Develop A Research Question, Provide A Brief Description ✓ Solved

Develop a research question, provide a brief description of

Develop a research question, provide a brief description of the research, and state whether you will use a qualitative or quantitative approach and justify why that approach is appropriate.

Paper For Above Instructions

Introduction

Chronic diseases such as type 2 diabetes mellitus (T2DM) show important differences in incidence, presentation, and outcomes across sexes and genders, which has implications for prevention, screening, and management (Kautzky-Willer et al., 2016; Mauvais-Jarvis, 2015). This assignment develops a focused research question, presents a concise study description, and justifies a methodological approach appropriate to answering the question.

Research Question

Are there sex-based differences in the average age of onset of type 2 diabetes mellitus among adults aged 30–70 in the United States, and how do body mass index (BMI) and socioeconomic status (SES) modify those differences?

Rationale and Background

Understanding age of onset by sex is valuable for tailoring screening recommendations and early-intervention strategies. National surveillance indicates rising diabetes prevalence and shifting demographics (CDC, 2020; IDF, 2021). Biological sex and gender-related exposures influence metabolic risk, adiposity distribution, and care access, potentially shifting age at presentation (Kautzky-Willer et al., 2016; Mauvais-Jarvis, 2015). BMI and SES are established determinants of diabetes risk and may differentially affect men and women, making them important effect modifiers to examine (WHO, 2016; NCD Risk Factor Collaboration, 2016).

Brief Description of the Study

This study will be a cross-sectional analysis using a large, nationally representative dataset (for example, the National Health and Nutrition Examination Survey, NHANES). The sample will include adults aged 30–70 with diagnosed T2DM. Primary outcome is self-reported or clinically identified age at diagnosis. Primary exposure is biological sex (male/female), and covariates include current BMI, SES indicators (education, income), race/ethnicity, and health behaviors (smoking, physical activity).

Analytic steps will include descriptive comparisons of mean age at diagnosis by sex and multivariable linear regression to estimate adjusted differences in age at onset associated with sex. Interaction terms will be used to test whether BMI or SES modifies the sex–age relationship. Sensitivity analyses will use alternative specifications (e.g., categorizing BMI, restricting to clinical diagnosis dates) to assess robustness.

Approach to Be Taken and Justification

A quantitative approach is appropriate and will be used because the research question asks about measurable differences in the average age of onset and the modifying effect of quantifiable variables (BMI, SES). Quantitative methods allow precise estimation of mean differences, hypothesis testing, and adjustment for confounding variables (Creswell & Creswell, 2017). Large survey data provide statistical power to detect moderate differences and support generalizability to the U.S. adult population (CDC, 2020).

Specifically, multivariable regression enables control for potential confounders (e.g., race/ethnicity, lifestyle) and estimation of interaction effects, which addresses the second component of the question concerning modification by BMI and SES (Vittinghoff et al., 2012). Use of survey weights will ensure that estimates are population-representative. Quantitative results can directly inform screening age thresholds and public health messaging.

Methods Overview

Data source: NHANES cycles from 2011–2020 (or most recent datasets available). Inclusion criteria: adults aged 30–70 with diagnosed T2DM. Outcome: age at diagnosis (years). Exposure: sex (male, female). Covariates: BMI (continuous and categorical), income-to-poverty ratio, educational attainment, race/ethnicity, smoking, physical activity, and comorbid conditions.

Statistical analysis: compute weighted means and 95% confidence intervals for age at diagnosis by sex. Fit weighted multivariable linear regression models to estimate adjusted mean differences in age at diagnosis between sexes. Include interaction terms sexBMI and sexSES to evaluate effect modification; present stratified estimates if interactions are significant. Conduct diagnostic tests for model assumptions and sensitivity analyses using alternative model forms (quantile regression, age-at-onset categories).

Ethical Considerations

Analyses will use de-identified, publicly available survey data; institutional review board (IRB) exemption will be obtained or confirmed as appropriate. Interpretations will emphasize that “sex” in the dataset refers to biological sex as recorded and may not capture the full complexity of gender identity; limitations related to measurement of gender will be acknowledged (Mauvais-Jarvis, 2015).

Limitations

The cross-sectional design and reliance on self-reported age at diagnosis can introduce recall bias and preclude causal inference about determinants of earlier onset. Residual confounding by unmeasured factors (e.g., early-life exposures) is possible. The binary sex variable may not capture gender diversity. Despite these limitations, the proposed quantitative analysis yields actionable descriptive information relevant to screening policy.

Expected Outcomes and Implications

The study will estimate whether men and women differ in average age at T2DM diagnosis and whether BMI or SES moderates these differences. If significant sex differences are observed, findings could inform sex-specific screening recommendations and targeted prevention efforts. Identifying SES-related modification could support equity-focused interventions aimed at delaying onset in vulnerable groups (WHO, 2016; ADA, 2023).

Conclusion

The proposed quantitative study addresses a clear, measurable question about sex-based differences in age at T2DM onset and the modifying roles of BMI and SES. Using nationally representative survey data and multivariable modeling will provide robust, generalizable estimates that can inform clinical and public health practice.

References

• American Diabetes Association. Standards of Medical Care in Diabetes—2023. Diabetes Care. 2023.
• Centers for Disease Control and Prevention (CDC). National Diabetes Statistics Report, 2020. Atlanta, GA: CDC; 2020.
• Creswell JW, Creswell JD. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. 5th ed. Sage Publications; 2017.
• International Diabetes Federation. IDF Diabetes Atlas, 10th ed. Brussels: IDF; 2021.
• Kautzky-Willer A, Harreiter J, Pacini G. Sex and gender differences in risk, pathophysiology and complications of type 2 diabetes mellitus. Endocrine Reviews. 2016;37(3):278–316.
• Mauvais-Jarvis F. Sex differences in metabolic homeostasis, diabetes, and obesity. Biology of Sex Differences. 2015;6:14.
• NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity. Lancet. 2016;387(10026):1377–1396.
• Vittinghoff E, Glidden DV, Shiboski SC, McCulloch CE. Regression Methods in Biostatistics: Linear, Logistic, Survival, and Repeated Measures Models. 2nd ed. Springer; 2012.
• World Health Organization. Global Report on Diabetes. Geneva: WHO; 2016.
• Goff DC Jr., et al. Trends in diabetes incidence and prevalence: implications for public health policy. Journal of Public Health Policy. 2014;35(2):123–136.