Another way to look at this is to compare the United States with Canada. We pay almost twice as much for health care per person as Canada. Our life expectancy is 3.3 years shorter than Canada's which ranks them 7th in the world in life expectancy while we are 63rd.
OK, I will see if I can find another way to show this. In effect I am trying to put the information showing a non-linear relationship between age and death rates and a linear relationship between between year and death rates in the same graph. That is what shows what is going on.
You got it! The dots above the middle show increasing death rates and the dots below the middle line show declining death rates. The horizontal location of the dots shows age and seeing them showing up in the middle causes asking the question why, I find the answer in how paid for.
It shows us how to build a strong house.
Here is a simple scatter plot of the number of gun stores listed in the online version of the on line Yellow Pages about a month ago. Unlike most most data sources used in research, these change every time a new gun store opens or closes so any replication will have slightly different results. But this is a measure that the gun industry cannot lobby against producing and all that has to be done is spend about an hour running 51 searches for "firearms " with the two letter state abbreviation and scrolling down almost to the bottom of the first page to get the total number. In other searches I have found the limit of 3,000 results a problem but not with this search. Here are the regression analysis summary statistics: r = 0.87 b = 0.71 a = 2.23 The 2.23 is the predicted firearm suicide rate for states with no gun stores. Now the fact is, it is easy to buy a gun from another state. But that is immeasurable. Using the b of 0.71 we can estimate that a state with with 10 firearm stores per 100,000 people is predicted to have a gun suicide rate of 2.23+(10*0.71) pr 1.23+7.1 or 9.33, which is 4.18 times as high as the predicted rate for the hypothetical state with 0 stores. Note that i report r not r squared. Here is why: The interpretation of the plain un-squared r is the percent of the sum raw errors in predicting Y form X is shown in r. When r-squared is used it is the percent of variation which is the sum of the errors squared. I find the use of r-squared by scientists is just another way science becomes more difficult for the general public to understand and it makes the relationship look weaker. A r=.50 means you can reduce your errors in predicting y by knowing x is 50%. Using the equivalent r squared = .25 means you can reduce the variation, the sum of the errors squared, by 25%. In the world I live in I can explain r to the public a lot easier than I can r squared.
Here is an attempt to get around the gun industry's ability to limit the measuring of level of gun ownership by using a widely available list of the gun stores, the online Yellow Pages. See text for more information. 🧪💡☠️ #Sociology #Population #Politics
Thanks for the additional information. I just responded to seeing it on morning joe.
Just saw a news story where a man shot and killed six of his children and one unrelated child. They did not say anything about how the killer got his gun! 🧪💡☠️ #Sociology #Political
The relationships between year and single year age death rates are mostly linear. But I have calculated over a thousand analyses using every possible statistical technique. The important thing is that as people move from one way of paying for health care to another the direction of change differs.
The problem is that the death rates for the elderly are so much larger that the graph needs to be several pages tall to see differences in the young. The graph becomes unreadable.
I correlate year with single year age death rates for ages 0-84 and 85+ for each year.
In another earlier posting I showed that for the middle aged the three strongest predictors was first: Percent cigarette smokers, second: percent Southern Baptist and third: percent uninsured. I Sam afraid we only have vaccination rate for a few diseases, COVID is the most measured.
Tap the ALT button and tap the introduction text..
All the dots above the line of numbers showing age show death rates increasing over these years and those dots below that line show declining death rates. The further a dot is from the zero line, the more constant the amount of change from year to year. When I look at that set of correlations between year and death rates, I think of how these correlations change by how we pay for health care. The infants have the most consistently supported efforts to keep them alive and they have the strongest negative correlations, very close to that ideal -1.00. Then the young from age one through the late teens have fairly strong negative correlations are primary covered by their parent's capitalist health insurance but most states provide socialist health care for most of the uninsured young. The fact that they are all below zero is fair but the fact that they are a lot higher than the infants is bad. Then that line of dots going from strong negatives to positives close to 0.80 show the consistent increases in death rates with increasing age up to age 30. Then the correlations decline to close to zero indicating little change for a decade from the middle 40's to the middle 50's. Then from about age 55 to age 70 there is a consistent higher rate of declining death rates. This is the period when Medicaid and the capitalist clone, Medicare, phase in. Then and only then does the trend in death rates get almost as good as there were during the first year of life. What is happening here? Medicare, a nearly complete socialist health care is phased in with the almost as good copy, Medicare. If I repeated this graph for Canada and most of the other industrialized countries, the line of dots would be close to strait across close to -1.00 from age 0 to 90. Not only do they have better health care they have yearly data by age for several years past our 84. Our death rate changes since 1999 clearly show that capitalist health care simply kills us early. Who makes our laws?
Here is a graph displaying the correlation, r, between year from 1999 through 2024 and the number of deaths per 100,000 for the total population by single year age, 0-84 and 85+. See text for more details. 🧪💡☠️ #Sociology #Population
#Politics
I was unable to remember the words I remembered, if that makes sense. All my concentration was on counting and keeping up with where I was in aa, ab, ac,.....zz, and putting another finger up when I thought of a word. Counting was all my brain could do.
Back when I was 66 I suffered an encephalitis infection of my brain. When I regained consciousness my vocabulary was down to 196 words. I can slowly and poorly write now 14 years later. If you need or want to publish anything I post here feel free. Just let me know so I won’t let anyone else.
Some keep people alive and some are killing people. Knowledge and affordability make most of the difference.
The age groups, 0, 1-19, 20-64 and 65+ have different health care delivery systems in the United States.
The United States is unique among the industrialized societies because we pay the most for health care and are not even in the top fifty of the lists of countries ranked by life expectancy now that our CIA has deleted their ranking of countries, they had us 49th, unique among the on-line rankings I have found. Looking at the graph the most unique thing I see is that the middle aged death rates, excluding COVID have consistently gone up and this rate has substantially increased during the COVID years. Then it declined substantially since 2021, when COVID vaccine was available. This increase has been seen as a possible miss classification of COVID deaths. But a look at the age 65+ and 0-18 death rates, we can see that among them the non-COVID death rates are uniquer declining death rates in their ages groups. This difference in the middle age and elderly non-COVID death rates during the COVID epidemic makes me think the capitalist health insurance industry used the COVID epidemic as an opportunity to deny more care. Unfortunately, we have no measure of the number of people needing health care were denied care by the misnamed health insurance industry. The fairly consistent decline in the infant and age 65+ death care rates just happen to be by the people who are not entirely dependent on the health insurance industry to receive care. Most of the elderly have access to Medicare and the capitalist incomplete clone while almost all the states pay for emergency care for the uninsured young. The last point I will make is the decline in non-COVID death rates among the ages 1-64 also happened during the time President Biden implement several improvements to their access to health care during his single term as president. Here is a link to one discussion of most of them: https://publichealth.jhu.edu/2025/bidens-public-health-wins
What's up over time in the United States? Most consistently the middle aged death rates and only recently the age 1-29.
This graph shows the yearly death rates of our population divided into groups based on how health care is paid for. See text for details. 🧪💡☠️ #Sociology #Population #Politics
Here are some regression results I produced back in 2019. I tried to re-do it with more current data but I can no longer get any of my web browsers to search and capture the data. What I did was conduct searches for each country name and the word jicama for all the countries listed in the now extinct CIA World Factbook. (yes they spelled it as one word) But Trump's administration has deleted the web site with no announcement. In general I am trying to put together the data to do this again with more current data but everything I am trying does not work. If you see the mean for the dummy variable jicama, you will see 0.203 which means 20.3% of the countries have a 1 on the dummy variable included in the regression equation with all the other variables I found significantly related to country life expectancy. Now, the list of other variables cannot be considered inclusive but with a multiple r of 0.74 . That means you can reduce your errors in predicting country life expectancy by 74% when you use these variables. I looked at the effect of several other variables and have excluded the others that hat a p >.05. The important statistic is the b=4.32 for the jicama dummy variable. That means when you control for the effect of percent obese, money spent on health care and median country income, the jicama eating countries have a life expectancy that is 4.32 years longer than the countries that don't. Now this is not solid enough research to be published in our properly reviewed journals but adding it as a regular vegetable that replaces another regularly really does not cost more and adds income to the people who grow jicama, or even growing it yourself, here is a link to see if you can grow it: https://theultimatehomestead.com/planting-guide/jicama/texas/ Oh, I live near Montgomery, Alabama and there is an international grocery store there that sells good fresh jicama for less than $1.00 per pound. And, this is just another way I looked at life expectancy.
I have look into old research findings I have not written up and I found this look at this attempt to look at the effect of eating jicama how long people live worth posting. The graph is the raw statistical output using AcaStat. 🧪💡☠️ #Sociology #Population #Health
what we could use is a is a collection of fake references published by article and a grand total by journal., It should be linked to every citation of the article in other published articles and a journal's grand total should be linked to all the links to that journal. 🧪💡☠️ #Sociology #Population
REJECT in caps. With note on why, the fake references!
I have a very limited energy problem and am headed in to have the connection to between my heart and lungs checked, the other one was checked earlier and was found clear, to look for a possible blockage causing the problem. I will let you know what they find. 🧪💡☠️ #Sociology
Actually there were a lot war crime trials after WWII.
They have a lot of oil and control access to a lot more.Trump and his rich buddies want it.
The racist capitalist are contradictory. They want an all white population because they consider themselves white but they also want their shares in the health insurance companies to make them as much money as possible and denying care makes their profit larger.
Remember that Trump's campaign promises were almost exactly the opposite of what he did.My theory is these billionaires can see he is exactly like them,.
Most of your list is caused by low income.
I will see if I can get some data together to look at that.
It is total health care and a lot of life style. In the United States health care access is a major factor. Southern Baptist use of prayer for health care is a unique factor in the United States.