As articulated in the Unintended Consequences of Coronavirus, we see a major decline in care caused by the reaction to the COVID-19 pandemic. Not in deaths associated directly with the virus itself, but rather in deaths caused by the narrow focus on the virus by the medical community, fear of interactions with clinicians, and the corresponding lack of emphasis of ongoing health needs. While articulated elsewhere, the care needs of America have been severely impacted by the COVID-19 coronavirus. Fewer cancer screenings, fewer ER visits for heart attacks and strokes, fewer “elective” procedures such as heart stents and bypass surgeries, all have major implications to the overall health of America… and the overall mortality rate.
While it is difficult to know the impact of the reductions in care provided under this fog of COVID-19, we can gain insights into the potential risk by looking at data we do possess. To perform a broader analysis on the potential impacts to care, and ultimately the mortality implications to such, we looked at the impacts of reduced healthcare professionals at the disease (cause of death) level.
As detailed in our discussion on the Unintended Consequences of COVID-19 we discuss the potential increase in deaths caused by the population reaction and government policies related to the coronavirus pandemic. More traditional health events are suffering greatly by the hyper-focus on COVID-19, with routine healthcare measures suffering dramatically. Missed appointments, screenings, ER visits, “optional” procedures such as bypass surgeries, heart stents and the like, all leading to serious reductions in the amount of care being provided across the county. And make no mistake, with 2.8 million people dying in our country annually, small changes in routine care can have devastating effects. But to what degree? How can we quantify this reduction in care in order to develop mitigation strategies?
The overall quality of care and availability of healthcare professionals in the US is among the highest in the world. However, it is recognized that the quality of care received by some is not equivalent to that provided to others. The Health Resources & Services Administration created scoring mechanisms to identify these underserved communities. In some cases, the focus of the scoring is related to communities within a population, based more on socioeconomic issues. In other cases, the score focuses on shortages due to population sparsity and available healthcare professionals. The former, the MUA (Medically Underserved Area), and in the latter the HPSA (Health Professional Shortage Area) score.
For the purposes of our analysis we elected to look at the HPSA score to determine the risk to the population should with reduced quality of care. This provides the best existing representation of impact to mortality resulting from reduced care. We posited that we would see progressive mortality impacts the more underserved a geographic area was, and that this could identify the slope of progression in mortality the greater the shortage. To account for aging populations in specific geographic areas, we used the age-adjusted mortality rates provided by the CDC.
We also note that of the total number of deaths that occur in the US each year, those referred to as “deaths of despair”, such as suicide and drug overdoses would be further impacted by financial and social implications caused by loss of work and unemployment. However, for the purpose of our study, we focused exclusively on the impacts due to the reduction in care. Other models have been developed highlighting these other impacts
Furthermore, there are mitigation plans in place such as unemployment insurance, payroll protection, and others, to mitigate these challenges in the short-term. To what degree these plans work, and for what duration is still unknown. It is a safe assumption that there will also be deaths resulting from these factors in addition to those we highlight.[/vc_column_text]
As anticipated, there is indeed a correlation between mortality and lack of healthcare professionals. It is a consistent trend across nearly all categories of cause of death. It is important to note that there are some challenges in data availability to rely on this data as being 100% accurate. Due to potential privacy concerns, the CDC suppresses causes of death in a geographic query when the number of deaths is less than 10. The smaller US counties, therefore, are most susceptible to this suppression, and those with higher HPSA scores are often more likely to be smaller counties.
The following highlights the mortality rates by counties based on HPSA score for key classifications of cause of death.
You will note that the slope of virtually all classifications of cause of death show an increase in mortality the fewer care professionals. While the slope may seem fairly shallow, it is important to understand that the difference indicates an increase in numbers of deaths per 100,000 (of which there are approximately 3,200 instances of 100K for the entire 320 million population), so an increase of 2 deaths per hundred thousand represents 6,400 overall country-wide.
A larger view of the various causes of death demonstrates the degree of the trend more fully. For example, the following highlights Diabetes as an example.
As a result of the above finding, Lifemesh came to the conclusion that the HPSA score and corresponding annual mortality rates do indeed represent a strong proxy for impacts of reduced care. We therefore developed our model to anticipate the reduction in care caused by COVID-19 to correspond to the overall slope of decline by each cause of death, which can then be applied to the overall mortality assumptions for the unintended consequences of COVID-19.