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HMGT 400 Research and Data Analysis in Health- FINAL EXAM

Data File: HMGTFINALEXAM.csv (Request from Professor after you submit Group Assignment #3)

Software Tool: Analysis ToolPak, RegressItLogistic or XLMiner Analysis ToolPak

Question #1 (15 credits):



The FINAL EXAM data file provides data on hospitals in 2011 and 2012. Analyze the FINAL EXAM dataset. You may calculate the ?Hospital Beds per Population (Per Capita)? variable by dividing ?total_hospital_beds by tot_population. Use the analysis results to complete Table 1 (template) below. Net Hospital benefits = Hospital Revenues minus Hospital Costs.

Table 1. Comparing hospitals in 2011 with hospitals in 2012

2011

2012

t Value (Pr<|t|)

N

Mean

St. Dev

N

Mean

St. Dev

Hospital Characteristics

1. Hospital beds

1078

229.3831

206.7204

921

1301.885

32944.54

0.285168

2. Number of paid Employee

945

1146.951

1414.9

354

653.2254

875.9757

1.18516E-09

3. Number of non-paid Employee

80

48.1

68.10483

115

41.87826

43.81463

0.438670

4. Internes and Residents

279

79.65591

138.8227

44

4.340909

3.863624

0.000377

5. System Membership

1079

0.60797

0.552588

922

0.646421

0.680688

0.092113986

6. Total hospital cost

1078

203721472.4

303617442.7

919

183732389.6

265000930.8

0.122213

7. Total hospital revenues

1078

477069773.3

1034436756

919

471954365.8

1093163806

0.917651

8. Net Hospital benefits

1078

919

9. Available Medicare days

1068

16538.37734

19225.12023

919

16538

0

0.99643

10. Available Medicaid days

1052

5311.276616

9190.481016

919

5311

0

0.994757

11. Total Hospital Discharge

1074

9312.395717

10711.43761

919

9345

0

0.927247

12. Medicare discharge

1070

3206.281308

3380.252336

920

111902.162

3296796.503

0.280894

13. Medicaid discharge

1064

1253.279135

1900.277427

905

1171.319337

1762.961121

0.34953

Socio-Economic Variables

14. Hospital Beds per Population (Per Capita)

1078

0.002338

0.004095

919

0.002349

0.003575

0.9515

15. Percent of population in poverty

1078

25.96855288

9.66858643

919

25.79129489

9.528074007

0.6901

16. Percent of Female population in poverty

1078

15.87625232

5.562371262

919

15.7773667

5.444037856

0.6696

17. Percent of Male population in poverty

1078

10.09230056

4.321048879

919

10.01392818

4.316069396

0.6933

18. Median Household Income

1078

50137.41373

13656.34485

919

49714.11099

12863.14896

0.4460


Then please answer the following questions.


A) What are the significant differences for each of the hospital characteristics between 2011 and 2012?

The number of paid employees, on the other hand, was larger in 2011 than in 2012, indicating a considerable difference. Between the two years, there is a major difference between Internes and Residents.

B) What are the significant differences for each of the socio-economic variables between 2011 and 2012?

There are no significant differences for the socio economic variables between 2011 and 2012.

C) Based on your findings did hospitals (in general) have a better performance in 2011 or 2012? Explain.

2011 hospital performance looks better based upon findings. In 2011 there were more hospital beds, more paid employees, more interns and residents, more revenue, more Medicaid and Medicare discharges.

D) How does hospital performance relate to the hospital characteristics and how does it relate to the socio-economic characteristics?


Question #2 (15 credits):


In the data file, create a new variable called ?hospital net benefits?. Do this by subtracting ?hospital costs? from ?hospital revenues?.




Analyze the dataset and then complete Table 2 (template). In the last column report the T-test results comparing hospital characteristics between 2 groups of hospitals ?1. For-profit & Other ownership?, 2.?Non-profit & public? hospitals. Note in the data file:

If own = 0 then the hospital is non-profit owned

If own = 1 then the hospital is for-profit owned

If own = 2 then the hospital is public (government) owned

If own = 3 then the hospital is owned by some other organization

Table 2. Comparing ?For-profit & Other? hospital group to ?Non-profit & Public? hospital group in two years (2011, 2012)

For Profit & Other

Non-Profit & Public

t Value (Pr<|t|)

N

Mean

St. Dev

N

Mean

St. Dev

Hospital Characteristics

1. Hospital beds



2. Number of paid Employee

3. Number of non-paid Employee

4. Internes and Residents

5. System Membership

6. Total hospital cost

7. Total hospital revenues

8. Net Hospital Benefits

9. Available Medicare days

10. Available Medicaid days

11. Total Hospital Discharge

12. Medicare discharge

13. Medicaid discharge

Socio-Economic Variables

14. Per Capita Hospital Beds

15. % population in poverty

16. % Female population in poverty

17. % Male population in poverty

18. Median Household Income


Then answer the following questions:

1) What are the main significant differences between the ?For-profit & Other? hospital group and the ?Non-profit & Public? hospital groups? Which test is the best fit test? Why?

2) Use a column plot or graph to compare ?Net Hospital Benefits? between ?for-profit & other? and ?non-profit & public? hospitals.

3) Create a scatter plot with the correct variable and axes labels and legend. Use the plot to compare hospital cost (x-axes) and revenue (y-axes) and discuss your findings?

4) If we assume that hospital net-benefit is a good measure of hospital performance, which type of hospitals had the better performance between ?for-profit & other? and ?non-profit & public? hospitals?

5) Overall, what are the main statistically significant differences between ?for-profit & other? and ?non-profit & public? hospital groups?

Question #3 (15 credits):


Our data file provides the variable herf_ins called the Herfindahl?Hirschman Index which measures market concentration for the health insurance market. Please note that unlike the class exercise in which you used herf_cat, which measured market concentration for the hospital market, in this assignment you are using herf_ins which measures market concentration for the health insurance market.

Analyze the data to complete Table 3 (template) below:

Table 3. Comparing hospitals in High, Moderate, and Low Insurance Market Concentration in two years (2011, 2012)

High Concentration Insurance Market

Moderate Conc. Insurance Market

Low Concentration Insurance Market

ANOVA

Count

Mean

STDev

Count

Mean

STDev

Count

Mean

STDev

F Value (Pr<|F|)

Hospital Characteristics

1. Hospital beds

2. No. of paid Employees

3. No. of non-paid Employees

4. Internes and Residents

5. System Membership

6. Total hospital cost

7. Total hospital revenues

8. Net Hospital benefits

9. Available Medicare days

10. Available Medicaid days

11. Total Hospital Discharges

12. Medicare discharge-ratio

13. Medicaid discharge-ratio

Socio-Economic Variables

14. Per Capita Hospital Beds

15. Median Household Income


Then answer the following questions:

1) In a short paragraph describe and explain what the Herfindahl index is. You can use the reference provided in the class exercise or any other citation.

2) Compare the following information between hospitals located in high, moderate, and low concentration health insurance markets?

a) What are the main significant differences between hospitals in the different insurance markets? (use the ANOVA test)

b) What is the impact of being in a high-concentration health insurance market on hospital revenues and costs?

c) Does being in a high concentration market have a positive impact on net hospital benefits?

d) What about the number of Medicare and Medicaid discharges? Are hospitals in high concentration insurance markets more likely to accept Medicare and Medicaid patients?

e) What is the impact of other variables?

(Note: to answer the last question, please compute Medicare-discharge ratios and Medicaid-discharge ratios first and then run two t-Tests (high concentration vs. moderate concentration, and high vs. low concentration market). Please support your findings with an illustrative graph.

Question #4 (Credits 20)



Regression Models



Analyze the data by running a regression model with ?Net Hospital Benefits? as the dependent variable and present your results using the Table 4 template below.


Table 4 ? Regression Model 1

Coefficient

ST. ERR

T Stats

P-values

Lower 95%

Upper 95%

Intercept/Constant

Total Hospital beds

Teaching Hospital Dummy

Count (N) =

R Square =

a) Describe and discuss your findings.

b) Do the number of hospital beds or whether a hospital is a teaching hospital or not have a positive or negative impact on hospital net-benefit. (Hospital Performance)? In answering this question, consider statistical significance.

Regression Model 2:

Analyze the data by running a linear regression model and present your results using the Table 5 template below.

Table 5 ? Regression Model 2

Coefficient

ST. ERR

T Stat

P-values

Lower 95%

Upper 95%

Intercept/Constant

Total Hospital beds

Non-Teaching Hospital Dummy

Count (N) =

R Square =

a) Describe and discuss your findings.

b) Do the number of hospital beds or whether a hospital is a non-teaching hospital or not have a positive or negative impact on hospital net-benefits. (hospital performance)? In answering this question, consider statistical significance.

c) Use the results from your Regression model 1 and regression model 2 to comment on any differences or similarities in impact of teaching hospital status or non-teaching hospital status on hospital net-benefits. (hospital performance)?

Regression Model 3:

Analyze the data by running a linear regression model and present your results using the Table 6 template below.

Table 6 ? Regression Model 3

Coefficient

ST. ERR

T Stat

P-values

Lower 95%

Upper 95%

Intercept/Constant

Total Hospital beds

Teaching Hosp. Dummy

Medicare discharge ratio

Medicaid discharge ratio

Count (N) =

R Square =

a) Describe and discuss your findings.

b) Do the number of Medicare or Medicaid patients in a teaching hospital have a positive or negative impact on hospital net-benefits. (hospital performance)? In answering this question, consider statistical significance.

Regression Model 4:

Analyze the data by running a linear regression model and present your results using the Table 7 template below.

Table 7 ? Regression Model 4

Coefficient

ST. ERR

T Stat

P-values

Lower 95%

Upper 95%

Intercept/Constant

Total Hospital beds

Non-Teaching Hosp. Dummy

Medicare discharge ratio

Medicaid discharge ratio

Count (N) =

R Square =

a) Describe and discuss your findings.

b) Do the number of Medicare or Medicaid patients in a non-teaching hospital have a positive or negative impact on hospital net-benefit. (hospital performance)? In answering this question, consider statistical significance.

c) Based on your findings please recommend three policies to improve hospital performance. Please make sure to use the final model for your recommendation?

Question #5 (Credits 20)

Logistic Regression Models

Analyze the data by running a linear regression model and present your results using the Table 8 template below.

Use ?being a member of a hospital network? (system_member) as the dependent variable. and the independent variables presented in Table 8 template below

Table 8 ? Logistic Model 1

Coefficient

ST. ERR

P-Value

Exp (coeff)

Exp (z SE)

Exp (Std. Coeff.)

Intercept/Constant

Total Hospital costs

Count (N) =

R Square =

a) Describe and discuss your findings.

Logistic Model 2:

Analyze the data by running a linear regression model and present your results using the Table 9 template below.

Table 9 ? Logistic Model 2

Coefficient

ST. ERR

P-Value

Exp (coeff)

Exp (z SE)

Exp (Std. Coeff.)

Intercept/Constant

Total Hospital Costs

Total Hospital Revenue

Count (N) =

R Square =

a) Describe and discuss your findings.

Logistic Model 3:

Analyze the data by running a linear regression model and present your results using the Table 10 template below.

Table 10 ? Logistic Regression Model 3

Coefficient

ST. ERR

P-Value

Exp (coeff)

Exp (z SE)

Exp (Std. Coeff.)

Intercept/Constant

Total Hospital Costs

Total Hospital Revenue

Medicare discharge ratio

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