First, a quick visualization to get a perspective of where people live, and how many people get born and die each year. Each line represents 10 million people.
More people die each year (57m) than the entire populations of Australia and New Zealand (43m), and more than twice that many are being born each year (140m). The populations of Europe and Latin America are relatively similar (747m and 653m), and each of which is about twice the population of North America (369m), and half the population of Africa (1.3b). Asia (4.6b) makes up more than half of the total population of Earth (7.8b).
*Source: United Nations. Downloaded 21/02/2020.
Apart from the total populations of each country and region, it's interesting to note a few more factors. Population density, which measures how many people live per square kilometre, so basically, how crowded is it there. Fertility rate which indicates how many children on average are born per woman. A number of around 2 would indicate a stable population, with a larger number signalling a population increase and vice versa. Life expectancy is the number of years someone can expect to live being born on the given year and place. Median age is an indicator of how old a population is, with smaller numbers indicating younger populations and vice versa. Lastly, mortality percentage is the percentage of the total population that dies each year.
It’s hopeful that there seems to be an optimistic trend in all parameters, with life expectancy stealing the show. Despite the fact that fertility rates have been dropping in most of the world, the world’s population has soared mostly due to the impressive increase in life expectancy. We are getting more simply by getting older. And with so low median ages and high fertility rates, we can expect much more growth coming in the following years from Africa.
Half of the following statements are false. Find out which by interacting with the visualisation dashboard below.
Attributing a single cause of death for each death happening is a useful simplification that allows us to compare causes of death and get a bird’s eye view of what is killing us. But it also hides a lot of what is going on. Does a HIV patient who contracts viral Hepatitis die from HIV or Hepatitis? Does a cancer patient who contracts the flu, which then develops to pneumonia die from cancer, the flu, or pneumonia? Does a driver having a fatal road accident while under the influence of alcohol die from the car crash, or the alcohol? Does a child who is severely underfed die from malnutrition or from poverty? To navigate this landscape, we need some conventions, and the dominating one is to single out the most severe cause, the one having the greatest impact on one’s health. As a result, in this dataset, the first death would be counted under HIV, the second under cancer, the third as a transport accident (with alcohol an associated risk) and the last a nutritional deficiency (with poverty ignored as a factor).
The examples above show two important shortcomings of such datasets. The simplification of a single cause of death, and the disregard of certain socioeconomic contributors (such as poverty). This last bit is important because big scale tragedies like poverty, famine, war, child labour and so on will not show up either as risks or causes of death. Another, is that death itself is a very narrow point of view for disease, and more broadly, suffering. Patients can live for years with ALS, decades with HIV and many overcome cancer. So this data is not a good indication of how many people get impacted by a disease (e.g. you cannot tell how many people get cancer), or how much it impacts them. Ultimately this data can only help us understand mortality.
Historical death data is nowhere close to the quality and detail of what is presented above, but you can see a rought estimate of the major causes of death for the US in 1900 below.