, Basdevant, Charles, Vray, Balkau and Borys40, Reference Heitmann, Lissner and Osler41.
The strengths of this investigation are the large sample size, the coverage of several countries with harmonised protocols, and the investigation of several variables with potential predictive importance. A major limitation of our study, shared by all cross-sectional investigations, is that causal associations have to be inferred, rather than documented, in the absence of demonstrable time sequences. Relying on a single 24-HDR (a prevalence entity) rather than patterns of OH eating (the more appropriate cumulative incidence entity) is also a limitation. The availability of only one 24-HDR has more serious consequences whenever the intra-individual variability is large compared with the inter-individual variability. Thus, associations may be underestimated, but it is unlikely that significant results would be generated when in reality these do not existReference Armitage and Berry42–Reference Willett and Willett44. Moreover, mean values cannot be affected by intra-individual random or systematic misclassification, although the corresponding standard deviations (and standard errors) will tend to increase with the degree of random misclassificationReference Willett and Willett43–Reference Buzzard and Willett45. An additional, but probably minor, limitation is the arbitrariness in operationally defining as substantial OH eaters those receiving at least 25% of their daily energy intake through eating out. This arbitrariness could affect the odds ratio estimates, but it is unlikely that it would have generated quantitatively contradictory results if the underlying pattern is monotonic as the empirical evidence suggests it is. Another limitation is that the study population is relatively old and unequally distributed across centres; although controlling for age in the analyses preserves internal validity, the generalisibility, particularly to very young persons, is questionable. Other limitations are the self-reporting of weight and height in the determination of BMI (although these variables are generally correctly reported), comparison of data collected over a 5-year period against the background of an increasing secular trend of OH eating, and the lack of temporal correspondence between 24-HDRs and some of the evaluated predictor variables. The collective impact of these limitations is likely to be an underestimation of the reported associations.
These arguments rely on the assumption that intra-individual variability of reported intakes is random, an assumption that may not always apply with respect to particular foodsReference Ferrari, Slimani, Ciampi, Trichopoulou, Naska and Lauria46. The focus of our investigation, however, is on eating at home or eating out of home – a situation in which systematic errors, particularly when averaged over several individuals, are likely to be less important. Finally, our analyses relied on country-specific samples that were not representative of the corresponding general populations. However, unless the selection factors were strongly associated with eating out of home in ways not explained by the control variables already included in Table 6 (sex, age, education, physical activity, smoking, etc.) distortions are unlikely to be substantial.
In conclusion, we have investigated the pattern of OH eating in 10 European countries and found evidence that it is associated with sedentary lifestyle and increased energy intake. Eating out of home is particularly common among the young and concerns several food groups, but particularly coffee/tea/waters and sweets. To our knowledge, this is the first European study that compares the frequency and the characteristics of eating out among various European populations. However, additional and preferentially longitudinal work is needed on assessing the relationship of obesity, physical activity or other personal characteristics and lifestyle choices with substantial OH eating.
Sources of funding: This study was supported by the ‘Europe against Cancer’ Programme of the European Commission (DG SANCO) for the project EPIC, coordinated by the International Agency for Research on Cancer (World Health Organization); the Greek Ministry of Health and the Greek Ministry of Education (Greece); a fellowship honouring Vasilios and Nafsika Tricha (Greece); the Danish Cancer Society (Denmark); Ligue contre le Cancer (France); Société 3M (France); Mutuelle Générale de l’Education Nationale (France); Institut National de la Santé et de la Recherche Médicale (INSERM) (France); Gustave Roussy Institute and several General Councils in France (France); German Cancer Aid (Germany); German Cancer Research Center (Germany); German Federal Ministry of Education and Research (Germany); Associazione Italiana per la Ricerca sul Cancro (AIRC), Milan (Italy); Associazione Italiana per la Ricerca contro il Cancro (AIRC) in Florence (Italy); Compagnia di San Paolo (Italy); Regione Sicilia, Associazione Italiana Ricerca Cancro and Avis-Ragusa (Italy); the Dutch Ministry of Public Health, Welfare and Sports (The Netherlands); the National Cancer Registry and the Regional Cancer Registries of Amsterdam, Utrecht, East and Maastricht (The Netherlands); Health Research Fund (FIS) of the Spanish Ministry of Health (Spain); the Spanish Regional Governments of Andalucia, Asturias, Basque Country, Murcia and Navarra (Spain); the ISCIII Network Red de Centros RCESP (C03/09) (Spain); Swedish Cancer Society (Sweden); Swedish Scientific Council, City of Malmö (Sweden); the Regional Government of Skåne (Sweden); Cancer Research UK (UK); the Medical Research Council (UK); the Stroke Association (UK); the British Heart Foundation; the Department of Health (UK); the Food Standards Agency (UK); the Wellcome Trust (UK); the Norwegian Cancer Society; and the Norwegian Research Council.
The funding sources had no involvement in the study design, in the collection, analysis and interpretation of data, in the writing of the report and in the decision to submit the paper for publication. The author(s) is (are) solely responsible for the publication and the publication does not represent the opinion of the Community. The Community is not responsible for any use that might be made of data appearing in this work.
Conflict of interest declaration: None of the authors has declared a conflict of interest.
Authorship responsibilities: All authors made substantial contribution to conception, design and interpretation of the data, in critically revising the article for important intellectual content, and in finally approving the version to be published.