Showing posts with label health. Show all posts
Showing posts with label health. Show all posts

Wednesday, March 26, 2014

Correlation of the Concentration of Hate Groups with Health Outcomes

This post is a cross post with other blog on the concentration of hate groups in each state (adjusted for population) and their health outcomes.  Pennsylvania is slightly ahead of the national rate at 3.12 groups per million.

This is a follow up on the last post on the number of hate groups (such as the Ku Klux Klan and the Westboro Baptist Church) in each state that are being watched by the Southern Poverty Law Center.  Some may not agree with the inclusion of African American separatists like the Nation of Islam.  If these groups are excluded from the national total (115 out of 939). Computing the population adjusted rate per million gives a rate of 2.62 groups per million for the US.

The state with the highest previous rate of 23.72 groups per million was the District of Columbia.  One possible criticism is that they have a large African American population and that they are not technically a state.  If the four black separatist groups in DC are excluded from their total of 15, it still has a rate of 17.40 groups per million which is well above the national rate.  I decided to look at which other state level variables are correlated with the rate of hate groups in each state.

I combined this data set with a state level health and income data set and several of them are significantly correlated with the health measures.  The strongest of these effects was the one between infant mortality and hate groups per million accounting for 40.9% of the variability.  In the chart on the left, DC is an outlier on both variables. 

The correlation was rerun with DC excluded.  The relationship was still significant but with 12.3% of the variability accounted.  This indicates that the relationship is weaker with DC excluded but still present.


The relationship between hate groups and state level life expectancy was also significant with 29.4% of the variability accounted in a negative relationship where as the number of hate groups increases, the state's life expectancy decreases.  Like the previous graph, DC is an outlier on hate groups per million.  When DC is removed from the graph, 30.2% of the variability is accounted for in a relationship that is still negative.  This suggests that  DC has high influence but is not poorly fit to the data.

There was no significant correlation between state level per capita income and the rate of hate groups.  Other health related outcomes were significantly associated.  These individual correlations are not described in detail here for space considerations.

There is a more advanced method that can identify clusters of highly correlated variables.  It is called factor analysis.  There were two factors extracted which account for 68.8 % of the variability.  They are presented in the table below.


Rotated Factor Matrixa

Factor
Health
(46% of var explained)
Income
(22% of var explained)
Infant Mortality 2007 Deaths/1000
.909

Life Expectancy
-.817
-.462
% Low Birthweight Babies
.735
.245
Hate Groups per million
.709

Percent under age 65 in 200% of Poverty
.411
.862
Income
.140
-.727
Percent Uninsured in Demographic Group for All Income Levels
.140
.644
Expanding medicaid

-.314
Extraction Method: Principal Axis Factoring.
 Rotation Method: Varimax with Kaiser Normalization.a
a. Rotation converged in 3 iterations.

The first factor extracted has the health related variables loading on it and accounts for 46% of the total variance.  Infant mortality, life expectancy, % low birth weight babies, and the rate of hate groups load most strongly on this factor.  Percent within 200% of poverty, income, and % uninsured load most strongly on the second extracted factor (called an income factor) while accounting for 22% of the variability.  

The hate group rate does not load on the income factor but it does on the health suggesting an association with health related outcomes.  One must always be careful about inferring a cause and effect relationship based on correlational data. When DC was removed, the factor analysis did not run.

**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)


A Statistical Profile of the Uninsured in Washington, DC, New Mexico, and Texas

Wednesday, March 14, 2012

Romneycare: The Bottom Line

Sometimes we get so caught up in the details of health care reform that we forget the real purpose—to improve people's health. A new study by Charles Courtemanche and Daniela Zapata, published by the National Bureau of Economic Research, examines the health effects of Massachusetts' 2006 health care reform bill, commonly called “Romneycare.” This effects of this reform are important because it is nearly the same as the Affordable Care Act (ACA).

It may sound silly to ask whether expanding people's access to medical care improves their health, but the outcome of the study was not obvious. Critics of health care reform sometimes claim that people will not take advantage or make good use of medical care and their health will be unchanged. More importantly, the moral hazard hypothesis claims that when health care is available at low cost, people will take unnecessary health risks, such as smoking and overeating. The result could be a decline in their overall health, accompanied by an increase in medical spending.

Previous studies have generally found improvements in health following increased access to care. The most impressive is the Oregon Health Study, which found a 13% increase in the number of people reporting themselves to be in good or excellent health following enrollment in Medicaid.

The data for this study come from a survey conducted by state health departments and the Center for Disease Control between 2001 and 2010. It includes answers from 2.8 million respondents from all 50 states and the District of Columbia. The study is a time series design. Health care reform in Massachusetts went into effect in March 2006, but the implementation was gradual and was not completed until July 2007. The research question is whether there were any changes in the health of Massachusetts residents around those times that did not occur in other states. The analysis controls for irrelevant variables such as age, income and marital status.

The main dependent measure was a self-report health question asking respondents to classify their overall health as either poor, fair, good, very good, or excellent. The results showed an improvement in health while the reform was being implemented (from April 2006 to July 2007), and approximately twice as large an improvement after it was fully implemented. These changes did not occur in other states at the same time. To put this into perspective, it is estimated that 1.4% of the population went from being in either poor, fair or good health to either very good or excellent health. Considering the overall cost of the program, Massachusetts spent $9,782 per year for every individual whose health improved from poor, fair or good to very good or excellent. Of course, having more people in very good or excellent health might save money in the long run.

This analysis includes everyone. However, if you look specifically at those people who acquired health insurance as a result of the reform, their probabilities of being in poor, fair or good health went down by 6.2%, 9.8% and 8.5% respectively, while their probability of being in very good health and excellent health went up 8.5% and 16%. This is comparable to the results of the Oregon study.

The main concern about this measure is that self-report questions are subjective and can be influenced by various biases. For example, people might have expected their health to improve due to the reform. On the other hand, access to medical care might make people more aware of the health problems they have. Therefore, the survey included several other measures. People were asked the number of days out of the past 30 that they were not in good physical health, that they were not in good mental health, and that they experienced health-related functional limitations. They were also asked the number of minutes per week they spent in moderate and vigorous physical activity, whether they experienced joint pain, and whether they smoked. Finally, their body mass index (weight/height2), or BMI, was calculated. Since these questions are more specific, they should be less subject to bias.

The results showed significant improvements on all of these measures with the exception of vigorous exercise and smoking. The fact that smoking did not increase and that BMI was reduced casts doubt on the moral hazard hypothesis that people would take more health risks. In fact, the overall pattern suggests that people were heeding medical advice.

Finally, internal analyses showed that, while almost every subgroup showed improvements in health, women improved more than men, and people between 55 and 64 (the oldest group not eligible for Medicare) showed the greatest improvement. Those in the lowest income category, who were eligible for a state subsidy to help purchase their insurance, showed greater improvement, and blacks improved more than other races. The authors estimate that health care reform reduced black-white health inequality by 21.5%.

Does this study predict a positive effect of the ACA on health if it is implemented? Maybe. But the ACA includes cost-cutting measures that were not part of the Massachusetts plan, such as reductions in Medicare spending, which might reduce the gains that would otherwise be expected. On the other hand, Massachusetts had one of the lowest percentages of uninsured citizens of any state, so implementing these same reforms nationwide might result in a greater improvement in the health of the nation.

I presume these successful results will be embarrassing to Governor Romney, who has repudiated his own health care reform in search of the approval of the bewildered herd of Elephants.