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Applying Epidemiology to Program Design for Chronic Disease

Demie Alaekwe

Walden University

NURS 8310: Epidemiology and Population Health

Dr. Rodgers

July 3rd, 2025

Applying Epidemiology to Program Design for Chronic Disease

1. Introduction

Type 2 diabetes (T2D) is a major public health challenge in the United States, contributing to high healthcare costs and reduced quality of life. In Texas, this disease disproportionately affects racial and ethnic minorities, low-income groups, and those with limited healthcare access, deepening existing health disparities (Texas Health and Human Services, 2025). This paper examines the burden of T2D among Texas adults, explores demographic and geographic trends, and proposes Diabetes Self-Management Education and Support (DSMES) as an evidence-based intervention. By focusing on quality of life (as a patient outcome), DSMES empowers individuals through education, behavioral support, and culturally tailored strategies, fostering self-management, reducing complications, and promoting long-term health equity for T2D individuals.

2. Selected Chronic Health Issue and Population

The selected chronic illness is T2D, which is described as a metabolic disorder characterized by insulin resistance or insufficient insulin production, resulting in elevated blood glucose levels (American Diabetes Association, 2025). Unlike type 1 diabetes, which is autoimmune, T2D is primarily driven by a combination of lifestyle factors, including poor diet and physical inactivity, as well as genetic predisposition. The disease typically develops slowly, beginning with insulin resistance and often progressing through a stage of prediabetes before advancing to full diabetes. Without proper management, T2D can lead to serious complications, such as cardiovascular disease, kidney failure, neuropathy, and vision impairment. Diabetes is generally a growing public health crisis in the United States, affecting over 38 million Americans, and at least 98 million adults are living with prediabetes, placing them at high risk for developing the disease, and resulting in billions in healthcare costs (American Diabetes Association, 2025).

The target population for this proposed study is adults over 18 in Texas, where T2D poses a significant public health challenge. As per the American Diabetes Association (2025), approximately 11.1% of adults in Texas, which is about 2.55 million individuals, are living with diagnosed diabetes, with an estimated 135,300 new cases reported yearly. Texas Health and Human Services (2025) shows that obesity, which is a primary risk factor, affects 35.5% of Texas adults and is a key driver of this growing epidemic. Targeting this population is particularly important because early detection and interventions can reduce the risk of severe complications, lower healthcare costs, and improve long-term quality of life.

3. The Geographic Region and Population Characteristics

The geographic region for this proposed study is Texas, which is the second-largest US state by area and population. As of 2024, Texas was home to an estimated 31.3 million residents, and its population growth is fueled by domestic migration, economic opportunities, and natural increase (Fechter, 2024). According to the 2020 Census, Texas had 29.1 million people, with a relatively young median age of 35.9 compared to the national median of 39.2 (US Census Bureau, 2025). The state is among the most racially and ethnically diverse in the country: 40.2% Hispanic, 39.8% non-Hispanic White, 12.9% Black, and 5.5% Asian. Further, 17.9% of Texans are foreign-born, surpassing the national average of 14.3% (US Census Bureau, 2025). Spanish is widely spoken, with 28.2% of residents reporting it as their primary household language (Fechter, 2024).

In terms of population characteristics, Texas had 11.3 million households as of the 2020 Census and a poverty rate of 13.7%, which is higher than the US average of 12.5%. The state's median household income of $75,780 is slightly below the national median of $77,719 (US Census Bureau, 2025). It has the highest uninsured rate in the country, with 16.4% of residents lacking health insurance coverage. Educational attainment is modest, with 34.2% of adults holding a bachelor's degree or higher, compared to 36.2% nationally. The Texas workforce is largely private-sector based (69.2%), with healthcare (21.7%), retail (10.8%), and manufacturing (8.7%) serving as the state's leading industries (US Census Bureau, 2025). These demographic and economic patterns highlight both opportunities and challenges for statewide development.

4. The Patterns of T2D

4.1 Person

T2D in Texas shows significant demographic disparities, particularly among racial and ethnic groups. According to the CDC's Behavioral Risk Factor Surveillance System (2021), the prevalence of T2D is highest among non-Hispanic Black adults (13.8%), followed by Hispanic (12.5%) and non-Hispanic White adults (10.6%) (Hackl et al., 2024). The disease develops earlier in minority populations, with 43.4% of Hispanic cases occurring under age 45, compared to 35.7% of White cases. Gender disparities are also pronounced, with 55.7% of Black women and 60.4% of Hispanic women affected compared to 48.6% of White women (Neupane et al., 2024). Also, socioeconomic factors primarily influence outcomes, with prevalence nearly doubling (20.5% vs. 12.4%) among Texans earning less than $25,000 yearly (Hackl et al., 2024).

4.2 Place

Texas has a higher age-adjusted diabetes prevalence of 12.9% than the national average of 11.3%, as per Hackl et al. (2024), but with notable regional clusters. Some places like the Rio Grande Valley, a Hispanic-majority area, report rates up to 18.3%, while East, which is largely composed of majority-Black counties, sees rates between 15% and 17% (Hackl et al., 2024). Urban centers like Houston and Dallas also exhibit high prevalence, particularly in food deserts affecting low-income neighborhoods. Structural determinants compound these geographic patterns, including limited healthcare access (23.2% of Hispanic and 24.3% of Black adults are uninsured), high food insecurity (14.3% of households), and uneven access to diabetes education programs, as explained by Hackl et al. (2024).

4.3 Time

According to America's Health Rankings (2025), Texas' diabetes epidemic has evolved in three phases since 1995. During the early epidemic (1995 to 2010), prevalence more than doubled from 4.0% to 10.0%, coinciding with an increase in obesity from 28% to 33% (America's Health Rankings, 2025). In the endemic phase (2011–2019), rates stabilized around 10% but rose to 12% by 2019 as obesity reached 35.5% (America's Health Rankings, 2025). The pandemic (2020 to 2023) saw a sharp spike to 14.0% in 2021 due to COVID-19 care disruptions, followed by a decline to 12.5 to 13.0% (America's Health Rankings, 2025). Disparities enlarged, with Black adults experiencing a 21% increase compared to 15% among Whites, reflecting persistent structural barriers (Neupane et al., 2024).

5. One Health Outcome

The health outcome of interest in this population is quality of life (QoL), a multidimensional measure that reflects physical, emotional, and social well-being (Hackl et al., 2024). For adults with T2D, QoL is often compromised due to the disease's symptoms, complications, and ongoing management demands. Gregg et al. (2023) explain that, physically, chronic hyperglycemia can cause fatigue, neuropathy, pain, and mobility limitations, while microvascular complications and macrovascular events further reduce independence and functional health. In terms of emotions, the prevalence of depression and anxiety among T2D patients is 2 to 3 times higher than in the general population, and is mainly driven by the stress of continuous glucose monitoring, medication adherence, and fear of complications, as discussed by Neupane et al. (2024). Also, social and economic factors contribute to another layer of burden, whereby financial constraints from expensive medications, specialist visits, and reduced productivity particularly affect low-income groups, while dietary restrictions and stigma can lead to isolation and reduced social engagement (Neupane et al., 2024). For these reasons, improving QoL is a priority for better glycemic control and fewer hospitalizations.

6. Summary of Current Evidence

Diabetes compromises QoL through its physical, psychological, and social burdens. Even though researchers consistently show that depression, poor glycemic control, and lack of social support are key predictors of diminished QoL (Chen et al., 2024; Alsudairy et al., 2024), many analyses fail to comprehensively account for the cumulative effect of socioeconomic disparities and healthcare inequities. In particular, elderly patients, women, and individuals with comorbidities are more likely to experience reduced outcomes, as complications like neuropathy, retinopathy, and cardiovascular disease reduce independence and functional capacity (Panahi et al., 2024; Garg & Duggal, 2022). Researchers, however, tend to overemphasize individual behaviors, such as diet and exercise (Garg & Duggal, 2022), while underexploring structural barriers like healthcare costs, access to education, and systemic issues.

Depression remains the most significant psychological challenge affecting QoL among diabetes patients. Network analysis by Chen et al. (2024) highlights strong associations between depression, maladaptive coping mechanisms, and reduced overall well-being. Nonetheless, cross-sectional studies cannot determine whether depression is a cause or consequence of poor diabetes management (Alsudairy et al., 2024). Alsudairy et al. (2024) found that anxiety affected 39% of patients with diabetes in Saudi Arabia, while Panahi et al. (2024) observed that Iranian elderly with diabetes reported markedly lower mental health scores tied to depression, which helps to highlight how cultural and healthcare system differences play a role in mental health outcomes.

Social and economic determinants also shape QoL in patients with T2D. Chen et al. (2024) and Garg & Duggal (2022) found that strong family and community support improve coping strategies, such as problem-focused confrontation, while financial strain, low education, and healthcare inaccessibility predict poorer QoL. In Saudi Arabia, 28% of patients reported difficulties maintaining social interactions, a figure likely underestimated due to stigma and cultural norms (Alsudairy et al., 2024). While research often acknowledges socioeconomic disparities, few studies assess how interventions, such as culturally tailored education programs or financial assistance, directly influence QoL (Alsudairy et al., 2024). Hence, addressing these gaps requires moving beyond descriptive data to develop and evaluate community-based strategies that are sensitive to cultural values and economic constraints contributing to diabetes.

7. Evidence-based Program

The selected evidence-based program is Diabetes Self-Management Education and Support (DSMES), which is designed to enhance QoL for adults with T2D by empowering them with the skills, knowledge, and confidence needed for effective self-management. According to Huber et al. (2022), DSMES integrates personalized education, behavioral coaching, and psychosocial support, directly addressing the physical, emotional, and social challenges that reduce QoL, such as poor glycemic control, diabetes distress, and lifestyle limitations. In Texas, where high obesity rates (35.5%), as per the Texas Health and Human Services (2025), cultural diversity and socioeconomic barriers worsen diabetes outcomes, DSMES is particularly valuable. Kanny et al. (2025) explain that programs like the Health Extension for Diabetes (HED), which is part of DSMES, have shown improvements in QoL scores (SF-12), demonstrating that structured self-care interventions reduce stress, improve daily functioning, and promote healthier behaviors. By tailoring DSMES to diverse cultural and economic contexts, using bilingual educators, community health workers, and group-based learning, patients experience better clinical outcomes (Kanny et al., 2025) and improved emotional well-being and social participation.

8. Data Collection

Primary and secondary data will be collected to assess the impact of DSMES on adults with T2D in Texas. Primary data would include patient-reported outcomes such as QoL, and will be assessed using validated tools like the SF-12 Health Survey and the Diabetes Quality of Life (DQOL) questionnaire (Alzahrani et al., 2023). Clinical indicators, including HbA1c levels, BMI, blood pressure, and the presence of complications (Attal et al., 2019), would be obtained through partnerships with local healthcare providers and DSMES program sites across Texas. Surveys and interviews with participants, particularly in high-risk regions such as the Rio Grande Valley and East Texas, would capture first-hand insights on cultural barriers, diabetes distress, and access to care. Secondary data would complement this by drawing from the CDC's Behavioral Risk Factor Surveillance System (BRFSS) (CDC, 2021), the Texas Diabetes Surveillance System, and peer-reviewed studies on DSMES outcomes.

9. Short-Term, Long-Term Objectives, and Stakeholders

The short-term objectives include:

· By the end of three months, 85% of DSMES participants will complete at least four structured self-management education sessions focused on glycemic control, healthy eating, and medication adherence.

· By the end of four months, 80% of participants will demonstrate improved diabetes knowledge, as measured by pre- and post-program assessments.

· Within six months, 75% of participants will report improved QoL using validated tools such as SF-12 or DQOL scores.

· By the end of six months, at least 70% of participants will achieve a reduction in HbA1c of 0.5 to 1.0%.

The long-term objectives include:

· By the end of year one, 85% of participants will maintain improved glycemic control through sustained lifestyle changes and follow-up sessions.

· By 18 months, 90% of participants will report sustained improvements in QoL, including reductions in diabetes-related distress and increased social engagement.

· In two years, DSMES will achieve a 25% reduction in diabetes-related hospitalizations and emergency visits among enrolled participants.

· In two years, 85% of participants from underserved Texas communities will demonstrate sustained self-management behaviors, such as regular glucose monitoring, medication adherence, and healthy eating habits.

9.1 Stakeholders

Stakeholder	Roles
· Primary healthcare providers	Physicians, nurses, and diabetes educators who deliver education, monitor progress, and support clinical care.
· Community health workers (CHWs)	Provide culturally tailored education and outreach, especially for underserved and bilingual populations.
· Patients and family caregivers	Actively participate in self-management and support behavior changes within high-risk families.
· Public health agencies	Texas Department of State Health Services and CDC guide policy alignment and provide resources.
· Local healthcare organizations	Clinics and hospitals implement DSMES programs and provide access to low-income communities.
· Policy makers and insurers	Ensure funding, Medicaid coverage, and supportive policies for DSMES programs.
· Nonprofit organizations	Groups like the ADA and local health foundations provide community outreach and additional resources.

Table 1: Stakeholders

10. Program Planning Model

The selected program planning model from Curley's Chapter 7 is the Logic Model, developed by the Kellogg Foundation (2004) (Rossi & Curley, 2020). This model provides a visual framework that links program resources, activities, and outcomes, enabling planners to clearly define goals and assess progress. As explained by Rossi and Curley (2020), it is widely used for program planning, management, and evaluation because it aligns interventions with measurable results and clarifies the underlying theory of change. For the DSMES program, the Logic Model will be particularly valuable due to the complexity of T2D, as it highlights key assumptions, ensures stakeholder alignment, and supports systematic evaluation of outcomes such as improved quality of life.

Based on this model, planning for DSMES will begin with describing the problem: the high prevalence of T2D in Texas (12.9%), particularly among underserved Hispanic and Black populations, which leads to reduced quality of life and preventable complications. The second phase will involve identifying population needs by addressing barriers such as health literacy gaps, limited healthcare access, and cultural or socioeconomic factors. The third phase will be defining desired results, which may include improved glycemic control, enhanced self-care behaviors, and measurable quality of life improvements. Factors influencing change, including social determinants of health, cost barriers, and cultural attitudes toward chronic disease management, will also be described. Best practices, including culturally tailored education, will form the foundation for intervention strategies. Also, assumptions underlying the program will be defined, including patient education, behavioral coaching, and psychosocial support. Implementation will involve multidisciplinary teams of healthcare providers and community health workers delivering structured sessions in group and one-on-one formats, supported by bilingual materials, telehealth options, and partnerships with local stakeholders.

Program evaluation will include formative, outcome, and impact assessments. Formative evaluation will rely on participant feedback through surveys and interviews. Outcome evaluation will measure short-term changes, such as HbA1c reduction, increased self-care practices, and improved quality of life, using validated tools like the SF-12 and DQOL, as discussed by Karki et al. (2023). Impact evaluation will focus on long-term results, including reduced hospitalizations, lower diabetes-related complications, and sustained lifestyle improvements over 12 to 24 months. Both primary data (patient surveys, clinical records) and secondary data (Texas Diabetes Surveillance System, BRFSS) will be analyzed using descriptive and inferential statistics. In general, continuous monitoring will ensure alignment with program goals and identify opportunities for scaling to other states.

11. Relevant Cultural and Ethical Considerations

Cultural and ethical considerations will be fundamental in the DSMES program design and implementation, particularly for Texas's diverse populations, such as Black, Hispanic, and Native American communities. For instance, cultural differences in dietary habits, such as high-carbohydrate staples in Hispanic diets, Southern-style foods rich in fats and sugars among Black communities, and traditional indigenous foods among Native Americans, require culturally sensitive education, as discussed by Ojo et al. (2023). The program will emphasize adapting traditional meals into healthier versions while respecting cultural preferences to promote sustainable behavior change. Ethically, the program will uphold participant confidentiality, ensuring that all personal health information remains secure. It will follow the principle of beneficence, focusing on maximizing benefits such as improved quality of life and reduced complications, while ensuring no harm by avoiding stigmatization or culturally insensitive messaging (Vandecasteele et al., 2024). Bilingual educators and tribal health leaders will be engaged to ensure equitable access and culturally appropriate care.

12. Funding the Program

The DSMES program will be funded through a combination of grants, government support, and partnerships. Federal and state funding sources, such as the CDC's Diabetes Prevention Program (DPP) grants and the Texas Department of State Health Services, can provide core financial support. Local governments may contribute through public health initiatives aimed at reducing chronic disease burdens. Additionally, non-governmental organizations (NGOs) like the American Diabetes Association (ADA) and local health foundations can offer grants, training resources, and educational materials. Partnerships with hospitals, community clinics, and private donors will further sustain the program, ensuring resources for underserved populations and program scalability.

13. Strategies for Marketing the Program

Effective marketing of the DSMES program requires a combination of offline and online strategies to reach diverse communities across Texas. Offline approaches include partnering with local healthcare providers, clinics, and community centers to distribute flyers, posters, and brochures. Also, hosting free health fairs, educational workshops, and diabetes screening events can raise awareness while building trust within high-risk populations. Collaborating with churches, tribal organizations, and cultural groups can further extend outreach.

As well, online strategies focus on leveraging digital platforms to engage a broader audience. Social media campaigns can share success stories, educational tips, and program details using targeted ads to reach specific demographics. A user-friendly website with resources, enrollment information, and testimonials will enhance visibility. Additionally, email newsletters and webinars can provide ongoing education and program updates. Combining offline community engagement with a strong digital presence ensures maximum program reach.

14. Conclusion

T2D is a serious public health challenge in Texas, with significant disparities impacting minority and low-income populations. The proposed DSMES program provides an evidence-based approach to improving self-management and quality of life by addressing physical, emotional, and social barriers. Through culturally sensitive education, community collaboration, and ongoing evaluation, the program aims to reduce complications, lower healthcare costs, and encourage lasting behavioral change. Supported by federal and state funding, stakeholder engagement, and targeted outreach, DSMES can effectively reach high-risk communities. Investing in prevention and management is vital for advancing health equity and creating healthier populations across Texas.

15. References

Alsudairy, N. M., Alzaidi, S. M., Alghamdi, A. G., Alrashidi, M. S., & Altashkandi, D. A. (2024). Quality of life and its determinants among individuals with diabetes in Saudi Arabia: A cross-sectional online survey.  Cureus.  https://doi.org/10.7759/cureus.69394

Alzahrani, O., Fletcher, J. P., & Hitos, K. (2023). Quality of life and mental health measurements among patients with type 2 diabetes mellitus: A systematic review.  Health and Quality of Life Outcomes,  21(1).  https://doi.org/10.1186/s12955-023-02111-3

American Diabetes Association. (2025). Diabetes Research, Education, Advocacy | ADA.  https://diabetes.org/sites/default/files/2024-03/adv_2024_state_fact_texas.pdf

America's Health Rankings. (2025).  Explore diabetes in Texas | AHR.  https://www.americashealthrankings.org/explore/measures/Diabetes/TX

Attal, S., Mahmoud, M. H., Aseel, M. T., Candra, A., Amuna, P., Elnagmi, M., Abdallah, M., Ismail, N., Abdelrazek, A., Albaw, D., Albashir, A., & Elmahdi, H. (2019). Indicators of quality of clinical care for type 2 diabetes patients in primary health care centers in Qatar: A retrospective analysis.  International Journal of Endocrinology,  2019, 1-9.  https://doi.org/10.1155/2019/3519093

CDC. (2021, July 21).  2021 BRFSS survey data and documentation. Centers for Disease Control and Prevention | CDC.  https://www.cdc.gov/brfss/annual_data/annual_2021.html

Chen, L., Li, L., Qiao, K., Qiao, Z., Xiang, Y., Zhou, J., Bu, T., Hu, X., Ke, S., Kan, Y., Liu, X., Ji, Y., Qiu, X., & Yang, Y. (2024). Exploring factors influencing the quality of life in diabetes: A network analysis-based study.  Frontiers in Psychiatry,  15.  https://doi.org/10.3389/fpsyt.2024.1431772

Fechter, J. (2024, December 19).  Texas is now home to 31 million people even as population growth slows. The Texas Tribune.  https://www.texastribune.org/2024/12/19/texas-population-31-million/

Garg, P., & Duggal, N. (2022). Type 2 diabetes mellitus, its impact on quality of life and how the disease can be managed-a review.  Obesity Medicine,  35, 100459.  https://doi.org/10.1016/j.obmed.2022.100459

Gregg, E., Buckley, J., Ali, M. K., Davies, J., Flood, D., Mehta, R., ... & HPAAC Collaboration. (2023). Improving health outcomes of people with diabetes mellitus: global target setting to reduce the burden of diabetes mellitus by 2030.  Lancet (London, England),  401(10384), 1302. https://doi.org/10.1016/S0140-6736(23)00001-6

Hackl, C. M., Lee, W., Sallam, H. S., Jneid, H., Campbell, K. M., & Serag, H. (2024). Racial disparities in selected complications and comorbidities among people with type 2 diabetes.  Healthcare,  12(8), 846.  https://doi.org/10.3390/healthcare12080846

Huber, C., Montreuil, C., Christie, D., & Forbes, A. (2022). Integrating self-management education and support in routine care of people with type 2 diabetes mellitus: A conceptional model based on critical interpretive synthesis and a consensus-building participatory consultation.  Frontiers in Clinical Diabetes and Healthcare,  3.  https://doi.org/10.3389/fcdhc.2022.845547

Kanny, S., Hall, L., Blackhurst, D., & Sherrill, W. W. (2025). Community-based diabetes self-management and support program: Addressing quality of life and social vulnerability.  BMC Public Health,  25(1).  https://doi.org/10.1186/s12889-025-22627-1

Karki, A., Vandelanotte, C., Khalesi, S., Dahal, P., & Rawal, L. B. (2023). The effect of health behavior interventions to manage type 2 diabetes on the quality of life in low-and middle-income countries: A systematic review and meta-analysis.  PLOS ONE,  18(10), e0293028.  https://doi.org/10.1371/journal.pone.0293028

Neupane, S., Florkowski, W. J., & Dhakal, C. (2024). Trends and disparities in diabetes prevalence in the United States from 2012 to 2022.  American Journal of Preventive Medicine,  67(2), 299-302.  https://doi.org/10.1016/j.amepre.2024.04.010

Ojo, A. S., Nnyanzi, L. A., Giles, E. L., Ells, L. J., Awolaran, O., Okeke, S. R., Afaya, A., & Bolarinwa, O. A. (2023). Perceptions of dietary intake amongst Black, Asian and other minority ethnic groups in high-income countries: A systematic review of qualitative literature.  BMC Nutrition,  9(1).  https://doi.org/10.1186/s40795-023-00743-8

Panahi, N., Ahmadi, M., Hosseinpour, M., Sedokani, A., Sanjari, M., Khalagi, K., ... & Ostovar, A. (2024). The association between quality of life and diabetes: the Bushehr Elderly Health Program.  BMC geriatrics,  24(1), 267. https://doi.org/10.1186/s12877-024-04878-6

Rossi, L., & Curley, A. L. C. (2020). Concepts in program design and development. In  Population-based nursing: Concepts and competencies for advanced practice (3rd ed., pp. 159-182). Springer.

Texas Health and Human Services. (2025).  Diabetes. Texas Department of State Health Services (DSHS).  https://www.dshs.texas.gov/diabetes

US Census Bureau. (2025).  Explore census data. Explore Census Data.  https://data.census.gov/profile/Texas?g=040XX00US48

Vandecasteele, R., Robijn, L., Willems, S., De Maesschalck, S., & Stevens, P. A. (2024). Barriers and facilitators to culturally sensitive care in general practice: A reflexive thematic analysis.  BMC Primary Care,  25(1).  https://doi.org/10.1186/s12875-024-02630-y