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

Damage to the median and ulnar nerves after a snake bite

[no abstract available

Unexpected inching explained by an ulnar nerve anatomic variant documented by sonography

[no abstract available

Isolated musculocutaneous nerve injury in a kickboxer

[no abstract available

Painful legs and moving toes syndrome: putative underlying pathophysiology as a hint for combined pharmacological treatment?

[no abstract available

Nerve ultrasound findings differentiate Charcot-Marie-Tooth disease (CMT) 1A from other demyelinating CMTs

OBJECTIVE: Ulnar/median motor nerve conduction velocity (MNCV) is 6438\u202fm/s in demyelinating Charcot-Marie-Tooth disease (CMT). Previous nerve high resolution ultrasound (HRUS) studies explored demyelinating CMT assuming it as a homogeneous genetic/pathological entity or focused on CMT1A. METHODS: To explore the spectrum of nerve HRUS findings in demyelinating CMTs, we recruited patients with CMT1A (N\u202f=\u202f44), CMT1B (N\u202f=\u202f9), CMTX (N\u202f=\u202f8) and CMT4C (N\u202f=\u202f4). They underwent nerve conduction study (NCS) and HRUS of the median, ulnar, peroneal nerve, and the brachial plexus. RESULTS: Median, ulnar and peroneal MNCV significantly differed across CMT subtypes. Cross sectional area (CSA) was markedly and diffusely enlarged at all sites, except entrapment ones, in CMT1A, while it was slightly enlarged or within normal range in the other CMTs. No significant right-to-left difference was found. Age had limited effect on CSA. CSAs of some CMT1A patients largely overlapped with those of other demyelinating CMTs. A combination of three median CSA measures could separate CMT1A from other demyelinating CMTs. CONCLUSIONS: Nerve HRUS findings are heterogeneous in demyelinating CMTs. SIGNIFICANCE: Nerve HRUS may separate CMT1A from other demyelinating CMTs. The large demyelinating CMTs HRUS spectrum may be related to its pathophysiological variability

Nerve size correlates with clinical severity in Charcot\u2013Marie\u2013Tooth disease 1A

INTRODUCTION: Nerve cross sectional area (CSA) is larger than normal in Charcot-Marie-Tooth disease 1A (CMT1A), although to a variable extent. We explored whether CSA is correlated with CMT clinical severity measured with neuropathy score version 2 (CMTNS2) and its examination subscore (CMTES2) in CMT1A. METHODS: We assessed 56 CMT1A patients (42 families). They underwent nerve conduction study (NCS) and nerve high-resolution ultrasound (HRUS) of the left median, ulnar and fibular nerves. RESULTS: Univariate analysis showed NCS and HRUS variables to be significantly correlated with CMTNS2 and CMTES2 and with each other. Multivariate analysis showed ulnar motor nerve conduction velocity (\u3b2: -0.19) and fibular compound muscle action potential amplitude (-1.50) to significantly influence CMTNS2 and median forearm CSA to significantly influence CMTNS2 (\u3b2: 5.29) and CMTES2 (4.28). DISCUSSION: Nerve size is significantly associated with clinical scores in CMT1A, suggesting it might represent a potential biomarker of CMT damage and progression. This article is protected by copyright. All rights reserved

Cognition and emotional decision-making in chronic low back pain: an ERPs study during Iowa gambling task

Previous reports documented abnormalities in cognitive functions and decision-making (DM) in patients with chronic pain, but these changes are not consistent across studies. Reasons for these discordant findings might include the presence of confounders, variability in chronic pain conditions, and the use of different cognitive tests. The present study was aimed to add evidence in this field, by exploring the cognitive profile of a specific type of chronic pain, i.e.: chronic low back pain (cLBP).Twenty four cLBP patients and 24 healthy controls underwent a neuropsychological battery and we focused on emotional DM abilities by means of Iowa gambling task (IGT). During IGT, behavioral responses and the electroencephalogram (EEG) were recorded in 12 patients and 12 controls. Event-related potentials (ERPs) were averaged offline from EEG epochs locked to the feedback presentation (4000 ms duration, from 2000 ms before to 2000 ms after the feedback onset) separately for wins and losses and the feedback-related negativity (FRN) and P300 peak-to-peak amplitudes were calculated. Among cognitive measures, cLBP patients scored lower than controls in the modified card sorting test (MCST) and the score in this test was significantly influenced by pain duration and intensity. Behavioural IGT results documented worse performance and the absence of a learning process during the test in cLBP patients compared to controls, with no effect of pain characteristics. ERPs findings documented abnormal feedback processing in patients during IGT.cLBP patients showed poor performance in the MCST and the IGT. Abnormal feedback processing may be secondary to impingement of chronic pain in brain areas involved in DM or suggest the presence of a predisposing factor related to pain chronification. These abnormalities might contribute to the impairment in the work and family settings that often cLBP patients report

Impatto a breve termine dell'inquinamento dell'aria nelle citt\ue0 coperte dalla sorveglianza epidemiologica EpiAir2

OBIETTIVO: stimare l\u2019impatto a breve termine dell\u2019inquinamento atmosferico sulla popolazione adulta di 23 citt\ue0 italiane nel periodo 2006-2009 nell\u2019ambito del progetto EpiAir2. DISEGNO, MATERIALI E METODI: per ogni citt\ue0 inclusa nello studio \ue8 stato calcolato l\u2019impatto dell\u2019effetto a breve termine dell\u2019inquinamento atmosferico sulla mortalit\ue0. In particolare, sono stati calcolati i decessi attribuibili a concentrazioni delle polveri (PM10 e PM2.5) superiori a soglie differenti definite dalla legislazione europea o nell\u2019ambito delle linee guida dell\u2019Organizzazione mondiale della sanit\ue0 (per il PM10: 20 e 40 \u3bcg/m3, riduzione del 20% ad arrivare a 20 \u3bcg/m3 e superamento del limite di 35 giorni con concentrazioni medie di 50 \u3bcg/m3; per il PM2.5: 10, 18 e 25 \u3bcg/m3, riduzione del 20% ad arrivare a 18 \u3bcg/m3). La stima di impatto \ue8 stata ottenuta combinando la stima di effetto delle polveri, il livello di mortalit\ue0 osservato e i livelli di concentrazione degli inquinanti misurati dalle reti di monitoraggio urbane. Per quanto riguarda le stime di effetto, sono state utilizzate le distribuzioni a posteriori specifiche per citt\ue0 risultanti da una metanalisi bayesiana. L\u2019incertezza sulle stime di impatto \ue8 stata calcolata con metodi Monte Carlo. RISULTATI: nell\u2019insieme delle 23 citt\ue0 valutate nel presente studio il numero di decessi attribuibili agli effetti a breve termine delle concentrazioni di PM10 superiori a 20 \u3bcg/m3 e di PM2.5 superiori a 10 \u3bcg/m3 nel periodo 2006-2009 \ue8 risultato rispettivamente pari allo 0,9% (assumendo indipendenza tra citt\ue0 l\u2019intervallo di credibilit\ue0 all\u201980% \ue8 0,4-1,4) e allo 0,8% (ICr80% 0,2-1,3) della mortalit\ue0 naturale. L\u2019impatto delle concentrazioni di polveri PM10 e PM2.5 \ue8 risultato concentrato nelle citt\ue0 della Pianura Padana, della Piana fiorentina, e nelle grandi realt\ue0 metropolitane di Roma, Napoli e Palermo: per il PM10 la percentuale sui decessi \ue8 risultata 1,0% (ICr80% 0,4-1,5) contro 0,4% (ICr80% 0,2-0,7) nelle altre citt\ue0 analizzate. Se i livelli di concentrazione delle polveri fossero stati inferiori del 20%, complessivamente l\u2019impatto si sarebbe ridotto del 42% per il PM10 e del 51% per il PM2.5. CONCLUSIONI: i livelli di inquinamento osservati nel periodo in studio sono stati responsabili di un numero importante di decessi nelle citt\ue0 analizzate. Politiche di contenimento basate sulla diminuzione percentuale delle concentrazioni annuali di polveri interesserebbero tutte le citt\ue0 coperte dallo studio e potrebbero ridurre in modo importante l\u2019impatto dell\u2019inquinamento sulla salute

Indicatori ambientali nello studio EpiAir2: I dati di qualit\ue0 dell'aria per la sorveglianza epidemiologica

OBIETTIVO: costruzione di indicatori ambientali di inquinamento aerodiffuso per finalit\ue0 di sorveglianza epidemiologica in 25 citt\ue0 italiane per il progetto EpiAir2 (2006-2010) e presentazione dei dati di dieci anni di sorveglianza in 10 citt\ue0 italiane (2001-2010). DISEGNO: sono stati raccolti dati di particolato (nelle frazioni PM10 e PM2.5 ), biossido di azoto (NO2 ) e ozono (O3 ), considerati fattori di rischio per la salute. I datimeteorologici considerati come confondenti nell\u2019analisi dell\u2019effetto degli inquinanti sono stati: temperatura, umidit\ue0 relativa (e la variabile derivata \u201ctemperatura apparente\u201d) e pressione barometrica. I criteri per la selezione delle stazioni dimonitoraggio e imetodi di calcolo per la costruzione di indicatori ambientali a partire dalle serie giornaliere disponibili sono stati scelti in continuit\ue0 con la precedente edizione di EpiAir. Per tutte le citt\ue0, \ue8 stata verificata l\u2019omogeneit\ue0 dei dati selezionati nel rappresentare l\u2019esposizione delle popolazioni. SETTING E PARTECIPANTI: il progetto EpiAir2 coinvolge per gli anni 2006-2010 le citt\ue0 diMilano,Mestre-Venezia,Torino, Bologna, Firenze, Pisa, Roma,Taranto,Cagliari e Palermo, gi\ue0 presenti nello studio EpiAir. A questo elenco vanno aggiunte le citt\ue0 di Treviso, Trieste, Padova, Rovigo, Piacenza, Parma, Ferrara, Reggio Emilia, Modena, Genova, Rimini, Ancona, Bari, Napoli e Brindisi. RISULTATI: nel periodo considerato \ue8 stato osservato un decremento delle concentrazioni di particolato nella maggior parte delle citt\ue0 in analisi, mentre non si pu\uf2 giungere a conclusioni cos\uec nette per NO2 e ozono. L\u2019analisi dell\u2019andamento temporale degli indicatori ha evidenziato valori medi annuali di PM10 superiori ai 40 \u3bcg/m3 in alcune citt\ue0 della Pianura Padana, e valori medi annuali di NO2 costantemente superiori ai 40 \u3bcg/m3 nelle citt\ue0 di Trieste, Milano, Padova, Torino, Modena, Bologna, Roma e Napoli. CONCLUSIONE: l\u2019ampliamento del progetto EpiAir, con l\u2019inclusione di ulteriori 13 citt\ue0, ha permesso di evidenziare peculiarit\ue0 legate alle differenti aree geografiche in studio e numerose situazioni di criticit\ue0 con superamenti dei valori di concentrazione limite fissati dalla legislazione corrente. I risultati dello studio EpiAir2 confermano la necessit\ue0 di un sistema di sorveglianza dell\u2019inquinamento aerodiffuso nei centri urbani e industriali al fine di ottenere stime affidabili dell\u2019esposizione della popolazione residente e di monitorarne l\u2019andamento nel tempo