Investigate the prevalence and treatment landscape of diabetes through an extensive literature review integrated with statistical analysis.

Your focus in healthcare revolves around Diabetes, a topic rich in data analytics.

Utilize platforms like Data.gov, Statista.com, or Kaggle.com to procure datasets for your research.

Your submission should encompass six or more resources (articles, websites, databases) to build a comprehensive understanding.

Craft a narrative around your topic, setting the foundation for further exploration in healthcare.

Research Structure:

I. INTRODUCTION: Define the study`s purpose and significance. II. METHODS: Outline the research methodology employed. III. DISCUSSION: Interpret the findings and their implications. IV.

RESULTS: Present the discovered insights from the study.

Research Queries:

1. Explore the prevalence of diabetes within specific demographics, such as age, gender, ethnicity, and other determinants. Utilize survey data, medical records, or similar sources for analysis.
2. Identify and address the risk factors associated with diabetes onset using epidemiological studies, observational research, or clinical trials.
3. Evaluate the efficacy of various diabetes treatments and assess methods for measuring their effectiveness.
4. Examine the impact of diabetes on health outcomes, including mortality rates, morbidity, quality of life, and healthcare expenditures.
5. Design and evaluate diabetes prevention and management programs to maximize their efficacy and cost-effectiveness.

Resource: Refer to the Health Information Management Research Guide Resource.

Research Approach:

1. Initiate the research journey with the CINAHL database, renowned for indexing top journals in HIM and health informatics.
2. Conduct preliminary searches using key phrases like "data analytics method*" or "big data in healthcare" to identify relevant methodologies.
3. Expand your search by incorporating keywords and MESH headings aligned with your topic to draw in data analysis-related citations.

In chronic disease research, data analysis entails gathering and processing extensive datasets from diverse sources, including electronic health records, population surveys, clinical trials, and administrative databases. Incorporate essential data analysis techniques prevalent in chronic disease research:

1. Descriptive Analysis: Summarize data using tables, graphs, and charts for clarity.
2. Inferential Analysis: Draw conclusions about the population based on sample data.
3. Predictive Modeling: Develop mathematical models to forecast disease likelihood or treatment effectiveness.

Include your research statement, detailed data analysis, comprising descriptive and inferential statistics, along with conclusive insights derived from the data analysis process.