COVID-19 landed in a United States that is deeply divided in opportunity, health, and hope; a reality that is manifest in the million lives lost to deaths of despair in the past decade. We explore the places and populations most vulnerable to COVID and where they coincide with vulnerability to despair deaths. We use well-being metrics to explore spillover effects from the confluence of COVID and despair. Our earlier research finds that metrics like lack of hope and worry track with mortality patterns, with minorities more optimistic and less likely to die of despair deaths than whites. Using EMS first responder data, we compare trends in 2020 with those in 2018 to 2019, assessing excess deaths of despair and new survey data to explore changes in well-being. Remarkably, the cohorts with the highest COVID death rates—low-income Blacks—still report more optimism than other cohorts.
BackgroundIn reviewing health inequalities it is not sufficient to simply focus on those most affected, instead there should be a whole systems approach to have the maximum effect upon the population (Marmot, 2010). Within such an approach ‘the mechanisms giving rise to inequalities are still imperfectly understood’ (Woodward & Kawachi, 2000). Further it has been suggested that evidence remains to be gathered on the effectiveness of interventions to reduce inequalities (Gottfredson, 2004), particularly within end of life care (EOLC) (Care Quality Commission, 2016).MethodologyTo understand the mechanisms giving rise to inequalities in EOLC we utilised an adapted process map of the EOLC journey (Trebble et al., 2010). Within this map we highlighted pinpoints. Pinpoints are places within the journey where a clinician decides whether there is a referral to another service. At each pinpoint a patient profile was taken looking at their basic demographics (including disease and place of death) and index of multiple deprivation score.ResultsThe initial data review illustrated similarities across all the pinpoints with the exception of hospice care services that showed a significant increase in the number of patients classified within the 7–10 on the index of multiple deprivation and cancer diagnosis.Further ResearchAt present statistical analysis is on going into the differences between the pinpoints. At the same time a literature review sought to analyse if there was evidence of a similar anomaly within another healthcare setting; in which it was identified that implicit bias was a potential cause (Fitzgerald & Hurst, 2017). The next phase of the research looks to test, using an Implicit Association Test method, whether implicit bias is present in the EOLC journey and develop an intervention to remove it.
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