The proportion of Hispanic surveys completed per strata matched our proposed distribution: 7% for strata 1, 30% for strata 2, 58% for strata 3 and 83% for strata 4. The final sample included 106 Hispanic and 111 non-Hispanic participants. The proposed sample included 109 Hispanic and 107 non-Hispanic participants to be recruited from 44 Census blocks. To ensure a balanced sample of both ethnic groups, we designed an area stratified random sampling procedure involving three stages: (1) division of the sampling area into non-overlapping strata based on Hispanic household proportion using GIS software (2) random selection of the designated number of Census blocks from each stratum and (3) random selection of the designated number of housing units (i.e., survey participants) from each Census block. We conducted a community based survey to collect and examine social determinants of health and their association with obesity prevalence among a sample of Hispanics and non-Hispanic whites living in a rural community in the Southeastern United States. We describe the use of a novel Geographic Information System (GIS)-based population based sampling to minimize selection bias in a rural community based study. Conducting community based surveys to study these determinants must ensure representativeness of disparate populations. An explanation of how this tool works and the publications it is based on can be found here: How Create Spatially Balanced Points works.Most studies among Hispanics have focused on individual risk factors of obesity, with less attention on interpersonal, community and environmental determinants. Due to this, the Create Spatially Balanced Points tool exists within the Geostatistical Analyst toolbox. Also, not all of them guarantee that the sampling design will be spatially balanced (that is, that the design will sample the entire population, due to the inherent randomness of selecting a site to sample). These methods do not easily account for variations in the probability of a site to be selected (other than splitting the study area into strata, which usually requires manual inspection of the study site and good knowledge of the process under study). This method is easy to implement in practice as many samples are collected from nearby locations (unlike a simple random sample pattern, where sample sites may occur anywhere in the study area). This can be done by generating randomly placed centers using the Minimum Allowed Distance of the Create Random Points tool and allocating additional samples within a specified distance from each center. Clustered random sampling: The location for a group of sites is selected at random, and sites within each group are then located relatively close to one another.The method is simple and provides designs that are spatially well balanced (well distributed in space). Systematic random sampling: An initial sample site is picked at random, and all other sites are selected so that they are located according to some regular pattern (for example, on the vertices of equilateral triangles, squares, hexagons, and so forth).Other types of designs can be relatively easily generated using simple scripts or models: Strata can be adjusted based on prior knowledge of the phenomenon (for example, concentric circles can be made larger as the distance from a point source emission increases), providing some spatial structure to the sample. Stratified random sampling: The study area is split into strata and random samples are generated within each stratum. The method is simple and flexible, but the outcome of one realization may include areas where samples are clustered and other areas that are devoid of samples. A similar outcome could be obtained by using the Create Random Raster tool and a probability cutoff value (note that the ArcGIS Spatial Analyst extension version of the Create Random Raster tool uses a uniform random number, whereas the Data Management toolbox's version of the Create Random Raster tool supports several different distributions).
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