What does your walking say about your true age?

By Daniela Weber, research scholar in the IIASA World Population Program.

Did you ever notice that you walk faster than your friends, or are you the one lagging behind? Your walking speed doesn’t only reveal how fast you walk; it can predict survival and the “slowing down process” and – according to a recent study – it can tell how old you are in terms of your physical fitness. If you are highly educated, or work in a non-manual job, you could be up to 15 years younger when it comes to your “walking age”—the age you are supposed to be according to your physical performance.

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Your chronological age does not necessarily reflect your “walking age,” which can be much younger depending on your lifestyle.

For almost all of us, walking is necessary for our everyday lives. For instance, did you know that walking at about 1.1 meters per second (m/s) means that you can cross the street safely in time before the light changes? The decline in walking speed starts around age 60, previous research has found, and a speed of less than 0.6 m/s is associated with substantial impairments to daily life. In my new study, I investigated physical aging and particularly the walking age of older adults in England.

It’s well documented that higher socioeconomic status is positively associated, for older individuals especially, with better physical functioning, such as faster walking. My results confirmed the effects of education, occupation, and regional wealth on physical aging. For instance, a more highly educated 70 year old walked around 0.1 m/s faster at their normal pace than a less educated person of the same age. I noticed a gap of a similar magnitude comparing manual and non-manual workers, with a higher walking speed in those that worked in non-manual jobs.

Interestingly, living in a wealthier region was also associated with higher walking speed, although the difference is only minor. The positive effect of living in a wealthier area might be down to fitness as a result of daily routine. People living in wealthier, urban areas are more likely to walk to the shops or to catch public transport than their counterparts living in rural areas, where people tend to use their cars for shopping or commuting.

What does a difference of for instance 0.1 m/s mean and why is it important? I highlighted the differences by converting them into years of age. In a nutshell, less educated 70-year-olds walked on average at the same pace as more highly educated individuals who were 6.5-9 years older. At higher ages they had lost 2.5 years of walking age, which has been linked to many general health issues. Overall, the advantage of more education, non-manual work, or living in a wealthier region comprises up to 15 walking age years for men and up to 10 years for women. However, the size of the advantage declines with increasing age.

More information: IIASA Research Project: Reassessing aging from a population perspective

Reference
Weber D (2016) Differences in physical aging measured by walking speed: evidence from the English Longitudinal Study of Ageing. BMC Geriatrics, 16(1):31 [December 2016] doi:10.1186/s12877-016-0201-x

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Emission rates of VW models in Europe as high as in the USA

© kichigin19 | dreamstime.com

© kichigin19 | dreamstime.com

By Jens Borken-Kleefeld, IIASA Mitigation of Air Pollution and Greenhouse Gases Program

Earlier this week, Volkswagen admitted fraudulent software causing high exhaust emissions of nitrogen oxides (NOx) from several of its diesel car models during normal driving. That diesel cars emit many times more NOx in normal driving than their legal limit has been known for more than a decade in Europe. The surprise to me is that the enforcement of these legal limits is pursued now from the USA and not from a European authority, and that – in the face of a public outcry – the automaker admitted the same software was not only in US models.

Following this announcement, I took a second look into the on-road emission data from Europe and compared it with data collected by colleagues in the USA. We find that VW diesel cars in Europe emit as much NOx as the incriminated models in the USA, as shown in the chart for VW Golf, Jetta and Passat models model years 2009 to 2013.

emissions

On-road data US: Peter McClintock, remote sensing campaign by Envirotest Inc. for Colorado (2013). On-road data Europe: Jens Borken-Kleefeld, analyzing remote sensing campaigns by AWEL Zurich (2009-2013). Each filtered for normal driving conditions.

We measured significant differences between manufacturers, yet on the whole the gap between officially certified and real-driving NOx emissions from diesel cars in Europe has been growing. The few models with low emissions are by far outnumbered by cars with high NOx emissions. Yet, VW’s emission levels are not even the worst in class.

References:
US EPA Notice of Violation, 18 Sept 2015. http://www3.epa.gov/otaq/cert/documents/vw-nov-caa-09-18-15.pdf

Announcement by VW: http://www.volkswagenag.com/content/vwcorp/info_center/en/news/2015/09/Volkswagen_AG_has_issued_the_following_information.html

Chen and J. Borken-Kleefeld, “Real-Driving Emissions from Cars and Light Commercial Vehicles – Results from 13 Years Remote Sensing at Zurich/CH,” Atmospheric Environment 88 (May 2014): 157–64. http://dx.doi.org/10.1016%2Fj.atmosenv.2014.01.040

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Journey of your life: Demography for the demos

By Samir K.C., IIASA World Population Program

How old are you? This is the most basic demographic question about an individual, and an easy one to answer. What is the population of the world or your country? Well, many who read the news roughly know the number, about seven billion for the world and more than a billion in China and India. But when asked more detailed questions about demography, “What percentage of people are younger than you in the world or your country?” or “What’s the remaining life expectancy for you in your country and the world?” the eyes start rolling. Such questions are important because they lead to better knowledge and awareness about the population, especially the question of life expectancy.

(Photo: UN Photo/Sebastiao Barbosa)

(Photo: UN Photo/Sebastiao Barbosa)

This is why I, with my colleagues Wolfgang Fengler (World Bank), Benedikt Gross (data visualization designer), and many others, have developed a website where people can find out their respective place in the world population or the country population: population.io.  The website was launched last Saturday at the TEDxVienna.

How long will we live? Most of us in the general public do not know the answer.  But demographers and actuaries can actually project the expected date of death for populations, based on factors such as place of residence, age, and sex. Demographers use data on deaths occurring during a period and the population structure to estimate death rates. These death rates are then included in the life table calculations that show, among other details, expected number of years of remaining life given one’s place of residence, age, and sex.

On population.io, you can find your own expected death date, based on population projections and details such as where you were born, where you live, and your sex. Of course, this date is just an average with a distribution. If the remaining life expectancy for a 40-year-old is 30 more years,  that does not mean that all today’s 40-year-olds will die in 2044: roughly half will die earlier and half later. But we hope that exploring this tool will give people some insight into the world and their country’s population and their place within it.

How do we know how long you will live?
To answer this question, we use population projections. To make good population projections, demographers need information about the demographic structure, including current age and sex structure and assumptions about the future scenarios of mortality,  fertility, and migration. A “cohort component” method is then applied to calculate the future population size and structure and to obtain number of births, deaths, and migration. This method projects each cohort born in the same one- or five-year period forward in time, to replace the older cohort occupying the age. In the process some die or migrate out (population decreases) and some migrate in (population increases), while women in reproductive age groups might give birth to children, who will then enter the population as a new cohort. All of these numbers and assumptions are needed for many purposes within and outside the discipline of population studies including for a proper answer to our question, “How long will I live?”

Here’s how the calculations behind population.io work. As an example, I’ll take myself: For a male of my age,  40 years old, on average according to the current global mortality rates, my remaining life expectancy would be about 37 years. This is bit scary for me – that means as an average “global citizen, I would die at age 77. In Nepal, where I am from, my life expectancy would be a little more than one year less. However, since I will most likely live in Austria, my remaining life expectancy increases to 43 years, an increase of 7.4 years due to migration.

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On population.io, you can explore–among lots of other population data–how living in a different country would affect your life expectancy. Click to try it yourself!

Now, if I add that I belong to the highest category in terms of education, what will happen to my life expectancy? Though education is not yet included in the population.io, it turns out that that also depends to a large degree on where I live. In Portugal or Italy, a person with a university degree would have lesser advantage compared to those with lower secondary education or below (2.5 and 2.6 years more respectively) than someone living in Estonia (13.8 years more) or the Czech Republic (12.5 years), Hungary and Bulgaria (12.1 years).

What if I am a smoker? Do not exercise? These factors too play an important role in future life expectancy, and we plan to add them soon to the population.io Web site.

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

How to save lives—and money – by addressing India’s air pollution

By Erich Striessnig, Research Assistant, IIASA World Population Program
We have all heard about the terrible air pollution in India’s cities. Average concentrations of particulate pollution exceed World Health Organization guidelines through most of India, most of the time. So why hasn’t anything been done? Is it really too expensive?

In a recent publication with fellow IIASA Population Program researcher Warren Sanderson and IIASA Mitigation of Air Pollution and Greenhouse Gases Program researchers Wolfgang Schöpp and Markus Amann, we set to find out. In the study, published in the journal Environmental Science and Technology, we showed that in fact, policy reforms in India targeted at reducing emissions of dangerous fine particulate matter could save thousands of lives, and at the same time save money.

Mark Danielson

Air pollution in India exceeds World Health Organization limits much of the time, which contributes to health problems and premature deaths. Photo Credit: Mark Danielson via Flickr (Creative Commons License)

Due to their very small size, small particles released by cars, factories, and other combustion can travel very deep down into people’s lungs and cause or worsen all sorts of health issues. In Indian cities, where concentrations of these pollutants are already quite high, the expected increase in economic output over the next two decades will be accompanied by an enormous increase in air pollution, leading to a higher number of sick days or even deaths.

Both of these effects could be prevented or at least reduced if stricter regulations on emission limits – already in place in other countries – were imposed. The new study shows that if India enacted pollution controls as stringent as according to European legislation, by the year 2030, the end of the study period, up to 2.5 million premature deaths would be prevented.

So how do pollution controls save money? Healthier people are more productive because they are sick less often. People who can expect to live longer in a cleaner environment are more likely to make investments which would again create jobs and boost the economy. Our study shows that by 2030 such investments would in fact more than pay for themselves, when the economic benefits of a healthier population are considered.

So why haven’t politicians started doing something already much earlier? One answer might be that such reforms initially only produce costs, whereas the benefits typically don’t crystallize before the next elections. Hopefully, this latest scientific evidence from a collaboration of IIASA population and air pollution researchers can offer these politicians an impetus to act. Read more on the IIASA Web site.

Reference Warren Sanderson, Erich Striessnig, Wolfgang Schoepp, and Markus Amann. 2013. Effects on Well-Being of Investing in Cleaner Air in India. Environmental Science and Technology. 47 (23), pp 13222–13229 DOI: 10.1021/es402867r

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.

Healthy living in hard times?

By Raya Muttarak, Research Scholar, IIASA World Population Program

  Some rights reserved by danieljordahl on FlickrFor many years social scientists have observed a connection between economic downturns and a reduction in both unhealthy behaviors and mortality—a paradigm known as “healthy living in hard times.” One possible explanation for such counterintuitive findings was that people when people lose their jobs, have more spare time to dedicate to physical activities. Moreover, under an increased threat of unemployment, those who were still employed might limit their smoking and alcohol consumption in order to reduce their chances of being laid off. So the general agreement among social scientists has been that we should not worry too much about the impact of an economic crisis on health and mortality.

Our current global economic recession is the worst economic recession in contemporary history, however. And in a new study we found out that in fact, its consequences for health are very different from previous economic downturns. Since 2008, the number of smokers has increased substantially along with the increase in unemployed people.

In the new study, published in the journal Tobacco Control, we analyzed the effects of the current economic crisis on smoking in the United States, using  data for around 2 million people from the Center for Disease Control’s Behavioral Risk Factor Surveillance System (BRFSS) survey for 2005 to 2010. In contrast to previous economic literature, we found an estimated increase of around 600,000 smokers due to the economic crisis.

Our study took into account the increase of the US population, the pre-crisis trends in smoking prevalence and the change in the distribution of population by socio-demographic characteristics.

We did find that “healthy living in hard times” still holds true for the employed individuals. Perhaps for fear of being laid off due to insurance reasons, these people maintain their healthy lifestyle. On the other hand, once without a job, people either started smoking or relapsed, possibly because of stress related to their new economic situation.

© Alexander Babich | Dreamstime.com

In contrast to previous economic literature, the new study finds an estimated increase of around 600,000 smokers due to the economic crisis. © Alexander Babich | Dreamstime.com

Since smoking prevalence by employment status remained more or less the same as before the crisis, a sharp increase in the number of unemployed individuals consequently led to a massive rise in the number of smokers. This has counterbalanced the trend in declining smoking rate among the employed.

Two main lessons can be drawn from our findings. First of all, YES…. this time it is different. The magnitude of the crisis has substantially changed the share of those with and without a job, with the latter being much more likely to engage in risky health behaviors. This in turns reversed most of the conclusions drawn by the previous literature. Second, we should not underestimate the impact of job-related stress factors on healthy behaviors. The idea that joblessness could be seen as a holiday where someone can engage in self-empowering activities is nice and reassuring, but it is meant to fail when unemployment is essentially unexpected and unwanted.

Reference
Gallus S, Ghislandi S, Muttarak R. Effects of the economic crisis on smoking prevalence and number of smokers in the USA. Tob Control. 2013 Aug 16. doi: 10.1136/tobaccocontrol-2012-050856. [Epub ahead of print] PubMed PMID: 23956058.

About the author
Raya Muttarak is a Research Scholar with the World Population (POP) Program. She came to IIASA in September 2011. Her current research covers three broad themes: 1) educational inequality and vulnerability and adaptation to climate change, 2) immigrant integration and ethnic inequality in education, the labor market and climate change adaptation, and 3) socioeconomic determinants of health risks and behaviors. More>>

Note: This article gives the views of the author, and not the position of the Nexus blog, nor of the International Institute for Applied Systems Analysis.