Read “The Reversal of Fortunes” by Ezzati, et al., located in the Reading & Study folder for this module/week. Discuss the following points in your thread. Review the Discussion Board Instructions before posting your thread.
- In general public health measures appear to be working. Not only has US life expectancy increased over the past half century, but mortality rates of major lifestyle-related diseases—particularly heart disease and stroke—have decreased in both men and women. In the 1980s, however, a disconcerting “reversal of fortunes” began to occur in some vulnerable populations in some regions of the US. Describe what happened and give the proximal (immediate) influences for this backward trend.
- What do you think are the distal (ultimate) influences for the “reversal of fortunes” described in the article? Suggest a comprehensive intervention plan to reverse this reversal. Upon which theoretical framework or model would it be based? Why?
Replies needed as well:
DB Forum 3: The Reversal of Fortunes Article Review
Based on the article review, the reversal of fortunes there appears to be an increase in mortality rates amongst counties. The U.S. Census and vital statistics data were used to consider poverty impacts amongst gender and race groups. U.S. Census data is self-reported and may not provide most accurate information. However, mortality data is more accurate since vital records provide death certification. Consider data is analyzed over four decades and mortality rates were well documented over a time span to provide statistical review. Once it was determined trends do not reflect similar patterns amoungs mostly ethic and gender groups issues of reverse fortune occur, “Between 1961 and 1999, average life expectancy in the United States increased from 66.9 to 74.1 y for men and from 73.5 to 79.6 y for women.”  Give credit to the researchers ability to withdraw inferences where data did not reflect actual reality. Income is a key indicator which helps determine poverty between groups. Once you compare the income “ Between 1961 and 1983, counties with life expectancy improvement above and below the national average had relatively similar income levels” Since health, poverty, and income are closely related as social determinants of public health. A closer look at ethnicity and gender profile is a requirement to consider, “Black women formed a larger proportion of the population in counties with above-average life expectancy improvement than in those counties with below-average life expectancy change; the pattern was reversed for men. After 1983, gain in life expectancy was positively associated with county income.” The increase to life expectancy related to county income is a key parameter to realize how greatly income affects a persons individual function and behavioral outcomes. The reverse of fortune was not beneficial to all because life expectancy gains were not applicable to all groups. The adverse mortality rates impact the population groups labeled disadvantaged due to existing inequalities. The increase to other disease factors did not help considering “Higher HIV/AIDS and homicide deaths also contributed substantially to life expectancy decline for men, but not for women. Alternative specifications of the effects of migration showed that the rise in cross-county life expectancy SD was unlikely to be caused by migration.The increase in communicable diseases women .”
The idea of ruling of migration as a factor in life expectancy provides more reason to focus on the segment of population categorized as disadvantaged. The distal influence was income since wages impact quality of life. In any environment poverty can impact health outcomes. In the counties impacts may spread to even larger community scope, “ Poverty contributes to epidemic disease and epidemic disease contributes to poverty:causation is bi-directional and occurs through many different pathways. For example,loss of labour from a farming system may result in failure to maintain infrastructure such as terracing, leading to soil erosion, and decreasing agricultural productivity.” There appears to be some change built on more recent research mentions improvement in children poverty rates in the U.S. compared to historical data, “Turning to an analysis of age-specific mortality rates, we show that among adults age 50 and over, mortality has declined more quickly in richer areas than in poorer ones, resulting in increased inequality in mortality. This finding is consistent with previous research on the subject.”
Faith in progress continuously provides a best outcomes Ephesians 4:13-14 Till we all come in the unity of the faith, and of the knowledge of the Son of God, unto a perfect man, unto the measure of the stature of the fulness of Christ: That we henceforth be no more children, tossed to and fro, and carried about with every wind of doctrine, by the sleight of men, and cunning craftiness, whereby they lie in wait to deceive. To understand that change is constantly happening “We also show that there have been stunning declines in mortality rates for African Americans between 1990 and 2010, especially for black men. The fact that inequality in mortality has been moving in opposite directions for the young and the old, as well as for some segments of the African-American and non-African-American populations, argues against a single driver of trends in mortality inequality, such as rising income inequality.” Implementing a community intervention program health belief model could help improve information and access to ways to supplement poverty related outcomes.
- Ezzati M, Friedman AB, Kulkarni SC, Murray CJL. Correction: The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States. PLoS Medicine. 2008: 5(4): e66. https://journals.plos.org/plosmedicine/article?id=…
- Barnett, T., & Whiteside, A.2002; poverty and HIV/AIDS: impact, coping and mitigation policy. In G. AIDS, Public
Policy and Child Well-Being.
- Currie, Janet, and Hannes Schwandt. 2016; Mortality inequality: the good news from a county-level approach. Journal of Economic Perspectives .30(2): 29–52.
- Holy Bible
In the 1980’s a disconcerting “reversal of fortunes” began to occur in some vulnerable populations in some regions of the US. For example, the difference between life expectancies of the countries that make up the 2.5 % of the US populations with the lowest and highest mortality each year rose from 9.0 years in 1983 and to 11.0 years in 1999 for men, and from 6.7 years to 7.5 years for women.1 This was caused by stagnating improvements in life expectancy among the worst off, while the best off experienced consistent mortality decline.1 The stagnation of mortality among the worst off was primarily caused by a slowdown or halt of the earlier decline of cardiovascular mortality, coupled with a moderate rise in number of other chronic diseases for both sexes as well as HIV/AIDS and homicide for men.
After 1983 the decline in female life expectancy was caused by a rise in mortality from lung cancer, COPD, diabetes, and a range of other non-communicable diseases in the older ages. Mortality from diabetes, cancers and COPD in the older ages also worsened in men but these continued to be countered by relatively large reductions in male cardiovascular mortality. 1 In the article, there were several different factors that influence the “reversal of fortunes” which include income levels and sociodemographic factors, data that was unaccounted for, cause of death coding, and statistical uncertainty in death rates.
This week’s reading included discussion on two stage theories that predominate in health promotion research and practice: the Trans theoretical Model of Change (TMC) and the Precaution Adoption Process Model (PAPM).2 Both of these theories have been used to successfully change a diverse array of health behaviors, either facilitating the elimination of health risk behaviors or the adoption of health protective behaviors.
- Ezzati M, Friedman AB, Kulkarni SC, Murray CJL. The Reversal of Fortunes: Trends in County Mortality and Cross-County Mortality Disparities in the United States. PLoS Medicine. 2008;
- DiClemente RJ, Salazar LF, Crosby RA. Health Behavior Theory for Public Health. Burlington, MA: Jones & Bartlett Learning; 2013