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Assignment #3 – Group Project

This is not more than 20-page submission. In this assignment, students will go through the steps to set up a quantitative research study. The Instructor will divide the class into groups to complete the assignment in week 1. Each group should submit 1 topic selection in week 1, and 1 final report. This report should include qualitative and quantitative sections. Here are the main steps to perform this team project:

Step #1: Topic selection

The instructor will provide a list of topics; the team also should submit the team agreement plan with the selected topic in week 1 for the faculty feedback and approval.

Step #2: Performing Qualitative analysis

2.1. This section of the assignment is aimed at giving students an opportunity to select and analyze at least 5 articles using the ‘Review Manager 5’ tools to analyze the risk of bias for 5 selected articles following these steps:

- The methodological quality and risk of bias evaluation of the selected studies should be conducted independently by two team members, following the Cochrane Handbook for Systematic Reviews (Higgins, 2011), a domain-based evaluation for each study should be done across five domains:

- Selection bias,

- Performance bias,

- Detection bias,

- Attrition bias and,

- Reporting bias.

The judgment of studies for potential bias should be indicated by assigning ‘low risk’, ‘high risk, or ‘unclear risk’, for each respective source of bias.

Reference

Cochrane Handbook for Systematic Reviews of Interventions. (n.d.). Retrieved July 12, 2019, from https://training.cochrane.org/handbook

2.2. Report your finding using the Bias-Tables and Bias-Plots.

Table 1: Literature Review Analysis

Authors, Year of Publication

Intervention/

Policy evaluated

Study design/

Time Period

Data/

Study Population

Relevant Findings/

Recommendations

Author1, YYYY

Author2, YYYY

Author3, YYYY

Author4, YYYY

Author5, YYYY

Table 2. Literature Review, Table of Biases

Selection Bias

Performance Bias

Detection Bias

Attrition Bias

Reporting Bias

Authors

Systematic differences between baseline characteristics of the groups that are compared.

Systematic differences between groups in the care that is provided, or in exposure to factors other than the interventions of interest.

Systematic differences between groups in how outcomes are determined.

Systematic differences between groups in withdrawals from a study.

Systematic differences between reported and unreported findings.

Author1, 2019

Y

Y

Y

Y

Y

Author2, 2019

Author3, 2019

Author4, 2019

Author5, 2019

Note:

Y: Low risk

N: High risk

U: Unclear

Step #3: Performing Quantitative data analysis design, this is very similar to the Individual Assignments 1 and 2 (please look at the instruction for more details).

For this section:

3.1. Select one of the class data sets

3.2. Identify relevant variables

3.3. Choose the statistical method you plan to use for your analysis

3.4. Identify statistical software your team will use to run the statistical analysis focusing on EXCEL or RStudio (BONUS points) as the main software (you can use any other software such as SAS, STATA or SPSS, but the master-code will be available only for RStudio)

3.5. Analyze the data, state your conclusions and support them, this section should be included:

3.5.1. Hypothesis or research questions with a short paragraph to discuss the issue.

3.5.2. Research Method for this section first report the table of variables, then define the variables using the example provided in step-by-step instruction.

3.5.3. Report the software and type of analysis you performed in this section included descriptive table and plots

3.5.4. Discuss your findings

3.5.5. Discussion: for this section compare your finding with the LR (literature review) you performed in the first section

3.5.6. Recommendations: suggest a few policy recommendations based on your finding and LR.

Note: Make sure to submit your RStudio codes for your instructor review (for BONUS points).

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HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible 1 between Montana and Alaska HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska Tien Bui Junau Louis-Jean Lateen Keys Janaye Mack Suzanna March Dr. Mojgan Azadi April 20, 2020 HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska 2 Introduction The topic being investigated is the percent of Medicare beneficiaries Part A eligible between Montana and Alaska. Medicare is a government funded health insurance program for individuals who are 65 years old and older, individuals with certain disabilities, and individuals with End-Stage Renal Disease and require kidney transplant or dialysis. There are three different parts of Medicare, all of which cover different healthcare services, Medicare Part A, B, and D. Medicare Part A is for hospitalization, Part B is for Medical and outpatient services, and Part D is for prescription medication. Medicare is the largest payor for healthcare in the United States for the over 65 population. Medicare Part A is a provision that generally covers for inpatient hospitalization. Most people over 65 are eligible for free Medicare Part A coverage. These individuals become eligible by working more than 10 years and paying appropriate Medicare taxes while employed. Those who do not have enough years of paid employment, may elect to purchase Part A by paying a monthly premium. The premium is currently about $450 a month for those with less than 30 paid quarters of Medicare contributions and about $250 for those with 30-39 quarters of contributions. In order to elect to purchase Part A, you generally have to also purchase Part B (Medicare Costs at a Glance, n.d.). As a healthcare provider, a facility is in the business of wanting to provide good health to its customers. The absence of Part A coverage may lead an individual to defer needed care due to costs. For an inpatient facility, it is important to understand the demographic of payors accessing your facility. Medicare Part A coverage provides 100% of allowed amounts for the first 60 days of care (after the deductible) (Medicare Costs at a Glance, n.d.). This creates a steady and HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible 3 between Montana and Alaska predictable revenue source for a facility. In contrast, the elderly who are underinsured without Part A or other on par coverage, may create a large “charity care” or “indigent care” burden on the facility that is required to provide stabilizing care. This topic has been researched by others using a quantitative method. An article titled, A Dozen Facts About Medicare Advantage in 2019, researches the increase of Medicare Part A beneficiaries in the U.S. by state. According to this article, “Medicare Advantage enrollment is relatively low (20 percent or lower) in 14 states and the District of Columbia, including two mostly rural states where it is virtually non-existent (AK and WY)”. Literature Review Through literature review and data analysis, this study seeks to answer a critical question: Is there a significant difference between Alaska and Montana individuals in terms of Medicare Eligibility. Through a literature review relevant studies will be analyzed relevant to understanding this question. Table 1: Literature Review Analysis Intervention/ Authors, Year Policy evaluated of Publication Gretchen Jacobson, Meredith Freed, Anthony Damico, and Tricia Neuman, 2019 Medicare Advantage enrollment Study design/ Time Period Data/ Relevant Findings/ Study Population Recommendations Quantitative Centers for There is an increase Descriptive Medicare and in the percentage of Research Design Medicaid Services Medicare (CMS) Medicare beneficiaries Advantage enrolling in Medicare Enrollment, Benefit Advantage programs and Landscape files which provide extra benefits that may not be covered under the HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible 4 between Montana and Alaska Intervention/ Authors, Year Policy evaluated of Publication Study design/ Time Period Data/ Relevant Findings/ Study Population Recommendations traditional Medicare Programs Author2, YYYY Author3, YYYY Author4, YYYY Author5, YYYY Table 2. Literature Review, Table of Biases Selection Bias Performance Bias Detection Bias Attrition Bias Systematic differences between Systematic groups in the differences care that is between provided, or in Systematic Systematic baseline exposure to differences differences characteristics factors other between groups between of the groups than the in how groups in that are interventions outcomes are withdrawals Authors compared. of interest. determined. from a study. Gretchen U Jacobson, Meredith U U U Reporting Bias Systematic differences between reported and unreported findings. Y HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska Performance Selection Bias Bias Detection Bias Attrition Bias Systematic differences between Systematic groups in the differences care that is between provided, or in Systematic Systematic baseline exposure to differences differences characteristics factors other between groups between of the groups than the in how groups in that are interventions outcomes are withdrawals Authors compared. of interest. determined. from a study. Freed, Anthony Damico, and Tricia Neuman, 2019 Author2, 2019 Author3, 2019 Author4, 2019 Author5, 2019 Note: (Y: Low risk, N: High risk, U: Unclear) Research Question or Research Hypothesis 5 Reporting Bias Systematic differences between reported and unreported findings. HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska 6 The research question or research hypothesis is “Is there a significant difference between the percentage of Medicare beneficiaries Part A eligible between Montana and Alaska”. Significance is a term used in statistics to describes how sure you are that there is a difference or relationship present and it only provides part of the story, not the full picture. Significance helps identify whether the difference is big, moderate, or small. The sample size may help determine the if significance small, moderate or large. There are two outputs to help determine if the difference noted is statistically significant. Confidence interval or CI around the difference helps identify statistical difference. P-value is the primary output and represents probability value. In order to analyze the data quantitatively, this study will test the null hypothesis: There is no significant relationship between the states of Alaska and Montana and who eligible Medicare. The study will be looking at the data at the 0.05 significance level. The results from each approach will then be analyzed to develop a summary of findings that can inform health care entities related to the topic. Research Method The primary source of the data set is the Medicare National Data CSV. This data set provides information regarding number of beneficiaries overall, number of beneficiaries black, number of beneficiaries white, per ambulatory visit overall, per ambulatory visit black, per ambulatory visit white, number of diabetic overall, number of diabetic black, number of diabetic white, per hemoglobin overall, per hemoglobin black, per hemoglobin white, per eye exam overall, per eye exam black, per eye exam white, per lipid overall, per lipid black, per lipid white, number of female Medicare overall, number of female Medicare black, number of female HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska 7 Medicare white, per mammogram overall, per mammogram black, per mammogram white, number of Part A beneficiaries overall, number of Part A beneficiaries black, number of Part A beneficiaries white, number of discharges overall, number of discharges black, number of discharges white, per diabetic black, and per diabetic white. The writer is using Medicare National Data which included 56 observations of Montana Medicare Part A beneficiaries and 29 observations for Alaska Medicare Part A beneficiaries. The variables that are being analyzed Montana residents who are Medicare Part A eligible beneficiaries and Alaska residents who are Medicare Part A eligible beneficiaries. Both variables are numerical. To determine whether there’s a significant difference between the percentage of Medicare beneficiaries Part A eligible between Montana and Alaska, we will use a t-test, to determine whether differences across the states are statistically significant. The t-test is used to determine the difference between two samples which in this case is Montana and Alaska. The statistical alternative hypothesis for this research question would be that there is no significant difference between the percentage of Medicare beneficiaries Part A eligible between Montana and Alaska. The research method for analysis is quantitative. This quantitative study will be conducted using statistical analysis of publicly available data from the Centers for Medicare and Medicaid Services that contains the data needed for this study. A box plot can also be used in order to compare the data. The data will be analyzed to determine the average, standard deviation and HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible 8 between Montana and Alaska number of data points. This test is a statistical tool that evaluates the likelihood that a difference between two averages could have occurred by chance. A larger t-score implies a greater variation between the two means. The t value can then be used to find a p-value. The p value indicates the probability that the two means belong to the same data group. The smaller the p value the more likely the results are to indicate a significant difference between the means (Statistics How-To, n.d.). This method provides explanations for phenomena’s by using data collection and statistical based methods used to analyze data. Qualitative data cannot be analyzed using statistics. The statistical method, p-value, will be used during this study to help determine whether there is a significant difference. The statistical packaged used in Excel is called the Analysis ToolPak. This statistical package is an add-in feature that must be installed prior to use. This package allows users to create charts for statistical data already on an active excel worksheet. The Analysis ToolPak is mainly used to calculate t-test, chi-square tests, and correlations. Below is a table showing the variables, the definitions of the variables, the description of code, the source, and the year for the analysis. Table 1. List of variables used for the analysis Variable Montana Medicare beneficiaries Part A Definition Total number of Montana residents in all counties who are Medicare Part A beneficiaries Description of code Source Numeric Medicare National Data Year HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible 9 between Montana and Alaska Variable Alaska Medicare beneficiaries Part A Definition Total number of Alaska residents in all counties who are Medicare Part A beneficiaries Description of code Numeric Source Year Medicare National Data Source: UMUC, 2019 Results According to the results, the test statistic is 0.292756 and the critical two tail value is 2.011741. When the absolute value of the test statistic is not larger than the critical two-tail value, the two populations means are not statistically different. In this case, based on the results, there is no statistical significance between the percentage of Medicare beneficiaries Part A eligible between Montana and Alaska. Below is a table providing the descriptive analysis. Table 2. Descriptive analysis to compare percentage of Medicare beneficiaries Part A between Montana and Alaska. Variable Montana Medicare beneficiaries Part A Alaska Medicare beneficiaries Part A Obs . Mean SD 56 2309.161 3575.915 29 2028.345 4478.925 Source: UMUC, 2019 t-Test: Two-Sample Assuming Unequal Variances P-value P(T<=t ) onetail P(T<=t ) twotail 0.38549 9 0.77099 7 HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska Mean Variance Observations Hypothesized Mean Difference df t Stat P(T<=t) one-tail t Critical one-tail P(T<=t) two-tail t Critical two-tail 10 Variable Variable 1 2 2309.161 2028.345 12787168 20060767 56 29 0 47 0.292756 0.385499 1.677927 0.770997 2.011741 The results present evidence that contradicts the null hypothesis that there is a significant difference between the percentage of Medicare beneficiaries Part A eligible between Montana and Alaska. Conclusion In conclusion, Medicare is a federal health insurance program available throughout the United States for people who are 65 years or older. Medicare is divided into three different parts: Medicare Part A (for hospital insurance), Medicare Part B (for Medical insurance), and Medicare Part D (for prescription drug coverage). The focus of this paper is on Medicare Part A, and the percent of beneficiaries eligible between Montana and Alaska. According to Medicare.gov, “Part A covers inpatient hospital stays, care in a skilled nursing facility, hospice care, and some home health care” (Medicare.gov, 2020). Based on the findings on the percentage of Medicare beneficiaries Part A eligible between Montana and Alaska, it was found that there is no statistical significance between Montana and Alaska Medicare Beneficiaries. The limitations that may exist within this study is that there is limited information provided on the actual year of this data set. Also, the observed HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska 11 sample size was relatively small. This research analysis is based mainly on numerical data and the information provided is generally gathered in a ridged manner. One can easily assume that because of the difference of the geographic areas of Montana and Alaska, that this information would show more significant difference, however, using the dataset provided, it shows that that is not the case in this situation. Reference How to Use the Analysis ToolPak in Excel 2019. (2020). Retrieved 11 April 2020, from https://www.universalclass.com/articles/computers/excel/how-to-use-the-analysistoolpak-in-excel-2019.htm Jacobson, G., Freed, M., Damico, A., & Neuman, T. (2019). A Dozen Facts About Medicare Advantage in 2019. Retrieved 20 April 2020, from https://www.kff.org/medicare/issuebrief/a-dozen-facts-about-medicare-advantage-in-2019/ Medicare costs at a glance. (n.d.). Retrieved from https://www.medicare.gov/your-medicarecosts/medicare-costs-at-a-glance Medicare.gov. (2020). What’s Medicare? Retrieved from https://www.medicare.gov/whatmedicare-covers/your-medicare-coverage-choices/whats-medicare Statistics How-To. (n.d.). T Test (Student's T-Test): Definition and Examples. Retrieved April 19, 2020, from https://www.statisticshowto.datasciencecentral.com/probability-andstatistics/t-test/ HMGT 400 Group Assignment: Comparing percent of Medicare beneficiaries Part A eligible between Montana and Alaska Zare, H., (2019, May). MN Hospital Report Data. Data posted in University of Maryland University College HMGT 400 online classroom, archived at: http://campus.umuc.edu 12 ...
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