Spatial pattern of BMI among adults in Northern California

Laraia, B. A., S. D. Blanchard, A. J. Karter, J. C. Jones-Smith, M. Warton, E. Kersten, M. Jerrett, H. H. Moffet, N. Adler, D. Schillinger, and M. Kelly. 2014. Spatial pattern of Body Mass Index among adults in the Diabetes Study of Northern California (DISTANCE). International Journal of Health Geographics 13:48 doi:10.1186/1476-072X-13-48

clustering of high and low BMIThe role that environmental factors, such as neighborhood socioeconomics, food, and physical environment, play in the risk of obesity and chronic diseases is not well quantified. Understanding how spatial distribution of disease risk factors overlap with that of environmental (contextual) characteristics may inform health interventions and policies aimed at reducing the environment risk factors.

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Income and ethnicity differences among people with diabetes

Jones Smith, J. C., M. Wharton, M. Kelly, E. Kersten, A. Karter, N. Adler, D. Schillinger, H. Moffett, B. A. Laraia. 2013. Obesity and the food environment: income and ethnicity differences among people with diabetes, the Diabetes Study of Northern California (DISTANCE). Diabetes Care 36:2697-2705

The objectives of this study were to test whether there was an association between food environments and obesity among adults with diabetes and whether this relationship differed according to individual income or race/ethnicity. We found that more healthful food environments were associated with lower obesity in the highest income groups among whites, Latinos, and Asians. The association was negative, but smaller and not statistically significant, among high-income blacks. In contrast, a more healthful food environment was associated with higher obesity among participants in the lowest-income group which was statistically significant for black participants in this income category. These findings suggest that the availability of healthful food environments may have different health implications when financial resources are severely constrained. Journal link.

How do we characterize food stores for public health research?

Kersten, E., B. Laraia, M. Kelly, N. Adler, and I. Yen. 2012. Small food stores and availability of nutritious foods: A comparison of database and in-store measures, Northern California, 2009. Preventing Chronic Disease 9:120023. DOI: http://dx.doi.org/10.5888/pcd9.120023

Small food stores are prevalent in urban neighborhoods, but the availability of nutritious food at such stores is not well known. The objective of this study was to determine whether data from 3 commercially available sources would yield a single, homogenous, healthful food store category that can be used to accurately characterize community nutrition environments for public health research. We conducted in-store surveys in 2009 on store type and the availability of nutritious food in a sample of nonchain food stores (n = 102) in 6 predominantly urban counties in Northern California (Alameda, Contra Costa, Marin, Sacramento, San Francisco, and Santa Clara). We compared survey results with commercial database information and neighborhood sociodemographic data by using independent sample t tests and classification and regression trees. We found that commercial databases alone may not adequately categorize small food stores and the availability of nutritious foods. Alternative measures are needed to more accurately inform research and policies that seek to address disparities in diet-related health conditions. 

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Use of obia in public health - a review, and call for more

Kelly M., S. Blanchard. E. Kersten and K. Koy. 2011. Object-based analysis of imagery in support of public health: new avenues of research. Remote Sensing 3:2321-2345

The benefits of terrestrial remote sensing in the environmental sciences are clear across a range of applications, and increasingly remote sensing analyses are being integrated into public health research. This integration has largely been in two areas: first, through the inclusion of continuous remote sensing products such as normalized difference vegetation index (NDVI) or moisture indices to answer large-area questions associated with the epidemiology of vector-borne diseases or other health exposures; and second, through image classification to map discrete landscape patches that provide habitat to disease-vectors or that promote poor health. In this second arena, new improvements in object-based image analysis (or “OBIA”) can provide advantages for public health research. This paper provides a brief review of what has been done in the public health literature with continuous and discrete mapping, and then highlights the key concepts in OBIA that could be more of use to public health researchers interested in integrating remote sensing into their work.

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