The Relatiohship Between Healthcare Costs and Good Health (Part VI)

To summarize the results of the running a linear regression analysis on the model shown in Part I of this series of blogs are as follows:


Variable 2009 Result ObamaCare Projection
--------------------------------------------------------------------------------------------------------------------
Healthcare Insurance Premium $13,375 $14900
Doctors Per Capita (per 1000 people) 2.4 2
Nurses Per Capita (per 1000 people) 9.7 8.1
Life Expectancy 78 76.2
Obesity 28% 32.7%
Healthcare % of GDP 16.2% 16.8%
Healthcare Costs Per Capita $7700 $9000

These results are the consequence of moving 30 million uninsured Americans into government run insurance plans such as Medicaid. The results indicate that there will be a 17% rationing of doctors; a 17% rationing of nurses; an 12% increase in insurance premiums; a 1.8 year reduction in the life expectancy rate; a 4.2% increase in the national obesity rate; a 0.6% or 80 billion dollar annual increase in the size of the healthcare industry compared to our gross domestic product (This may not sound like much, but the United States annual GDP grows about 3% per year or 400 billion dollars annually. Thus, the annual United States medical industry is growing by nearly 500 billion dollars!); and finally there will be a 17% increase in the annual healthcare expenditures per person.

The bottom line; any government attempt to take over a large portion of the healthcare industry will result in much higher expenses for every American while at the same time risking the wellbeing of every American. This is a lose – lose scenario. Some may argue the claims in this model are false, but it does accurately follow what happened when the state of Massachusetts implemented a similar law a few years back. It also tracks the trends of other socialized healthcare systems around the globe (i.e. Great Britain and Canada). Thus, the fears of many Americans that ObamaCare will lead to higher costs, shoddier care, and rationing are all well founded.

Since the ObamaCare law is so complex, it is very difficult to predict how these variables will actually trend. However, there is nothing wrong with looking at present healthcare trends to forecast the potential risks of creating a massive government run healthcare program. I seriously doubt anyone took the time to evaluate these important trends prior to developing and implementing the ObamaCare legislation.

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The Relationship Between Healthcare Costs and Good Health (Part V)

Below are the results of running a linear regression analysis on the model posted in Part I of this series of blogs solving for No Ins (the number Americans without insurance), Gov Total (the number of Americans with government health insurance plans), and Priv Total (the number of Americans with private health insurance plans). The units for all three categories (No Ins, Priv Total, and Gov Total) are in thousands of people.


No Insurance

R2 1
SE 225.4

Coefficient Value 2009 No Insurance Projection No Insurance
Intercept 143447.00000 1 143447 1 143447
Priv Total -0.60350 194545 -117407.9075 194545 -117407.9075
Gov Total -0.86580 93167 -80663.9886 124000 -107359.2
Costs GDP 7867.00000 16.2 127445.4 17 133739
Expense Per Capita -37.06000 7700 -285362 9000 -333540
Doctors Per Capita 24614.00000 2.4 59073.6 2 49228
Health Ins Rate 18.21000 13375 243558.75 15000 273150
Obesity 63.97000 28 1791.16 33 2111.01
Nurses Per Capita -13362.00000 9.7 -129611.4 8 -106896
Life Expectancy 1134.00000 78 88452 76 86184

Result 50722.6139 22655.9025


Government Insured

R2 1
SE 258.3

Coefficient Value 2009 Government Insured Projection Government Insured
Intercept 159489.0000 1 159489 1 159489
Priv Total -0.6906 194545 -134352.777 194545 -134352.777
No Ins -1.1370 50674 -57616.338 20000 -22740
Costs GDP 9095.0000 16.2 147339 17 154615
Expense Per Capita -42.7800 7700 -329406 9000 -385020
Doctors Per Capita 28303.0000 2.4 67927.2 2 56606
Health Ins Rate 20.9900 13375 280741.25 15000 314850
Obesity 70.6200 28 1977.36 31 2189.22
Nurses Per Capita -15426.0000 9.7 -149632.2 8 -123408
Life Expectancy 1368.0000 78 106704 76 103968

Result 93170.495 126196.443





Private Insured

R2 1
SE 258.3

Coefficient Value 2009 Private Insured Projection Private Insured
Intercept 125153.0000 1 125153 1 125153
Gov Total -1.2930 93167 -120464.931 124000 -160332
No Ins -1.4890 50674 -75453.586 20000 -29780
Costs GDP 10651.0000 16.2 172546.2 17 181067
Expense Per Capita -48.9800 7700 -377146 9000 -440820
Doctors Per Capita 26540.0000 2.4 63696 2 53080
Health Ins Rate 23.8200 13375 318592.5 15000 357300
Obesity 547.4000 28 15327.2 33 18064.2
Nurses Per Capita -16762.0000 9.7 -162591.4 8 -134096
Life Expectancy 3008.0000 78 234624 76 228608

Result 194282.983 198244.2

The results from all three linear regression analyses above illustrate very good correlation because the R² variable is equal to 1. These results were computed to check the validity of the prior analyses done in Parts II, III, and IV of this series of blogs since the expected number of people with no insurance is to decline from 50 million to 20 million; the number of people with government insurance plans is expected to increase from 94 million to 124 million; and the number of people with private insurance plans is expected to stay the same at 194 million. For the most part these results correlate since No Ins dropped to 22 million, Gov Total increased to 126 million, and Priv Total increased to 198 million. These results are within 4% of what was expected and the difference can be easily explained by the upward population growth trend in the model. All of the results in Part I through Part V of this blog series on healthcare will be summarized in Part VI.

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The Relationship Between Healthcare Costs and Good Health (Part IV)

Below are the results of running a linear regression analysis on the model posted in Part I of this series of blogs solving for Obesity (percentage of Americans with a body mass index over 30), Costs GDP (the annual health industry market size as a percentage of GDP), and Exp Per Capita (the average annual healthcare expenditure per person):


Obesity

R2 1
SE 0.22

Coefficient Value 2009 Obesity Percent Projection Obesity Percent
Intercept 174.000000000 1 174 1 174
Priv Total 0.000217900 194545 42.3913555 194545 42.3913555
Gov Total 0.000052627 93167 4.903099709 124000 6.525748
No Ins 0.000062590 50674 3.17168566 20000 1.2518
Costs GDP 1.504000000 16.2 24.3648 17 25.568
Exp Per Capita -0.009257000 7700 -71.2789 9000 -83.313
Health Ins Rate 0.005210000 13375 69.68375 15000 78.15
Life Expectancy -2.741000000 78 -213.798 76 -208.316
Doctors Per Capita 19.380000000 2.4 46.512 2 38.76
Nurses Per Capita -5.351000000 9.7 -51.9047 8 -42.808

Result 28.04509087 32.2099035


Cost Per GDP

R2 1
SE 0.03

Coefficient Value 2009 Costs Per GDP Projection Costs Per GDP
Intercept -21.490000 1 -21.49 1 -21.49
Priv Total 0.000064 194545 12.36605838 194545 12.36605838
Gov Total 0.000102 93167 9.4564505 124000 12.586
No Ins 0.000115 50674 5.8477796 20000 2.308
Expense Per Capita 0.004766 7700 36.6982 9000 42.894
Doctors Per Capita -3.524000 2.4 -8.4576 2 -7.048
Health Ins Rate -0.002351 13375 -31.444625 15000 -35.265
Obesity 0.022540 28 0.63112 33 0.74382
Nurses Per Capita 1.778000 9.7 17.2466 8 14.224
Life Expectancy -0.059710 78 -4.65738 76 -4.53796

Result 16.19660348 16.78091838


Expense Per Capita

R2 1
SE 0.007

Coefficient Value 2009 Expense Per Capita Projection Expense Per Capita
Intercept 4672.0000 1 4672 1 4672
Priv Total -0.0128 194545 -2484.33965 194545 -2484.33965
Gov Total -0.0209 93167 -1944.39529 124000 -2587.88
No Ins -0.0237 50674 -1203.00076 20000 -474.8
Costs GDP 208.1000 16.2 3371.22 17 3537.7
Doctors Per Capita 762.7000 2.4 1830.48 2 1525.4
Health Ins Rate 0.4942 13375 6609.925 15000 7413
Obesity -6.0600 28 -169.68 33 -199.98
Nurses Per Capita -375.0000 9.7 -3637.5 8 -3000
Life Expectancy 8.3980 78 655.044 76 638.248

Result 7699.7533 9039.34835


The above model results have excellent correlation as illustrated by the R² variable equal to 1. The Obesity rate will increase from 28% to 32.2%, the healthcare industry will grow from 16.2% to 16.8% of GDP, and the average annual healthcare expense per person will increase from 7700 to 9000 dollars by moving 30 million Americans with no healthcare insurance to a government run healthcare insurance plan. It has already been determined that Exp Per Capita (the average annual healthcare expenditures per capita), Costs GDP (the healthcare industry market size as a percent of GDP), Health Ins Rate (the annual family health insurance premium), and the Obesity rate will all increase using this model. On the other hand, it was determined that the Life Expectancy, the Doctors Per Capita, and Nurses Per Capita will all go down. Those numbers are updated in the projection column in the above table.

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The Relationship Between Healthcare Costs and Good Health (Part III)

Below are the results of running a linear regression analysis on the model posted in Part I of this series of blogs solving for Doctors Per Capita (the average number of doctors per 1000 people), Nurses Per Capita (the average number of nurses per 1000 people), and Life Expectancy (the average life span for Americans):


Doctors Per Capita

R2 1
SE 0.007

Coefficient Value 2009 Doctors Per Capita Projection Doctors Per Capita
Intercept -6.69700000 1 -6.697 1 -6.697
Priv Total 0.00000987 194545 1.919633879 194545 1.919633879
Gov Total 0.00001970 93167 1.8353899 124000 2.4428
No Ins 0.00002249 50674 1.139708934 20000 0.44982
Costs GDP -0.21950000 16.2 -3.5559 17 -3.7315
Exp Per Capita 0.00108800 7700 8.3776 9000 9.792
Health Ins Rate -0.00054610 13375 -7.3040875 15000 -8.1915
Obesity 0.01810000 28 0.5068 33 0.5973
Nurses Per Capita 0.43080000 9.7 4.17876 8 3.4464
Life Expectancy 0.02561000 78 1.99758 76 1.94636

Result 2.398485213 1.974313879


Nurses Per Capita

R2 1
SE 0.02

Coefficient Value 2009 Nurses Per Capita Projection Nurses Per Capita
Intercept 13.630000 1 13.63 1 13.63
Priv Total -0.000030 194545 -5.928369785 194545 -5.928369785
Gov Total -0.000052 93167 -4.890708498 124000 -6.509256
No Ins -0.000060 50674 -3.0252378 20000 -1.194
Costs GDP 0.541500 16.2 8.7723 17 9.2055
Exp Per Capita -0.002616 7700 -20.1432 9000 -23.544
Health Ins Rate 0.001300 13375 17.3875 15000 19.5
Obesity -0.024430 28 -0.68404 33 -0.80619
Doctors Per Capita 2.107000 2.4 5.0568 2 4.214
Life Expectancy -0.005975 78 -0.46605 76 -0.4541

Result 9.708993917 8.113584215


Life Expectancy

R2 1
SE 0.07

Coefficient Value 2009 Life Expectancy Projection Life Expectancy
Intercept 45.6900000 1 45.69 1 45.69
Priv Total 0.0001132 194545 22.022494 194545 22.022494
Gov Total 0.0000964 93167 8.982789472 124000 11.955584
No Ins 0.0001049 50674 5.3157026 20000 2.098
Costs GDP -0.3766000 16.2 -6.10092 17 -6.4022
Exp Per Capita 0.0012130 7700 9.3401 9000 10.917
Health Ins Rate -0.0003751 13375 -5.0169625 15000 -5.6265
Obesity -0.2591000 28 -7.2548 33 -8.5503
Doctors Per Capita 2.5920000 2.4 6.2208 2 5.184
Nurses Per Capita -0.1237000 9.7 -1.19989 8 -0.9896

Result 77.99931357 76.298478


The above model results have excellent correlation as illustrated by the R² variable equal to 1. The doctors per capita will decrease from 2.4 to 2, the nurses per capita will decrease from 9.7 to 8.1, and the life expectancy will decrease from 78 to 76.2 years old by moving 30 million Americans with no healthcare insurance to government run healthcare insurance plans. It has already been determined that Exp Per Capita (the annual average healthcare expenditures per capita), Costs GDP (the healthcare industry size as a percent of GDP), Health Ins Rate (the annual family healthcare insurance premium rate), and the Obesity rate will increase in this model. On the other hand, it was determined that the Life Expectancy, the Doctors Per Capita, and Nurses Per Capita will all go down. Those numbers are updated in the projection column in the above table.


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The Relationship Between Healthcare Costs and Good Health (Part II)

Below is the result of running a linear regression analysis on the model posted in Part I of this series of blogs solving for Health Ins Rate (the average annual health insurance rate for American families):


n 11

R2 1.00
Adjusted R2 1.00
SE 11.5

Term Coefficient 95% CI SE t statistic DF p
Intercept -9818 -121313 to 101678 8774.9 -1.12 1 0.4643
Priv Total 0.02536 -0.23553 to 0.28625 0.020532 1.24 1 0.4333
Gov Total 0.04183 -0.15634 to 0.24000 0.015596 2.68 1 0.2272
No Ins 0.04766 -0.18862 to 0.28393 0.018595 2.56 1 0.2368
Costs GDP -419.5 -1044.8 to 205.7 49.21 -8.53 1 0.0743
Exp Per Capita 2.019 0.853 to 3.185 0.0918 22.01 1 0.0289
Doctors Per Capita -1564 -9780 to 6652 646.6 -2.42 1 0.2496
Nurses Per Capita 761.2 -247.6 to 1769.9 79.39 9.59 1 0.0662
Life Expectancy -10.61 -2143.47 to 2122.25 167.860 -0.06 1 0.9598
Obesity 13.93 -618.89 to 646.75 49.804 0.28 1 0.8263


Source of variation Sum squares DF Mean square F statistic p
Model 67361655.0 9 7484628.3 56285.47 0.0033
Residual 133.0 1 133.0
Total 67361788.0 10

Coefficient Value 2009 Health Insurance Rate Projection Health Insurance Rate
Intercept -9818 1 -9818 1 -9818
Priv Total 0.02536 194545 4933.6612 194545 4933.6612
Gov Total 0.04183 93167 3897.17561 124000 5186.92
No Ins 0.04766 50674 2415.12284 20000 953.2
Costs GDP -419.5 16.2 -6795.9 17 -7131.5
Exp Per Capita 2.019 7700 15546.3 9000 18171
Obesity 13.93 28 390.04 33 459.69
Life Expectancy -10.61 78 -827.58 76 -806.36
Doctors Per Capita -1564 2.4 -3753.6 2 -3128
Nurses Per Capita 761.2 9.7 7383.64 8 6089.6

Result 13370.85965 14910.2112


The above model has excellent correlation as illustrated by the R² variable equal to 1. The results indicate the annual health insurance rate will go up by over 1500 dollars annually by moving 30 million people with no health insurance to a government run health insurance plan. It has already been determined that the Exp Per Capita (annual average healthcare expenditure per capita), Costs GDP (healthcare costs as a percent of GDP), and the Obesity rate will all go up in this model. On the other hand, it was determined that the Life Expectancy, the Doctors Per Capita, and Nurses Per Capita will all go down. Those numbers are updated in the projection column in the above table.

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The Relationship Between Healthcare Costs and Good Health

What is the relationship between healthcare costs and good health? They are not only closely related, they correlate perfectly. Data was broken down into 10 classifications and was evaluated over the past 11 years (1999 – 2009 – there was not much data on the subject earlier than 1999). This data was obtained from multiple sites including the Bureau of Economic Analysis, the Bureau of Labor Statistics, the Census Bureau, and the Organization for Economic Cooperation and Development . A linear error model was created to evaluate the data to see the impact of moving millions of uninsured persons into government run healthcare (this is an attempt to model the potential outcome of the recently passed ObamaCare legislation). To understand how to create and evaluate a linear error model please refer to my blogs: Creating an Election Model Part I, Part II, and Part III on Oct 4, 5, and 6 2010. Below is the data used in the linear error model:


Year Priv Total Gov Total No Ins Costs GDP Exp Per Capita Doctors Per Capita Nurses Per Capita Life Expectancy Obesity Health Ins Rate
2009 194,545 93,167 50,674 16.2 7,700 2.40 9.7 78 28 13375
2008 200,992 87,411 46,340 16.0 7,538 2.40 9.5 78 27.5 12680
2007 201,991 83,031 45,657 15.7 7,285 2.40 9.5 77.9 26.6 12106
2006 201,690 80,270 46,995 15.5 6,931 2.40 9.6 77.7 26.2 11480
2005 201,167 80,213 44,815 15.4 6,563 2.40 9.9 77.4 25.3 10880
2004 200,924 79,486 43,498 15.4 6,196 2.40 9.8 77.4 24.3 9950
2003 199,871 76,755 43,404 15.3 5,852 2.40 9.7 77.1 23.5 9068
2002 200,891 73,624 42,019 14.8 5,453 2.40 9.3 76.9 23.8 8003
2001 201,695 71,295 39,760 14.1 5,052 2.40 9.0 76.9 22.9 7061
2000 202,794 69,037 38,426 13.4 4,703 2.30 8.7 76.8 21.8 6438
1999 200,721 67,683 38,767 13.2 4,500 2.30 8.7 76.7 21 6000

Where Priv Total equals the number of Americans who own private healthcare insurance plans (in thousands of people); Gov Total equals the number of Americans who own government healthcare insurance plans (in thousands of people); No Ins equals the number of Americans who do not have any healthcare insurance (in thousands of people); Costs GDP equals the overall costs of running the healthcare industry as a percentage of the United States Gross Domestic Product (GDP); Exp Per Capita equals the annual expenditures for Americans on healthcare (in dollars); Doctors Per Capita equals the number of doctors per 1000 people; Nurses per Capita equals the number of nurses per 1000 people; Life Expectancy is average age of the American life span; Obesity is the percent of Americans who have a body mass index greater than 30; and Health Ins Rate is the average annual expense American families spends on health insurance premiums.

In Part II of this post we will begin to run various linear regression analyses on the above model to see the potential trends of implementing the ObamaCare legislation.

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