![](https://blogs.iu.edu/ecohealth/files/2021/05/brocoli-vs-burger.jpg)
Background:
Fast-food establishments have significantly negative impacts on both human health and the environment. Areas with densely populated fast-food restaurants and limited access to fresh and healthy food have been shown to have populations with lower life expectancy rates. In addition to the impacts on human health, fast-food establishments negatively affect the environment through its use of land, wastes, use of limited resources, as well as its greenhouse gas emissions. The effects of fast-food establishments are not sustainable for both the health of the human population and the environment.
A study done by Jay Maddock found that there is a correlation between state-level obesity with both fast-food restaurants per square mile and population per fast-food restaurant. This study implemented a cross-sectional analysis to study the density of fast-food restaurants per square mile and population density per fast-food. The study also took into account measurements of ethnicity, age, gender, physical inactivity, fruit and vegetable intake, as well as obesity rates. The results showed that at the state level, fast-food restaurants per square mile and population per restaurant accounted for 6% of the variance in obesity rates. These were results after controlling other factors such as ethnicity, age, physical inactivity, etc (Maddock, 2004). It is well accepted that above moderate consumption of fast-food has been linked to obesity which puts an individual at higher risk to a variety of adverse health outcomes that cause premature morbidity such as cardiovascular diseases and diabetes. Low income areas densely populated with fast-food restaurants are at higher risk of being predisposed to these adverse health effects, especially when considering people’s tendency to make food choices based on the food outlets that are available in their neighborhood (Furey,2001). This tendency is extremely problematic as various studies have shown that low-income, urban areas, highly populated with minorities and marginalized groups have higher densities of fast-food restaurants and corner stores (Hendrickson et al., 2006). As dietary intake is considered one of the major determinants of health outcomes and life expectancy, it is detrimental to study socioeconomic status and access to affordable healthy food in various regions to understand how they play a role in creating health disparities between counties in the same state.
An article by Joel Fuhrman evaluates various primary studies to discuss the dangers of fast-food and processed food especially in low-income urban areas that have limited access to fresh fruits and vegetables. According to Fuhram, areas with limited access to supermarkets and fresh fruits and vegetables are coined as “food deserts” and residents of these areas are at risk of early-life stroke by up to seven times. These individuals are also two times more at risk of heart attack and diabetes, and four times more at risk of renal failure. The article includes that fast-food is not exclusive to food acquired from fast-food restaurants but also pertains to any food that is high-calorie, low-nutrient foods that people eat multiple times a day. These could include chips, frozen pizza, french fries, white-flour baked goods, cookies, soda, etc. These foods are easily accessible and affordable in these urban areas where supermarkets that provide access to high-quality, nutritious, and fresh food are limited and grocery stores, corner stores, and convenience stores are prevalent. This article also emphasizes that ample research has determined that compared to areas in America with easy access to supermarket food, that the years of potential life lost for overweight diabetic individuals residing in these “food desert” zones was 45 years (Fuhrman, 2018). Similar trends have been resulted from other research such as one by the American Journal of Health Promotion. This research did a cross-sectional study which studied 1221 residents from 120 neighborhoods. The results showed that neighborhoods densely populated with fast-food restaurants were associated with unhealthy lifestyles and increased risk of obesity among older adults (Li et al., 2009). These researches are important as it further elaborates on how food environment drastically affects health outcomes.
In addition to effects of health outcomes, fast-food restaurants are also great contributors to greenhouse gas emission and pollution. One recent review done by the World Obesity Federation discusses both global warming and the obesity epidemic together and possible links between the two. Recent studies, which the World Obesity Foundation discusses during their review, question whether global warming and the obesity epidemic share common determinants, or whether one influences the other. They constructed a conceptual model taking into account the following: fossil fuel economy, population growth, impacts of industrialization land use and urbanization, agricultural productivity and their greenhouse gas emissions, obesity epidemic by a transition in nutrition, and many more. This review emphasizes how these two problems interchangeably affect each other. Global warming directly influences food supply or price fluctuations while the obesity epidemic influences global warming by increasing energy consumption. On the other hand, this review also considers how this relationship can vary across geographical locations and population subgroups (An et al., 2017). Despite these variations, fast-food restaurants are a great source of pollution and greenhouse gas emissions. Both obesity and global warming have been studied extensively but this article evaluates both phenomenon together which sheds light on how both can possibly have similar determinants and can influence each other.
Fast-food establishments use of land, and use of meat and dairy products have hit unsustainable levels. Various organizations such as Ceres and the FAIRR Initiative have reached out to various fast-food restaurants to report and reduce greenhouse gas emissions and freshwater impacts. The amount of energy and resources consumed for the farming, production, processing, and transportation of meat and dairy products are unsustainable and the greenhouse gas emitted during these processes are uncapped for fast-food restaurants (Bonnet et al.,2020). Majority of models to reduce greenhouse gas emissions through these systems is to reduce meat consumption and sustain a balanced diet that features plant based food which are sustainable and low in greenhouse gas emissions (Bonnet et al., 2020). The benefits of reducing fast-food establishments can aid in both the promotion and protection of human health and create a more sustainable environmental system. One of the greatest solutions to these issues is to increase the density of supermarkets in relation to their population densities while reducing or maintaining the current amount of fast-food establishments to promote a more sustainable food consumption system for the environment.
The study I propose will focus on examining the population and the areas in which they reside in. The areas’ fast-food restaurant, supermarket, and local farm densities will be studied. Studying populations and their access to variations of food can help give us a better scope of variations in life expectancy rates between counties within the same state. In addition, studying land use and its effects to human health and environment can give us better insight on how these can affect each other and promote mitigation of global warming and reduction of premature morbidity.
Methods:
The following research methodologies were conducted:
(1) A dashboard for Marion County and Hamilton County was created through a site called SAVI which utilizes information and data from various researches, nonprofit organizations, and government organizations. The dashboard created is interactive and can depict various data trends in the two counties and for their designated census tracts. The following data and information was acquired for each county and for each of their census tracts:
- Total population and population density
- Median household income
- Life expectancy at birth
- Fast-food sites (only for Marion County)
- Grocery sites (only for Marion County)
- Farmers market sites
- Percentage of population far from grocery access (more than 1 mile away)
- Rate of obesity among population 18 and older
- Percentage of population 18 and older with heart disease
- Percentage of population 18 and older with diabetes
- Deaths caused by heart disease per 100,000
- Deaths caused by diabetes per 100,000
- Food desert population by poverty status (only for Marion County)
(2) A t-test was conducted analyzing information acquired through the U.S Department of Agriculture’s Food Environment Atlas. The total amount of fast-food restaurants during 2011 and 2016 was acquired for both counties. The fast-food restaurants/1,000 population for each county and for each year was acquired and used to conduct the T-test: Two-Sample Assuming Unequal Variances. A significant difference between the two counties can allow this study to suggest fast-food density as a factor affecting the difference in adverse health outcomes and life expectancies between the counties.
(3) A survey was conducted with responses from 35 participants whose primary residencies are in either Hamilton County or Marion County. Each participant was asked to answer six questions regarding their general eating habits and access to grocery stores. The survey’s multiple choice questions and results will be shown in the results portion.
Results:
(1) Dashboard links and results:
- Marion County: https://profiles.savi.org/sharabledashboard.html?boundaryId=4027889
- Hamilton County: https://profiles.savi.org/sharabledashboard.html?boundaryId=4028108
- Highlighted information found on the dashboard:
![Map 1: Access to Assets in Marion County](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-11-1.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-12.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-16.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-19.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-20-1.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-18-1.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-25.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-26.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-24.png)
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-23-1.png)
(2) Data Analysis of U.S Department of Agriculture’s 2011 and 2016
The data gathered for the analysis were derived from the USDA’s Food Environment Atlas for the years 2011 and 2016. In the year 2011, Marion County had 776 total fast-food restaurants which approximated to 0.85 fast-food restaurants per 1,000 population and Hamilton County had 187 total fast-food restaurants which approximated to 0.66 fast-food restaurants per 1,000 population. In 2016, Marion County had a total of 832 fast-food restaurants which approximated to 0.88 fast-food restaurants per 1,000 population and Hamilton County had a total of 247 fast-food restaurants which approximated to 0.78 fast-food restaurants per 1,000 population (USDA, 2020). The t-test conducted used the fast-food restaurants per 1,000 population for both counties for each year
Null Hypothesis: There is no significant difference between Marion County and Hamilton County’s number of fast-food restaurants per 1,000 population.
![](https://blogs.iu.edu/ecohealth/files/2021/05/Screenshot-21.png)
As shown in Figure 4, the p-value of 0.128 is greater than the alpha value of 0.05, therefore we fail to reject the null hypothesis. With the data provided, there is insufficient evidence to assume that there is a significant difference between Marion County’s and Hamilton County’s density of fast-food restaurants per 1,000 population during 2011 and 2016.
(3) Survey:
- In general, out of 3 meals per day, around how many are home cooked meals?
- all 3
- 2
- 1
- How often do you eat store bought, microwave ready or frozen processed meals? (e.g. pizza rolls, hot pockets, chicken nuggets, frozen premade meals, etc)
- rarely
- sometimes
- often 68%
- never
- How often do you go to the grocery store each week?
- once
- twice
- three or more
- don’t go every week
- How often do you eat fast-food?
- 1-2 times a week
- 3 or more times a week
- less than once a week
- How far of a drive is the nearest grocery store from your home?
- 5 minutes or less
- 10-15 minutes
- more than 15 minutes
- How often do you eat out? (sit in restaurants or take out food from restaurants)
- 1-2 times a week
- 3 or more times a week
- less than once a week
In general, majority of the 35 participants consume fast-food, processed food, frozen meals, or “junk-food” on a regular basis throughout the week. Only 20% of the participants consume fast-food less than once a week; 48.6% claim to consume one to two times a week and 31.4% consume three of more times a week. More than half (68%) of the participants claim to consume store bought microwave ready, frozen meals, or processed meals (e.g. pizza rolls, hot pockets, chicken nuggets, etc.) often. Despite of 91.4% of the participants being five minutes or less of a drive from the nearest grocery store, 45.7% of participants only go to the grocery store once a week. In addition, 68.6% of participants eat at sit in restaurants or takeout food from restaurants at least once a week.
Discussion:
There are significant disparities in life expectancy in various counties in Indiana. Hamilton County and Marion County are approximately 30 miles apart but differ in life expectancy rates by 6.2 years (Weather et al., 2015); the National Center for Health Statistics show life expectancy rates to be 81.6-82.2 for Hamilton County and 75.5-75.9 for Marion County (County Health Ranking, 2020). Disparities in health outcomes between populations that reside closely together have been linked to various social determinants of health; one of which is access to food (WHO, 2007).
Indianapolis was rated worst in the nation for food deserts in 2014 and had food insecurity rates higher than the national average during 2013-2015. Indiana’s rate of 14.8% is greater than the national rate of 13.7%; within Indiana, Marion County had the highest rate (19.4%) while Hamilton County had the lowest (9.4%) (County Health Rankings, 2020)(Miltko, 2017) . These trends are consistent when examining the data depicted in Map 1 where food deserts are congested within Marion County. The USDA defines the criteria food deserts as areas that meet both low-access and low-income thresholds; the threshold for low-income are those with poverty rates of 20% of greater, or a median family income at or below 80% of the statewide metropolitan area median family income. The threshold for low-access are urban areas with a population of at least 500 living more than 1 mile from a supermarket/large grocery store or areas where at least 33% of the population living more than 1 mile from a supermarket/large grocery stores; for rural areas, populations less than 10 miles from the nearest supermarket/large grocery store (USDA, 2011)
As shown in Figure 1 there are only 204 grocery stores and 20 farmers markets for Marion County’s population of 951,861. This indicates that there is approximately 0.214 grocery stores and 0.021 farmers markets per 1,000 population. Figure 1 also shows that 184,385 of the population reside in food deserts; 57,302 of which are also living in poverty. Map 2 also exhibits 2020 trends for population percentage who are far away from grocery stores based on the USDA’s criteria; as depicted, Marion County is mostly congested with census tracts with rates of 56.7%-80.9% or 80.9%-100%. Hamilton County has areas census tracts with similar trends which are mostly congest south of the county closer to Marion County. Although there was no recent data that was acquired through SAVI’s database for Hamilton County’s population in food deserts, data was acquired from USDA Food Environment Atlas which showed that population percentage with low-income and low access to grocery stores in Marion County were 8.06% and 4.19% in Hamilton County; the population percentage with low access to grocery stores and do not own vehicles were 1.54% in Marion County and 0.79% in Hamilton County (Miltko, 2017). Although there is much improvement needed for both counties, Marion County’s rates are almost more than two times than Hamilton County’s.
Access to food requires nutritious and healthy food be both accessible nearby and affordable. Low income areas are at greater risk when they have limited access to grocery stores and high densities of fast-food restaurants. Those with low income are automatically limited to access in food which is exacerbated by coexisting factors like limited access to vehicles which limits transportation to grocery stores (Saksena et al., 2018). High density of fast-food restaurants per unit of population has been shown to account for 6% of the variance in obesity rates (Maddock, 2004). In addition, limited consumption of nutritious and healthy food along with above to moderate consumption of processed food and food with low-fiber, high-cholesterol, refined carbohydrates are linked to adverse health outcomes like obesity, diabetes, and heart disease. Food with little to no nutritional value are mostly found in convenience stores, corner stores, fast-food restaurants, and small grocery stores with little to no quality produce (Saksena et al., 2018). These products are also available at lower cost making them the most accessible source of food for low income individuals. As shown in Map 3 which depicts trends in obesity rates in both counties, Hamilton County only has one census tract that has obesity rates labeled within the range of 33.0%-37.6% mean while Marion County is congested with rates ranging from 37.6%-42.9% or 42.9%-54.0%. Map 4 which depicts diabetes rates and Map 5 which depicts heart disease rates show similar trends to the obesity rates depicted in Map 3. Overall, the maps depict similar trends in health outcomes for each county and their census tracts. These maps depict the obvious disparities in health outcomes in Marion County which could be attributed by their access to food and density of fast-food.
The trends in adverse health outcomes are consistent to trends in population far from grocery stores which is depicted in Map 2 for Marion County but are not consistent for Hamilton County. The areas and census tracts in the central regions of Marion County with high rates of obesity, diabetes, and heart disease are the same areas and census tracts with higher percentages of population far from grocery stores. The trends in adverse health outcomes (Map 3,4,5) and grocery store access (Map 2) are not consistent for Hamilton County as lower grocery access is greatest in the southern census tracts of Hamilton County but rates of diabetes, obesity, and heart disease are the lowest in these regions; this region also has census tracts with life expectancies greater compared to the rest of the county which is depicted in (Map 7). These contrasts in Hamilton County’s southern region highlights how income is a more influential factor in health outcomes that can buffer the effects of limited food access from grocery stores. Hamilton County’s southern census tracts have the highest ranges of household income yet have the highest population percentage far from groceries within the county; majority of the census tracts are shown to fall mostly in the range of $115,931-$167,546 for income and 56%-80.9% or 80.9%-100% for population percentage away grocery stores, both of which are significantly higher than the middle and northern census tracts of the county (Map 2,6). This could account for their significantly lower rates of adverse health effects and higher life expectancy (Map 3,4,5,7). With higher income comes greater access to food and less risks for adverse health outcomes for a variety of reasons. Despite limited availability of grocery stores, higher income decreases the limitations of food affordability; it also increases the ability to afford and access private vehicles to use as transportation to grocery stores. Income is also a significant social determinant of health linked to a multitude of factors that decrease the likelihood of adverse health outcomes such as education, reduced financial stress, access to healthcare and medication, etc (WHO,2007).
Although the results from the dashboard suggests income’s dominating effects in buffering food availability’s impact of health outcomes, it still highlights how income can also drastically exacerbate low food availability . One in specific is the census tract in the northern region of Hamilton County near the town of Arcadia. This census tract has a range of 34.8% – 56.7% for percentage population far from grocery stores which is significantly less than the Hamilton County census tracts in the southern region (Map 2).This census tract’s income range of $40,000-$59,764 is significantly lower than the county’s southern census tracts (Map 6). These trends remain consistent with their rates of diabetes and heart disease which are higher than the rest of Hamilton County. The percentage of population with diabetes for this census tract falls in the range of 11.6%-14.6% which is significantly higher compared to the surrounding and southern census tracts which fall in the ranges between 3.3%-11.5% (Map 5). The percentage of population with heart disease also falls in the range of 7.0%-8.7% which is significantly higher than the surrounding and southern census tracts which falls in the ranges between 1.6%-5.6% (Map 4). This suggests the role of low income in exacerbating the effects of low food access to health outcomes and emphasizes the need for both greater grocery store availability and higher income.
A more comprehensive dashboard can be developed with the acquisition of more data and information. There was substantial information on food deserts and food access for Marion County, but the information for Hamilton County was very limited. There was no data gathered on populations living in food deserts and density and locations of fast-food restaurants, grocery stores, and farmers markets for the county. Therefore it was difficult to compare and visualize food access and food assets for the two counties. Data and information on the densities of fast-food restaurants and grocery stores and their specific locations for Hamilton County would have allowed for a more comprehensive comparison against Marion County. Density and location of grocery stores for Hamilton County for the same year as Marion County in the dashboard would have provided greater insight on differences in access to food. Although the dashboard illustrates the percentage of population far from grocery stores for both counties, there was no information found on areas with low access to grocery stores and access to vehicles. This could have shown which areas in Hamilton and Marion County have populations with limited access to grocery stores and limited transportation. No information on the prevalence, densities, and locations of food deserts in Hamilton Country were found which disabled to ability to compare to Marion County and provide an analysis of whether these food deserts contribute to the difference in life expectancies. Lastly, the program used did not allow for layering information in dashboard maps. This feature could highlighted areas overlapping or coexisting factors like limited food access, low income, and higher rates of adverse health outcomes. On the other hand, data was acquired from The USDA’s Food Environment Atlas for fast-food restaurants per 1,000 population for the years 2011 and 2016 which enabled a comparison analysis between the two counties. This information was used to conduct a t-test but there was no information on the locations of these fast-food restaurants during these years which disabled any visualizations.
Although there is strong scientific evidence from previous studies on the link between fast-food restaurant density per population and rates of obesity, this study’s data analysis failed to substantiate any significant difference between the fast-food density per population in Marion County and Hamilton County. The overall obesity rate for Marion County is 33% which is greater than Hamilton County’s which is 25.7% (Figure 2 and 3) but with the results of the t-test and limitations in date, this study is not able to suggest any associations to the differences in health outcomes and life expectancies between Hamilton County and Marion County. The results of the statistical analysis could have been affected by the scarcity of data. A more ideal approach for future studies would be to locate and determine the total amount of fast-food restaurants for each counties’ census tracts and their populations with the most recent data. This could provide the value of fast-food restaurant per census tract population for all the census tracts for each county. A larger data set could provide more accurate and reliable results.
The surveys results showed majority of participants consuming fast-food or just food at above moderate levels. The survey also shows that consuming from fast-food restaurants and dine in restaurants are a prevalent part of participants’ diet. Around 80% of participants consume fast-food at least once a week which can be somewhat acceptable but 31% of the participants also claim to consume three or more times a week. What was most interesting was how majority of participants claim to be within five minutes away from the nearest grocery store but majority of participants only visit once a week. This information along with the 31% who consume fast-food three times a week show that the participants go to fast-food restaurants more than the grocery store during the week. Lastly, the percentage of the participants who often consume microwave ready food, frozen meals, or processed meals is more than half which indicates that even in the cases participants eat at home, they are still consuming food that are mostly processed, low in nutritional value, and high in cholesterol and refined carbohydrates. On the other hand, these results could show more about behavior and lifestyle of the specific demographic of the participants as almost all participants were college students who’s primary residencies were either in Marion County or Hamilton County. These findings could mostly suggest dietary intake based on the lifestyle and circumstance of college students. If this survey was to be redone, a recall diary of food intake for a certain period of time would be more comprehensive. A recall diary could be deduced to keeping track of their daily meal intake based on certain categories such as fast-food, restaurant dine in or takeout, homemade, or microwave ready/frozen food. In addition, surveying a more diverse demographic would be extremely beneficial and should ideally include adults outside of college.
Limitations: Connection to Environment
One of the significant limitations of this research was its inability to tie into the environmental effects of land use in regards to fast-food establishments. Initially, this research aimed to shed led on the negative impacts of high density fast-food establishments on both population health and the environment but information was not implemented onto the research as there were limited resources and data for quantifying the greenhouse gas emissions, wastes, water use, and consumption of agricultural goods and livestock of fast-food establishments. There is a great number of fast-food establishments for each county and limited resources and information on quantifying environmental effects. There are strong evidence that show the environmental effects of fast-food restaurants and even the mass production of processed goods which makes this topic an extremely important topic to study further. Future studies would require dissection of reports on fuel consumption, heat value, and emission factors to calculate greenhouse gas emissions and these values are found through the Environmental Protection Agency (EPA).
The guidelines and criteria for greenhouse gas reporting mandates have limited this study and could limit future studies that have been suggested. Although various nongovernmental agencies such as Ceres and FAIRR Initiative have reached out to various fast-food restaurants to report and reduce their emissions but these are voluntary and are not mandated (Bonnet et al.,2020). The EPA issued the Greenhouse Gas Reporting Rule in 2009 which requires the reporting of greenhouse data and other relevant information to determine greenhouse gas emission from large sources and suppliers in the United States (EPA, 13). All the data gathered from businesses are accessible to the public under the Clean Air Act unless certain information is extremely confidential (EPA, 2014). There are extensive criteria for businesses required to report their greenhouse gas information, but overall, the facilities are required to submit annual reports if GHG emissions exceed 25,000 metric tons of carbon dioxide per year, supply of certain products would results in over 25,000 metric tons of carbon dioxide emissions if products were released, combusted, or oxidized, or if the facility receives 25,000 metric tons or more of carbon dioxide for underground injection (EPA, 15). Majority of small businesses fall below the 25,000 metric ton threshold and are not required to report to the EPA and majority of industries required are those who manufacture or produce resources such as fuel combustion sources, chemicals, compounds, electric transmission and distribution equipment, fuel etc (EPA, 16)(EPA,17).
Although large fast-food establishments such as McDonald’s and Taco Bell would possibly have emissions or would result in wastes producing emissions exceeding 25,000 metric tons because of their food processing, transport and export, fuel consumption for production and for maintenance of franchises, specific reports on greenhouse gas emissions for each franchise from reliable sources such as the EPA would be limited or non-existent. Large fast-food businesses (e.g. the McDonald’s Corporation) which includes all their corporations’ franchises in the nation would meet the criteria to report to the EPA, but each individual franchise of these corporations would not meet the criteria exceeding 25,000 metric tons. These specific franchise locations (which could be owned by independent business owners) of fast-food corporations in each census tract would show similar trends of emissions to small business which will not meet the criteria. This has limited the data available for this research and could greatly limit future studies on the environmental effects of fast-food establishments in counties or census tracts.
Conclusion:
This study has visualized evident disparities in social determinants of health between Marion and Hamilton County that can account for their differences in health outcomes and life expectancies. Having food access means having nutritious and healthy food both available nearby and affordable. From this study’s results as well as information from other studies and data, limited food access and food insecurity are at significantly greater rates in Marion County especially in the central region. These are the same areas with higher rates of obesity, diabetes, and heart disease as well as lower life expectancies. Greater percentages of population in Marion County are limited by both scarcity of accessible grocery stores and low income. Trends of low grocery store access in Hamilton County’s southern census tracts are buffered by their significantly higher income; their high income can account for their lower rates of adverse health affects and higher life expectancy rates despite having limited grocery access. This reveals the buffering effect income has on the health effects of limited food access. The census tract in the northern region of Hamilton County with health outcome trends inconsistent with the rest of the county can be accounted for by both lower income and lower access to grocery stores. Although the dashboard emphasized the dominating role of income in buffering limited food availability, it emphasizes how low income can exacerbate the effects of low food availability.
Even without data on Hamilton County’s fast-food density, there is sufficient data from this study on the disparities in income and grocery store access between Hamilton and Marion County to assume that Marion County overall has lower food access. It is also safe to suggests that Marion County residents have more access to fast-food restaurants than grocery stores considering fast-food density is over two times greater than grocery store density and over twenty-five times more than farmers market density. Food choices and dietary habits are influenced by food outlets available in the near vicinity as well as food affordability (WHO,2007)(Furey, 2001), therefore it is safe to suggest that because of Marion County’s significant rates of high poverty, low income, low grocery store access, and high fast-food to grocery store ratio, residents are more inclined to consume high cholesterol, high trans and saturated fat, high sodium, and high refined sugars which is found in fast-food products as they are both affordable and accessible. Although there is no data on food deserts in Hamilton County, it is safe to assume that food deserts do not exist within the county’s southern region despite of extremely high rates of low grocery store access, because it only meets one of the two criteria, the low-access threshold but not the low-income threshold (USDA, 2011).
There were some significant limitations to this study and its findings such as: incomplete data, information, and location needed to quantically and visually compare the two counties assets in fast-food and grocery stores, inability to connect fast-food density to environmental effects, lack of demographic diversity in surveys, limited and outdated data in statistical analysis. For future studies, it would be ideal to minimize the scope of this study and focus on only a handful of census tracts with varying life expectancies and disparities in health outcomes as this could provide more comprehensive and specific analyses. Gathering data in a smaller population would provide more specific life expectancies and health outcomes; in addition it would be easier to locate fast-food establishments, grocery stores/supermarkets with a smaller parameter and relate it back to its specific location; this could provide comprehensive data on densities of fast-food establishments per square mile per population similar to previous studies (Maddock,2004). Focusing on census tracts outside of the college demographic for surveys on meal recall diaries could also provide more diverse information on eating habits and fast-food consumption. On the other hand, challenges in acquiring accurate and reliable data on each fast-food franchise per census tracts could arise because of the limitations of the EPA guidelines on greenhouse gas reporting.
Importance of Future Studies:
There is a bidirectional relationship between humans and the environment and within this relationship, fast-food plays a role in affecting human health and the environment and exacerbates unsustainable circumstances for both humans and their environment. Further studies are especially important now to shed light on progressive and feasible solutions, policies, and practices that can mitigate problems associated with fast-food; these issues include but are not limited to: the decline of quality of population health and life expectancies; environmental issues such as climate change and depletion of resources. These problems play a role in the network between humans and environment. There is limited access to healthy, nutritious, and quality food such as fruits and vegetables but also high access and densities of fast-food restaurants that produce large amounts of wastes and overconsume limited resources such as livestock, water, fuel, and land.
The tendency to make food choices based on the food outlets available nearby (Furey, 2001) are problematic for the health of populations in areas with limited grocery stores but high densities of fast-food restaurants as people consume less nutrient dense food such as fruits, vegetables, whole grains, and lean meat from grocery stores and consume more food high in sodium, saturated fat, trans fats, cholesterol, refined sugars from fast-food restaurants. The growth and expansion of these establishments have exacerbated their contribution to environmental effects through the increase in their GHG emissions and wastes as well as consumption of limited resources. This in turn affects a variety of environmental systems that the human population rely on such as clean water, agricultural goods, clean air, etc. Without further studies and policies to moderate fast-food consumption and the environmental effects of fast-food establishments, humans, the systems in which they rely on, and the environment in which they habituate will continue to decline.
References:
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- Weathers T.D., Leech T.G.J., Staten L.K., Adams E.A., Colbert J.T., Comer K.F. (2015) Worlds Apart: Gaps in Life Expectancy in the Indianapolis Metro Area. Available from the SAVI Community Information System at: https://savi.org
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