Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Uva exhibits consistent activity across multiple song renditions at multiple recording sites.

<p><b>(A)</b> Activity in Uva is consistent across multiple bouts. From top to bottom, spectrogram of a single motif, multiple traces of time-warped, smoothed and rectified multiunit activity and an average trace in blue. Red arrows indicate motifs that occur at an end of a rendition. Orange bars mark out each syllable. <b>(B)</b> Cross-correlation across multiple renditions (shaded region indicating SEM) <b>(C)</b> Coherence across multiple renditions (shaded region indicating the 95% percentile of the null distribution corrected for multiple testing). <b>(D)</b> Activity in Uva is consistent across different recording sites. An average trace of time-warped multiunit activity at each recording site is shown in blue. Individual traces are shown in gray. Diagram in upper left-hand corner represents the relative position of each recording site within Uva. <b>(E)</b> Cross-correlation and (<b>F</b>) coherency across multiple recording sites.</p

Multi-unit recordings in Uva during singing.

<p><b>(A)</b> Simplified schematic view of the oscine song control system. Multiunit recordings were made in HVC-projecting core of Uva, which was antidromically identified by electrical stimulation in HVC. <b>(B)</b> Antidromic activation of neurons in Uva. Traces shows the response in Uva across sequential stimulations. Red arrow indicates a trial during which a spontaneous spike occurred, preventing in antidromic response in Uva. <b>(C)</b> Latency and jitter of antidromic responses in awake and anesthetized birds.</p

The avian premotor song circuit can be viewed as a combination of a feed-forward premotor pathway (gray arrows) combined with a feedback pathway (brown arrows).

<p>HVC (used as proper name); RA (robust nucleus of the arcopallium); nXIIts (tracheosyringeal portion of the hypoglossal nucleus); PAm (nucleus parambigualis); RAm (nucleus retroambigualis); DM (dorsomedial medial nucleus of the intercollicular complex); Uva (nucleus Uvaeformis); NIf (nucleus interface).</p

Uva activity exhibits no significant correlations with acoustic transitions or gesture trajectory extrema (GTEs).

<p>(<b>A</b>) Many syllables in a song may exhibit one or more acoustic transitions. Red lines mark acoustic transitions within long syllables. Trial-averaged song amplitude and smoothed, rectified neural activity also shown. (<b>B</b>) Examples of syllables where peaks in Uva activity are associated with acoustic transitions. Trial-averaged song amplitude and smoothed, rectified neural activity also shown. (<b>C</b>) Examples of syllables where acoustic transitions are not associated with peaks in Uva activity. Trial-averaged song amplitude and smoothed, rectified neural activity also shown. (<b>D</b>) GTE were identified using automated algorithm. Red lines mark GTEs that were identified by the automated algorithm from <i>Boari et al</i>. Also shown is a trial-averaged song amplitude and smoothed, rectified neural activity. (<b>E</b>) Uva activity does not exhibit any significant correlation with GTE times in individual birds and (<b>F</b>) across all birds when compared to a surrogate datasets in which GTE times were randomly shuffled within song syllables (gray shading: 99% confidence interval of surrogate dataset distribution; yellow: surrogate dataset distribution; blue: cross-correlation between Uva activity and GTE times).</p

Uva activity peaks prior to syllable onset and dips prior to syllable offset.

<p>(<b>A</b>) Above is a spectrogram of a single motif. Red bars represent the syllable lengths, with syllable labels below. Also showed is the trial-averaged smoothed, rectified neural activity in Uva. The song shown here is the same as that shown in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0169568#pone.0169568.g005" target="_blank">Fig 5A</a> <b>(B)</b> Uva activity peaks prior to syllable onset. Raster(top) represents the power of neural activity during each syllable rendition. Red line marks syllable onset and white line marks syllable offset. Syllables are grouped based on identity, arranged from longest to shortest syllable in descending order and then aligned to syllable onset. Individual syllables have been identified and labeled. Below is a syllable onset aligned multiunit trace averaged across all syllables. Also shown is the 95% confidence interval of baseline activity during vocalization determined from random shuffling of multiunit activity (yellow trace). <b>(C)</b> Uva activity dips prior to syllable offset. Heat raster (top) shows all syllables aligned to syllable offset. Average trace (below) shows a dip prior to syllable offset. Black line represents syllable offset. <b>(D)</b> Syllable onset aligned multiunit trace averaged across all birds. <b>(E)</b> Syllable offset aligned multiunit trace averaged across all birds.</p

Premotor activity in Uva.

<p><b>(A)</b> A trace of neural activity in Uva during a single bout. Song spectrogram (top) of an adult bird (>90dph) followed by a song amplitude trace. Immediately below that is the raw neural activity followed by a smoothed and rectified neural trace. Orange bars mark out individual syllables. Detailed examination of Uva activity during a song motif reveals peaks in activity prior to syllable onsets. The last syllable in the song bout is followed by a period of depressed neural activity in Uva lasting for approximately 200ms. <b>(B)</b> Uva exhibits activity prior to onset of introductory notes. At the top is a spectrogram of a single, example introductory note. Raster represents the power of neural activity during each rendition of an introductory note. Red line marks introductory note onset and white line marks introductory note offset. Below is a note onset aligned multiunit trace averaged across all renditions. Also shown is the baseline activity during vocalization determined from random shuffling of multiunit activity (yellow; shading indicates 95% confidence interval for maxima and minima anywhere in this window). <b>(C)</b> Uva activity during distance calls. Same as (<b>B</b>) but with distance calls instead of introductory notes. Note the peak in activity prior to call onset.</p

Quantification of rhythmic activity in Uva.

<p><b>(A)</b> Cross‐correlation function between Uva activity and sound amplitude averaged across 5 birds (green: mean cross-correlation, shaded region: SEM; peak correlation = 0.40±0.04, mean lag at peak correlation = 42±6ms). <b>(B)</b> normalized power spectra of the song amplitudes (red) and Uva multiunit activity (blue), averaged across n = 5 birds. A broad peak in the power spectrum is seen in both the song amplitude and neural data, centered ~10Hz. <b>(C)</b> Cross-spectrum between Uva activity and sound amplitude averaged across n = 5 birds (black: mean, yellow: null cross-spectrum, shaded region: 95% percentile corrected for multiple testing) <b>(D)</b> Coherency between Uva activity and sound amplitude averaged across n = 5 birds (black: mean, yellow: null cross-spectrum, shaded region: 95% percentile corrected for multiple testing). Note a large, significant peak is observed in both the cross-spectrum and the coherence at ~10Hz (F_peak = 8.8Hz, C_peak = 0.75, p<0.01, phase = -0.77π).</p