How to measure premature mortality? A proposal combining "relative" and "absolute" approaches.
Hierarchical model
Mixture model
Premature mortality
Journal
Population health metrics
ISSN: 1478-7954
Titre abrégé: Popul Health Metr
Pays: England
ID NLM: 101178411
Informations de publication
Date de publication:
26 10 2021
26 10 2021
Historique:
received:
04
02
2021
accepted:
22
09
2021
entrez:
27
10
2021
pubmed:
28
10
2021
medline:
1
2
2022
Statut:
epublish
Résumé
The concept of "premature mortality" is at the heart of many national and global health measurement and benchmarking efforts. However, despite the intuitive appeal of its underlying concept, it is far from obvious how to best operationalise it. The previous work offers at least two basic approaches: an absolute and a relative one. The former-and far more widely used- approach sets a unique age threshold (e.g. 65 years), below which deaths are defined as premature. The relative approach derives the share of premature deaths from the country-specific age distribution of deaths in the country of interest. The biggest disadvantage of the absolute approach is that of using a unique, arbitrary threshold for different mortality patterns, while the main disadvantage of the relative approach is that its estimate of premature mortality strongly depends on how the senescent deaths distribution is defined in each country. We propose to overcome some of the downsides of the existing approaches, by combining features of both, using a hierarchical model, in which senescent deaths distribution is held constant for each country as a pivotal quantity and the premature mortality distribution is allowed to vary across countries. In this way, premature mortality estimates become more comparable across countries with similar characteristics. The proposed hierarchical models provide results, which appear to align with related evidence from specific countries. In particular, we find a relatively high premature mortality for the United States and Denmark. While our hybrid approach overcomes some of the problems of previous measures, some issues require further research, in particular the choice of the group of countries that a given country is assigned to and the choice of the benchmarks within the groups. Hence, our proposed method, combined with further study addressing these issues, could provide a valid alternative way to measure and compare premature mortality across countries.
Sections du résumé
BACKGROUND
The concept of "premature mortality" is at the heart of many national and global health measurement and benchmarking efforts. However, despite the intuitive appeal of its underlying concept, it is far from obvious how to best operationalise it. The previous work offers at least two basic approaches: an absolute and a relative one. The former-and far more widely used- approach sets a unique age threshold (e.g. 65 years), below which deaths are defined as premature. The relative approach derives the share of premature deaths from the country-specific age distribution of deaths in the country of interest. The biggest disadvantage of the absolute approach is that of using a unique, arbitrary threshold for different mortality patterns, while the main disadvantage of the relative approach is that its estimate of premature mortality strongly depends on how the senescent deaths distribution is defined in each country.
METHOD
We propose to overcome some of the downsides of the existing approaches, by combining features of both, using a hierarchical model, in which senescent deaths distribution is held constant for each country as a pivotal quantity and the premature mortality distribution is allowed to vary across countries. In this way, premature mortality estimates become more comparable across countries with similar characteristics.
RESULTS
The proposed hierarchical models provide results, which appear to align with related evidence from specific countries. In particular, we find a relatively high premature mortality for the United States and Denmark.
CONCLUSIONS
While our hybrid approach overcomes some of the problems of previous measures, some issues require further research, in particular the choice of the group of countries that a given country is assigned to and the choice of the benchmarks within the groups. Hence, our proposed method, combined with further study addressing these issues, could provide a valid alternative way to measure and compare premature mortality across countries.
Identifiants
pubmed: 34702295
doi: 10.1186/s12963-021-00267-y
pii: 10.1186/s12963-021-00267-y
pmc: PMC8547117
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
41Commentaires et corrections
Type : ErratumIn
Informations de copyright
© 2021. The Author(s).
Références
United Nations. Transforming our world: the 2030 agenda for sustainable development. United Nations: Technical report; 2015.
OECD. Health at a Glance 2009: OECD Indicators. OECD Publishing. 2009.
Murray CJL, Ezzati M, Flaxman AD, Lim S, Lozano R, Michaud C, Naghavi M, Salomon JA, Shibuya K, Vos T, Wikler D, Lopez AD. Gbd 2010: design, definitions, and metrics. The Lancet. 2012;380(9859):2063–6. https://doi.org/10.1016/S0140-6736(12)61899-6 .
doi: 10.1016/S0140-6736(12)61899-6
WHO: Targets and Indicators for Health 2020. WHO Regional Office for Europe; 2016.
Eurostat: Health Statistics—Atlas on Mortality in the European Union. European Communities, 2009.
Lexis W. Sur la durée normale de la vie humaine et sur la théorie de la stabilité des rapports statistiques [on the normal human lifespan and the theory of the stability of the statistical ratios]. Annales de Démographie Internationale. 1878;2(6–7):447–60.
Kannisto V. Measuring the compression of mortality. Demogr Res. 2000;6(3):1–24.
Kannisto V. Mode and dispersion of the length of life. Popul Engl Sel. 2001;13(1):159–72.
Cheung SLK, Cheung J-M, Tu EJ-C, Caselli G. Three dimension of the survival curve: horizontalization, verticalization, and longevity extension. Demography. 2005;42:243–58.
doi: 10.1353/dem.2005.0012
OECD: Mortality amenable to health care in 31 OECD countries: estimates and methodological issues. Technical report, OECD Health Working Papers. 2011;No. 55.
Eurostat: Amenable and preventable deaths statistics. Technical report, Eurostat Statistics Explained. 2016.
Best A, Haozous EA, Berrington de Gonzalez A, Chernyavskiy P, Freedman ND, Hartge P, Thomas D, Rosenberg PS, Shiels MS. Premature mortality projections in the USA through 2030: a modelling study. Lancet Public Health. 2018;3:374–84.
doi: 10.1016/S2468-2667(18)30114-2
Mackenbach JP, Kulhánová I, et al. Trends in inequalities in premature mortality: a study of 3.2 million deaths in 13 European countries. J Epidemiol Community Health. 2015;69:207–17.
doi: 10.1136/jech-2014-204319
Case A, Deaton A. Rising morbidity and mortality in midlife among white non-Hispanic Americans in the 21st century. PNAS. 2015;112(49):15078–83.
doi: 10.1073/pnas.1518393112
Gardner JW, Sanborn JS. Years of potential life lost (ypll): what does it measure? Epidemiology. 1990;1(4):322–9.
doi: 10.1097/00001648-199007000-00012
Martinez R, Soliz P, Caixeta R, Ordunez P. Reflection on modern methods: years of life lost due to premature mortality: a versatile and comprehensive measure for monitoring non-communicable disease mortality. Int J Epidemiol. 2019;48(4):1367–76. https://doi.org/10.1093/ije/dyy254 .
doi: 10.1093/ije/dyy254
pubmed: 6693813
pmcid: 6693813
Case A, Deaton A. Deaths of despair and the future of capitalism. Princeton: Princeton University Press; 2020.
doi: 10.2307/j.ctvpr7rb2
Pearson K. the chances of death and other studies in evolution, vol. I. London: Edward Arnold; 1897.
Zanotto L, Canudas-Romo V, Mazzuco S. A mixture-function mortality model: illustration of the evolution of premature mortality. Eur J Popul. 2020 (to appear).
Basellini U, Camarda CG. Modelling and forecasting adult age-at-death distributions. Popul Stud. 2019;73(1):119–38. https://doi.org/10.1080/00324728.2018.1545918 .
doi: 10.1080/00324728.2018.1545918
Mazzuco S, Scarpa B, Zanotto L. A mortality model based on a mixture distribution function. Popul Stud. 2018;72(2):191–200. https://doi.org/10.1080/00324728.2018.1439519 .
doi: 10.1080/00324728.2018.1439519
Pascariu MD, Lenart A, Canudas-Romo V. The maximum entropy mortality model: forecasting mortality using statistical moments. Scand Actuar J. 2019;2019(8):661–85. https://doi.org/10.1080/03461238.2019.1596974 .
doi: 10.1080/03461238.2019.1596974
Azzalini A. A class of distribution which includes the normal one. Scand J Stat. 1985;12:171–8.
Siler W. A competing-risk model for animal mortality. Ecology. 1979;60(4):750–7. https://doi.org/10.2307/1936612 .
doi: 10.2307/1936612
Heligman L, Pollard JH. The age pattern of mortality. J Inst Actuar. 1980;107(1):49–80.
doi: 10.1017/S0020268100040257
Camarda CG, Basellini U. Smoothing, decomposing and forecasting mortality rates. Eur J Popul. 2021. https://doi.org/10.1007/s10680-021-09582-4 .
doi: 10.1007/s10680-021-09582-4
pubmed: 34421446
pmcid: 34421446
Remund A, Camarda CG, Riffe T. A cause-of-death decomposition of young adult excess mortality. Demography. 2018;55(3):957–78. https://doi.org/10.1007/s13524-018-0680-9 .
doi: 10.1007/s13524-018-0680-9
pubmed: 29869068
pmcid: 29869068
Megyesiova S, Lieskovska V. Premature mortality for chronic diseases in the EU member states. Int J Environ Res Public Health. 2019. https://doi.org/10.3390/ijerph16204021 .
doi: 10.3390/ijerph16204021
pubmed: 6843938
pmcid: 6843938
Case A, Deaton A. Mortality and morbidity in the 21st century. Brook Pap Econ Act 2017, (2017)
Li N, Lee R. Coherent mortality forecasts for a group of populations: an extension of the lee-carter method. Demography. 2005;42:575–94.
doi: 10.1353/dem.2005.0021
Azzalini A, Capitanio A. The skew-normal and related families. Cambridge: Cambridge University Press; 2013. https://doi.org/10.1017/CBO9781139248891 (Cambridge Books Online).
doi: 10.1017/CBO9781139248891
Lindahl-Jacobsen R, Rau R, Jeune B, Canudas-Romo V, Lenart A, Christensen K, Vaupel JW. Rise, stagnation, and rise of Danish women’s life expectancy. PNAS. 2016;113(5):4015–20.
Mackenbach JP, Looman CWN, Janssen F, Kunst AE, Nusselder WJ. Stagnation in mortality decline among elders in The Netherlands. The Gerontologist. 2003;43(5):722–34. https://doi.org/10.1093/geront/43.5.722 .
doi: 10.1093/geront/43.5.722
pubmed: 14570968
pmcid: 14570968
Palloni A, Pinto G, Beltrán-Sánchez H. Latin American mortality database (LAMBdA). Madison: University of Wisconsin; 2014.
Gelman A, Rubin DB. Inference from iterative simulation using multiple sequences. Stat Sci. 1992;7:457–511.
Leger A, Mazzuco S. What Can We Learn from the Functional Clustering of Mortality Data? An Application to the Human Mortality Database. Eur J Popul. 2021. https://doi.org/10.1007/s10680-021-09588-y .
doi: 10.1007/s10680-021-09588-y
pubmed: 8575745
pmcid: 8575745