Showing posts with label Mortality. Show all posts
Showing posts with label Mortality. Show all posts

Monday, December 3, 2018

Assorted Links on mortality

  1. The case for monitoring life-span inequality, via José Manuel Aburto

  2. Mortality from road crashes in 193 countries: a comparison with other leading causes of death, ht Romulo Krafta

  3. The slowing pace of life expectancy gains since 1950

  4. How life expectancy in U.S. counties compares to other countries via Simon Kuestenmacher

  5. R package: mortAAR, for archeological demography. It provides functions for the analysis of archaeological mortality data See Chamberlain (2006). There is a vignette on Lifetables and an Extended Discussion

  6. R package: MortalityLaws, for downloading data from the Human Mortality Database and building parametric mortality models and life tables, by Marius Pascariu

  7. R package: MortCast, for estimating and Projecting of age-specific mortality rates, by Hana Sevcikova, Nan Li and Patrick Gerland (paper here) - via Adrian Raftery‏

  8. Beautiful Lexis-surface plot showing age and period specific mortality ratios of females and males, by Jonas Schöley (interactive version at the Human Mortality Explorer)


image credit: Jonas Schöley

Monday, January 15, 2018

Wednesday, November 8, 2017

How Pollution Compares With Other Causes Of Global Deaths

The Lancet Commission on pollution and health has recently published a report that calls attention to the fact that pollution kills 3 times more than AIDS, tuberculosis and malaria combined. There is a good summary of the report in this piece, by NPR.

This chart below was created by Brittany Mayes and Matthew Zhang for the NPR piece, and it is based on data from the Global Health Data Exchange. Thanks Linsey Marr for the pointer on Twitter.


[click on the image to enlarge it]

Saturday, August 12, 2017

An R library to analyze and map John Snow's 1854 Cholera data

As many of you will know, an English physician called John Snow mapped the cholera outbreak in the Soho district of London in 1854. That map would later be a key element in the discovery that cholera was caused by contaminated water, not air. It's fair to say this map somehow changed history not only because of the lives it helped save, but perhaps more importantly because of the ways it opened human imagination to the role of spatial analysis in science and human development. Steven Johnson has written a book about the story of this map and its influence on modern science and cities. If you are short in time, there is a great 9-minute video summary of the book here.

All this introduction to say that now there is an R library that allows you to analyze and map John Snow's 1854 Cholera data yourself. Thanks Bob Rudis for calling attention to this library on twitter. Dani Arribas-Bel also pointed out to this chapter / online notebook that presents the documented code for a reproducible spatial analysis of John Snow’s map using mostly Python. This is great material for teaching.

update 16 Aug 2017: RJ Andrews has also pointed me to this paper analyzing the mortality rates and the space-time patterns of John Snow’s cholera epidemic map.



Sunday, November 13, 2016

The world’s most violent cities

For the most part, the most violent cities are either in deep economic crisis (or long economic stagnation), with high levels of inequality or involved with drug gangs. via The Economist. You can follow their daily charts on twitter here.


Wednesday, February 3, 2016

Pedestrian fatality risk as function of car impact speed

quick post today: Mikefc has created a very nice interactive chart showing pedestrian fatality risk as function of car impact speed using multiple risk models. Some of you might find it useful, specially for those involved in discussions/studies on road speed limits.


Thursday, October 8, 2015

The hidden inequality of who dies in car crashes

Emily Badger has written an interesting piece on The Washington Post covering a recent study that discuss socioeconomic inequalities in car crashes in the US between 1995 and 2010.



Friday, April 26, 2013

Latin American Human Mortality Database

Some of you have probably heard about the Human Mortality Database. Now, Prof. B. Piedad Urdinola (National University of Colombia) and Prof. Bernardo Lanza Queiroz (Cedeplar/UFMG, Brazil) have recently started a new project with a similar idea but covering Latin American countries. It's the Latin American Human Mortality Database.

It brings tons of information and it's really well organized with easy navigation.


"At present the database contains detailed information on mortality for five countries in Latin America: Argentina, Brazil, Colombia, Mexico and Peru. All information is broken down by age, sex, region and cause of death. Additionally there is information on the academic literature on the study of mortality for these same countries."


Long live the project!