| Course | DPH 810 Advanced Epidemiology in Public Health |
|---|---|
| Module | Module 1 |
| Paper type | Doctoral epidemiological hypothesis paper |
| Length | About 1,046 words, 6 pages |
| Format | APA 7 student paper |
| School | Aspen University |
| Program | Doctor of Public Health |
| Updated | September 2026 |
Free sample paper for DPH 810 Module 1
From Curiosity to Hypothesis: Food Swamps and Incident Type 2 Diabetes in a Composite County
Student Name
Doctor of Public Health Program, Aspen University
DPH 810: Advanced Epidemiology in Public Health
Instructor Name
Month Day, Year
From Curiosity to Hypothesis: Food Swamps and Incident Type 2 Diabetes in a Composite County
Every epidemiological study begins with a question precise enough to answer. This paper develops a question and testable hypothesis for a doctoral project: whether adults living in food swamps, neighborhoods saturated with fast-food and convenience outlets relative to healthy food stores, develop type 2 diabetes at higher rates than adults in neighborhoods with healthier food environments in a composite county of 480,000.
Why This Question
Type 2 diabetes incidence in the composite county is highest in neighborhoods with the most fast-food outlets, and local planners are considering zoning changes. Evidence to guide such policy is limited. An analysis of US counties found that the presence of food swamps predicted adult obesity rates more strongly than the absence of grocery stores, especially in counties with greater income inequality and less mobility (Cooksey-Stowers et al., 2017).
Prior Evidence
Cohort studies suggest neighborhood food environments matter for diabetes. In a multi-ethnic US cohort, better neighborhood resources for physical activity and healthy foods were associated with a 38% lower incidence of type 2 diabetes over about five years (Auchincloss et al., 2009). A Swedish national cohort found that moving into, or living in, areas that gained more health-harming food outlets was associated with higher odds of developing diabetes (Mezuk et al., 2016).
Defining Exposure
Exposure will be the food swamp ratio: the number of fast-food restaurants and convenience stores divided by the number of supermarkets and produce markets within one mile of home for urban residents and five miles for rural residents. Residents will be classified into quartiles, comparing the highest with the lowest.
Defining Outcome
The outcome will be incident type 2 diabetes, defined by a new diagnosis code on two occasions, a hemoglobin A1C of 6.5% or higher or a new prescription for diabetes medication, among adults free of diabetes at baseline. Using several criteria improves sensitivity while requiring confirmation improves specificity.
The Question in Structured Form
The table states the question in PECO format.
| Element | Definition |
|---|---|
| Population | Adults aged 30 to 70 without diabetes, receiving care in the county's two largest health systems |
| Exposure | Highest quartile of food swamp ratio around home address |
| Comparison | Lowest quartile of food swamp ratio |
| Outcome | Incident type 2 diabetes over five years |
Hypotheses
Null hypothesis: the five-year incidence of type 2 diabetes does not differ between adults in the highest and lowest food swamp quartiles after adjustment for confounders. Alternative hypothesis: adults in the highest quartile have higher incidence. The primary effect measure will be the adjusted hazard ratio with its 95% confidence interval. Secondary hypotheses concern stronger associations in lower-income and rural neighborhoods.
A Causal Diagram
A causal diagram clarifies assumptions. Food swamp exposure is hypothesized to raise diabetes risk through diet and weight. Individual income, education, race and ethnicity and age may influence both where people live and diabetes risk, so they are potential confounders. Body mass index lies on the causal pathway and should not be adjusted for in the main analysis, since doing so would remove part of the effect.
Neighborhood Research Challenges
Studying neighborhood effects raises distinct problems: defining the relevant area, separating neighborhood effects from the characteristics of people who live there and accounting for residential mobility. A widely cited review warned that area effects can be confounded by individual factors and urged careful definition of areas and measures (Diez Roux, 2001).
Feasibility
Electronic health record data from two health systems covering about 60% of county adults, linked to geocoded addresses and a commercial business database of food outlets, make the study feasible within a doctoral timeline. The health systems have agreed in principle to provide de-identified data. Outlet data for each year of the study period are available, allowing exposure to be measured over time.
Ethical Considerations
Findings could stigmatize neighborhoods if framed as describing residents rather than environments. Results will be communicated as evidence about retail environments and policy, with community input on messaging. Data will be de-identified and geocoded at a level that prevents identifying individuals.
Refining the Question
Early versions of the question were broader, asking whether the food environment affects health. Narrowing to one exposure measure, one outcome, a defined population and a time frame made the question answerable. Further refinement may examine whether associations differ by neighborhood income or urban and rural setting, which would inform where policy could help most.
Biological Plausibility
The hypothesized pathway is plausible: frequent access to energy-dense, low-fiber foods and sugary drinks promotes weight gain and insulin resistance, which lead to type 2 diabetes. Neighborhood environments also shape stress and physical activity, which affect metabolic health. Plausibility does not prove causation but supports investigating the question.
Stakeholders
County planners, the health department, community food access groups, retailers and residents of affected neighborhoods all have stakes in the answer. Consulting them early helped refine the exposure definition, for example by including dollar stores that sell mainly packaged foods.
Anticipated Contribution
Most US evidence on food swamps comes from area-level analyses. A cohort study following individuals over time in a mixed urban and rural county would add stronger evidence and directly inform the county's zoning debate.
Scope and Boundaries
The project will not study children, individual diet interventions or food prices directly. Keeping the scope on adult incidence and neighborhood exposure makes the question answerable with available data and keeps the doctoral project manageable.
Alternative Hypotheses
Competing explanations must be considered from the start. The association could reflect neighborhood poverty rather than food outlets, differences in access to health care that affect diagnosis or the preferences of people who choose to live near fast-food outlets. Stating these alternatives now shapes the design and analysis choices in later modules.
Next Steps
The next module documents a literature search built on this question, testing whether the evidence gap is as large as it appears and refining exposure and outcome definitions based on how prior studies measured them.
Conclusion
The project asks whether living in a food swamp raises the incidence of type 2 diabetes among adults in the composite county. With clear exposure and outcome definitions, a PECO statement, stated hypotheses, a causal diagram and awareness of neighborhood research pitfalls, the question is ready to guide the literature search and design that follow.
References
Auchincloss, A. H., Diez Roux, A. V., Mujahid, M. S., Shen, M., Bertoni, A. G., & Carnethon, M. R. (2009). Neighborhood resources for physical activity and healthy foods and incidence of type 2 diabetes mellitus: The Multi-Ethnic Study of Atherosclerosis. Archives of Internal Medicine, 169(18), 1698-1704. https://doi.org/10.1001/archinternmed.2009.302
Cooksey-Stowers, K., Schwartz, M. B., & Brownell, K. D. (2017). Food swamps predict obesity rates better than food deserts in the United States. International Journal of Environmental Research and Public Health, 14(11), Article 1366. https://doi.org/10.3390/ijerph14111366
Diez Roux, A. V. (2001). Investigating neighborhood and area effects on health. American Journal of Public Health, 91(11), 1783-1789. https://doi.org/10.2105/AJPH.91.11.1783
Mezuk, B., Li, X., Cederin, K., Rice, K., Sundquist, J., & Sundquist, K. (2016). Beyond access: Characteristics of the food environment and risk of diabetes. American Journal of Epidemiology, 183(12), 1129-1137. https://doi.org/10.1093/aje/kwv318
DPH 810 Module 1 instructions, in plain terms
Aspen's catalog says DPH 810 develops the hypothesis, literature search and study design that lead to the doctoral project, and since the first module's prompt stays inside the course, this sample frames a question and hypothesis. Hypothesis papers usually ask you to explain why the question matters, summarize prior evidence, define exposure and outcome precisely and state null and alternative hypotheses. Choose a question tied to a real decision. Define every term so another researcher could measure it. Put the question in a structured format. Draw or describe a causal diagram showing confounders and mediators. Say which effect measure you will report. Address feasibility and ethics briefly but concretely. Keep the time frame explicit, because incidence questions need a defined follow-up window. Mention the data you expect to use so a reader can judge whether the question is answerable within the program.
Inside the DPH 810 Module 1 example
Roughly 1,050 words run through seventeen headings, with a two-column PECO table stating population, exposure, comparison and outcome. The paper explains why the question matters, reviews prior evidence, defines exposure and outcome and presents the table, then states hypotheses, describes a causal diagram and covers neighborhood research challenges, feasibility and ethics. Refining the question, biological plausibility, stakeholders, anticipated contribution, scope, alternative hypotheses and next steps follow. A margin comment explains that tying the question to a live policy decision shows why it matters. The conclusion hands the question to the literature search. A short section on alternative hypotheses names reverse causation and residential self-selection before any data are gathered. The PECO table is placed early so every later heading can refer back to its four cells without repeating definitions.
Where the marks sit in the DPH 810 Module 1 rubric
Hypothesis papers at the doctoral level are graded on a focused, answerable question, precise definitions, grounding in prior evidence, correctly stated hypotheses and awareness of design implications. Its evidence comes from a national county study of food swamps, the MESA cohort's findings on neighborhood resources and diabetes, a Swedish cohort on changing food environments and a review of neighborhood research methods, all in APA form. The PECO table makes the question testable. The causal diagram separates confounders from mediators. Ethical framing avoids stigmatizing neighborhoods, a point graders notice. Instructors also look at whether the effect measure fits the design, so the paper names the hazard ratio and explains why incidence rather than prevalence is the outcome. Feasibility is argued with the county's actual record systems, which keeps the question grounded instead of hypothetical.
Common DPH 810 Module 1 mistakes, and how to avoid them
Many first drafts pose a question too wide to settle, such as whether food affects health. Another is vague exposure definitions that could not be measured the same way twice. Narrow the population, exposure, comparison, outcome and time frame. Define each measurably. State both hypotheses. Identify at least three confounders and one mediator. If you are unsure how to draw a causal diagram, our tutors can sketch one with you from your variables. End with the single sentence that states your hypothesis. Some students write a hypothesis that is really a prediction about a single number rather than a comparison between groups. Frame it as a contrast between exposed and unexposed people. Ask a classmate to restate your question from the PECO table alone; if they cannot, tighten a definition before you submit.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Aspen University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
More DPH 810 and Doctor of Public Health sample papers
- DPH 810 Module 2: Documented Literature Search
- DPH 810 Module 3: Measures of Occurrence and Risk
- DPH 810 Module 4: Choosing a Study Design
- DPH 810 Module 5: Designing a Community Survey
- DPH 810 Module 6: Bias, Confounding and Alternatives
- DPH 810 Module 7: Staged Study Proposal
- DPH 810 Module 8: Doctoral Project Topic and Committee
- DPH 850 Module 8: Health Data Policy and Governance
- DPH 840 Module 8: Immersion and DPH Project Progress
- DPH 890 Module 3: Discussion and Implications for Practice
- DPH 870 Module 6: A Synthesis Matrix by Theme
DPH 810 Module 1 questions, answered
What does DPH 810 Module 1 usually ask for?
Aspen's DPH 810 covers developing a hypothesis toward the doctoral project, so framing an epidemiological question and hypothesis is a typical first assignment. Follow your classroom prompt.
What is a food swamp?
A neighborhood with many fast-food and convenience outlets relative to stores selling healthy food.
What is PECO?
A format for stating an epidemiological question: population, exposure, comparison and outcome.
Where can I find a free DPH 810 Module 1 sample paper?
The food swamp hypothesis paper is posted above, complete with its PECO table.
How is an epidemiological hypothesis framed in DPH 810 Module 1?
By defining the population, exposure, comparison and outcome, stating null and alternative hypotheses and choosing an effect measure, supported by prior evidence and a causal diagram.