Change in Bankruptcy and Health Outcomes

Rotated Factor Matrixa
	Factor
	1  (52% of Variance)	2 (21% of Variance)
Infant Mortality 2007 Deaths/1000	.915	.157
Life Expectancy	-.824	-.473
% Low Birthweight Babies	.700	.341
Groups per million	.612
% change in bankruptcy	-.441
Percent Uninsured in Demographic Group for All Income Levels		.979
Percent under age 65 in 200% of Poverty	.372	.753
Extraction Method: Principal Axis Factoring.  Rotation Method: Varimax with Kaiser Normalization.a
a. Rotation converged in 3 iterations.

I've added the change in bankruptcy rates to the factor analysis for health and hate groups.  I had to remove income from the analysis so the model would converge.  The factor structure did not change radically otherwise.  The % change in bankruptcy loaded weakly and negatively on the factor with health outcomes and hate groups.  Of all the factors entered, life expectancy correlated most strongly with the % change in bankruptcy followed by infant mortality. 

The graph for life expectancy suggests a positive relationship with the change in bankruptcy.  The increased change in bankruptcy could be due to more permissive laws in granting bankruptcy protection from creditors as can be seen in the video below.  These states may tend to have better quality of life characteristics than those who do not.  For example, Hawaii had the highest life expectancy and an increase in bankruptcies of 129% (and zero hate groups).  As can be seen in the above graph, this relationship is far from perfect accounting for only 15.2% of the variability,

There is also a significant negative correlation with infant mortality and change in bankruptcy accounting for 13.4% of the variance.  The District of Columbia is an outlier with a rate of 13.1 deaths per 1,000 live births but an increase of 26% of bankruptcies (DC also has the highest rate of hate groups)/  Removing DC gives a slightly stronger relationship with 14.9% of the variability accounted.

**Related Posts**

A Wave of Hate Groups in California? No in Washington, DC

 

How do the States Stack Up on Infant Mortality? (Cross Post with PUSH)

 

Correlation with the Number of Hate Groups per Million, Poor Health Suggests More Hate

Personal and Medical Bankruptcies: A Follow Up

 

Income and Life Expectancy. What does it Tell Us About US?

New Health Data from the WHO (World Health Organization)

The World Health Organization has come out a new report on life expectancy, infant mortality, and other health statistics for 2012 which can be read here.  Their image above shows the countries top 10 for men and women.  As with previous estimates of life expectancy, the US is not in the top 10 for either men or women.  I haven't seen where the US ranks but the life expectancy but for US men it is 76 years and for US women it is 81 years which is 3 years below the tenth ranked country for each gender.

The report states that for the US and the rest of the world the health measures have improved but disparities remain. Lloyd Stires has done posts detailing studies which show a 3% decrease in mortality in Massachusetts.  Would a nationwide decrease in nationwide mortality result in the US placing in the top 10 countries?  In many of the countries that rank above the US, their medical bankruptcy rates are zero.  

Mike Stout will be having a benefit concert for Healthcareforallpa.org
which is working to correct these discrepancies in PA.  It will be held on June 7 at 8:30PM at the First Unitarian Church, 605 Morewood Ave., Pittsburgh, PA 15213.
 

**Related Posts**

Health Insurance and Mortality, Part 2 

 

Health Insurance and Mortality, Part 1

 

The Affordable Care Act (ACA) having little effect on PA's Uninsured Rate So Far

 

How does Pennsylvania Measure Up on Infant Mortality?

Are You Feeling Lonely?

Outlier (noun): (1) a person whose residence and place of business are at a distance; (2) something (as a geological feature) that is situated away from or classed differently from a main or related body; (3) a statistical observation that is markedly different in value from the others of the sample.

The Organization for Economic Cooperation and Development (OECD) has released Health at a Glance, its 2013 report on the performance of health systems in its member nations. Here is one of the charts.

The vertical dimension is life expectancy in years. On the horizontal is health care spending per person in US dollars. The line is the curve which best fits the data. It has both a linear component—as health care spending goes up, life expectancy increases—and a quadratic component—as spending increases, each dollar spent has diminishing returns. The United States is an outlier. We are spending quite a bit more (about $2400 per person more) than any other country, but our life expectancy is 26^th out of the 40 countries, almost one year lower than the international average. Another way of looking at it is that the two countries closest to us in life expectancy, Chile and the Czech Republic, are spending less than a quarter of what we're spending per capita on health care.

We're spending lots of money, but not getting a good return on our investment. A substantial portion of the money is being spent not on improved health care services, but rather on ever higher profits for private insurers, drug companies, and giant hospital chains.

The next time a politician says we have “the best health care system in the world,” you are free to burst out laughing.

You may also be interested in reading:

Where's the Outrage?

Why Are American Health Care Costs So High?

Lessons from HBO's 'Weight of the Nation'

This past week HBO debuted a four part documentary called The Weight of the Nation on the obesity epidemic in the US.  The clip above from part four talks about how geography can have a big effect on one's health due to the socioeconomic factors which surround these areas.  The fourth episode which deals with public health challenges can be seen below and is relevant to much of the research I have been doing for PUSH-Healthcare for All PA on Pennsylvania's uninsured.  The episode can be seen below.  All four parts can be seen at the above link in italics.  I'll review this episode in particular.

The program does a good job of presenting the data and issues related to the obesity epidemic in the US.  The impacts of their actions, intentional or not, are discussed at length including those on health care costs.  Various solutions to the problem are discussed such as ending farm subsidies, creating more park space in inner city areas such as Philadelphia County, and adding more bike trails.  While all of these are good things which I fully support, how much does the obesity epidemic really contribute to the high cost of health?  According to The Incidental Economist only around $25 billion in extra health care spending in 2004 can be attributed to health problems related to obesity because other non obesity related diseases such as prostate cancer are just as prevalent in the US relative to other countries with universal care such as Japan, Germany and the UK as can be seen in the graph below.  Diseases below the horizontal line in the graph such as Hepatitis B and Bladder Cancer are more prevalent in those countries.  You can see more cost analysis at this page.

In the opening credits of the episode above we can see that one of the sponsors of this documentary is Kaiser Permanente which was skewered for its profiteering practices in the film Sicko by Michael Moore.  The practices of the health insurance, pharmaceutical, and agribusiness industries to maximize profits often overlap.  I credit the filmmakers for skewering the food industry.  Is the Kaiser Permanente using this documentary to distract individuals from their own practices?  Congress only turned on the tobacco industry when the costs to the health care system became clear.

**Related Posts**

Evergreening

 

Moving Backward 

 

Unbelievable Promises Monopolized Care—UPMC

WaPo Interactive International Cost Graphic