Perhaps there is a drug that can prolong your life—it's called money

A wise man once said that "mo' money, mo' problems" (Wallace, 1997). However, despite increases in supposed problems, one of the major benefits is increased life expectancy.

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.

There are some pretty striking findings. Those in the top 1% were found to live an average of 14.6 years longer for men, and 10.1 years longer for women, compared to those in the bottom 1%. Put into context, while a man in the top 1% can expect to live to the ripe old age of 87.3, and a woman in the top 1% can expect to live until they are 88.9 years of age, their compatriots in the bottom 1% can only expect to live to 72.7 years of age for men, and 78.8 years of age for women – considerably less. As pointed out in this piece on IFLS, this is "similar to the reported male life expectancy in Sudan and Pakistan" (which is a pretty stigmatizing statement, but I'll leave that for another time). They also found this has got worse over time. Between 2001 and 2014, those in the top 5% have had their life expectancy increase by around 2.5 years (2.34 for men and 2.91 for women), while the life expectancy for those in the bottom 5% have increased by 0.32 years for men and 0.04 years for women.

Now here's where things get interesting.

They then looked at how life expectancy varied across geographic areas, to see how someone in the bottom 20% of income living in one city compared to someone in the bottom 20% of another city. They found that the difference between the highest and lowest life expectancy was approximately 4.5 years, despite them existing in the same income quintile (i.e., they were all in the < 20% of income bracket). For an excellent interactive tool to examine this, check out this Time article. They tracked this over time, and found that some areas had their life expectancy increase by around 4 years, while others saw a decrease by more than 2 years. The researchers further unpacked this, and found that differences decreased if you had a higher immigrant population, more college graduates, and higher government expenditure. As said by the authors:

The strongest pattern in the data was that low-income individuals tend to live longest (and have more healthful behaviors) in cities with highly educated populations, high incomes, and high levels of government expenditures, such as New York, New York, and San Francisco, California. In these cities, life expectancy for individuals in the bottom 5% of the income distribution was approximately 80 years. In contrast, in cities such as Gary, Indiana, and Detroit, Michigan, the expected age at death for individuals in the bottom 5% of the income distribution was approximately 75 years. Low-income individuals living in cities with highly educated populations and high incomes also experienced the largest gains in life expectancy during the 2000s (emphasis added).

So what do we do with this? The article raises some interesting points. For example, of the 10 metro areas with the higher life expectancy for the poor, six are in California. The other four are Miami, New York, Newark and Boston. While these cities do have a lot of internal income inequality, they are also cities with very aggressive public health policies, such as smoking bans. It might be that these cities have specific policies in place that help those who are in the lower end of the income spectrum. Further research is required to see what these areas are doing differently, and whether their lessons can be applied to the areas with low life expectancy.

This story is republished courtesy of PLOS Blogs: blogs.plos.org.