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

Factors associated with recurrence in patients with oral cancer in Mongolia

Abstract Introduction In Mongolia, there has been limited research on the posttreatment survival rate, recurrence, and occurrence of oral cancer. The goal of this study is to investigate the risk factors that contribute to the recurrence of oral cancer to increase survival rates, facilitate early detection, and improve treatment accuracy. Method A retrospective cohort method was used, with medical records from 173 patients diagnosed with squamous cell carcinoma of the mouth at the National Cancer Center of Mongolia’s Department of Head and Neck Surgery, Radio, and Chemotherapy between 2012 and 2017. The Mongolian National University of Medical Sciences’ Research Ethics Committee approved the project. Results The findings revealed that 109 cases (63.0%) were men and 64 (37.0%) were females, with a large proportion of patients (28.3%) falling between the ages of 61 and 70. Men had a 3.8 times higher risk of cancer recurrence than women (OR = 3.79, CI = 1.24–11.57). Furthermore, lymph node metastases and treatment were linked to oral cancer recurrence. Conclusion This study offers light on the factors that influence the recurrence of oral cancer, giving useful insights for improving patient outcomes through early detection and proper treatment

Skin aging risk factors: A nationwide population study in Mongolia risk factors of skin aging

The world population is aging and no country is immune to the consequences. We are not aware of any country-specific skin aging risk factors data for the Mongolian people. Thus, we aimed to study the risk factors associated with skin aging in the Mongolian population. A population-based cross-sectional study of 2720 study participants 18 years of age and older was performed evaluating the severity of skin aging based on cutaneous microtopography. Questionnaire data and skin physiological measurements were obtained. The odds ratios for skin aging grades associated with risk factors were estimated using ordinal logistic regression. Study participant’s mean age was 45 years, ranging from 18 to 87. After adjustment for known risk factors, skin aging was associated with demographic risk factors such as increasing age (aOR = 1.19, 95% CI 1.18–1.20), living in an urban area (aOR = 1.31, 95% CI 1.12–1.55) and lifestyle factors including being a smoker (aOR = 1.32, 95% CI 1.09–1.61), having a higher body mass index (aOR = 1.04, 95% CI 1.02–1.06) and higher levels of sun exposure time (aOR = 1.03, 95% CI 1.00–1.06) were significantly associated with higher skin aging grades. Having dry (aOR = 1.94, 95% CI 1.45–2.59) and combination skin (aOR = 1.62, 95% CI 1.22–2.16) types were also independent risk factors associated with skin aging. Having very low skin surface moisture at the T-zone (aOR = 2.10, 95% CI 1.42–3.11) was significantly related to skin aging. Older age, urban living and toxic working conditions were independent demographic risk factors related to skin aging. Smoking, higher BMI, greater levels of sun exposure were significant lifestyle risk factors. Having a skin type other than normal was a physiologic risk factor for skin aging.</jats:p

Integrating Quantitative and Qualitative Approaches to Explore Absenteeism Attributed to Air Pollution and its Attributed Direct and Indirect Costs Among Private Sector Companies in Ulaanbaatar, Mongolia

Abstract Background Ulaanbaatar, Mongolia, the coldest national capital city, has the highest winter seasonal mean concentrations of PM2.5 and PM10. During January, the coldest month, peak pollution levels are &gt; 8 times higher than the World Health Organization (WHO) guideline values are reached, on average, 15.7 times. Over 80% of this seasonal air pollution is due to domestic heating with coal stoves in large ger residential communities that surround much of the city. This report presents an analysis of the direct and indirect costs of wintertime seasonal air pollution due to the absenteeism of private-sector employees.Methods Questionnaire data were obtained for 1330 employees working for private sector companies over six economic sectors. To assess employee’s direct and indirect costs, healthcare-related costs such as cost per hospitalization, medication, and outpatient visits were calculated using the Cost-of-Illness approach. Non-healthcare costs, such as transportation and food, were also estimated in the study. Individual Indirect costs were calculated with the Human Capital Approach, which estimates the hours of work lost by the person due to disease and then multiplies total lost hours by the hourly wage.Results Approximately 60% of employee absences occurred during the coldest and hence most air polluted time of 4 months of the year from November to February. Female employees were proportionately more likely to be absent than their male counterparts. Individual direct healthcare costs attributed to air pollution related-sickness absences totaled 1,005,000₮ ($361.50) per year due to being absent from work an average of 3 days three times during the winter in Ulaanbaatar. The median cost of lost wages for 3 days’ absence is 120,000₮ ($43.20).Conclusions We conclude that wintertime pollution has a major impact on absenteeism rates among private-sector employees, and therefore, we postulate that this must be a significant driver of opportunity costs, affecting not only corporate bottom lines but also employees.</jats:p