This website began on 8 May 2008 as a collection of graphs showing trends in mortality rates for a couple of countries, compiled—as a pastime—by
New Zealand-born Oxford-based
epidemiologist, Gary Whitlock.
Initially called MORtrends (www.mortrends.org), it became Mortality Trends (www.mortality-trends.org)
on 6 March 2010, and by the end of that year, the main, Choose a graph, page encompassed full sets of graphs for 40 countries.
The website was comprehensively updated and revamped on 5 May 2012. At present, the site contains just over 18,200 unique graphs.
By coincidence, Gary Whitlock experienced a series of health crises during much the same period as the site was being developed. He had a melanoma removed from his left leg on 21 February 2008, and a carcinoma removed from his left parotid gland (a salivary gland on the side of the neck and face) on 18 August 2010. His general health began to collapse in February 2012, and metastases were discovered in his lungs on 21 March 2012. After several investigations, including two different types of lung biopsy, the origin of the metastases at last became clear: it was the parotid, and the prognosis was grim. His wish was, however, that this website continue for at least a few years after his death, in much the same form as he left it. A personal farewell to family and friends.
Trends in mortality rates can reflect changes in disease occurrence or treatment, in which case they represent something real about a population's health. However, trends can also reflect alterations in how causes of death were defined or coded. These are artefacts, and are of little interest except simply to know that each is there. If on one of these graphs you see a jagged corner, a crooked step, or a fleeting dip, very probably the excursion is an artefact, not something of real significance. Many such artefacts are marked here by dotted lines, but most are not—there are simply too many of them.
'His [Percy Stock's] success owed much to his ability to distinguish the important data and methods, and to avoid the lure of trivia.'
—D.D. Reid on Percy Stocks (pictured right),
statistician and interpreter of national mortality data, British Journal of Preventive & Social Medicine (1975)
A common type of artefact is that caused by changes to how the underlying cause of death is specified when a disease is complicated by another life-threatening condition. For example, acute stroke is often complicated by pneumonia, and this is sometimes a fatal development. For such deaths, the underlying cause might have been coded as stroke in one era, but as pneumonia in another. On this website, mortality rates for specific causes are for what was considered to be the underlying cause at the time the death was recorded.
Not all genuine trends are of major significance to the health of the population in question: some are, but most are not. The key to gauging the public health significance of a trend is to measure the absolute size of any change, referring to the scale on the vertical axis. For example, a fall in the annual mortality rate for a particular disease from 500 to 400 per 100,000 would be an even greater public health gain than a fall for another disease from 10 to 1 per 100,000 (even though it is a fall of just 20% compared with 90%), as out of every 100,000 living it would represent 100 deaths a year averted, in comparison with 9.
Provided your purpose is non-commercial, you are welcome to copy any of the graphs on this website.
Most of the graphs were produced using data from the World Health Organization (WHO) and the United Nations Population Division ('World Population Prospects: The 2010 Revision'), but apart from providing the relevant data, these two bodies had no role either in the production of the graphs or in their interpretation on this website. For all of these graphs, an appropriate acknowledgement would be:
'From www.mortality-trends.org, using WHO and
UN Population Division data'
For the few graphs based on data from other sources (eg, the Human Mortality Database), an appropriate acknowledgement is shown below the relevant image.
Under no circumstances will permission be granted for any of these graphs, or information related to them, to be used for commercial purposes. This website is provided as a public good.
Definitions of the different causes of death are given here.
Most of this website's graphs show time trends in mortality rates. For each calendar year, a mortality rate was calculated as the number of deaths divided by the number of people in the relevant population, and then multiplied by 100,000. The relevant population was all those people who would have been counted among the deaths had they died in that year. Thus, the population is only of males if the deaths were male, and only of people aged 60-69 years if the deaths were at this age.
Mortality rates were standardised for age by taking the unweighted mean of the component 5-year rates. For example, the rate for age 60-69 years is the unweighted mean of the rates for 60-64 and 65-69 years. (A slight exception is for age 1-14 years, where age 1-4 receives only four-fifths weighting compared with ages 5-9 and 10-14.) All-cause mortality rates standardised in this way can be used to calculate the probability of death in a particular age band, as follows: P = 1 - e-Y.R/100,000, where Y is the number of years in the age band, R/100,000 is the age-standardised mortality rate expressed as annual deaths per 100,000, and P is the probability that a person at the very start of the age band would die before reaching its end if R were to obtain throughout the whole period. For example, the age-standardised mortality rate for UK men at age 50-59 in 2005 was 593 per 100,000 (actually a smoothed rate—see below—but the principle broadly holds nonetheless), so the probability of a 50-year old man dying before 60 was, at 2005 mortality rates, 6%. The corresponding probability for UK women was (based on a rate of 384 per 100,000) 4%. Half a century earlier the probabilities were, respectively, 13% and 7%. Mortality rates for the first year of life were not age-standardised because this age band is extremely narrow.
The mortality rates were then smoothed before being plotted. The smoothed rate for a particular year is a weighted average of the rate in that year (weight = 3), the preceding year (weight = 2), and the year before that (weight = 1). The smoothed rate for a particular year was not calculated if any of these three components was missing. (This is why, for example, graphs start plotting at 1952 if the first available data were for 1950.)
A: The graphs were produced by Gary Whitlock using a computer programme he wrote in VB.Net. Jill Boreham made many practical suggestions relating to the methods of analysis, and Richard Peto provided the core idea of focussing on mortality rates that are age- and sex-specific.
'The effects of real enthusiasm and real determination are incalculable. In the realm of the just possible they are sometimes decisive.'
—Michael Frayn, Copenhagen (1998)
A: Gary Whitlock designed, compiled and maintains the website. Jill Boreham made numerous useful suggestions about content and formatting, and Ben Cairns came up with the idea for a 'Jump' button on the Random graph page.
A: Mortality trends that are not for particular ages generally conceal important differences between trends at different ages.
A: The age groups correspond reasonably to what may be considered the main phases of life, and each group tends to have its own, distinctive, mortality pattern. For example, the most common causes of death in 'middle age' (on this website taken to mean the age range 35-69 years) remain largely the same throughout middle age, but are quite different from those in early adulthood (15-34 years). On the Choose a graph and Random graph pages, however, no information is given for deaths after age 79 years because information about cause of death becomes less reliable at advanced ages.
'Questions remain—in what sense is anything separable? What is it that is separable?—but it is the job of first philosophy to answer them.'
—Aristotle, Physics (about 330 BC) [transl. Robin Waterfield, 1996]
A: As with age, mortality trends often differ so much between males and females that merging these distinct groups conceals more than it reveals.
A: The choice is entirely arbitrary.
A: The main sets of graphs have been restricted to causes of death that are (or were) at least moderately common, and for which time-series data generally extend back several decades. The usual constraining factor is how mortality data were coded and aggregated in the ICD-7, -8 or -9 eras.
'Changing knowledge and subsequent change in nomenclature lead to irreconcilable discontinuities in the quantification of
mortality from various diseases over an extended period of time.'
—PD Sorlie and EB Gold, American Journal of Public Health (1987)
A: These 40 countries and territories have provided WHO with mortality data that can be adapted to standard ICD and WHO formats, are of at least moderately high quality (see article by Mathers et al. in the Bulletin of the World Health Organization, 2005; 83: 171-7), and extend back several decades. In the absence of any of these criteria, the main type of graph on this website would frequently be uninformative or misleading. As of 2012, few (if any) other countries met all three of these criteria.
'There is no national science, just as there is no national multiplication table; what is national is no longer science.'
—Anton Chekhov, Notebooks 1894-1902
A: Having this degree of flexibility, while at the same time ensuring high quality control for every potential graph, was—with the available resources—technically infeasible. Some inter-country comparisons are, however, given on the Special graph page.
A: The mortality data shared by national authorities with the World Health Organization begin in various years. (Poland's data start in 1959, but the data for 1959-65 were considered too unreliable to be graphed here.)
A: The reported number of deaths is for the specified year only (say, 2010), whereas the rate is a weighted average across that (2010) and the preceding two (2009 and 2008) years. So, if there were deaths in one or both of the earlier years, the average rate would not be 0.
A: The percentage of all deaths (ie, at any age) for the particular sex in that year.
A: The population and mortality data from WHO both exclude Singapore's large number of temporary migrant workers, whereas the population data from UNDP include this group.
A:
It randomly selects a graph from among the thousands of different graphs that can be called up
on the Choose a graph page. However, it does not select graphs for categories of residual or ill-defined causes
(eg, 'other vascular diseases', 'other respiratory diseases', 'ill-defined cause'), or graphs with especially few deaths in the particular age group.
A:
These occasional 'treats' (Easter eggs) are brief pieces of text that might be considered apt to the disease, age group or country in question.
Quotes posted in memory of particular individuals appear every time that the relevant combination of factors is selected;
other quotes appear only once every few such times, on a random basis.
Enquiries and suggestions can be emailed to: info@mortality-trends.org
@mortrends: the Twitter account for Mortality Trends
World Health Organization (WHO): a major source of data for Mortality Trends
United Nations Population Division: a major source of data for Mortality Trends
Human Mortality Database: an additional source of data for Mortality Trends
Clinical Trial Service Unit (CTSU): a medical research unit at Oxford University
Deaths from Smoking: the toll of tobacco deaths in different countries
National Obesity Observatory for England: useful information on obesity
Understanding Uncertainty: making sense of chance, risk and probability
Gapminder: animated graphs making a wide range of international comparisons
Paul McGale's astronomy images: a repository of interesting night-sky pictures
Astronomy Picture of the Day: a new astronomy image is posted each day
'The heaventree of stars hung with humid nightblue fruit.'—James Joyce, Ulysses (1922)