Holistic depiction of time-trends in average mortality rates, and absolute and relative inequalities, is challenging.
We outline a typology for situations with falling average mortality rates (m↓; e.g., cardiovascular disease), rates stable over time (m-; e.g., some cancers), and increasing average mortality rates (m↑; e.g., suicide in some contexts). If we consider inequality trends on both the absolute (a) and relative (r) scales, there are 13 possible combination of m, a, and r trends over time. They can be mapped to graphs with relative inequality (log relative index of inequality [RII]; r) on the y axis, log average mortality rate on the x axis (m), and absolute inequality (slope index of inequality; SII; a) as contour lines. We illustrate this by plotting adult mortality trends: (1) by household income from 1981 to 2011 for New Zealand, and (2) by education for European countries.
Types range from the “best” m↓a↓r↓ (average, absolute, and relative inequalities all decreasing; southwest movement in graphs) to the “worst” m↑a↑r↑ (northeast). Mortality typologies in New Zealand (all-cause, cardiovascular disease, nonlung cancer, and unintentional injury) were all m↓r↑ (northwest), but variable with respect to absolute inequality. Most European typologies were m↓r↑ types (northwest; e.g., Finland), but with notable exceptions of m-a↑r↑ (north; e.g., Hungary) and “best” or southwest m↓a↓r↓ for Spain (Barcelona) females.
Our typology and corresponding graphs provide a convenient way to summarize and understand past trends in inequalities in mortality, and hold potential for projecting future trends and target setting.
Supplemental Digital Content is available in the text.
From the aDepartment of Public Health, University of Otago, Wellington, New Zealand; and bDepartment of Public Health, Erasmus MC, Rotterdam, Netherlands.
Editor’s Note: A commentary on this article appears on p. 604.
Submitted 11 April 2016; accepted 3 April 2017.
The NZCMS is conducted in collaboration with Statistics New Zealand and within the confines of the Statistics Act 1975. This study was supported by the Ministry of Health, New Zealand [425630/34738]. The European data were collected in the DEMETRIQ project, which was financially supported by a grant (FP7-CP-FP Grant No. 278511) from the European Commission Research and Innovation Directorate General.
Access to the data used in this study was provided by Statistics New Zealand under conditions designed to give effect to the security and confidentiality provisions of the Statistics Act 1975. The results presented in this study are the work of the authors, not Statistics New Zealand. SAS code to generate mortality rates, slope indices, and relative indices of inequality, and typology plots, is available from the authors on request.
The authors report no conflicts of interest.
Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com).
Correspondence: Tony Blakely, Department of Public Health, University of Otago, P.O. Box 7343, Newtown, Wellington 6242, New Zealand. E-mail: firstname.lastname@example.org.