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Up until the 1980s, the United States was witnessing an increase in the life expectancy of the men and women, as well as the decrease of mortality rates of major lifestyle-related diseases across several different counties. However, disparities in the mortality in the United States, measured as the distribution of life expectancy and as the difference between best-off and worst-off counties began to decline and the trend was reversed.¹ During this time period, the counties that proved to follow this reverse trend were the counties in the Deep South, along the Mississippi River, and in Appalachia, extending into the southern portion of the Midwest and into Texas.¹ The reason for the reversal of the trend and for greater disparity was because of stagnation or increase in mortality within the population located in the worst-off segment counties in the United States.¹ Life expectancy of about 4% of the men and 19% of the women in the population either statistically declined or there was stagnation.¹ Although the stagnation and decline in mortality had an effect on a small portion of the nation’s population, it is still alarming and an issue considering the aim of the United States health system is to improve the health of all people, especially those at greater risk of health disparities.¹ The proximal (immediate) influences for this backward trend are the rise in mortalities caused by cancers, diabetes, COPD, and a reduction in the rate of decline of cardiovascular diseases, mainly prevalent in the female population.¹ HIV/AIDS and homicide are other proximal influences that affected men directly.¹

Some distal (ultimate) influences for the “reversal of fortunes” can be attributed to migration and socioeconomic status. With the migration of individuals between differing counties, it was observed and reported that migration play a role in the rise of inequality and the reversal trend, even if migrants were reported to have better life expectancy.¹ What this reveals and causes for deeper investigation in, is the effect that cross-county migration can have on the rates of mortality and the causation of mortality in different counties.¹ Another variable that was taken into account, was the analysis of income, which indicated that most migrant families had incomes that were $500 lower than nonimmigrant families. This is important because the socioeconomic status of a family and county play a significant role in the access to health that individuals obtain, the quality of health that individuals obtain, and the resources that are available, not just to treat patients, but to aid in the eradication or decrease of different disease through preventative health measures.² Socioeconomic status is not limited to a family’s income or their relative position in society, but also effects their education and the amount of opportunities present to break free from their low income.² In order to combat these issues, it would be imperative for different counties to implement different policies to aid families receive better health care and obtain access to preventative care education and resources.²

References

1. Ezzati et al. The reversal of fortunes: Trends in county mortality and cross-county mortality disparities in the United States. PloS Med. 2008;5(4): e66.
2. Saydah et al. Socioeconomic status and mortality: Contribution of health care access and psychological distress among U.S. adults with diagnosed diabetes. Diabetes Care. 2013;36: 49-55.

It may be safe for me to say that one of my favorite takeaways from The Reversal of Fortunes peer-reviewed study was the statement, “Programs that increase the coverage of interventions for chronic disease and injury risk factors in the worst-off counties, states, and regions should be established and regularly monitored and evaluated with respect to their local, versus aggregate only, impacts.”.¹ Over the past few years of studying Public Health, I’ve really began to notice my almost immediate habit of reviewing firstly any identified possibilities of future improvements outlined within the discussion section of peer-reviewed publications– I do this because I like to then imagine how the published study could have varied had there been XYZ improvements implemented, at the time of the original study. The researchers outlined future improvements, for counties in every corner of the United States (US), to prioritize the infinite need to tailor public health measures/programs to their specific vulnerable population/s. In the 1980’s, it was notated that the differences in reported life expectancy began transitioning from a narrow-scope into a widened-scope as being a “reversal of fortunes”.¹

This reversal of fortunes was an expression used to refer to a point in history in which there was an increased shift of mortality rates amongst/across hundreds of counties; Which came as quite the surprise since the US had previously stayed fairly consistent for 4 decades prior. A number of generalized counties classified as being “worse-off”, were suddenly seeing more of a stagnancy in death rates; Rates that were a proximal reflection of a flat-lined pace in deaths from cardiovascular disease (CVD), but also an increase of diseases like lung cancer, chronic lung disease, HIV/AIDS, etc. Due to that break in patterns of newly irregular reportings seen between the 1980’s and the 1990’s, the dire need to both closely monitor and closely measure any/all health inequalities became emphasized amongst all people/counties/populations. Counties who fell into the “worse-off” classification weren’t even seen to benefit from the increase of life expectancies that the advantaged neighboring counties became first-hand witnesses to, and those who began within those more disadvantaged counties later reported becoming less and less well-off as time continued to pass.

I believe that the reversal, that Ezzati, et al., spoke of, shines an added amount of light on the backpedaling that had transpired; in terms of previous progress/equality/advancements throughout the US, prior to the early 1980’s. The distal influences for this backward trend may call direct attention to various social determinant such as: social inequality, income inequality, or lack of economic opportunity. The researchers summarized all of the data that had been collected and the conclusions made after 1983 stating, “Gain in life expectancy was positively associated with county income.”. I believe that health inequality is a rippling effect.

In an attempt to reverse this reversal of fortunes, I would implement a behavioral health intervention plan which would open the door to exploring pre-motivational health factors and socio-cognitive theories to explain potential behavioral changes; And implemented using a theoretical framework within the Integrated Change Model (I-Change Model or ICM). Kawachi and Kennedy were two researchers who had published a peer reviewed journal in that late 90’s that addressed income inequality stating, “Reducing income inequality offers the prospect of greater social cohesiveness and better population health.”². I stand with their conclusion and agree that greater social cohesiveness and population health are attainable and within reach, but how?

Implementing this plan would require populations (little or small) to review and complete the I-Change Model so that all informative factors (i.e. personal factors, message factors, channel factors, and source factors) are addressed alongside all preceding factors (biological factors, psychological factors, behavioral factors, and environmental factors). After taking into consideration the differing responses, it would be possible at that point to address the 4 major constructs: (1) Awareness; Inclusive of cognizance, knowledge, risk perceptions, and perceived cues.; (2) Motivation; Inclusive of attitude, social support, self-efficacy, and intention.; (3) Action; Inclusive of action planning, plan enactment, skills, and barriers.; and lastly, (4) Behavior. By using this comprehensive intervention plan, we will be able to develop self-improvement instead of seeking self-contentment. Actions speak louder than words and a behavioral change will be a great first step in making an impact/change in health inequalities in the US and around the world. David says in Psalm 51:10, “Create in me a clean heart, O God, and renew a right spirit within me.”³. How influential can a change of heart be?

References

1. Ezzati M, Friedman AB, Kulkarni SC, Murray CJL (2008) The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States . PLOS Medicine 5(4): e66. doi: https://doi.org/10.1371/journal.pmed.0050066

2. Kawachi Ichiro, Kennedy Bruce P. Socioeconomic determinants of health: Health and social cohesion: why care about income inequality? BMJ 1997; 314:1037. doi: : https://doi.org/10.1136/bmj.314.7086.1037

3. Psalm 51:10 (NIV)