Comparing Different Residential Neighborhood Definitions and the Association Between Density of Restaurants and Home Cooking Among Dutch Adults

The definition of neighborhoods as areas of exposure to the food environment is a challenge in food environment research. We aimed to test the association of density of restaurants with home cooking using four different definitions of residential neighborhoods. We also tested effect modification by age, length of residency, education, and income. This innovative cross-sectional study was conducted in the Netherlands (N = 1245 adults). We calculated geographic information system-based measures of restaurant density using residential administrative neighborhood boundaries, 800 m and 1600 m buffers around the home and respondents’ self-defined boundaries (drawn by the respondents on a map of their residential area). We used adjusted Poisson regression to test associations of restaurant density (tertiles) and the outcome ”weekly consumption of home-cooked meals” (six to seven as compared to five days per week (day/week) or fewer). Most respondents reported eating home-cooked meals for at least 6 day/week (74.2%). Regardless of the neighborhood definition used, no association between food environment and home cooking was observed. No effect modification was found. Although exposure in terms of density of restaurants was different according to the four different neighborhood definitions, we found no evidence that the area under study influences the association between density of restaurants and home cooking among Dutch adults.


Identification of the data fields used in analyses.
✓ Original purpose of the data (e.g. food hygiene regulation enforcement or commercial business data). ✓ Methods used by the data creator to collect the data/compile the dataset (e.g. audits conducted by data creator). ✓ Prevalence of missing data (e.g. number of entries with incomplete address information). Methods for handling missing data (e.g. case-wise deletion, or use of secondary sources to impute missing data). ✓ Information on the accuracy of the data e.g. via reference to one or more validation studies or acknowledgement that data accuracy is unknown. ✓ 2. EXTRACTING FOOD OUTLETS Essential Desirable Description of methods used to extract food outlets of interest from dataset (e.g. search for specific proprietary classifications or store names). ✓ If outlets were extracted using search terms (e.g. proprietary classifications or store names): • An exhaustive list of search terms (where proprietary classifications are used, it should be made explicitly clear that the classifications listed are those of the data provider).

N/A
If outlets were extracted based on proprietary classifications: • A copy of the proprietary classification scheme, optionally including exemplary outlets falling within each classification; OR, • A discussion of any notable categories excluded from analyses (e.g. pubs, pharmacies, mobile food vendors etc. have been applied to the data and description of the methods used to apply the scheme. • Description of any other methods used (note methods based on subjective criteria are discouraged). Examples of outlets falling within each construct such that the scope of each construct can be more readily interpreted. For example, if the construct 'fast food outlet' includes 'traditional' burger and fried chicken outlets, and also coffee shops and sandwich shops then well-known chains falling within each such sub-type could be listed. Identification of any additional data sources used to group outlets into constructs e.g. use of Google Street View, business directories etc.
N/A Description of how any additional data sources were linked to the food outlet data (e.g. by matching store names and/or addresses).

N/A
Where proprietary classifications are used to define constructs, a copy of the entire proprietary classification scheme. 4. GEOCODING METHODS Essential Desirable Acknowledgement of whether any data has been geocoded. ✓ The address model used (e.g. areal unit, street segment, land parcel, address point). ✓ The match rate achieved. N/A The environmental context, including details on how this was defined e.g. the study area was urban/rural, defined based on population density. ✓ Geocoding software used, including the version number. ✓ The source of geocoding reference data (e.g. street line segment data), including publication date. 5. ACCESS METRICS Essential Desirable Definition of the conceptual environment being measured e.g. home, school, work etc. ✓

Intensity Metrics
If areal zoning system used: • The type of areal zoning system (e.g. government districts, census tracts etc.) • The source of boundary data, including the publication date or other version identifier.
✓ If buffer zoning system used: • The buffer size.
• The type of distance measure (e.g. Euclidian or network).

✓
The units of the intensity metric(s) (e.g. count per unit area, as measured in meters) or formula indicating how they were calculated. ✓ If network data was used (i.e. to calculate network distances): • The source and publication date of network data.
• The types of road/path included.

N/A
Rationale for the choice of zone type (e.g. areal vs buffer) and/or size as applicable. ✓

Proximity Metrics
The type of distance measure (Euclidian vs network). N/A If network data was used (i.e. to calculate network distances): • The source and publication date of network data.
• The types of road/path included.
N/A Gravity Metrics The zone radius. N/A The decay coefficient.
N/A 6. UNKNOWN DETAILS Essential Desirable Any items noted as essential, but that are unknown should be highlighted as a limitation.
N/A Table S2. Sensitivity analyses performed only with individuals who drew self-defined neighbourhoods that were within the range of area sizes for administrative neighbourhood boundaries (0.25 to 4.14 km²) (n=720).

Frequency of home cooking (6 -7 days per week)
Covariates   1.00 (0.92 -1.11) 1.00 (0.90 -1.10) 0.91 (0.81 -1.03) 0.89 (0.77 -1.03) 0.87 (0.75 -1.01) Q1, Q2, Q3, Q4 and Q5 are quintiles of densities, where individuals in Q1 have the lowest density of restaurants and individuals in Q5 the highest density; Model 1: adjusted for age, sex, education, income, household composition, employment status, spare time spent in the neighbourhood, years of residency in the neighbourhood, presence of restaurants was a reason for choosing the neighbourhood. Model 2: additionally adjusted for the density of grocery stores. Model 3: additionally adjusted for the density of all other food retailers.