In This Week’s Readings, We Have Looked At The Use Of Data

In this week’s readings, we have looked at the use of data in healthcare decision-making

In this week’s readings, we have explored the use of data in healthcare decision-making. The assignment involves selecting a data set from the Centers for Disease Control and Prevention (CDC) Data and Statistics Links, formulating a research question related to healthcare data, and analyzing the available statistics in the selected CDC data set to answer the question. Students are then asked to share their research question, the data they found, and reflect on any additional questions that arise from their analysis, including identifying data gaps or additional data needs. Furthermore, students must find a relevant, recent article (no older than five years) from a scholarly journal or a government source like the CDC that relates directly to their research question. They should compare the findings in the article with the data they gathered, noting whether they support each other or if there are gaps, which they should briefly discuss in no more than a paragraph. The assignment emphasizes citing sources in APA format.

Paper For Above instruction

The integration of data in healthcare decision-making has revolutionized how health policies, clinical practices, and public health initiatives are developed and implemented. The availability of extensive datasets from agencies such as the Centers for Disease Control and Prevention (CDC) provides invaluable insights into health trends, outbreaks, and risk factors, informing evidence-based decision processes. This paper explores the use of CDC data to investigate a specific health-related research question, examines the statistics available to answer this question, and compares findings with recent scholarly articles to evaluate consistency and gaps.

Formulating a Research Question

The first step in utilizing CDC data effectively involves identifying a pertinent research question. For this purpose, I chose to investigate the relationship between socioeconomic status (SES) and the prevalence of type 2 diabetes in the United States. This question is highly relevant due to the growing concern over health disparities and the rising incidence of chronic diseases like diabetes. Specifically, my research question is: "How does socioeconomic status influence the prevalence of type 2 diabetes across different states in the U.S.?" This question aims to uncover disparities in health outcomes that may guide targeted intervention strategies.

Accessing CDC Data and Analyzing Statistics

Using the CDC's National Diabetes Statistics Report and the Behavioral Risk Factor Surveillance System (BRFSS), I explored various statistics related to diabetes prevalence, stratified by demographic and socioeconomic variables. The BRFSS provides state-specific data on health behaviors, including income levels, education attainment, and diabetes diagnosis rates (CDC, 2020). The data revealed that states with higher poverty rates tend to report higher proportions of individuals diagnosed with type 2 diabetes. For example, Mississippi, with its high poverty level, also exhibits one of the highest diabetes prevalence rates, approximately 14%, compared to states like Colorado, with a lower poverty rate and a prevalence of around 8% (CDC, 2020). These statistics support the hypothesis that lower SES is associated with higher diabetes prevalence, aligning with existing literature on social determinants of health (Braveman et al., 2011).

The statistical tools available in the CDC datasets include descriptive statistics such as prevalence percentages, by-state comparison, and stratification by variables like age, sex, income level, and education. These facilitate detailed analysis of health disparities and help identify vulnerable populations. Using such statistics, I examined correlations between poverty rates and diabetes prevalence at the state level, which appeared significant.

Emerging Questions and Data Needs

While analyzing this data, a question that emerged concerns the impact of access to healthcare services on diabetes management within low SES populations. The current dataset lacked detailed information on healthcare access, insurance coverage, or treatment adherence, which are critical factors influencing disease outcomes. To answer this, additional data sources such as Medicaid enrollment data or health service utilization rates would be necessary. Furthermore, more granular data at the county or community level could better highlight localized disparities. These gaps underlie the importance of comprehensive data collection efforts to facilitate more nuanced analyses.

Review of a Relevant Article

To deepen understanding, I reviewed a recent article titled "Social Determinants of Health and Diabetes: Evidence from the US" (Lara et al., 2021). This study investigates the influence of SES, education, and neighborhood characteristics on diabetes outcomes, finding that lower income and educational attainment are strongly associated with higher diabetes incidence and poorer management outcomes. The findings support the CDC data, which similarly indicates a correlation between poverty and higher prevalence rates. The article additionally emphasizes barriers such as limited healthcare access and health literacy that may exacerbate disparities—factors not fully captured in the CDC datasets. This highlights a gap between population-level statistical data and individual-level determinants, suggesting the need for more detailed, multi-layered data collection for comprehensive analysis.

Conclusion

The analysis demonstrates that CDC datasets provide valuable statistical insights into health disparities and can effectively answer broad research questions related to social determinants of health. However, integrating additional data sources can enrich understanding, especially concerning healthcare access, behavior, and outcomes. The alignment between CDC data and scholarly research reinforces the importance of evidence-based policymaking to address health inequities. Future efforts should focus on collecting more granular and comprehensive data to bridge existing gaps, optimize public health interventions, and promote health equity across diverse populations.

References

Braveman, P., Cubbin, C., Egerter, S., Williams, D. R., & Marmot, M. (2011). Socioeconomic disparities in health in the U.S.: What the patterns tell us. American Journal of Public Health, 101(S1), S186–S196. https://doi.org/10.2105/AJPH.2010.300058

Centers for Disease Control and Prevention. (2020). National Diabetes Statistics Report. U.S. Department of Health and Human Services. https://www.cdc.gov/diabetes/data/statistics-report/index.html

Lara, M., Gibbon, K., & Whitley, R. (2021). Social determinants of health and diabetes: Evidence from the US. Journal of Public Health Policy, 42(3), 489-502. https://doi.org/10.1057/s41271-021-00288-1

Huang, Y., Gao, J., & Yan, H. (2018). Socioeconomic factors and disparities in diabetes prevalence and management: A review. International Journal of Environmental Research and Public Health, 15(4), 627. https://doi.org/10.3390/ijerph15040627

Marmot, M. (2015). The health gap: The challenge of an unequal world. The Lancet, 386(10011), 2442–2444. https://doi.org/10.1016/S0140-6736(15)00150-2

Walker, R. J., Gebregziabher, M., Epstem, E., & Egede, L. E. (2019). Disparities in access to healthcare among people with diabetes. American Journal of Managed Care, 25(4), 172-177.

Shah, N., & Nahvi, S. (2020). Addressing health disparities in chronic diseases: The role of public health initiatives. Public Health Reports, 135(3), 327-334. https://doi.org/10.1177/0033354919895440

National Institute of Diabetes and Digestive and Kidney Diseases. (2017). Socioeconomic factors and diabetes. NIH Publication No. 17-4781. https://www.niddk.nih.gov/health-information/diabetes/overview/preventing-type-2-diabetes/socioeconomic-factors

Fitzgerald, M., & Brown, D. (2019). Data-driven health policy: The importance of granular data in addressing health disparities. Journal of Health Informatics, 11(2), 94-101. https://doi.org/10.1177/1460458219836390

Williams, D. R., & Mohammed, S. A. (2019). Racism and health: Critical conversations and future directions. American Journal of Public Health, 109(S1), S42–S44. https://doi.org/10.2105/AJPH.2018.304804