Unequal impact of COVID-19 on excess deaths, life expectancy, and premature mortality in Spanish regions (2020-2021)
Bibliographic record
Abstract
We aimed to estimate regional inequalities in excess deaths and premature mortality in Spain during 2020 and 2021, before high vaccination coverage against COVID-19. With data from the National Institute of Statistics, within each region, sex, and age group, we estimated the excess deaths, the change in life expectancy at birth ( e 0 ) and age 65 ( e 65 ) and years of life lost as the difference between the observed and expected deaths using a time series analysis of 2015-2019 data and life expectancies based on Lee-Carter forecasting using 2010-2019 data. From January 2020 to June 2021, an estimated 89,200 (men: 48,000; women: 41,200) excess deaths occurred in Spain with a substantial regional variability (highest in Madrid: 22,000, lowest in Canary Islands: −210). The highest reductions in e 0 in 2020 were observed in Madrid (men −3.58 years, women −2.25), Castile-La Mancha (−2.72, −2.38), and Castile and Leon (−2.13, −1.39). During the first half of 2021, the highest reduction in e 0 was observed in Madrid for men (−2.09; −2.37 to −1.84) and Valencian Community for women (−1.63; −1.97 to −1.3). The highest excess years of life lost in 2020 was in Castile-La Mancha (men: 5370; women: 3600, per 100 000). We observed large differences between reported COVID-19 deaths and estimated excess deaths across the Spanish regions. Regions performed highly unequally on excess deaths, life expectancy and years of life lost. The investigation of the root causes of these regional inequalities might inform future pandemic policy in Spain and elsewhere. Estimar las desigualdades regionales en exceso de muertes y mortalidad prematura en España entre enero de 2020 y junio de 2021, antes de la vacunación poblacional masiva contra la COVID-19. Con datos del Instituto Nacional de Estadística se estimaron el exceso de muertes con respecto a las muertes esperadas por regione, sexo y grupos de edad, el cambio en la esperanza de vida al nacer ( e 0 ) y a los 65 años ( e 65 ), y los años de vida perdidos, mediante series temporales (2015-2019) y el pronóstico de Lee-Carter (2010-2019). En el periodo del estudio, el exceso de muertes fue de 89.200 (hombres 48.000, mujeres 41.200), con una gran variabilidad regional (desde Madrid con 22.000 hasta las Islas Canarias con −210). Las mayores reducciones de e 0 en 2020 fueron en Madrid (hombres −3,58 años, mujeres −2,25), Castilla-La Mancha (−2,72, −2,38) y Castilla y León (−2,13, −1,39), y en el primer semestre de 2021 fueron en Madrid (hombres −2,09; −2,37 a −1,84) y en la Comunidad Valenciana (mujeres −1,63; −1,97 a −1,3). El mayor exceso de años de vida perdidos en 2020 se produjo en Castilla-La Mancha (hombres 5370, mujeres 3600, por 100.000). Hubo grandes diferencias entre las muertes por COVID-19 notificadas y el exceso de muertes. Se observaron enormes desigualdades regionales en el exceso de muertes, la esperanza de vida y los años de vida perdidos. La determinación de las causas fundamentales de estas desigualdades podría servir para mejorar las políticas de salud pública en pandemias futuras.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".