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      Exploring absolute and relative measures of exposure to food environments in relation to dietary patterns among European adults

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          Abstract

          Objective

          To explore the associations of absolute and relative measures of exposure to food retailers with dietary patterns, using simpler and more complex measures.

          Design

          Cross-sectional survey.

          Setting

          Urban regions in Belgium, France, Hungary, the Netherlands and the UK.

          Participants

          European adults ( n 4942). Supermarkets and local food shops were classified as ‘food retailers providing healthier options’; fast-food/takeaway restaurants, cafés/bars and convenience/liquor stores as ‘food retailers providing less healthy options’. Simpler exposure measures used were density of healthy and density of less healthy food retailers. More complex exposure measures used were: spatial access (combination of density and proximity) to healthy and less healthy food retailers; density of healthier food retailers relative to all food retailers; and a ratio of spatial access scores to healthier and less healthy food retailers. Outcome measures were a healthy or less healthy dietary pattern derived from a principal component analysis (based on consumption of fruits, vegetables, fish, fast foods, sweets and sweetened beverages).

          Results

          Only the highest density of less healthy food retailers was significantly associated with the less healthy dietary pattern ( β = −129·6; 95 % CI −224·3, −34·8). None of the other absolute density measures nor any of the relative measures of exposures were associated with dietary patterns.

          Conclusions

          More complex measures of exposure to food retailers did not produce stronger associations with dietary patterns. We had some indication that absolute and relative measures of exposure assess different aspects of the food environment. However, given the lack of significant findings, this needs to be further explored.

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          Most cited references30

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          To GEE or not to GEE: comparing population average and mixed models for estimating the associations between neighborhood risk factors and health.

          Two modeling approaches are commonly used to estimate the associations between neighborhood characteristics and individual-level health outcomes in multilevel studies (subjects within neighborhoods). Random effects models (or mixed models) use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. These methods are used in place of basic regression approaches because the health of residents in the same neighborhood may be correlated, thus violating independence assumptions made by traditional regression procedures. This violation is particularly relevant to estimates of the variability of estimates. Though the literature appears to favor the mixed-model approach, little theoretical guidance has been offered to justify this choice. In this paper, we review the assumptions behind the estimates and inference provided by these 2 approaches. We propose a perspective that treats regression models for what they are in most circumstances: reasonable approximations of some true underlying relationship. We argue in general that mixed models involve unverifiable assumptions on the data-generating distribution, which lead to potentially misleading estimates and biased inference. We conclude that the estimation-equation approach of population average models provides a more useful approximation of the truth.
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            Using the outcome for imputation of missing predictor values was preferred.

            Epidemiologic studies commonly estimate associations between predictors (risk factors) and outcome. Most software automatically exclude subjects with missing values. This commonly causes bias because missing values seldom occur completely at random (MCAR) but rather selectively based on other (observed) variables, missing at random (MAR). Multiple imputation (MI) of missing predictor values using all observed information including outcome is advocated to deal with selective missing values. This seems a self-fulfilling prophecy. We tested this hypothesis using data from a study on diagnosis of pulmonary embolism. We selected five predictors of pulmonary embolism without missing values. Their regression coefficients and standard errors (SEs) estimated from the original sample were considered as "true" values. We assigned missing values to these predictors--both MCAR and MAR--and repeated this 1,000 times using simulations. Per simulation we multiple imputed the missing values without and with the outcome, and compared the regression coefficients and SEs to the truth. Regression coefficients based on MI including outcome were close to the truth. MI without outcome yielded very biased--underestimated--coefficients. SEs and coverage of the 90% confidence intervals were not different between MI with and without outcome. Results were the same for MCAR and MAR. For all types of missing values, imputation of missing predictor values using the outcome is preferred over imputation without outcome and is no self-fulfilling prophecy.
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              Eating out of home and its association with dietary intake: a systematic review of the evidence.

              During the last decades, eating out of home (OH) has gained importance in the diets worldwide. We document the nutritional characteristics of eating OH and its associations with energy intake, dietary quality and socioeconomic status. We carried out a systematic review of peer-reviewed studies in eight databases up to 10 March 2011. Of the 7,319 studies retrieved, 29 met the inclusion criteria and were analysed in this review. The quality of the data was assessed and a sensitivity analysis was conducted by isolating nationally representative or large cohort data from 6 and 11 countries, respectively. OH foods were important sources of energy in all age groups and their energy contribution increased in adolescents and young adults. Eating OH was associated with a higher total energy intake, energy contribution from fat in the daily diet and higher socioeconomic status. Two large studies showed how eating OH was also associated with a lower intake of micronutrients, particularly vitamin C, Ca and Fe. Although the studies were cross-sectional and heterogeneous in the way they classified eating OH, we conclude that eating OH is a risk factor for higher energy and fat intake and lower micronutrient intake. © 2011 The Authors. obesity reviews © 2011 International Association for the Study of Obesity.
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                Author and article information

                Journal
                Public Health Nutr
                Public Health Nutr
                PHN
                Public Health Nutrition
                Cambridge University Press (Cambridge, UK )
                1368-9800
                1475-2727
                07 December 2018
                April 2019
                : 22
                : 6
                : 1037-1047
                Affiliations
                [1 ] Amsterdam UMC, Vrije Universiteit Amsterdam , Department of Epidemiology and Biostatistics, Amsterdam Public Health, De Boelelaan 1089a, Amsterdam, The Netherlands
                [2 ] Sorbonne Université , Institute of Cardiometabolism and Nutrition, Department of Nutrition, Paris, France
                [3 ] Equipe de Recherche en Epidémiologie Nutritionnelle (EREN), Centre de Recherche en Epidémiologie et Statistiques , Inserm (U1153), Inra (U1125), Cnam, COMUE Sorbonne Paris Cité, Université Paris 13, Bobigny, France
                [4 ] Université Paris Est , Lab-Urba, UPEC, Créteil, France
                [5 ] Department of Preventive Medicine, Faculty of Public Health, University of Debrecen , Debrecen, Hungary
                [6 ] Department of Social and Policy Sciences, University of Bath , Bath, UK
                [7 ] Department of Movement and Sport Sciences, Faculty of Medicine and Health Sciences, Ghent University , Ghent, Belgium
                [8 ] Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht , Utrecht, The Netherlands
                [9 ] Amsterdam School of Communication Research (ASCoR), University of Amsterdam , Amsterdam, The Netherlands
                [10 ] Faculty of Geosciences, Utrecht University , Utrecht, The Netherlands
                Author notes
                [* ] Corresponding author: Email matiasdepinho@ 123456vumc.nl
                Author information
                https://orcid.org/0000-0003-4928-0851
                https://orcid.org/0000-0002-2783-721X
                Article
                S1368980018003063 00306
                10.1017/S1368980018003063
                6536821
                30523774
                54387750-d895-4833-bb04-8676c37733f4
                © The Authors 2018

                This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 18 March 2018
                : 19 September 2018
                : 02 October 2018
                Page count
                Figures: 1, Tables: 5, Pages: 11
                Categories
                Research Paper
                Nutritional Epidemiology

                Public health
                food environment,exposure,relative/absolute measures,dietary patterns,european adults

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