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Environmental factors play a surprisingly large role in the disease burden with which humankind must cope.
In 2012, 12.6 million deaths worldwide (22.7% of all deaths) were attributable to environmental causes, as were 596 million disability-adjusted life years (DALYs) (21.8% of all DALYs)*. (Figure 1) So says a report issued in 2016 by the World Health Organization. I reviewed some of the report’s findings in the previous post. In this post, I turn to its findings regarding specific disease conditions.
The report authors found that 13 types of diseases or disease groups had the highest preventable disease burden from environmental risks. Figure 2 shows the raw number of DALYs attributable to environmental factors by disease group. Cardio-vascular diseases account for the largest disease burden worldwide, causing 119 million DALYs in 2012, some 60% more than unintentional injuries, the second largest category. Road injuries were counted separately from unintentional injuries, however. I suspect that road injuries are mostly unintentional (though road conditions and driving habits may sometimes argue otherwise). If you combine the two categories, then accidents account for 105 million DALY’s just slightly less than cardio-vascular diseases.
Figure 3 shows the same data, but in a different way. For each of the disease groups, it shows the percentage of total cases that can be attributed to the environment. Thus, 57% of all diarrheal diseases can be attributed to environmental causes, the highest fraction. Fifty percent of all unintentional injuries have environmental causes (I think this means that they can be attributed to unsafe conditions that could be remedied, like working in the diamond mines of the Ivory Coast).
Many of the conditions shown in Figures 1 and 2 are disease groups. Looking at specific individual conditions, the report found that fully 76% of fires and burnings could be attributed to environmental conditions, as could 73% of drownings and 57% of diarrheal diseases. The data start to sound almost like public safety or public health issues, as opposed to what we typically think of as “environmental” here in America. And perhaps, in many parts of the world, that is exactly right.
The authors make recommendations regarding which environmental interventions they thought would be most likely to significantly reduce the burden of environmentally caused disease for each of the 13 disease groups. The recommendations are shown in Figure 4. For those conditions most relevant in the United States (cardio-vascular disease and cancer) it is interesting to see that, along with second-hand smoke, household and ambient air pollution were thought to be important. I’ve discussed the progress Missouri has made in improving its ambient air quality several times, most recently here. We often ignore indoor air quality when we discuss air pollution, however. I don’t know how you would measure it across millions of buildings, but it is a very important environmental issue. If anybody knows about studies of indoor air pollution across Missouri or across the USA, please let me know.
I’ll try to bring all of this home to Missouri a little bit in the next post.
*Disability-Adjusted Life Year (DALY). Disability-adjusted life year is a measure used to estimate the number of years lost to early death, combined with the number of years lost to disability. To determine the number of years lost to death for an individual, subtract the age of death from the normal life expectancy. The result represents the number of years lost to death. For disabilities, subtract the age at which the disability occurred from normal life expectancy, then multiply the result by a “disability factor,” which represents the severity of the disability. The result represents the years lost to disability. Add the years lost to death and the years lost to disability, and you have the disability-adjusted life years (DALYs) for that individual. Do this calculation for every individual in the group, and sum the results across the group, and you have the DALYs for the group.
Prüss-Ustün, A., J. Wolf, C. Corvalán, R. Box, and M. Neira. 2016. Preventing Disease Through Health Environments: A Global Assessment of the Burden of Disease from Environmental Risks. WHO Press: Geneva. Downloaded 5/3/2017 online from http://www.who.int/quantifying_ehimpacts/publications/preventing-disease/en.
Wildfire statistics for Missouri confirm how different wildfire is here compared to wildfire in the national parks and forests of the Northern Rockies.
Wildfire statistics for Missouri are kept by two agencies: the National Interagency Fire Center (NIFC), and the Missouri Department of Conservation (MDC). Let’s look at the NIFC data first.
Most wildland fire in Missouri is either started by humans or requires human intervention to spread significantly (see previous post). The NIFC data calls fires that were not prescribed “wildland.” Figure 1 shows the number of square miles burned by wildland fire (blue) and prescribed fire (red). I have dropped linear regression trend lines on the data (the dashed lines).
You can see that the number of acres burned in wildland fires has varied widely, from a minimum of 1,660 in 2013 to a maximum of 55,395 in 2011. The number of acres burned in prescribed fires has also varied widely, from a minimum of 6 in 2003 to a maximum of 95,268 in 2009. In contrast to much of the rest of the country, Missouri does not appear to be experiencing an increase over time in the number of acres that were burned in wildland fires – the trend is basically flat. For acres burned in prescribed fires, however, there was a significant increase until 2011, and since then the number of acres has slowly decreased. Over the 13 years with data, wildland fires burned an average of 24,209 acres per year, and prescribed fires burned an average of 38,078 acres per year. Thus, for every 2 acres burned by wildland fires, more than 3 were burned by prescribed fires.
The number of acres burned by wildfire in Missouri is somewhat lower than in many western states. This year, 4 wildfires burning in Wyoming each burned more than 20,000 acres, and the Maple Fire by itself burned 45,425 acres. In California this year, the Soberanes Fire burned 132,127 acres.
The fire data from the NIFC includes fires managed by federal agencies (in Missouri principally the National Park Service, the U.S. Fish and Wildlife Service, and the U.S.Forest Service). It also includes a subset of fires managed by state agencies, although what is included in the subset is not clear. MDC’s data seems to come from fire reports by local and regional fire departments. Those reports appear to be voluntary, and I couldn’t find any guidance about what the local departments file reports on. I did notice that the data included reports from departments that were the primary responders to fires, and from departments that were assisting responders to fires. Thus, there could be duplication in the data, as well as inconsistencies from year-to-year in the participating departments. It is also unclear what lands are included (state lands? private lands? developed lands? undeveloped lands?). For these reasons, I can’t use MDC’s data to indicate either the absolute number of fires in Missouri, nor their trend over time in acreage burned. The data do indicate the cause of the fires. Despite the possible inconsistencies in the data, it seems to me that they can be used to give a rough indication of the causes of fire in Missouri, especially if summed over a number of years.
Figure 2 shows the percentage of Missouri fire from each cause from 2006-2015. The largest category is Unknown. After that, however, the largest category is Debris. This is where somebody burns something – a pile of brush they cleared from their land, some construction waste, etc. – and the fire escapes. The next largest category is Arson. Lightning accounts for only 1% of the fires counted by MDC. Dry lightning is common in the West (lightning from a thunderstorm that drops no rain), and it accounts for about 2/3 of western wildfires. It is rare in Missouri, however. In addition, Missouri’s overall climate is wetter and more humid. A lightning strike may cause a single tree to burn, but it rarely spreads into a significant fire.
I wish that the Missouri Department of Conservation’s data included a description of what the data counts. Despite repeated attempts, the director of their fire program and I have been unable to connect with each other, so I haven’t been able to clarify it.
The next post will explore why western wildfires have become larger and fiercer in recent years.
InciWeb Incident Information System. This is a data portal. To find wildfires in Wyoming, I selected “Wyoming” in the “Select a State” data field, and clicked “Go.” Data viewed 10/31/2016 at http://inciweb.nwcg.gov.
Missouri Department of Conservation. Wildfire Data Search. Data downloaded 10/31/2016 from http://mdc7.mdc.mo.gov/applications/FireReporting/Report.aspx.
National Interagency Fire Center. Statistics > Historical Year-End Fire Statistics by State. Data downloaded 10/31/2016 from https://www.nifc.gov/fireInfo/fireInfo_statistics.html.
I’m going to be hiking in the national parks for a few weeks. I’ll start posting again September 1. Have a nice August.
I just repaired a problem on the blog: when you clicked on a chart, it was no longer opening in a full-size version. The problem seems to have affected posts back to November of last year. I first became aware of it Tuesday night, 2/9, but it may have existed before that.
I’m sorry. If you are going to be able to read and understand the charts I embed in the blog, you have to be able to open them at full size.
If ever you can’t open a chart, please let me know by leaving a comment. And that goes for any other problem with the functionality of the blog.
Hopefully it is fixed now. Thanks for your understanding.
This is the last post in my series on Drought in California. I’ve been writing the series for just over 3 months – I can’t believe it has been that long! I’ve looked at California’s climate, projections for how California’s climate might change through mid-century, California’s water infrastructure, California’s water supply, and patterns of water consumption in California. I’ve calculated the size of the water deficit that California might experience by mid-century, and I’ve looked at various ways California might attempt to cover the deficit: enacting policies to stop population growth, stealing water from the environment, diverting additional water from rivers, desalinating water, reducing agricultural water consumption, and reducing urban water consumption.
I’m not aware of anything like my analysis. If you are, I would love to read it, and I think other readers of this blog might like to, also. Please comment and let us know where to find it.
It looks to me like California faces some really difficult challenges. By mid-century, they are going to face a decline in water supply due to climate change. Put the decline in supply together with the fact that the population is predicted to grow, and the fact that they already overdraft their water, and they face a very large future water deficit. California has built an amazing water infrastructure, but there are problems associated with every possible alternative for covering the water deficit. Only a few seemed realistically possible to me: desalination, urban conservation, and agricultural conservation.
I constructed three scenarios for policies California might follow, but again, only one of them seemed realistic to me: conserving water in both the urban and agricultural sectors, desalinating enough water to cover the resulting urban demand, and diverting the remaining water resources to agriculture. This scenario would provide sufficient water to urban areas, but California would lose slightly more than half of its agricultural sector. I calculated the impacts such a scenario might have on California’s economy, and found that it would probably cause the economy to start shrinking. The result would be a recession, and eventually a depression. The impact would be worst in the agricultural sector, but it would be felt statewide.
The bulk of the projected water deficit comes from a decline in the snowpack that is projected to occur due to climate change. Obviously, if that projection turns out to be wrong, the entire analysis would have to change. Even if it holds true, it is likely to be a slow-motion train wreck. As I have been writing this series, an El Niño has formed, and El Niños are typically associated with lots of rain in California. It hasn’t happened yet, but many are hoping for a wet winter.
For my analysis, it doesn’t matter a bit. The projected 40% decline in the snowpack is a 30-year average. There will be wetter years, not every year will be as bad as this year. Thus, the problems I foresee are likely have a slow onset, except for economic effects. The economy depends on psychology, and asset prices do so especially. Psychology can (and usually does) change very quickly – ask anybody who invests in the stock market! At some point, I expect people to lose confidence in California. When? Before mid-century, but precisely when I don’t know. Until then, the economy will be okay. After that, it won’t. Everybody thinks they will be able to get out in time, but they never do. It is like being caught in an avalanche: there is no avalanche until the rocks are already sliding down the mountain. But then it is too late, and the avalanche slides down the mountain very fast!
As I said, I don’t know of any other analysis like mine, thus it has been a really worthwhile exercise. But it has been a lot of territory for one person to cover, especially someone who is neither an engineer, a water expert, nor a climate expert. Along the way I have had to rely on publicly available data sources. Some of them have been of the highest quality available, but others have been less reliable. There have been instances when data was not available, and I have had to make assumptions or “guesstimates.” Further, the analysis has sometimes had to predict how people will respond to the problems they will face. Predicting human behavior is notoriously hard to do. Yet if people respond differently than anticipated, the whole analysis will have to be redone.
All of these issues affect the quality of my analysis, and the reliability of my conclusions. The two areas most seriously affected are the calculation of the future water deficit and the calculation of economic effects. Take what I have written as an interesting exercise, but only the future will reveal what will actually happen.
Thanks for reading this long excursion away from what this blog usually focuses on. I’m going on vacation now for a couple of weeks. When I return in late October, I plan to get back to reporting on large-scale studies about Missouri’s environment.
Man, it was cold in early January! What’s with the temperature? On January 3, 2015, the temperature hit 3°F with a windchill of -8 in St. Louis. In Kansas City, it was 3°F with a windchill of -11. In Springfield, MO, it was 2°F with a windchill of -14. Meanwhile, in Chicago it was -14°F with a windchill of -27. It was even worse last year: in January 2014, the temperature in St. Louis hit -8, while in Kansas City and Springfield it hit -11 and -10°F.
Why is it so cold? Isn’t there this thing called global warming going on?
First, it is not as cold as we all may think. There may have been days of bitter cold, but there were also days of unusual warmth: for instance, in St. Louis, Kansas City and Springfield the temperature hit 60, 57, and 62°F, respectively, in January 2014. The same thing may happen in 2015 before January is over.
For Missouri as a whole, during January 2014, the average temperature was 25.9°F, only 3.9° colder than average, ranking it as the 22nd coldest January in the 120 years since record keeping began (see first chart at right). For comparison, the coldest January in Missouri averaged 13.9°F, 12° colder. For the United States as a whole, January 2014 was slightly warmer than average, at 30.54°F, the 55th warmest on record.
(Click on chart for larger view.)
For the entire world, the average temperature for January 2014 was 0.64°C warmer than the long-term average, making 2014 the 5th warmest January on record (see second chart on right). Unfortunately, the Climate at a Glance data portal does not provide actual global temperature time series, only anomaly time series.
The third chart at left shows what is really happening in Missouri. The red line shows the average temperature for January from 1885 through 2014. The dashed red line shows the trend over that time period. Overall, the average temperature in Missouri during January does not seem to be changing much.
The blue line in the chart shows the difference between the highest temperature and the lowest temperature during January. This is a measure of how variable our temperature is from day-to-day. The dashed blue line shows the trend. Overall, the difference between the high and low is decreasing slightly, but that disguises what’s really happening. During the first half of the 20th Century, there was relatively little variation between years, the highest and lowest temperatures during January didn’t differ by too much. But since the 1960s, the peaks and valleys of the blue line have spread out, meaning that some years there is less than usual variation, some years there is more than usual. Some years January is pretty “all-the-same,” and other years it is “all over the place.”
What does this mean? I don’t know. It would be a great topic for a research paper. I do know that there is an explanation for the “all over the place” years, and that will be the subject of the next post.
Source for January 2014 and January 2015 high and low temperatures in St. Louis, Kansas City, and Springfield, MO: The NOW/data database published by the National Atmospheric and Oceanographic Administration. To access the NOAA Local Forecast Office for each city, google “weather [city name]. Select the option with the URL that begins “www.crh.noaa…” On the webpage of the forecast office, in the column on the left, select “climate:local.” On the Local Climate webpage, select the “NOWdata” tab.Use the query portal to define your search. I searched for the St. Louis, Kansas City, and Springfield Areas, Daily Data for A Month, and 2014-01.
Source for Missouri and USA data: Climate At A Glance data portal, National Atmospheric and Oceanographic Administration. http://www.ncdc.noaa.gov/cag.