Quick-Service Restaurant Density and Relation to Obesity and Disease Rates

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Obesity and other chronic diseases can have multifactorial contributing factors, one of which being environmental factors, notably available food sources such as supermarkets and restaurants. However, it is unclear the relation between the presence of subcategories of QSRs and the prevalence of certain common comorbidities. This study aims to assess the relationship between QSR density and the prevalence of obesity, hypertension (HTN), and type 2 diabetes mellitus (DM). Subjects/Methods : A cross-sectional analysis was conducted using data on adults in the United States. State-level disease prevalence in 2022 was obtained from the Trust for America’s Health 2023 State of Obesity Report . Restaurant density data by state were obtained from online databases, restaurant websites, and the US Census Bureau. Total QSR density by state, along with subcategory densities of “Burger,” “Pizza,” “Breakfast/Dessert,” and individual chain restaurants, were analyzed. Linear regression models were created, and statistical significance was determined using correlation coefficient and sample size. Results : There was a significant positive correlation between overall QSR density and the rate of DM across the country ( P = 0.007). QSR subset analysis revealed significant positive correlations between each QSR subcategory and obesity, HTN, and DM (all P < 0.05), with the exception of DM with Subway ( P = 0.100). Conclusions : These significant positive relationships between obesity rates and comorbidities with QSR prevalence may indicate an example of the influence of the surrounding environment on disease rates. These patterns may serve to promote future endeavors to change the restaurant industry in order to improve health outcomes. Health sciences/Health care/Public health/Epidemiology Health sciences/Medical research/Epidemiology quick service restaurant fast food obesity hypertension diabetes Figures Figure 1 Figure 2 Introduction Fast-food restaurants, specifically QSRs, have seen a dramatic rise in popularity globally since their founding. 1 Within the context of the fast-paced American work culture, many working individuals and their families increasingly rely on quick and convenient food options to accommodate demanding and time-constrained schedules. 2 , 3 Fast food, therefore, has become a staple in the American diet among both adults and children thanks to its quick service, ease of access, inexpensiveness, palatability, and consistency across franchises. 2 , 4 Many fast-food items are characterized by high fat content and a poor fatty acid composition, combined with large portion sizes, high energy density, substantial amounts of refined carbohydrates and added sugars, and a high glycemic load, while lacking dietary fiber and essential micronutrients. Fast-food consumption has thus been identified as a major contributor to higher fat and calorie intake, as well as reduced micronutrient density in the diet. 5 As a matter of fact, during the early 2000s, concurrent increases in both total food intake and fast-food consumption were observed, 3 and national dietary survey data indicate that the overall energy intake of the U.S. population has risen over the past several decades. 5 Several studies have shown that frequent fast-food consumption increases the risk of overweight, obesity, metabolic syndrome, DM, and cardiovascular disease. 5 Regular fast-food intake has also been linked to higher body mass index, and excessive body weight is associated with multiple comorbidities, including HTN, cardiovascular disease, DM, depression, infertility, and various cancers. These conditions collectively contribute to over 300,000 excess deaths and approximately $ 100 billion in annual medical costs. 4 , 6 However, the relationship between the density of subcategorized QSRs across the United States and factors such as obesity, HTN, and DM risk remains unstudied. Therefore, our aim was to study the density of various types of QSRs across the United States and its relation to obesity and other common comorbidities. Materials/Subjects and Methods Study Population The percentage of adults with obesity, HTN, and DM in 2022 in each state, as well as nationwide, was sourced from the Adult Obesity Rates and Related Health Indicators chart found in the Trust for America’s Health’s 2023 State of Obesity Report . 7 This report sources state-level adult obesity and health data from the Behavioral Risk Factor Surveillance System, which conducts telephone health surveys to retrieve self-reported height, weight, and other health data from adults living in each state, and each of the state survey results is representative of the population of that state. Outcomes and Variables Restaurant density data by state were calculated from multiple sources, including a pre-existing database compiled by NiceRx, a patient assistance and medication access company, summarizing total and select QSR regional densities across individual states for multiple popular restaurant brands. 8 This website uses numbers from the U.S. Census Bureau for total fast-food restaurants, and uses population figures from five-year estimates from the U.S. Census Bureau’s 2019 American Community Survey. 9 The database quantifies total QSR density as the amount of QSRs per 100,000 individuals living in each state, further providing the densities of each of the top 10 most popular QSRs in the U.S. per 100,000 people. The restaurants listed in this source were Subway, Starbucks, McDonald’s, Dunkin’ Donuts, Burger King, Taco Bell, Domino’s, Wendy’s, Dairy Queen, and KFC. As these restaurants had readily available data, we decided to primarily focus on these and group them into subcategories based on the genre of food they offered, which have been established in the food service literature. 10 The subcategories are reflected in Table 1 . Each subcategory density was quantified as the number of restaurants per 100,000 people in each state. As pizza restaurant chains are prevalent in the US, more data were sought to augment the data in this subcategory, specifically from Pizza Hut, a large competing national brand. Pizza Hut restaurant prevalence data were collected by gathering location numbers by state from the publicly available retail website. Population data by state were sourced from the 2020 United States census. State-level analysis was conducted by dividing the number of Pizza Hut locations by each state’s population, followed by normalization per 100,000 residents to calculate restaurant density. These density values were added to the existing Domino’s density values to attain the density of Pizza QSRs in each state. Table 1 The QSR subcategories and the restaurants which they include. QSR Subtype Restaurants Included Burger McDonald’s, Burger King Pizza Domino’s, Pizza Hut Breakfast/Dessert Starbucks, Dunkin’ Donuts, Dairy Queen Taco Bell Taco Bell KFC KFC Subway Subway QSR, quick-service restaurant. A total of 49 states, excluding Florida due to lack of data, were analyzed for relationships between QSR density and adult prevalence rates of obesity, HTN, and DM. Statistical Analysis Data were analyzed using Microsoft Excel (Microsoft Corporation, Redmond, WA). Linear regression analysis was performed by graphing scatter plots, then superimposing a line of best fit to study R 2 . Obesity, HTN, and DM prevalence in each of the 49 states (excluding Florida) were each graphed as a function of QSR density in that particular state. Correlations of QSR density and disease rate were analyzed using the Pearson correlation test, and significance was accepted as P ≤ 0.05. P values less than 0.001 are reported as P < 0.001. According to Cohen’s conventions, correlations were interpreted as weak (0.10 ≤ r < 0.30), moderate (0.30 ≤ r < 0.50), and strong ( r ≥ 0.50). Results Initial linear regression analysis assessing the relationship between total QSR density (number per 100,000 population) and disease prevalence showed a statistically significant moderate positive correlation with DM prevalence across the 49 states excluding Florida ( r = 0.30, R² = 0.092, P = 0.034). In contrast, the associations between total QSR density and the prevalence of obesity and HTN were weaker and not statistically significant. For obesity, the correlation coefficient was r = − 0.22 ( R² = 0.049, P = 0.126), and for HTN, r = 0.12 ( R² = 0.015, P = 0.402), suggesting that the overall presence of QSRs does not meaningfully account for differences in obesity or HTN rates between states. When Hawaii was excluded due to its classification as a statistical outlier, the correlation between total QSR density and DM prevalence strengthened further, increasing to r = 0.39 ( R² = 0.15, P = 0.007), indicating a moderate, statistically significant relationship. However, correlations with obesity ( r = − 0.10, R² = 0.01, P = 0.515) and HTN ( r = 0.20, R² = 0.04, P = 0.166) remained weak and nonsignificant. To refine the analysis, QSRs were analyzed according to the subtypes reflected in Table 1 . Correlations between the density of each QSR subcategory in 49 states (excluding Florida) and disease prevalence were assessed individually. The category of Burger QSRs (McDonald’s and Burger King) showed the strongest positive associations across all health outcomes. Obesity had a robust correlation ( r = 0.71, R² = 0.50, P < 0.001), indicating that 50% of the variation in obesity rates across states could be attributed to burger QSR density (Fig. 1). Similarly strong relationships were observed for HTN ( r = 0.72, R² = 0.51) and DM ( r = 0.65, R² = 0.43), both P < 0.001. Pizza QSRs (Domino’s and Pizza Hut) showed a relatively strong, statistically significant positive correlation with obesity ( r = 0.53, R² = 0.29, P = 0.047), and a moderate correlation with HTN ( r = 0.37, R² = 0.14, P = 0.009). However, the correlation with DM ( r = 0.28, R² = 0.080, P = 0.045) was small, but significant and in a positive direction (Table 2 ). Table 2 Pearson’s r , R² , and P values for correlations between QSR subcategory density and adult obesity or disease rates across 49 U.S. states (excluding Florida). Relationship Pearson’s r R 2 P value Obesity and Total QSR –0.22 0.05 0.126 HTN and Total QSR 0.12 0.02 0.402 DM and Total QSR 0.30 0.10 0.034* Obesity and Total QSR (excluding Hawaii) –0.10 0.01 0.515 HTN and Total QSR (excluding Hawaii) 0.20 0.04 0.166 DM and Total QSR (excluding Hawaii) 0.39 0.15 0.007* Obesity and Burger 0.71 0.50 < 0.001* HTN and Burger 0.72 0.51 < 0.001* DM and Burger 0.65 0.43 < 0.001* Obesity and Pizza 0.53 0.29 0.047* HTN and Pizza 0.37 0.14 0.009* DM and Pizza 0.28 0.08 0.045* Obesity and Breakfast/Dessert –0.58 0.34 < 0.001* HTN and Breakfast/Dessert –0.41 0.17 0.003* DM and Breakfast/Dessert –0.37 0.14 0.008* Obesity and Subway 0.62 0.38 < 0.001* HTN and Subway 0.37 0.14 0.009* DM and Subway 0.24 0.06 0.100* Obesity and Taco Bell 0.59 0.35 < 0.001* HTN and Taco Bell 0.56 0.31 < 0.001* DM and Taco Bell 0.55 0.30 < 0.001* Obesity and KFC 0.62 0.39 < 0.001* HTN and KFC 0.74 0.55 < 0.001* DM and KFC 0.73 0.54 < 0.001* QSR, quick-service restaurant; HTN, hypertension; DM, type 2 diabetes; Burger, McDonald’s and Burger King; Pizza, Domino’s and Pizza Hut; Breakfast/Dessert, Starbucks, Dunkin’ Donuts, and Dairy Queen. KFC locations demonstrated strong positive correlations with all three conditions, including obesity ( r = 0.62, R² = 0.39), HTN ( r = 0.74, R² = 0.55), and DM ( r = 0.73, R² = 0.54) (all P < 0.001). Taco Bell density also showed significant positive correlations with obesity ( r = 0.59, R² = 0.35), HTN ( r = 0.56, R² = 0.31), and DM ( r = 0.55, R² = 0.30), all P < 0.001. Similarly, Subway locations were strongly correlated with obesity ( r = 0.62, R² = 0.38, P < 0.001). However, there was only a moderate relationship with HTN ( r = 0.37, R² = 0.14, P < 0.009), and no significant relationship with DM ( r = 0.24, R² = 0.057, P = 0.100) (Table 2 ). Interestingly, the Breakfast/Dessert subcategory (Starbucks, Dunkin’ Donuts, Dairy Queen) was the only category to demonstrate a significant inverse correlation with all three diseases. The strongest negative association was observed with obesity ( r = − 0.58, R² = 0.34, P < 0.001) (Fig. 2), followed by moderate relationships with HTN ( r = − 0.41, R² = 0.17, P = 0.003) and DM ( r = − 0.37, R² = 0.14, P = 0.008). These findings will be discussed further in the Discussion section, as they may reflect confounding factors such as urban density, walkability, and socioeconomic differences. Discussion The increased prevalence of DM in states with greater total QSR densities may indicate that the presence of more QSRs is correlated to an increased DM risk in adults living in proximity to QSRs. The positive correlations between the density of various QSRs and the prevalence of obesity, HTN, and DM provide a glimpse into the link between environment and obesity and disease rate. This indicates a possible environmental influence on health, which can be a potential target to improve public health and mitigate the burden of disease. The inverse relationship between the prevalence of dessert and breakfast restaurants and obesity, HTN, and DM may reflect the physical activity of people in the areas which these restaurants predominate. Breakfast restaurants are commonly quick grab-and-go type establishments frequently situated in areas with high foot traffic. An increased activity level in areas with these breakfast locations may be one factor to explain this unexpected relationship. Similar studies have found that a greater presence of fast-food restaurants was found to have higher mortality and hospital admission rates for acute coronary issues and a higher risk of overweight and obesity, aligning with our findings. 5 , 11 Further, the greater prevalence of obesity, HTN, and DM in states with a greater amount of the specific subcategories of QSRs indicated in Table 2 may indicate that adults living in proximity to these specific subcategories of QSRs may have an increased risk of obesity, HTN, and DM. A possible explanation for these selective patterns may be due to the caloric and macro/micro- nutrient makeup of the available foods served at these QSRs. Highly caloric and hyper-palatable foods that may be served in larger quantities as well as the temporal availability and locational convenience of these specific QSRs may have an effect on the dietary patterns of adults living in proximity to these types of QSRs, increasing their risk of disease. 12 Studies have linked higher QSR density to lower quality diets and higher weight in local residents in the U.S. and Montreal. 13 Evidence has also shown that the variety of food options has an influence on food choices, potentially showing that higher QSR density may influence purchasing decisions and normalize fast-food consumption due to the higher availability of fast food and the drowning out of more healthy options. 14 Different eating patterns may exist between states or within states, which can be due to different cultural patterns, varying socioeconomic status, and types of transportation usage, explaining why people may choose to eat at QSRs. 15 – 17 Also transportation patterns such as increased driving patterns and highway usage, or increased walking, may affect people’s access to QSRs. 18 For example, the U.S. interstate highway system houses a significant amount of QSRs, and the normalization of the personal car in American culture has enabled access to these QSRs. Interestingly, we found that a higher prevalence of breakfast/dessert QSRs was negatively correlated with obesity, HTN, and DM, which may seem to contradict our other findings and the common literature stating that proximity to QSRs is linked to higher disease risk. For example, in the U.K., more fast-food outlets within walking distance of residents were associated with a higher chance of having DM or obesity. 19 However, literature seems to suggest that a higher presence of QSRs tend to be found within urban areas which tend to be more walkable. Although we did not locate peer-reviewed studies that specifically examine breakfast/dessert restaurants by urbanicity, a study in Wisconsin showed higher general restaurant density in urban areas and that urban and suburban neighborhoods had slightly healthier nutrition environment scores compared to rural neighborhoods. 20 Additionally, some studies have reported lower body weight among individuals living in areas with greater fast-food restaurant availability, suggesting that such areas may also be more walkable and therefore associated with higher physical activity levels and lower risks of DM and obesity. 14 , 21 This may present a confound in our study, as physical activity may reduce the risk for obesity, HTN, and DM despite a higher presence of QSRs. Other papers have also studied QSR density but in relation to other types of restaurants, such as full-service restaurants, which may explain consumer food choices and potentially drive health outcomes. In the U.S., the rise of QSRs through mass marketing and widespread franchising has led local businesses and full-service restaurants out of business and has solidified QSRs existence in the U.S. as a primary source of fast and accessible food and limiting consumers’ options to healthier alternatives. 3 , 22 In Canada, a higher concentration of fast-food restaurants within walking distance relative to other types of restaurants was correlated to a higher risk of developing DM in young adults; however, no link was found between the absolute amount of fast-food restaurants and DM. 14 Limitations One limitation of this study is the discrepancy in data collection years across sources. The 2023 State of Obesity Report reports disease prevalence for 2022, whereas the NiceRx database provides restaurant and population data based on the 2020 U.S. Census. 7 , 8 Consequently, changes in restaurant density or population characteristics between 2020 and 2022 may not be captured in the correlation analyses. In addition, because the data are cross-sectional, temporal trends in QSR density and disease prevalence within each state could not be assessed. Future studies could address this limitation by examining year-to-year changes in disease prevalence and QSR density within states. Further, the State of Obesity Report determines obesity using self-reported height and weight, which may result in a lower obesity rate than actual due to the tendency of individuals to overestimate their height and underestimate their weight. 7 Another limitation is that our analysis is only limited to the state level and may not reflect patterns at the community level. The QSR densities were calculated at the state level using the number of QSRs located in the state and the total population of the state. This may not reflect the distribution of people living in the state and the concentrations of QSRs in specific communities and cities. Factors such as average household income, race, gender, and healthcare accessibility are not accounted for, and may be a confounding variable in understanding the rates of obesity, HTN, and DM. Lower socioeconomic status may limit the ability to afford healthier food options, while some areas may function as food deserts or contain a high density of QSRs, further restricting access to nutritious foods and leading residents to rely more heavily on QSRs. Conclusion In conclusion, significant positive correlations were identified between overall QSR density and DM prevalence, as well as between QSR subcategory densities and rates of obesity, HTN, and DM. These positive relationships between obesity rates and comorbidities with QSR prevalence suggest that the surrounding food environment may influence disease prevalence within communities. Further research is needed to clarify the causal mechanisms linking QSR density and chronic disease risk. Nevertheless, the observed patterns highlight opportunities for public health initiatives and policy interventions aimed at improving dietary environments and health outcomes. Declarations Competing Interests: All the authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript. Funding: No funding was provided for this study. Author Contributions: AMM conceptualized the work and design and edited the final manuscript. PAG was responsible for data collection and formatting, statistical analysis, and manuscript writing. KYG was responsible for data formatting, statistical analysis, and manuscript writing and editing. SA was responsible for manuscript writing and editing. Data Availability Statement: Data are available upon reasonable request from the corresponding author. References What is Quick Service Restaurant? Types and Features. https://restorapos.com/blog/what-is-quick-service-restaurant (accessed 7 Sept2025). Rydell SA, Harnack LJ, Oakes JM, Story M, Jeffery RW, French SA. Why Eat at Fast-Food Restaurants: Reported Reasons among Frequent Consumers. J Am Diet Assoc 2008; 108: 2066–2070. Freund P, Martin G. Fast Cars/Fast Foods: Hyperconsumption and its Health and Environmental Consequences. Soc Theory Health 2008; 6: 309–322. Fleischhacker SE, Evenson KR, Rodriguez DA, Ammerman AS. A systematic review of fast food access studies. Obes Rev 2011; 12: e460–e471. Bahadoran Z, Mirmiran P, Azizi F. Fast Food Pattern and Cardiometabolic Disorders: A Review of Current Studies. 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1","display":"","copyAsset":false,"role":"figure","size":211909,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992721/v1/a0ad0102ae2aa09d8e54fdfd.jpg"},{"id":95800762,"identity":"47bd2ae6-f9ed-43c2-8d9e-03851703da8b","added_by":"auto","created_at":"2025-11-13 08:23:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216888,"visible":true,"origin":"","legend":"\u003cp\u003eLegend not included with this version.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992721/v1/79d071c64d524e35177c7c3f.jpg"},{"id":99318862,"identity":"beaa5106-df61-435e-9874-a1ef9912aa0a","added_by":"auto","created_at":"2025-12-31 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09:46:13","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":167451,"visible":true,"origin":"","legend":"Supplementary Figure 4","description":"","filename":"SupplementaryFigure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992721/v1/cb2b5c3fe72edb982524dad6.jpg"},{"id":95800991,"identity":"aecffbc8-7af5-4786-9103-3a86451f142f","added_by":"auto","created_at":"2025-11-13 08:24:07","extension":"jpg","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":170373,"visible":true,"origin":"","legend":"Supplementary Figure 5","description":"","filename":"SupplementaryFigure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992721/v1/5726ec7f5bac90e48632d0cb.jpg"},{"id":95722324,"identity":"8c1e74bc-e12b-45da-a8fd-75417767a1cf","added_by":"auto","created_at":"2025-11-12 09:46:13","extension":"jpg","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":166922,"visible":true,"origin":"","legend":"Supplementary Figure 6","description":"","filename":"SupplementaryFigure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7992721/v1/794da879ff9b8c232c8e97ff.jpg"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Quick-Service Restaurant Density and Relation to Obesity and Disease Rates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eFast-food restaurants, specifically QSRs, have seen a dramatic rise in popularity globally since their founding.\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e Within the context of the fast-paced American work culture, many working individuals and their families increasingly rely on quick and convenient food options to accommodate demanding and time-constrained schedules.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e Fast food, therefore, has become a staple in the American diet among both adults and children thanks to its quick service, ease of access, inexpensiveness, palatability, and consistency across franchises.\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eMany fast-food items are characterized by high fat content and a poor fatty acid composition, combined with large portion sizes, high energy density, substantial amounts of refined carbohydrates and added sugars, and a high glycemic load, while lacking dietary fiber and essential micronutrients. Fast-food consumption has thus been identified as a major contributor to higher fat and calorie intake, as well as reduced micronutrient density in the diet.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e As a matter of fact, during the early 2000s, concurrent increases in both total food intake and fast-food consumption were observed,\u003csup\u003e3\u003c/sup\u003e and national dietary survey data indicate that the overall energy intake of the U.S. population has risen over the past several decades.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eSeveral studies have shown that frequent fast-food consumption increases the risk of overweight, obesity, metabolic syndrome, DM, and cardiovascular disease.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Regular fast-food intake has also been linked to higher body mass index, and excessive body weight is associated with multiple comorbidities, including HTN, cardiovascular disease, DM, depression, infertility, and various cancers. These conditions collectively contribute to over 300,000 excess deaths and approximately \u003cspan\u003e$\u003c/span\u003e100\u0026nbsp;billion in annual medical costs.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eHowever, the relationship between the density of subcategorized QSRs across the United States and factors such as obesity, HTN, and DM risk remains unstudied. Therefore, our aim was to study the density of various types of QSRs across the United States and its relation to obesity and other common comorbidities.\u003c/p\u003e"},{"header":"Materials/Subjects and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Population\u003c/strong\u003e\u003cp\u003eThe percentage of adults with obesity, HTN, and DM in 2022 in each state, as well as nationwide, was sourced from the Adult Obesity Rates and Related Health Indicators chart found in the Trust for America\u0026rsquo;s Health\u0026rsquo;s 2023 \u003cem\u003eState of Obesity Report\u003c/em\u003e.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e This report sources state-level adult obesity and health data from the Behavioral Risk Factor Surveillance System, which conducts telephone health surveys to retrieve self-reported height, weight, and other health data from adults living in each state, and each of the state survey results is representative of the population of that state.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eOutcomes and Variables\u003c/strong\u003e\u003cp\u003eRestaurant density data by state were calculated from multiple sources, including a pre-existing database compiled by NiceRx, a patient assistance and medication access company, summarizing total and select QSR regional densities across individual states for multiple popular restaurant brands.\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e This website uses numbers from the U.S. Census Bureau for total fast-food restaurants, and uses population figures from five-year estimates from the U.S. Census Bureau\u0026rsquo;s 2019 American Community Survey.\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e The database quantifies total QSR density as the amount of QSRs per 100,000 individuals living in each state, further providing the densities of each of the top 10 most popular QSRs in the U.S. per 100,000 people. The restaurants listed in this source were Subway, Starbucks, McDonald\u0026rsquo;s, Dunkin\u0026rsquo; Donuts, Burger King, Taco Bell, Domino\u0026rsquo;s, Wendy\u0026rsquo;s, Dairy Queen, and KFC. As these restaurants had readily available data, we decided to primarily focus on these and group them into subcategories based on the genre of food they offered, which have been established in the food service literature.\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e The subcategories are reflected in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Each subcategory density was quantified as the number of restaurants per 100,000 people in each state. As pizza restaurant chains are prevalent in the US, more data were sought to augment the data in this subcategory, specifically from Pizza Hut, a large competing national brand. Pizza Hut restaurant prevalence data were collected by gathering location numbers by state from the publicly available retail website. Population data by state were sourced from the 2020 United States census. State-level analysis was conducted by dividing the number of Pizza Hut locations by each state\u0026rsquo;s population, followed by normalization per 100,000 residents to calculate restaurant density. These density values were added to the existing Domino\u0026rsquo;s density values to attain the density of Pizza QSRs in each state.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe QSR subcategories and the restaurants which they include.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eQSR Subtype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRestaurants Included\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBurger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMcDonald\u0026rsquo;s, Burger King\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePizza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDomino\u0026rsquo;s, Pizza Hut\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBreakfast/Dessert\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStarbucks, Dunkin\u0026rsquo; Donuts, Dairy Queen\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTaco Bell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaco Bell\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKFC\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSubway\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003eQSR, quick-service restaurant.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/p\u003e\u003cp\u003eA total of 49 states, excluding Florida due to lack of data, were analyzed for relationships between QSR density and adult prevalence rates of obesity, HTN, and DM.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003cp\u003eData were analyzed using Microsoft Excel (Microsoft Corporation, Redmond, WA). Linear regression analysis was performed by graphing scatter plots, then superimposing a line of best fit to study \u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e. Obesity, HTN, and DM prevalence in each of the 49 states (excluding Florida) were each graphed as a function of QSR density in that particular state. Correlations of QSR density and disease rate were analyzed using the Pearson correlation test, and significance was accepted as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.05. \u003cem\u003eP\u003c/em\u003e values less than 0.001 are reported as \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. According to Cohen\u0026rsquo;s conventions, correlations were interpreted as weak (0.10\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.30), moderate (0.30\u0026thinsp;\u0026le;\u0026thinsp;\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.50), and strong (\u003cem\u003er\u003c/em\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.50).\u003c/p\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eInitial linear regression analysis assessing the relationship between total QSR density (number per 100,000 population) and disease prevalence showed a statistically significant moderate positive correlation with DM prevalence across the 49 states excluding Florida (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.30, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.092, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034). In contrast, the associations between total QSR density and the prevalence of obesity and HTN were weaker and not statistically significant. For obesity, the correlation coefficient was \u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.22 (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.049, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.126), and for HTN, \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.12 (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.015, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.402), suggesting that the overall presence of QSRs does not meaningfully account for differences in obesity or HTN rates between states.\u003c/p\u003e\u003cp\u003eWhen Hawaii was excluded due to its classification as a statistical outlier, the correlation between total QSR density and DM prevalence strengthened further, increasing to \u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.39 (\u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.15, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), indicating a moderate, statistically significant relationship. However, correlations with obesity (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.10, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.01, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.515) and HTN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.04, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.166) remained weak and nonsignificant.\u003c/p\u003e\u003cp\u003eTo refine the analysis, QSRs were analyzed according to the subtypes reflected in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Correlations between the density of each QSR subcategory in 49 states (excluding Florida) and disease prevalence were assessed individually.\u003c/p\u003e\u003cp\u003eThe category of Burger QSRs (McDonald\u0026rsquo;s and Burger King) showed the strongest positive associations across all health outcomes. Obesity had a robust correlation (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.71, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.50, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that 50% of the variation in obesity rates across states could be attributed to burger QSR density (Fig.\u0026nbsp;1). Similarly strong relationships were observed for HTN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.72, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.51) and DM (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.65, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.43), both \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001.\u003c/p\u003e\u003cp\u003ePizza QSRs (Domino\u0026rsquo;s and Pizza Hut) showed a relatively strong, statistically significant positive correlation with obesity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.53, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.29, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.047), and a moderate correlation with HTN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009). However, the correlation with DM (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.28, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.080, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.045) was small, but significant and in a positive direction (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e, \u003cem\u003eR\u0026sup2;\u003c/em\u003e, and \u003cem\u003eP\u003c/em\u003e values for correlations between QSR subcategory density and adult obesity or disease rates across 49 U.S. states (excluding Florida).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRelationship\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePearson\u0026rsquo;s \u003cem\u003er\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Total QSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.126\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Total QSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.402\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Total QSR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.034*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Total QSR (excluding Hawaii)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.515\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Total QSR (excluding Hawaii)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.166\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Total QSR (excluding Hawaii)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.007*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Burger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Burger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Burger\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Pizza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.047*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Pizza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Pizza\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.045*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Breakfast/Dessert\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Breakfast/Dessert\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.003*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Breakfast/Dessert\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u0026ndash;0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.008*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Subway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Subway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.009*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Subway\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.100*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and Taco Bell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and Taco Bell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and Taco Bell\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eObesity and KFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHTN and KFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDM and KFC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eQSR, quick-service restaurant; HTN, hypertension; DM, type 2 diabetes; Burger, McDonald\u0026rsquo;s and Burger King; Pizza, Domino\u0026rsquo;s and Pizza Hut; Breakfast/Dessert, Starbucks, Dunkin\u0026rsquo; Donuts, and Dairy Queen.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eKFC locations demonstrated strong positive correlations with all three conditions, including obesity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.39), HTN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.74, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.55), and DM (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.73, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.54) (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Taco Bell density also showed significant positive correlations with obesity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.59, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.35), HTN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.56, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.31), and DM (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.55, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.30), all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Similarly, Subway locations were strongly correlated with obesity (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.62, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.38, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was only a moderate relationship with HTN (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.009), and no significant relationship with DM (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.057, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.100) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, the Breakfast/Dessert subcategory (Starbucks, Dunkin\u0026rsquo; Donuts, Dairy Queen) was the only category to demonstrate a significant inverse correlation with all three diseases. The strongest negative association was observed with obesity (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.58, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.34, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;2), followed by moderate relationships with HTN (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.41, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.17, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and DM (\u003cem\u003er\u003c/em\u003e = \u0026minus;\u0026thinsp;0.37, \u003cem\u003eR\u0026sup2;\u003c/em\u003e = 0.14, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.008). These findings will be discussed further in the Discussion section, as they may reflect confounding factors such as urban density, walkability, and socioeconomic differences.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe increased prevalence of DM in states with greater total QSR densities may indicate that the presence of more QSRs is correlated to an increased DM risk in adults living in proximity to QSRs.\u003c/p\u003e\u003cp\u003eThe positive correlations between the density of various QSRs and the prevalence of obesity, HTN, and DM provide a glimpse into the link between environment and obesity and disease rate. This indicates a possible environmental influence on health, which can be a potential target to improve public health and mitigate the burden of disease.\u003c/p\u003e\u003cp\u003eThe inverse relationship between the prevalence of dessert and breakfast restaurants and obesity, HTN, and DM may reflect the physical activity of people in the areas which these restaurants predominate. Breakfast restaurants are commonly quick grab-and-go type establishments frequently situated in areas with high foot traffic. An increased activity level in areas with these breakfast locations may be one factor to explain this unexpected relationship.\u003c/p\u003e\u003cp\u003eSimilar studies have found that a greater presence of fast-food restaurants was found to have higher mortality and hospital admission rates for acute coronary issues and a higher risk of overweight and obesity, aligning with our findings.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eFurther, the greater prevalence of obesity, HTN, and DM in states with a greater amount of the specific subcategories of QSRs indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e may indicate that adults living in proximity to these specific subcategories of QSRs may have an increased risk of obesity, HTN, and DM. A possible explanation for these selective patterns may be due to the caloric and macro/micro- nutrient makeup of the available foods served at these QSRs. Highly caloric and hyper-palatable foods that may be served in larger quantities as well as the temporal availability and locational convenience of these specific QSRs may have an effect on the dietary patterns of adults living in proximity to these types of QSRs, increasing their risk of disease.\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e Studies have linked higher QSR density to lower quality diets and higher weight in local residents in the U.S. and Montreal.\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e Evidence has also shown that the variety of food options has an influence on food choices, potentially showing that higher QSR density may influence purchasing decisions and normalize fast-food consumption due to the higher availability of fast food and the drowning out of more healthy options.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e\u003cp\u003eDifferent eating patterns may exist between states or within states, which can be due to different cultural patterns, varying socioeconomic status, and types of transportation usage, explaining why people may choose to eat at QSRs.\u003csup\u003e\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e–\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003eAlso transportation patterns such as increased driving patterns and highway usage, or increased walking, may affect people’s access to QSRs.\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e For example, the U.S. interstate highway system houses a significant amount of QSRs, and the normalization of the personal car in American culture has enabled access to these QSRs.\u003c/p\u003e\u003cp\u003eInterestingly, we found that a higher prevalence of breakfast/dessert QSRs was negatively correlated with obesity, HTN, and DM, which may seem to contradict our other findings and the common literature stating that proximity to QSRs is linked to higher disease risk. For example, in the U.K., more fast-food outlets within walking distance of residents were associated with a higher chance of having DM or obesity.\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e However, literature seems to suggest that a higher presence of QSRs tend to be found within urban areas which tend to be more walkable. Although we did not locate peer-reviewed studies that specifically examine breakfast/dessert restaurants by urbanicity, a study in Wisconsin showed higher general restaurant density in urban areas and that urban and suburban neighborhoods had slightly healthier nutrition environment scores compared to rural neighborhoods.\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e Additionally, some studies have reported lower body weight among individuals living in areas with greater fast-food restaurant availability, suggesting that such areas may also be more walkable and therefore associated with higher physical activity levels and lower risks of DM and obesity.\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e This may present a confound in our study, as physical activity may reduce the risk for obesity, HTN, and DM despite a higher presence of QSRs.\u003c/p\u003e\u003cp\u003eOther papers have also studied QSR density but in relation to other types of restaurants, such as full-service restaurants, which may explain consumer food choices and potentially drive health outcomes. In the U.S., the rise of QSRs through mass marketing and widespread franchising has led local businesses and full-service restaurants out of business and has solidified QSRs existence in the U.S. as a primary source of fast and accessible food and limiting consumers’ options to healthier alternatives.\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e In Canada, a higher concentration of fast-food restaurants within walking distance relative to other types of restaurants was correlated to a higher risk of developing DM in young adults; however, no link was found between the absolute amount of fast-food restaurants and DM.\u003csup\u003e14\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eOne limitation of this study is the discrepancy in data collection years across sources. The 2023 \u003cem\u003eState of Obesity Report\u003c/em\u003e reports disease prevalence for 2022, whereas the NiceRx database provides restaurant and population data based on the 2020 U.S. Census.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e Consequently, changes in restaurant density or population characteristics between 2020 and 2022 may not be captured in the correlation analyses. In addition, because the data are cross-sectional, temporal trends in QSR density and disease prevalence within each state could not be assessed. Future studies could address this limitation by examining year-to-year changes in disease prevalence and QSR density within states.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFurther, the \u003cem\u003eState of Obesity Report\u003c/em\u003e determines obesity using self-reported height and weight, which may result in a lower obesity rate than actual due to the tendency of individuals to overestimate their height and underestimate their weight.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e Another limitation is that our analysis is only limited to the state level and may not reflect patterns at the community level. The QSR densities were calculated at the state level using the number of QSRs located in the state and the total population of the state. This may not reflect the distribution of people living in the state and the concentrations of QSRs in specific communities and cities. Factors such as average household income, race, gender, and healthcare accessibility are not accounted for, and may be a confounding variable in understanding the rates of obesity, HTN, and DM. Lower socioeconomic status may limit the ability to afford healthier food options, while some areas may function as food deserts or contain a high density of QSRs, further restricting access to nutritious foods and leading residents to rely more heavily on QSRs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, significant positive correlations were identified between overall QSR density and DM prevalence, as well as between QSR subcategory densities and rates of obesity, HTN, and DM. These positive relationships between obesity rates and comorbidities with QSR prevalence suggest that the surrounding food environment may influence disease prevalence within communities. Further research is needed to clarify the causal mechanisms linking QSR density and chronic disease risk. Nevertheless, the observed patterns highlight opportunities for public health initiatives and policy interventions aimed at improving dietary environments and health outcomes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting Interests:\u003c/h2\u003e\u003cp\u003eAll the authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eNo funding was provided for this study.\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e\u003cp\u003eAMM conceptualized the work and design and edited the final manuscript. PAG was responsible for data collection and formatting, statistical analysis, and manuscript writing. KYG was responsible for data formatting, statistical analysis, and manuscript writing and editing. SA was responsible for manuscript writing and editing.\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e\u003cp\u003eData are available upon reasonable request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWhat is Quick Service Restaurant? Types and Features. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://restorapos.com/blog/what-is-quick-service-restaurant\u003c/span\u003e\u003cspan address=\"https://restorapos.com/blog/what-is-quick-service-restaurant\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (accessed 7 Sept2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eRydell SA, Harnack LJ, Oakes JM, Story M, Jeffery RW, French SA. 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Weight Status and Restaurant Availability: A Multilevel Analysis. \u003cem\u003eAm J Prev Med\u003c/em\u003e 2008; 34: 127\u0026ndash;133.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"quick service restaurant, fast food, obesity, hypertension, diabetes","lastPublishedDoi":"10.21203/rs.3.rs-7992721/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7992721/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground/Objectives:\u003c/h2\u003e\u003cp\u003eQuick-service restaurants (QSRs), food establishments that focus on efficient \u0026ldquo;on-the-go\u0026rdquo; service, are prevalent in the USA and have seen significant growth in the past decade due to its convenience, inexpensiveness, and food palatability. Obesity and other chronic diseases can have multifactorial contributing factors, one of which being environmental factors, notably available food sources such as supermarkets and restaurants. However, it is unclear the relation between the presence of subcategories of QSRs and the prevalence of certain common comorbidities. This study aims to assess the relationship between QSR density and the prevalence of obesity, hypertension (HTN), and type 2 diabetes mellitus (DM). \u003cb\u003eSubjects/Methods\u003c/b\u003e: A cross-sectional analysis was conducted using data on adults in the United States. State-level disease prevalence in 2022 was obtained from the Trust for America\u0026rsquo;s Health 2023 \u003cem\u003eState of Obesity Report\u003c/em\u003e. Restaurant density data by state were obtained from online databases, restaurant websites, and the US Census Bureau. Total QSR density by state, along with subcategory densities of \u0026ldquo;Burger,\u0026rdquo; \u0026ldquo;Pizza,\u0026rdquo; \u0026ldquo;Breakfast/Dessert,\u0026rdquo; and individual chain restaurants, were analyzed. Linear regression models were created, and statistical significance was determined using correlation coefficient and sample size. \u003cb\u003eResults\u003c/b\u003e: There was a significant positive correlation between overall QSR density and the rate of DM across the country (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007). QSR subset analysis revealed significant positive correlations between each QSR subcategory and obesity, HTN, and DM (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the exception of DM with Subway (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.100). \u003cb\u003eConclusions\u003c/b\u003e: These significant positive relationships between obesity rates and comorbidities with QSR prevalence may indicate an example of the influence of the surrounding environment on disease rates. These patterns may serve to promote future endeavors to change the restaurant industry in order to improve health outcomes.\u003c/p\u003e","manuscriptTitle":"Quick-Service Restaurant Density and Relation to Obesity and Disease Rates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-12 09:46:08","doi":"10.21203/rs.3.rs-7992721/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d7b8c7ab-08a0-4503-800e-aaf48c551637","owner":[],"postedDate":"November 12th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57294036,"name":"Health sciences/Health care/Public health/Epidemiology"},{"id":57294037,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2025-12-30T10:40:12+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-12 09:46:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7992721","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7992721","identity":"rs-7992721","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}