A series published in The Lancet recently investigated the effect of income inequality on the health of Americans. While incomes for those in the top have grown, extreme poverty has also grown in the US. In fact, more than 1.6 million households in the US survive on less than $2 per day; a number double that of the 1990s. The cycle is not likely to be broken either, barring major social change. Differences in aspects ranging from zoning laws, access and quality of education, and inheritance laws continue these inequalities through generations, making it more difficult to rise out of poverty.
When someone has a heart attack, every minute counts. The American Heart Institute guidelines say that for every minute, the chances of a victim surviving decrease by 7 to 10 percent. To help save lives, Automated External Defibrillators (AEDs) have become more and more ubiquitous, and now can be found in many different locations, including coffee shops, banks, malls, and sports complexes. When placing these devices though, a few issues need to be considered, including hours of operation, proximity of other AEDs, and being in high-traffic areas. To help inform these decisions, researchers from the University of Toronto recently conducted a very interesting study.
Using data on cardiac arrests that occurred outside of hospitals in Toronto from January 2007 to December 2015, they were able to place them on a map. They then identified businesses and municipal offices with at least 20 locations from sources such as the Yellow Pages, along with their hours of operation and geographic coordinates. For each site, they mapped the number of cardiac arrests that occurred within 100 m to identify which locations would be able to save the most lives. As a final test of these locations, they then looked at how the locations fared over time; determining if the locations relatively stable or if the AEDs have to be moved every year to continue to be effective.
I came across an interesting read last week in The Lancet. In it, Drs Allen and Feigl make an interesting case for changing how we refer to non-communicable diseases
The global health community does not spend much time on branding, which perhaps explains why existing classifications for the three largest groups of diseases are both outdated and counterproductive. The first Global Burden of Disease study described infectious diseases, non-communicable diseases (NCDs), and injuries. This grouping reflected a predominantly infectious disease burden in low-income and middle-income countries, which has since tilted towards NCDs. A name that is a longwinded non-definition, and that only tells us what this group of diseases is not, is not befitting of a group of diseases that now constitute the world’s largest killer.
Thanks to all our old and new Public Health Perspectives readers for your support over the year. Your tweets, Facebook comments, and feedback are all really appreciated. Lets wrap up the year by reviewing our most popular stories of 2016.
New research published in JAMA last week examined how big a difference earning more money makes in life expectancy, as well as how this changes by geographic location across the United States. Researchers collected tax records from 1.4 billion individuals from 1999 to 2014 aged 40 to 76. Of these, around 4 million men died, compared to 2.7 million women (mortality rates of 596.3 and 375.1 per 100 000 respectively). They examined these data to look at what predicted life expectancy at age 40, after adjusting for race and ethnicity.”
The Affordable Care Act was a landmark piece of legislation for the United States. While most other G-20 countries already have some form of universal healthcare (either through a single payer system, or mandatory insurance coverage), the US was one of the few countries that did not have one. Arguably, however, it didn’t go far enough, and therein lies its biggest problem.
One of the key provisions in Obamacare was that insurers could not deny coverage based on pre-existing conditions. This was a hugely important for those with serious or chronic illnesses, who would normally be denied coverage. For example, diabetes can cost someone approximately $7900 a year in direct medical expenses, which is a hefty sum if you don’t have insurance coverage. Obamacare mandating that these individuals, and others with similar conditions, have to be able to purchase coverage, is an excellent step forward. However, the business of insurance relies on those who enrol but do not require services subsidising those who enrol and do. In terms of healthcare, this would be low risk people paying and not using services, ensuring high-risk individuals are able to access services. As you can imagine, there is very little incentive for low risk individuals to enrol; a phenomena known as “adverse selection.”
Our current way of dealing with poverty is inefficient at best, with mountains of forms, paperwork, weighed down by bureaucracy and procedures. At worst, it’s stigmatising and judgemental, keeping people in poverty rather than giving them opportunities to break free and elevate themselves out of poverty. One possible solution is providing individuals with a Basic Income (click link for my previous post on the subject).
A selling point for Basic Income is that it can save the government money. By streamlining select government services into one agency, it can reduce inefficiency. In fact, this is something that people on both sides of the political spectrum can agree on – both those who want small government and those who want the government to support our most vulnerable citizens. One example is in Ontario, where a recently published report titled “Finding a Better Way: A Basic Income Pilot Project for Ontario” by Hugh Segal reported that a basic income guarantee would replace Ontario Works and the Ontario Disability Support Program, giving everyone an income of $1320 a month, with an extra $500 for those with a disability. This would effectively replace those two programs with one larger, more comprehensive program that doesn’t require the same intensive oversight and monitoring.