In this study using spatial geo-referenced data, we visualized, explored, and modeled the associations between selected socioeconomic and demographic attributes from by the U.S. Bureau of Census and reported Salmonella infections in three large counties in Michigan. GIS is emerging as an efficient tool to support the results of traditional descriptive epidemiology–it allows researchers to hypothesize meaningful associations from the spatial data, identify high risk areas, and help guide future research. We found a concentration of block groups with a higher proportion of educated individuals in the southern part of Oakland and eastern part of Wayne counties. A higher incidence of salmonellosis was seen in the block groups with high education compared to the less educated block groups (Figure 6). A large grouping of block groups with a high proportion of African-Americans was found in the Detroit metropolitan area.
Socioeconomic and demographic indicators can be used to predict which individuals and communities are at an increased risk of acquiring infections. Generally, low SES is an important predictor of several poor health outcomes including chronic diseases, mental illnesses, and mortality [11, 26]. However, our multivariate model revealed a higher rate of reported Salmonella infections in block groups where a greater proportion of individuals with high educational attainment resided.
No prior studies using group level data investigated the relation between SES and salmonellosis. However, using a spatial analysis technique similar to our study, Green et al. , found a positive association between enteric campylobacter infections and higher SES status in Manitoba, Canada (1996–2004). This greater incidence of Campylobacter infection for individuals of higher SES status compared to their counterparts was partially attributed to increased opportunities for foreign travel to areas where Campylobacter infection is endemic and frequent consumption of food prepared outside the home . Similar risk factors for Salmonella infections have been reported in other individual level epidemiologic investigations [28, 29]. In addition to these reported risk factors for salmonellosis, our findings may be explained in part by greater access to healthcare for those in the high education and income groups, which would increase the detection of Salmonella cases. Mead et al.  stated that only a small fraction of individuals with Salmonella infection are diagnosed and reported . It is possible that a large proportion of unreported cases, particularly the relatively less severe episodes, among residents of low education-block groups who often do not have health insurance, go unreported because they do not seek medical care that would lead to specimen collection, diagnosis, and reporting. This is corroborated by the findings from a recent study that revealed that cases of acute diarrheal illness ascertained through laboratory-based public health surveillance differ systematically from unreported cases by the health insurance factor . Similarly, residents of high education block groups may seek medical consultation even for mild to moderate symptoms of enteric infections, including salmonellosis, thus increasing their likelihood of becoming a reported case. In contrast, individuals in block groups having low education may tend to ignore mild symptoms of the disease, resulting in a larger proportion of unreported cases being missed by the existing laboratory-based passive surveillance system.
In addition, individuals of higher education block groups who also have greater discretionary income may eat outside the home frequently and be more likely to own pets considered reservoirs of Salmonella, which increase the likelihood of contracting Salmonella infections compared to their counterparts with lower levels of education. In a cross-sectional study based on a sample of veterinary hospital clients in Utah about 77% of exotic pet (e.g., ferrets, lizards, turtles) owners had at least some college education and that their mean family income ranged between $35,000 and $50,000 per year . The limited purchasing power and access to supermarkets and pet stores may reduce the exposure of lower SES and educational status populations to foods and pets that are frequently associated with salmonellosis.
Since income and education generally have a strong positive correlation , which is shown in our data as well (p-value < 0.01), we expected a similar effect of income on the rate of Salmonella infections at the block group level. However, in our final Poisson regression model, levels of income at the block group level did not demonstrate a consistent dose response gradient with the outcome variable as depicted in the case of levels of education. An explanation for this observation may be that education itself has a strong effect on health seeking behavior irrespective of the income level – better educated people are more likely to seek medical treatment than less educated individuals.
In recent years, racial disparities in healthcare in the U.S. have been a major focus in epidemiologic research . Although data on African-American and Caucasian differences in mortality from infectious diseases are available , few studies have investigated differences in food borne infections between Caucasians and other racial minorities [33, 34]. The incidences of food borne infections, including salmonellosis, may differ across racial and ethnic groups due to variations in food preferences, preparation methods, and handling among racial groups [33, 35]. However limited data exist to delineate specific food preparation and handling methods responsible for the acquisition of enteric infections for specific racial subgroup populations.
In the year 2000, a significantly higher rate of Salmonella infections among African-Americans compared to Caucasians was reported . In addition, Marcus et al. (2007) found the highest average annual incidence (1998–2000) of Salmonella serotype Enteritidis in African-Americans (2.0/100,000), followed by Hispanics (1.2/100,000), and Caucasians (1.1/100,000) . In contrast to the findings based on individual level studies, our group level data analysis did not find an association between race at the block group level and Salmonella infections.
In accordance with our previous individual level studies using Michigan data [17, 18], we did not find an association between Salmonella infections and urban or rural block groups. This suggests that populations in both of these settings are exposed to similar levels of potential sources of Salmonella infections.
Inherent limitations of GIS based data and our analysis should be considered when interpreting the results [20, 38]. We used the block group as the unit of analyses, but analyses based on other census geography (e.g., census tract or county) may provide different results. In any group level data, variations within the group are masked, limiting researchers' ability to study any differences within the unit of analysis (in our case block group). Additionally, the fact that individual level studies usually utilize more complete data on race and socioeconomic status cannot be overlooked. Since our study used a group level analysis, attempts to draw individual level inferences is inappropriate and may lead to a biased interpretation (ecological fallacy) . Our case data spanned the years from 1997 to 2006. However, we used the Census 2000 200 data for analyses in order to approximate a middle point between the years of the study, which would and minimize the effects of any population change that occurred over the study period. The Census data is also the best freely available, standard, population dataset that provided robust data on the scale levels that we wished to conduct our research. As mentioned earlier, we excluded 185 block groups with < 500 population from our analysis. Since population of excluded block groups accounted for 1.36% of the total population in the three county area, we believe that exclusion of these scarcely populated block groups have not effected our results.
The spatial aspects of this study are somewhat limited due to the scope of the study. Our primary objective was to study the association between socioeconomic variables (which were not available in the surveillance database) and reported cases of Salmonella infections using GIS technology. It should be noted that the spatial join used in this study can be done without the use of GIS, however, visualization of the data using maps allows researchers to examine visual association between the variable of interest and outcome in addition to statistical analyses.