Additional file 2 of Validation of retail food outlet data from a Danish government inspection database

Additional file 2. List of search terms and characteristics for identification, location and classification of food outlets in the Smiley Register; Search terms are mainly given for the moderate definitions. Further, search terms are given for coffee shops that are included in the broad definitions of restaurants and fast food

Additional file 1 of Validation of retail food outlet data from a Danish government inspection database

Additional file 1. Map displaying the Capital region of Denmark (excluding the island Bornholm) (within dotted lines) illustrating the geographical distribution of the randomly selected grid cells (black boxes) for the ground-truthing. Each cell is 250x250m and contain at least one type of food outlet. 336 grids were selected; of these 3 were mistakenly placed outside the Capital region, while 4 grids were placed at an amusement park (i.e. not accessible to the greater public). These were discarded leaving 329 grids. Additionally, 32 grids were selected as being “empty” according the Smiley Register 2021. Map created in ArcGIS PRO

Additional file 3 of Validation of retail food outlet data from a Danish government inspection database

Additional file 3. The classification tool behind the field survey applied during ground-truthing; By completing the survey, each food outlet is geographically located and automatically classified into type (white boxes) based on the combination of answers. The white boxes comprise the five most common food outlets classifications used in the literature i.e. fast food, restaurants, convenience stores, supermarkets, fruit and vegetable stores [16]. Light grey boxes supply information needed for the subsequent partitioning of each classification into three definitions; narrow, moderate and broad with inspiration from Wilkins et. al (2019a)

Additional file 3 of Validation of retail food outlet data from a Danish government inspection database

Additional file 3. The classification tool behind the field survey applied during ground-truthing; By completing the survey, each food outlet is geographically located and automatically classified into type (white boxes) based on the combination of answers. The white boxes comprise the five most common food outlets classifications used in the literature i.e. fast food, restaurants, convenience stores, supermarkets, fruit and vegetable stores [16]. Light grey boxes supply information needed for the subsequent partitioning of each classification into three definitions; narrow, moderate and broad with inspiration from Wilkins et. al (2019a)

Additional file 1 of Validation of retail food outlet data from a Danish government inspection database

Additional file 1. Map displaying the Capital region of Denmark (excluding the island Bornholm) (within dotted lines) illustrating the geographical distribution of the randomly selected grid cells (black boxes) for the ground-truthing. Each cell is 250x250m and contain at least one type of food outlet. 336 grids were selected; of these 3 were mistakenly placed outside the Capital region, while 4 grids were placed at an amusement park (i.e. not accessible to the greater public). These were discarded leaving 329 grids. Additionally, 32 grids were selected as being “empty” according the Smiley Register 2021. Map created in ArcGIS PRO

Additional file 2: of Perceived stress as a risk factor of unemployment: a register-based cohort study

Table S2. Hazard ratios (HR) and 95% confidence intervals (CI) of unemployment by perceived everyday life stress quintiles. Unadjusted (model 1) and adjusted for gender, age, education level, income level, smoking, BMI, alcohol consumption and self-rated health (model 2). Complete cases (N = 8046). (DOCX 14 kb

Additional file 2 of Validation of retail food outlet data from a Danish government inspection database

Additional file 2. List of search terms and characteristics for identification, location and classification of food outlets in the Smiley Register; Search terms are mainly given for the moderate definitions. Further, search terms are given for coffee shops that are included in the broad definitions of restaurants and fast food

Validation of retail food outlet data from a Danish government inspection database

Abstract Background Globally, unhealthy diet is one of the leading global risks to health, thus it is central to consider aspects of the food environment that are modifiable and may enable healthy eating. Food retail data can be used to present and facilitate analyses of food environments that in turn may direct strategies towards improving dietary patterns among populations. Though food retail data are available in many countries, their completeness and accuracy differ. Methods We applied a systematically name-based procedure combined with a manual procedure on Danish administrative food retailer data (i.e. the Smiley register) to identify, locate and classify food outlets. Food outlets were classified into the most commonly used classifications (i.e. fast food, restaurants, convenience stores, supermarkets, fruit and vegetable stores and miscellaneous) each divided into three commonly used definitions; narrow, moderate and broad. Classifications were based on branch code, name, and/or information on the internal and external appearance of the food outlet. From ground-truthing we validated the information in the register for its sensitivity and positive predictive value. Results In 361 randomly selected areas of the Capital region of Denmark we identified a total of 1887 food outlets compared with 1861 identified in the register. We obtained a sensitivity of 0.75 and a positive predictive value of 0.76. Across classifications, the positive predictive values varied with highest values for the moderate and broad definitions of fast food, convenience stores and supermarkets (ranging from 0.89 to 0.97). Conclusion Information from the Smiley Register is considered to be representative to the Danish food environment and may be used for future research

Validation of retail food outlet data from a Danish government inspection database

Abstract Background Globally, unhealthy diet is one of the leading global risks to health, thus it is central to consider aspects of the food environment that are modifiable and may enable healthy eating. Food retail data can be used to present and facilitate analyses of food environments that in turn may direct strategies towards improving dietary patterns among populations. Though food retail data are available in many countries, their completeness and accuracy differ. Methods We applied a systematically name-based procedure combined with a manual procedure on Danish administrative food retailer data (i.e. the Smiley register) to identify, locate and classify food outlets. Food outlets were classified into the most commonly used classifications (i.e. fast food, restaurants, convenience stores, supermarkets, fruit and vegetable stores and miscellaneous) each divided into three commonly used definitions; narrow, moderate and broad. Classifications were based on branch code, name, and/or information on the internal and external appearance of the food outlet. From ground-truthing we validated the information in the register for its sensitivity and positive predictive value. Results In 361 randomly selected areas of the Capital region of Denmark we identified a total of 1887 food outlets compared with 1861 identified in the register. We obtained a sensitivity of 0.75 and a positive predictive value of 0.76. Across classifications, the positive predictive values varied with highest values for the moderate and broad definitions of fast food, convenience stores and supermarkets (ranging from 0.89 to 0.97). Conclusion Information from the Smiley Register is considered to be representative to the Danish food environment and may be used for future research

Social capital and frequent attenders in general practice: a register-based cohort study

Abstract Background Frequent attendance to primary care constitutes a large use of resources for the health care system. The association between frequent attendance and illness-related factors has been examined in several studies, but little is known about the association between frequent attendance and individual social capital. The aim of this study is to explore this association. Methods The analysis is conducted on responders to the North Denmark Region Health Profile 2010 (n = 23,384), individually linked with information from administrative registers. Social capital is operationalized at the individual level, and includes cognitive (interpersonal trust and norms of reciprocity) as well as structural (social network and civic engagement) dimensions. Frequent attendance is defined as the upper-quartile of the total number of measured consultations with a general practitioner over a period of 148 weeks. Results Using multiple logistic regression, we found that frequent attendance was associated with a lower score in interpersonal trust [OR 0.86 (0.79–0.94)] and social network [OR 0.88 (0.79–0.98)] for women, when adjusted for age, education, income and SF12 health scores. Norms of reciprocity and civic engagement were not significantly associated with frequent attendance for women [OR 1.05 (0.99–1.11) and OR 1.01 (0.92–1.11) respectively]. None of the associations were statistically significant for men. Conclusion This study suggests that for women, some aspects of social capital are associated with frequent attendance in general practice, and the statistically significant dimensions belonged to both cognitive and structural aspects of social capital. This association was not seen for men. This indicates a multifaceted and heterogeneous relationship between social capital and frequent attendance among genders