By Xinyue Guo
Due to their ability to spend more on healthcare, it is commonly thought that wealthier people tend to have longer life expectancies than the rest of the population. However, regional data has recently shown that more spending does not necessarily produce more positive health outcomes.1 This expands on a 2016 study by Chetty et al.,2 which used overall Medicare reimbursements as a proxy for health access to demonstrate that there is no relationship between average reimbursements and life expectancy.
Diverse types of healthcare cost different amounts of money. For example, preventive care (such as vaccinations and regular medical exams) and maintenance care (ongoing treatments of health conditions, or exercise) are usually relatively inexpensive. Nevertheless, they still improve overall longevity and health by identifying medical issues before they can become problematic. Thus, these kinds of healthcare are worth investing in. In comparsion, putting money into inpatient and elderly care is less straightforward. Current end-of-life procedures can only extend life expectancy by a few months at most, but they are usually exorbitant. Accordingly, Barnard & Hagos recently proposed1 that economic and costly healthcare systems (as are observed in the US) are often mixed up in the academic community, failing to represent the relationship between spending and health. They later decided to test this relation.
Working from Princeton University, Barnard & Hagos started with the idea that health access and longevity have positive correlations. Health equality – regardless of a person’s race, gender, residency, etc. – is known to have a positive relationship with longevity. However, it is worth noticing that the quality of healthcare cannot be controlled. Even when two patients with cancer have access to chemotherapy, for example, the difference in the quality of their treatments at separate hospitals can result in varying impacts to their longevity. Consequently, Barnard & Hagos designed their experiment at the county level to minimise this variation.
Their research used two sets of data – ‘local life expectancy’ data and ‘cost of preventive care’ data. The former was mainly extracted from 1999-2014 IRS tax and death records of people at the age of 40 or over, subdivided by their income quartile, county, race, and ethnicity. For individuals over the age of 76, the authors used Gompertz parameters to deduce their life expectancy. The other data set featured data from the 2010 Dartmouth Atlas of Health Care county-level Medicare outpatient reimbursements. To analyse the population, Barnard & Hagos then divided it into six metrics: the percentage of individuals with a Primary Care Physician visit the percentage of women over 67 with a mammogram; the percentage of diabetics with a haemoglobin test; the percentage of diabetics with an eye exam; the percentage of diabetics with a lipid test; and the discharge rate for ambulatory-sensitive conditions, such as asthma.
The team’s objective was to find the correlation between Medicare outpatient reimbursements, the quality of primary care and average life expectancies at different income quartiles. For the bottom income quartile, life expectancy was seen to be negatively correlated with outpatient Medicare reimbursements, while diabetics with occasional eye or lipid tests tended to have a longer life expectancy. Surprisingly, however, individuals who receive primary care visits appeared to have comparatively shorter life expectancies. Women with mammograms and diabetics with blood tests further exhibited no significant correlation with longevity. Although men with primary care appeared to have shorter lives than women, more outpatient Medicare reimbursements resulted in even shorter male life expectancies.
On the other hand, for the top income quartile, diabetics with blood tests, lipid tests and eye tests – as well as women with mammograms and individuals with primary care visits – all tended to have greater longevities. In contrast, there was no relationship between outpatient Medicare reimbursements and longevity. High-earning women receiving primary care also tended to live longer, while the same conditions had no effect on male life expectancy. Outpatient Medicare reimbursements, however, affected neither sex’s lifespan. These patterns were constant for people in the second and third-income quartiles, with the rate of discharge of ambulatory-sensitive conditions remaining negatively correlated to life expectancy in all income quartiles.
The results offer insights for both wealthy and poor populations, particularly in communities where income levels play an essential role in their healthcare decision-making. Firstly, they tell us that there is a strong negative association at the county level between the cost of outpatient care and life expectancy for individuals in the bottom income quartile. Low-income men, especially, are more frequently subject to poorer health outcomes and are less likely to receive health care. This might be a consequence of them not promptly going to medical exams or checks to avoid associated costs. In addition, even if they are diagnosed with early-stage diseases or fatal conditions with the need for costly procedures, their income often prevents them from accepting further treatments, resulting in a more widespread reluctance to healthcare and a shorter life expectancy. On the contrary, wealthier people typically have sufficient funds to support their treatments, which explains why four out of six metrics depict a positive correlation between preventive care and life expectancy for the first-, second- and third-income quartiles. It is also interesting to see that healthier individuals are not likely to see a physician very often. This might be because their routine medical check-ups are already enough for them to maintain good health, further proving that spending money on preventive healthcare helps the population to achieve a higher life expectancy.
Alas, there are still multiple limitations in this research. Firstly, all observations with an income of $0 were excluded, as they could not be categorized into the bottom income quartile. They still represent a portion of the population, however – not to mention that their health situation is likely more extreme given their circumstances. Secondly, the data did not account for retirement, probably causing the study’s results to deviate from reality. Race and ethnicity are not really factored into the databases, either, meaning that adjustments based on country and region lack precision. There might also be social issues underlying health access and income equity, which the authors cannot predict. Altogether, these flaws represent a direction for future research, providing opportunities to incorporate more details and data to conduct a longitudinal investigation on the matter.
- Barnard MS & Hagos RM. The association between preventive health and outpatient spending and life expectancy by income quartile. International Journal for Equity in Health 2022;21:159. doi: 10.1186/s12939-022-01748-8
- Chetty R, et al. The Association Between Income and Life Expectancy in the United States, 2001-2014. JAMA 2016;315(16):1750–1766. doi: 10.1001/jama.2016.4226