US Racial-Ethnic Mortality Gap Adjusted for Population Structure.

BACKGROUND: US racial-ethnic mortality disparities are well documented and central to debates on social inequalities in health. Standard measures, such as life expectancy or years of life lost, are based on synthetic populations and do not account for the real underlying populations experiencing the inequalities. METHODS: We analyze US mortality disparities comparing Asian Americans, Blacks, Hispanics, and Native Americans/Alaska Natives to Whites using 2019 CDC and NCHS data, using a novel approach that estimates the mortality gap, adjusted for population structure by accounting for real-population exposures. This measure is tailored for analyses where age structures are fundamental, not merely a confounder. We highlight the magnitude of inequalities by comparing the population structure-adjusted mortality gap against standard metrics' estimates of loss of life due to leading causes. RESULTS: Based on the population structure-adjusted mortality gap, Black and Native American mortality disadvantage exceedsmortality from circulatory diseases. The disadvantage is 72% among Blacks (men: 47%, women: 98%) and 65% among Native Americans (men: 45%, women: 92%), larger than life expectancy measured disadvantage. In contrast, estimated advantages for Asian Americans are over three times (men: 176%, women: 283%) and, for Hispanics, two times (men: 123%; women: 190%) larger than those based on life expectancy. CONCLUSIONS: Mortality inequalities based on standard metrics' synthetic populations can differ markedly from estimates of the population structure-adjusted mortality gap. We demonstrate that standard metrics underestimate racial-ethnic disparities through disregarding actual population age structures. Exposure-corrected measures of inequality may better inform health policies around allocation of scarce resources.

The polarization of politics and public opinion and their effects on racial inequality in COVID mortality.

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors-e.g., geography, urbanicity, segregation or age-structures-that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization-the partisan divide in pandemic-related policies and beliefs-that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths-mostly among white Americans-rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were "unconcerned" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a "race to the bottom'' explanation as overall deaths increased and substantively swamping the effects of containment policies.

Missing Diagnoses during the COVID-19 Pandemic: A Year in Review.

The COVID-19 pandemic has had major impacts on population health not only through COVID-positive cases, but also via the disruption of healthcare services, which in turn has impacted the diagnosis and treatment of all other diseases during this time. We study changes in all new registered diagnoses in ICD-10 groups during 2020 with respect to a 2019 baseline. We compare new diagnoses in 2019 and 2020 based on administrative records of the public primary health system in Central Catalonia, Spain, which cover over 400,000 patients and 3 million patient visits. We study the ratio of new diagnoses between 2019 and 2020 and find an average decline of 31.1% in new diagnoses, with substantial drops in April (61.1%), May (55.6%), and November (52%). Neoplasms experience the largest decline (49.7%), with heterogeneity in the magnitudes of the declines across different types of cancer diagnoses. While we find evidence of temporal variation in new diagnoses, reductions in diagnoses early in the year are not recouped by the year end. The observed decline in new diagnoses across all diagnosis groups suggest a large number of untreated and undetected cases across conditions. Our findings provide a year-end summary of the impact of the pandemic on healthcare activities and can help guide health authorities to design evidence-based plans to target under-diagnosed conditions in 2021.

Years of life lost to COVID-19 in 81 countries.

Understanding the mortality impact of COVID-19 requires not only counting the dead, but analyzing how premature the deaths are. We calculate years of life lost (YLL) across 81 countries due to COVID-19 attributable deaths, and also conduct an analysis based on estimated excess deaths. We find that over 20.5 million years of life have been lost to COVID-19 globally. As of January 6, 2021, YLL in heavily affected countries are 2-9 times the average seasonal influenza; three quarters of the YLL result from deaths in ages below 75 and almost a third from deaths below 55; and men have lost 45% more life years than women. The results confirm the large mortality impact of COVID-19 among the elderly. They also call for heightened awareness in devising policies that protect vulnerable demographics losing the largest number of life-years.