What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database
Bibliographic Data
| ID | 11295010 |
|---|---|
| Authors | Ainhoa-Elena Leger (0000-0001-9005-4878, University of Padua, corresponding author), Stefano Mazzuco (0000-0002-1686-5477, University of Padua) |
| Year | 2021 |
| Volume | 37 |
| Issue | 4-5 |
| Pages | 769-798 |
| Publication date | 2021-11-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | European Journal of Population / Revue européenne de Démographie (JOURNAL) |
| Journal identifiers | ISSN: 0168-6577 • E-ISSN: 1572-9885 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s10680-021-09588-y |
| PMID | 34785997 |
| OpenAlex | W3176053783 |
| Language | EN |
| Citations received | 2 |
| References cited | 44 |
This study analyzed whether there are different patterns of mortality decline among low-mortality countries by identifying the role played by all the mortality components. We implemented a cluster analysis using a functional data analysis (FDA) approach, which allowed us to consider age-specific mortality rather than summary measures, as it analyses curves rather than scalar data. Combined with a functional principal component analysis, it can identify what part of the curves is responsible for assigning one country to a specific cluster. FDA clustering was applied to the data from 32 countries in the Human Mortality Database from 1960 to 2018 to provide a comprehensive understanding of their patterns of mortality. The results show that the evolution of developed countries followed the same pattern of stages (with different timings): (1) a reduction of infant mortality, (2) an increase of premature mortality and (3) a shift and compression of deaths. Some countries were following this scheme and recovering the gap with precursors; others did not show signs of recovery. Eastern European countries were still at Stage (2), and it was not clear if and when they will enter Stage 3. All the country differences related to the different timings with which countries underwent the stages, as identified by the clusters
Cluster (spacecraft · Cluster analysis · Database · Geography · Mortality rate · Principal component analysis · Computer Science · Demography · Global Health Care Issues · Health disparities and outcomes · Insurance, Mortality, Demography, Risk Management · Medicine · Artificial Intelligence
Functional Data Analysis
The case for monitoring life-span inequality
Broken Limits to Life Expectancy
Life Expectancy and Mortality Rates in the United States, 1959-2017
A universal pattern of mortality decline in the G7 countries
Life expectancy and disparity
Mortality in Europe
Mortality in Europe
Cause-specific mortality at young ages
A Mixture-Function Mortality Model
Coherent mortality forecasts for a group of populations
Lifespan Disparity as an Additional Indicator for Evaluating Mortality Forecasts
Extending the Lee-Carter Method to Model the Rotation of Age Patterns of Mortality Decline for Long-Term Projections
A Cohort Comparison of Lifespan After Age 100 in Denmark and Sweden
Losses of Expected Lifetime in the United States and Other Developed Countries
Coherent Mortality Forecasting
Applying Lee-Carter under conditions of variable mortality decline
Modelling and forecasting adult age-at-death distributions
A mortality model based on a mixture distribution function
Rectangularization of the survival curve reconsidered
Mode and Dispersion of the Length of Life
Demography, Measuring and Modeling Population Processes
Coherent forecasts of mortality with compositional data analysis
The compression of deaths above the mode
The modal age at death and the shifting mortality hypothesis
Decomposing changes in life expectancy
| Unique citing works | 2 |
|---|---|
| Citations per year | 1 |
| Citation span | 2024 - 2024 (1) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 2 |