Quantitative Analysis Of Eye Exams Between MD And VA

Quantitative Analysis Eye Exam Between Md And Va 1ass

Centers for Disease Control and Prevention has estimated that there are 37.3 million Americans aged 65 years or older, living with diabetes (Centers for Disease Control and Prevention. National Diabetes Statistics Report website, 2022). Virginia is ranked 23rd in the country for diabetes cases, with 11.1% of the population diagnosed (Diabetes in the United States, 2021). Maryland is ranked 29th in the country for diabetes cases, with 10.3% of the population diagnosed (Diabetes in the United States, 2021). Those diagnosed with diabetes are at a high risk for developing serious eye diseases (DIABETES AND YOU: Healthy Eyes Matter!, 2014).

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 (DIABETES AND YOU: Healthy Eyes Matter!, 2014). A dilated eye examination can help eye doctors detect and treat problems that will help to prevent patients from losing their vision due to diabetes (DIABETES AND YOU: Healthy Eyes Matter!, 2014). 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

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 DR 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?" 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 (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 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 , however, and that it was no longer linked to changes in the rates of eye examination for the cumulative study years of and . 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.

Performing Qualitative analysis

This section of the assignment is aimed at giving students an opportunity to select and analyze 3-5 peer-reviewed articles, each team member should summarize one article. To summarize an article, consider the following items: A short introduction (A structural summary of the article); research hypothesis or questions; methodology, including: data, year of data, research method; main findings; discussion; and 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

Primary data source: Medicare National Data by County 2012. The observations include hospitals located in counties across the U.S., specifically focusing on Maryland and Virginia. There were 24 counties in Maryland and 134 counties in Virginia, with 24 and 132 reporting data respectively, totaling 156 observations. The number of diabetic Medicare enrollees was 543,395 from Maryland and 786,645 from Virginia.

Variables: The key variables are the percentage of diabetic Medicare enrollees aged 65–75 having eye examinations in each state. The analysis involves the average annual percent of enrollees with eye exams for each state, as reported in the dataset.

Method: A quantitative approach using RStudio was employed to perform a two-sample t-test to compare the means of the two groups. The tests assess whether the difference in mean percentages is statistically significant, with the p-value guiding the conclusions.

Data Analysis: Coding was performed in RStudio utilizing the two-sample t-test functions. The analysis was based on the null hypothesis that the means are equal. The p-value obtained was 0.9988, indicating no statistically significant difference between Maryland and Virginia regarding the percentage of diabetic Medicare enrollees aged 65–75 with eye examinations.

Results

The analysis concludes that there is no significant difference between the two states. The p-value of 0.9988 exceeds the threshold of 0.05, further supporting the null hypothesis. Visual comparisons through box-plots and density plots visually demonstrated overlapping distributions with negligible differences, consistent with the statistical findings.

Discussion and Conclusion

Based on the analysis, there is insufficient evidence to reject the null hypothesis. The data suggest that the proportion of diabetic Medicare enrollees aged 65–75, having eye examinations, does not significantly differ between Maryland and Virginia in 2012. This finding aligns with some prior studies indicating uniformity in eye examination rates across regions, although disparities are known to exist when considering broader demographics and years.

The study limitations include reliance on reported data, which could contain errors or inconsistencies. The population size differences (Maryland’s 543,395 vs. Virginia’s 786,645) may also influence the results, and further research could explore other factors such as socioeconomic status, insurance coverage, and access to eye care providers.

In conclusion, the analysis corroborates the hypothesis that no significant difference exists between these states concerning this specific metric during the studied year. Policy implications suggest uniform efforts to improve eye care among diabetic populations are essential, regardless of state boundaries. Future studies should consider longitudinal data and broader geographic scopes to enhance understanding and develop targeted interventions.

References

• Centers for Disease Control and Prevention. (2022). National Diabetes Statistics Report. https://www.cdc.gov/diabetes/data/statistics-report/index.html
• Diabetes in the United States. (2021). State of Childhood Obesity. https://www.stateofchildhoodobesity.org
• DIABETES AND YOU: Healthy Eyes Matter! (2014). American Diabetes Association. https://www.diabetes.org/healthy-living/diagnosing-and-treating-conditions/eye-health
• National Institute of Standards and Technology. (n.d.). Two-Sample t-Test for Equal Means. https://itl.nist.gov/div898/handbook/eda/section3/eda366.htm
• Medicare National Data by County. (2012). Dartmouth Atlas of Health Care. https://www.dartmouthatlas.org/data/
• RStudio. (n.d.). RStudio: Open-source software for R. https://www.rstudio.com/
• Centers for Medicare & Medicaid Services. (2012). Medicare Data Files. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/medicare-physician-and-other-practitioner-average-claims-data
• Behavioral Risk Factor Surveillance System. (2017). CDC. https://www.cdc.gov/brfss/data_statistics/index.htm
• Centers for Medicare & Medicaid Services. (2012). Medicare Enrollee Data. https://www.cms.gov/research-statistics-data-and-systems/statistics-trends-and-reports/medicare-reports/index.html
• American Academy of Ophthalmology. (2019). Eye care utilization patterns and disparities. https://www.aao.org/eye-health/diseases/disparities