Introduction
Centers for Disease Control and Prevention has estimated that there are 37.3 million Americans aged 65 years or older, living with diabetes . Virginia is ranked 23rd in the country for diabetes cases, with 11.1% of the population diagnosed . Maryland is ranked 29th in the country for diabetes cases, with 10.3% of the population diagnosed . Those diagnosed with diabetes are at a high risk for developing serious eye diseases . Because serious eye problems occur more often among people with diabetes, eye examinations for this demographic are very important.
According to CDC, diabetes is the main cause of blindness among people younger than the age of 74 . A dilated eye examination can help eye doctors detect and treat problems that will help to prevent patients from to keep you from losing their vision due to diabetes . It is important for those that are diabetic to have regular eye exams in order to have eye problems detected and conditions treated early. Eye exams help patients to have a better chance at protecting themselves from eye damage and loss of vision due to eye diseases such as retinopathy, glaucoma, and cataracts.
We will use the data provided by Medicare National Data by County 2012 dataset, to analyze if there is a significance between the number of diabetic Medicare enrollees aged 65-75, from Maryland, having eye examinations, and diabetic Medicare enrollees aged 65-75, from Virginia, having eye examinations.
This type of research is important because it can help prepare for better health outcomes of a population. (Insert a few lines that talk about research done on this topic- based on the articles from literature review)
Literature Review
(all literature reviews will follow here)
Introduction
In the United States, diabetic retinopathy (DR.) is a common cause of blindness and vision impairment among people. Through dilated eye examinations, early detection of D.R. can lessen the risk of vision damage or loss. However, research has shown that eye examination rates vary by ethnicity and race, highlighting the significance of expanding minority insurance coverage and accessibility to care.
Research questions
RQ1: “Are there racial and ethnic disparities in eye examination rates among U.S. adults (age≥18 years) with diabetes across 2010-2017?”
RQ2: “Is ACA Medicaid expansion associated with changes in eye examination rates among U.S. adults with diabetes living below 138% of the federal poverty level (FPL)?”
Methodology
A trend analysis was used to assess the evolution of eye exam rates over time. A difference-in-difference (DiD) approach was used to explore causal linkages in public health contexts where randomized controlled trials are impractical or unethical.
Data Source
This analysis utilized data from the Medical Expenditure Panel Survey, 2010-2017. (MEPS).
Data
The inclusion criteria required that participants be at least 18 years old and diagnosed with diabetes. The total sample size was 21,612 people. The total sample size for the analysis was 14,380 observations. For Medicaid expansion analysis, the sample size was increased to 4,790 observations by restricting the sample to persons earning less than 138% of the FPL.
Discussion and Summary of Findings
Crude Trends and Racial and Ethnic Differences in Eye Examination Rates
Not many studies in the available literature look at current events in eye examination statistics segmented into ethnicity and race. The study is the first to assess trends in ethnic and racial disparities in eye examination rates amongst Americans aged 18 years who had diabetes between 2010 and 2017. According to the findings of this research, there was no discernible shift in the frequency of unrefined eye exams among the study population as a whole, nor among non-Hispanic blacks, non-Hispanic whites, or Hispanics. Similarly, research based on data gathered from the Behavioral Risk Factor Surveillance System (BRFSS) from 2001-2010 also observed no significant trends in crude eye examination proportions for the total population, Hispanics or non-Hispanic blacks.
Adjusted Trends and Racial and Ethnic Differences In Eye Examination Rates
Hispanics were the only population in the model with a lower likelihood of reporting eye examinations compared to non-Hispanic whites after the predisposing factors were adjusted. It suggests that the predisposing model has to confound factors that skew the existing relationship between ethnicity, race, and eye examination. Specifically, their connection with eye examination rates suggests that these factors skew the relationship between race and ethnicity and eye examination rates. In addition, the investigation revealed that education level and marital statuses were both strongly associated with the result when the models were adjusted.
Medicaid Expansion Analyses
There have only been a few studies done in the past that have looked at how the ACA has affected changes in the number of people getting eye exams. The increase in Medicaid was not shown to be linked with any differences in the prevalence of eye exams. The results of the DiD assessment indicated that expansion of Medicaid was correlated with a significant rise in the rates of eye examination for the period of 2014-2015, however, and that it was no longer linked to changes in the rates of eye examination for the cumulative study years of 2014-2016 and 2014-2017. This was found in the study’s findings regarding the lack of a correlation between the Medicaid expansion and shifts in the number of people getting eye exams may be attributable to shifts in the availability of eye care providers. It is possible that there are not enough eye care specialists accessible to fulfill the demand of the growing numbers of newly insured people who want eye tests due to advancements in the accessibility of insurance.
2. Literature Review (Performing Qualitative analysis)
This section of the assignment is aimed at giving students an opportunity to select and analyze 3-5 peer-reviewed
articles, depends on the size of the TEAM, each team member should summarize one article.
To summarize an article, consider the following items:
2.1.1. A short introduction (A structural summary of the article)
2.1.2. Research hypothesis or questions
2.1.3. Methodology, including:
2.1.3.1. Data
2.1.3.2. Year of data
2.1.3.3. Research method (type of analysis for example descriptive analysis, ttest, regression model, etc.)
2.1.4. Findings, please report main findings not more than 1 or 2 paragraphs
2.1.5. Discussion, the discussion is about 1 paragraph
2.1.6. Add the paper in reference list using APA style
Hypothesis
The main research question is: Is there a significant difference between the annual percent of diabetic Medicare enrollees in Maryland and Virginia, aged 65-75, having eye examinations?
For this research question the hypothesis is: There is no significant difference in the annual percent of diabetic Medicare enrollees, aged 65-75, having eye examinations, between Maryland and Virginia.
Materials and Method
Description of Data
The primary source of data for this research is Medicare National Data by County 2012. The observations from the Medicare National Data by County 2012 dataset includes information from hospitals that are all located in counties within the United States. From this data, we used two states, Maryland and Virginia. There were 24 counties listed on the data spreadsheet for Maryland, with 24 counties reporting data. There were 134 counties listed on the data spreadsheet for Virginia, with 132 counties reporting data. The total number of observations for this analysis is 156, with the number of diabetic Medicare enrollees from Maryland at 543,395, and the number of diabetic Medicare enrollees from Virginia at 786,645. The variables used in the dataset include the average percent of the number of Maryland Medicare enrollees, and Virginia Medicare enrollees aged 65-75 having eye examinations. The used dataset included Medicare enrollees from these two states within the United States for the year 2012.
Variables
The variables in play to answer this research question were the annual average percent of diabetic Medicare enrollees from Maryland, aged 65-75 having eye examinations and the annual average percent of diabetic Medicare enrollees from Virginia, aged 65-75, having eye examinations. (See Table 1).
Table 1. List of Variables Used for the Statistical Analysis
Variable Definition Description
of code Source Year
Medicare enrollees from Maryland, aged 65-75, having eye examinations
Average annual percent of diabetic Medicare enrollees in MD, aged 65-75, having eye examinations Numeric Medicare National Data by County 2012 2012
Medicare enrollees from Virginia, aged 65-75, having eye examinations Average annual percent of diabetic Medicare enrollees in VA, aged 65-75, having eye examinations Numeric Medicare National Data by County 2012 2012
Source: Medicare National Data by County 2012
Method
A quantitative analysis research method was used to formulate an answer to the research question “Is there a significant difference between the annual percent of diabetic Medicare enrollees in Maryland and Virginia, aged 65-75, having eye examinations,” and to test the hypothesis. Due to the nature of the numeric data that was obtained for this analysis, the most appropriate statistical test to apply to this study is a two-sample t-test. The sample of subjects from the dataset used in this analysis is represented by data from hospitals from counties in two states, Maryland and Virginia. The independent variables represented in this analysis are the diabetic Medicare enrollees, aged 65-75 from Maryland, and the diabetic Medicare enrollees, aged 65-75, from Virginia. The dependent variable that is also related to the outcome of the analysis is the eye examinations of the enrollees in each state. In this analysis, the relationship between diabetic Medicare enrollees, aged 65-75, from Maryland and diabetic Medicare enrollees, aged 65-75, from Virginia, and eye examinations was established.
Data Analysis Process
We used RStudio to conduct the analysis of statistical data and to process the information. RStudio is an open-source, integrated development environment for R programming, used for statistical computing and graphics . It was decided that a t-test was appropriate for this analysis because t-tests are used to determine whether means that belong to two separate groups are equivalent to one another. The codes used for this t-test analysis were created in RStudio. A two-sample test was used in this analysis as a two-sample test is ideal for use in an analysis to determine if two population means are equal . In this case, the means were between diabetic Medicare enrollees aged 65-75, having eye examinations from Maryland, and diabetic Medicare enrollees ages 65-75, having eye examinations from Virginia. The RStudio statistical package was used to analyze the data into graphs for this analysis.
Results
The focus of this analysis was the association between Medicare enrollees aged 65-75, from Maryland, having eye examinations, and Medicare enrollees aged 65-75, from Virginia, having eye examinations. Findings of the analysis conclude that there no significant difference between Medicare enrollees aged 65-75, from Maryland, having eye examinations, and Medicare enrollees aged 65-75, from Virginia, having eye examinations. A significance was not able to be determined because the p-value is greater than 0.05. When there is a p-value of >0.05, there is no significant difference. Therefore, the annual percent of diabetic Medicare enrollees aged 65-75, from Maryland, having eye examinations, and the annual percent of diabetic Medicare enrollees aged 65-75, from Virginia not significant. Table 2 shows “Descriptive Analysis of Eye Examinations of Medicare Enrollees Between MD and VA.” Calculations performed in the RStudio statistical package show the p-value is equal to 0.9988, which is greater than 0.05.
Table 2. Descriptive Analysis of Eye Examinations of Medicare Enrollees Between MD and VA
N (obs.) Mean SD p-value
Medicare enrollees, 65-75, having eye examinations, MD
24
68.45417
5.338619 0.9988
Medicare enrollees, 65-75, having eye examinations, VA
132
68.45227
5.824081
Source of findings: Medicare National Data by County 2012
Using the box-plots generated in RStudio, we were able to compare the percentage of diabetic Medicare enrollees, aged 65-75, living in Maryland and Virginia, with eye examinations. The box-plots that were generated in RStudio, give a visual representation of the data. Figure 1 with VA (representing Virginia) and MD (representing Maryland), shows that Virginia has a slightly higher, but not significant, annual percentage of eye examinations compared to Maryland. Similarly, Figure 2 showing the density plot between Virginia (shown in red) and Maryland (shown in blue), shows that diabetic Medicare enrollees from Virginia compared to diabetic Medicare enrollees from Maryland, has a slightly higher, but not significant, density in comparison.
6. Conclusion and Discussion
▪ Review your research questions or hypothesis. How has your analysis informed this question or hypothesis? Present your conclusion(s) from the results (presented above) and discuss the meaning of this conclusion(s) considering the research question or hypothesis presented in your introduction.
▪ Discuss the results of your statistical analysis considering the background information presented in the introduction you need to find at least one paper to support your findings.
▪ At the end of this section, add one or two sentences and discuss the limitations associated with this analysis and any other statements you think are important in understanding the results of this analysis.
Discussion
According to data analysis performed in RStudio, there is not enough evidence to reject the research hypothesis that there is a significant difference in annual percent of diabetic Medicare enrollees aged 65-75 having eye examination between Maryland and Virginia. Therefore, we fail to reject the null hypothesis that there is no significant difference between the annual percent of diabetic Medicare enrollees aged 65-75, from Maryland, having eye examinations and diabetic Medicare enrollees aged 65-75, from Virginia, having eye examinations. (Add more here if you like, see above instruction)
Limitation: This analysis was done using data that was provided through the reporting by two states, Maryland and Virginia. The number of enrollees for Maryland was 543,395 and the number of Medicare enrollees for Virginia was 786,645. These two numbers show a slightly high numerical difference in the number of Medicare enrollees, in this age group, between Maryland and Virginia. There is no way of knowing if less of a numerical difference in enrollees between these two states would change the results of this analysis. (Add more here if you like, see above instruction)
Conclusion
Using calculations in the RStudio software, the data shows that there is no significant difference between the annual percentage of diabetic Medicare enrollees aged 65-75, from Maryland, having eye examinations, and diabetic Medicare enrollees aged 65-75, from Virginia, having eye examinations. Therefore, we fail to reject the null hypothesis. (Per Dr. Zare, pick a couple of the articles to compare to the findings of analysis, at least on that goes with the null hypothesis and one against if available)
Medicare works with state agencies to educate and inform the population about diabetes and the conditions that people with diabetes should be regularly screened for. Both Maryland and Virginia offer programs that provide support to diabetic patients who are at risk eye diseases secondary to diabetes, such as retinopathy, cataracts, and glaucoma. (add more here, summary for CONCLUSION)
Enter suggestions or policies here.
References (to be finalized when all articles are complete and added)
Centers for Disease Control and Prevention. National Diabetes Statistics Report website. (2022, January 18). Retrieved from www.cdc.gov: https://www.cdc.gov/diabetes/data/statistics-report/index.html
DIABETES AND YOU: Healthy Eyes Matter! (2014, January). Retrieved from www.cdc.gov: https://www.cdc.gov/diabetes/ndep/pdfs/149-healthy-eyes-matter.pdf
Diabetes in the United States. (2021, September). Retrieved from stateofchildhoodobesity.org: https://stateofchildhoodobesity.org/diabetes/
itl.nist.gov. (n.d.). Two-Sample t-Test for Equal Means. Retrieved from National Institute of Standards and Technology: https://www.itl.nist.gov/div898/handbook/eda/section3/eda353.htm
Medicare National Data by County. (2012). Retrieved from Dartmouth Atlas of Health Care.
RStudio. (n.d.). Retrieved from rstudio.com: https://www.rstudio.com/products/rstudio/
