Field validation of secondary data sources: a novel measure of representativity applied to a Canadian food outlet database

International Journal of Behavioral Nutrition and Physical Activity

Yan Kestens¹  Christelle M Clary¹ 

[1] CRCHUM – Research Centre, Centre hospitalier de l’Université de Montréal, 3850 St-Urbain, Montreal, Quebec H2W 1T7, Canada

关键词: Representativity;    Positive predictive value;    Sensitivity;    Secondary database;    Food environment;    Field validation;   

Others  :  806855
DOI  :  10.1186/1479-5868-10-77

 received in 2012-11-28, accepted in 2013-06-11,  发布年份 2013

【 摘 要 】

Background

Validation studies of secondary datasets used to characterize neighborhood food businesses generally evaluate how accurately the database represents the true situation on the ground. Depending on the research objectives, the characterization of the business environment may tolerate some inaccuracies (e.g. minor imprecisions in location or errors in business names). Furthermore, if the number of false negatives (FNs) and false positives (FPs) is balanced within a given area, one could argue that the database still provides a “fair” representation of existing resources in this area. Yet, traditional validation measures do not relax matching criteria, and treat FNs and FPs independently. Through the field validation of food businesses found in a Canadian database, this paper proposes alternative criteria for validity.

Methods

Field validation of the 2010 Enhanced Points of Interest (EPOI) database (DMTI Spatial®) was performed in 2011 in 12 census tracts (CTs) in Montreal, Canada. Some 410 food outlets were extracted from the database and 484 were observed in the field. First, traditional measures of sensitivity and positive predictive value (PPV) accounting for every single mismatch between the field and the database were computed. Second, relaxed measures of sensitivity and PPV that tolerate mismatches in business names or slight imprecisions in location were assessed. A novel measure of representativity that further allows for compensation between FNs and FPs within the same business category and area was proposed. Representativity was computed at CT level as ((TPs +|FPs-FNs|)/(TPs+FNs)), with TPs meaning true positives, and |FPs-FNs| being the absolute value of the difference between the number of FNs and the number of FPs within each outlet category.

Results

The EPOI database had a "moderate" capacity to detect an outlet present in the field (sensitivity: 54.5%) or to list only the outlets that actually existed in the field (PPV: 64.4%). Relaxed measures of sensitivity and PPV were respectively 65.5% and 77.3%. The representativity of the EPOI database was 77.7%.

Conclusions

The novel measure of representativity might serve as an alternative to traditional validity measures, and could be more appropriate in certain situations, depending on the nature and scale of the research question.

【 授权许可】

   
2013 Clary and Kestens; licensee BioMed Central Ltd.

【 参考文献 】

• [1]Winson A: Bringing Political Economy into the Obesity Epidemic. Agr Hum Values 2004, 21:299-312.
• [2]Charreire H, et al.: Measuring the food environment using geographical information systems: a methodological review. Public Health Nutr 2010, 13(11):1773-1785.
• [3]McKinnon RA, et al.: Measures of the food environment: a compilation of the literature, 1990-2007. Am J Prev Med 2009, 36(4 Suppl):S124-S133.
• [4]Oakes JM, Masse LC, Messer LC: Work group III: Methodologic issues in research on the food and physical activity environments: addressing data complexity. Am J Prev Med 2009, 36(4 Suppl):S177-S181.
• [5]Lytle LA: Measuring the food environment: state of the science. Am J Prev Med 2009, 36(4 Suppl):S134-S144.
• [6]Han E, et al.: Classification bias in commercial business lists for retail food stores in the U.S. Int J Behav Nutr Phys Act 2012, 9(1):46. BioMed Central Full Text
• [7]Paquet C, et al.: Field validation of listings of food stores and commercial physical activity establishments from secondary data. Int J Behav Nutr Phys Act 2008, 5:58. BioMed Central Full Text
• [8]Liese AD, et al.: Validation of 3 food outlet databases: completeness and geospatial accuracy in rural and urban food environments. Am J Epidemiol 2010, 172(11):1324-1333.
• [9]Bader MD, et al.: Measurement of the local food environment: a comparison of existing data sources. Am J Epidemiol 2010, 171(5):609-617.
• [10]Lake AA, et al.: The foodscape: classification and field validation of secondary data sources. Health Place 2010, 16(4):666-673.
• [11]Cummins S, Macintyre S: Are secondary data sources on the neighbourhood food environment accurate? Case-study in Glasgow, UK. Prev Med 2009, 49(6):527-528.
• [12]Hosler AS, Dharssi A: Identifying retail food stores to evaluate the food environment. Am J Prev Med 2010, 39(1):41-44.
• [13]Powell LM, et al.: Field validation of secondary commercial data sources on the retail food outlet environment in the U.S. Health Place 2011, 17(5):1122-1131.
• [14]Lake AA, et al.: The foodscape: classification and field validation of secondary data sources across urban/rural and socio-economic classifications in England. Int J Behav Nutr Phys Act 2012, 9:37. BioMed Central Full Text
• [15]Block JP, Scribner RA, DeSalvo KB: Fast food, race/ethnicity, and income: a geographic analysis. Am J Prev Med 2004, 27(3):211-217.
• [16]Moore LV, et al.: Associations of the local food environment with diet quality–a comparison of assessments based on surveys and geographic information systems: the multi-ethnic study of atherosclerosis. Am J Epidemiol 2008, 167(8):917-924.
• [17]Wang MC, et al.: Socioeconomic and food-related physical characteristics of the neighbourhood environment are associated with body mass index. J Epidemiol Community Health 2007, 61(6):491-498.
• [18]Morland K, Wing S, Roux AD: The contextual effect of the local food environment on residents’ diets: the atherosclerosis risk in communities study. Am J Public Health 2002, 92(11):1761-1767.
• [19]Jeffery RW, et al.: Are fast food restaurants an environmental risk factor for obesity? Int J Behav Nutr Phys Act 2006, 3:2. BioMed Central Full Text
• [20]Jago R, et al.: Distance to food stores & adolescent male fruit and vegetable consumption: mediation effects. Int J Behav Nutr Phys Act 2007, 4(1):35. BioMed Central Full Text
• [21]Bodor JN, et al.: Neighbourhood fruit and vegetable availability and consumption: the role of small food stores in an urban environment. Public Health Nutr 2008, 11(4):413-420.
• [22]Kestens Y, et al.: Association between activity space exposure to food establishments and individual risk of overweight. PLoS One 2012, 7(8):e41418.
• [23]Parenteau MP, et al.: Development of neighborhoods to measure spatial indicators of health. URISA Journal 2008, 20(2):43-55.
• [24]Administration, U.S.D.o.L.O.S.H: SIC Division Structure. 2008. Available from: http://www.osha.gov/pls/imis/sic_manual.html webcite
• [25]Camilli G, Hopkins KD: Applicability of chi-square to 2× 2 contingency tables with small expected cell frequencies. Psychol Bull 1978, 85(1):163.
• [26]Lydersen S, Fagerland MW, Laake P: Recommended tests for association in 2x2 tables. Stat Med 2009, 28(7):1159-1175.
• [27]Burns CM, Inglis AD: Measuring food access in Melbourne: access to healthy and fast foods by car, bus and foot in an urban municipality in Melbourne. Health Place 2007, 13(4):877-885.
• [28]Donkin AJ, et al.: Mapping access to food in a deprived area: the development of price and availability indices. Public Health Nutr 2000, 3(1):31-38.
• [29]Zenk SN, et al.: Neighborhood racial composition, neighborhood poverty, and the spatial accessibility of supermarkets in metropolitan Detroit. Am J Public health 2005, 95(4):660-667.
• [30]Krieger N, et al.: On the wrong side of the tracts? Evaluating the accuracy of geocoding in public health research. Am J Public Health 2001, 91(7):1114-1116.
• [31]Wang MC, et al.: Changes in neighbourhood food store environment, food behaviour and body mass index, 1981–1990. Public Health Nutr 2008, 11(9):963-970.
• [32]Burgoine T, et al.: Changing foodscapes 1980-2000, using the ASH30 Study. Appetite 2009, 53(2):157-165.
• [33]Cohen DA: Obesity and the built environment: changes in environmental cues cause energy imbalances. Int J Obesity 2008, 32(Suppl 7):S137-S142.
• [34]Ohri-Vachaspati P, et al.: Improving data accuracy of commercial food outlet databases. Am J Health Promot 2011, 26(2):116-122.