Impact of the economic crisis on the health of older persons in Spain: research clues based on an analysis of mortality. SESPAS report 2014
Bibliographic record
Abstract
Older adults are seldom considered in studies on the health impact of economic recessions or crises. However, they constitute a population group that is highly vulnerable to decreases in investment in health and social services and social security. Our aim is to examine the relationship between the economic crisis starting in 2008 and the health status of older adults in Spain. More specifically, we analyze changes in trends of mortality in relation to the crisis, the specific impact of winter on mortality and gender differences in the crisis’ impact on mortality. Using data from the National Institute of Statistics of Spain on people over 60 years of age, the number of monthly deaths by age and sex from January 2005 to December 2012 was analyzed. Interrupted time series analyses and the “difference in differences” method were used. During the crisis, for adults 60 years and older: 1) the observed mortality seems to be decreasing at a slower rate than what would have been expected in the absence of the crisis; 2) there has been an increase in winter mortality; 3) the impact of the crisis has been greater for female than for male mortality. These results suggest sizable effects of the economic crisis on the mortality of older adults and argue for research done using more detailed analyses integrating economic indicators. Las personas mayores son rara vez consideradas en los estudios sobre los impactos sanitarios de las recesiones o crisis económicas a pesar de constituir un grupo de población vulnerable a las disminuciones de inversión en la oferta de servicios o a las cotizaciones sociales. Nuestro objetivo es examinar la relación entre la crisis económica iniciada en 2008 y la salud de la personas mayores en España. Se analizan los cambios en las tendencias de mortalidad en relación a la crisis, su impacto específico en la mortalidad invernal, y las diferencias de su impacto entre hombres y mujeres. Utilizando datos del Instituto Nacional de Estadística, se analizan las defunciones mensuales por edad y sexo (a partir de los 60 años) desde Enero de 2005 a Diciembre de 2012. Se realiza un análisis de series temporales interrumpidas y un análisis del tipo “diferencias de diferencias”. En personas mayores de 60 años y en el periodo de la crisis en curso: 1) la mortalidad observada parece disminuir más lentamente de lo que se hubiera esperado en ausencia de la crisis; 2) un aumento en el exceso de la mortalidad invernal y 3) un impacto más importante de la crisis en la mortalidad de las mujeres que en los hombres. Estos resultados sugieren efectos importantes de la crisis económica en la mortalidad de las personas mayores, así como pistas de investigación que se podrían explorar con análisis más detallados integrando indicadores económicos.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".