The radically unequal distribution of Covid-19 vaccinations: a predictable yet avoidable symptom of the fundamental causes of inequality

The Covid-19 pandemic—and its social and economic fallout—has thrust social and health-related inequalities into the spotlight. The pandemic, and our response to it, has induced new inequalities both within and between nations. However, now that highly efficacious vaccines are available, one might reasonably presume that we have in our hands the tools to address pandemic-associated inequalities. Nevertheless, two prominent social science theories, fundamental cause theory and diffusion of innovation theory suggest otherwise. Together, these theories predict that better resourced individuals and countries will jockey to harness the greatest vaccine benefit for themselves, leaving large populations of disadvantaged people unprotected. While many other life-saving prevention measures have been distributed unequally in ways these theories would predict, the COVID-19 vaccines represent a different kind of case. As the disease is so highly infectious and because mutations lead to new variants so rapidly, any inequality-generating process that leaves disadvantaged individuals and countries behind acts to put everyone—rich and poor—at risk. It is time that we ensure the equitable distribution of this life-saving benefit. As the fundamental cause and diffusion of innovation theories help illuminate processes that regularly produce inequities, we turn to them to reason about the rollout of the COVID-19 vaccines. Specifically, employ them to suggest countermoves that may be necessary to avoid an irrational and inequitable vaccine rollout that ends up unfavorably affecting all people.publishedVersio

Trends in absolute and relative educational inequalities in health during times of labour market restructuring in coastal areas : The HUNT Study, Norway

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Educational inequalities in diabetes mortality across Europe in the 2000s : the interaction with gender

To evaluate educational inequalities in diabetes mortality in Europe in the 2000s, and to assess whether these inequalities differ between genders. Data were obtained from mortality registries covering 14 European countries. To determine educational inequalities in diabetes mortality, age-standardised mortality rates, mortality rate ratios, and slope and relative indices of inequality were calculated. To assess whether the association between education and diabetes mortality differs between genders, diabetes mortality was regressed on gender, educational rank and 'gender x educational rank'. An inverse association between education and diabetes mortality exists in both genders across Europe. Absolute educational inequalities are generally larger among men than women; relative inequalities are generally more pronounced among women, the relative index of inequality being 2.8 (95 % CI 2.0-3.9) in men versus 4.8 (95 % CI 3.2-7.2) in women. Gender inequalities in diabetes mortality are more marked in the highest than the lowest educated. Education and diabetes mortality are inversely related in Europe in the 2000s. This association differs by gender, indicating the need to take the socioeconomic and gender dimension into account when developing public health policies.Peer reviewe

Subnational inequalities in years of life lost and associations with socioeconomic factors in pre-pandemic Europe, 2009–19:an ecological study

Background: Health inequalities have been associated with shorter lifespans. We aimed to investigate subnational geographical inequalities in all-cause years of life lost (YLLs) and the association between YLLs and socioeconomic factors, such as household income, risk of poverty, and educational attainment, in countries within the European Economic Area (EEA) before the COVID-19 pandemic. Methods: In this ecological study, we extracted demographic and socioeconomic data from Eurostat for 1390 small regions and 285 basic regions for 32 countries in the EEA, which was complemented by a time-trend analysis of subnational regions within the EEA. Age-standardised YLL rates per 100 000 population were estimated from 2009 to 2019 based on methods from the Global Burden of Disease study. Geographical inequalities were assessed using the Gini coefficient and slope index of inequality. Socioeconomic inequalities were assessed by investigating the association between socioeconomic factors (educational attainment, household income, and risk of poverty) and YLLs in 2019 using negative binomial mixed models. Findings: Between Jan 1, 2009, and Dec 31, 2019, YLLs lowered in almost all subnational regions. The Gini coefficient of YLLs across all EEA regions was 14·2% (95% CI 13·6–14·8) for females and 17·0% (16·3 to 17·7) for males. Relative geographical inequalities in YLLs among women were highest in the UK (Gini coefficient 11·2% [95% CI 10·1–12·3]) and among men were highest in Belgium (10·8% [9·3–12·2]). The highest YLLs were observed in subnational regions with the lowest levels of educational attainment (incident rate ratio [IRR] 1·19 [1·13–1·26] for females; 1·22 [1·16–1·28] for males), household income (1·35 [95% CI 1·19–1·53]), and the highest poverty risk (1·25 [1·18–1·34]). Interpretation: Differences in YLLs remain within, and between, EEA countries and are associated with socioeconomic factors. This evidence can assist stakeholders in addressing health inequities to improve overall disease burden within the EEA. Funding: Research Council of Norway; Development, and Innovation Fund of Hungary; Norwegian Institute of Public Medicine; and COST Action 18218 European Burden of Disease Network.</p

Subnational inequalities in years of life lost and associations with socioeconomic factors in pre-pandemic Europe, 2009–19: an ecological study

Background: Health inequalities have been associated with shorter lifespans. We aimed to investigate subnational geographical inequalities in all-cause years of life lost (YLLs) and the association between YLLs and socioeconomic factors, such as household income, risk of poverty, and educational attainment, in countries within the European Economic Area (EEA) before the COVID-19 pandemic. Methods: In this ecological study, we extracted demographic and socioeconomic data from Eurostat for 1390 small regions and 285 basic regions for 32 countries in the EEA, which was complemented by a time-trend analysis of subnational regions within the EEA. Age-standardised YLL rates per 100 000 population were estimated from 2009 to 2019 based on methods from the Global Burden of Disease study. Geographical inequalities were assessed using the Gini coefficient and slope index of inequality. Socioeconomic inequalities were assessed by investigating the association between socioeconomic factors (educational attainment, household income, and risk of poverty) and YLLs in 2019 using negative binomial mixed models. Findings: Between Jan 1, 2009, and Dec 31, 2019, YLLs lowered in almost all subnational regions. The Gini coefficient of YLLs across all EEA regions was 14·2% (95% CI 13·6–14·8) for females and 17·0% (16·3 to 17·7) for males. Relative geographical inequalities in YLLs among women were highest in the UK (Gini coefficient 11·2% [95% CI 10·1–12·3]) and among men were highest in Belgium (10·8% [9·3–12·2]). The highest YLLs were observed in subnational regions with the lowest levels of educational attainment (incident rate ratio [IRR] 1·19 [1·13–1·26] for females; 1·22 [1·16–1·28] for males), household income (1·35 [95% CI 1·19–1·53]), and the highest poverty risk (1·25 [1·18–1·34]). Interpretation: Differences in YLLs remain within, and between, EEA countries and are associated with socioeconomic factors. This evidence can assist stakeholders in addressing health inequities to improve overall disease burden within the EEA. Funding: Research Council of Norway; Development, and Innovation Fund of Hungary; Norwegian Institute of Public Medicine; and COST Action 18218 European Burden of Disease Network

Disease Burden Attributed to Drug use in the Nordic Countries: a Systematic Analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study 2019

The Nordic countries share similarities in many social and welfare domains, but drug policies have varied over time and between countries. We wanted to compare differences in mortality and disease burden attributed to drug use over time. Using results from the Global Burden of Disease (GBD) study, we extracted age-standardized estimates of deaths, DALYs, YLLs and YLDs per 100 000 population for Denmark, Finland, Iceland, Norway, and Sweden during the years 1990 to 2019. Among males, DALY rates in 2019 were highest in Finland and lowest in Iceland. Among females, DALY rates in 2019 were highest in Iceland and lowest in Sweden. Sweden have had the highest increase in burden since 1990, from 252 DALYs to 694 among males, and from 111 to 193 among females. Norway had a peak with highest level of all countries in 2001–2004 and thereafter a strong decline. Denmark have had the most constant burden over time, 566–600 DALYs among males from 1990 to 2010 and 210–240 DALYs among females. Strict drug policies in Nordic countries have not prevented an increase in some countries, so policies need to be reviewed.publishedVersio

Changes in life expectancy and disease burden in Norway, 1990–2019: an analysis of the Global Burden of Disease Study 2019

Bill & Melinda Gates FoundationpublishedVersio

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation