Health Archives - GeoCurrents

Hispanic Vs. Non-Hispanic White Life Expectancy in Texas

Article-Grid, Health, North America, Population Geography / By Martin W. Lewis

Life expectancy generally correlates with income, but other factors also play an important role. In the United States, non-Hispanic white households earn significantly more money than Hispanic households: $74,912 vs. $55,321 in 2020 (median household income). But Hispanics outlive non-Hispanic whites. The Wikipedia article on “Race and Health in the United States” notes that “as of 2020, Hispanics Life Expectancy was 78.8 years, followed by Non Hispanic Whites at 77.6 years and Non Hispanic blacks at 71.8 Years.” The table in the same article, however, puts the figures at 78.6 for non-Hispanic whites and 82.0 for Hispanics. It also lists Hispanics as outliving non-Hispanic whites in every state except New Mexico, where the gap was only one tenth of a year.

In Texas, Hispanics can be expected to outlive non-Hispanic whites by 2.8 years. The gap between the two groups, however, varies widely by county, as can be seen in the map posted here (derived from this data source). The patterns are clear and intriguing. In the most heavily Hispanic – and quite poor – counties of south Texas, non-Hispanic whites have the advantage. In east Texas counties with proportionally fewer Hispanics, Hispanics have a decided advantage.

Some of the data in this tabulation, however, must be questioned. In Lamar County, for example, Hispanics are listed as have a “100+” life expectancy, as opposed to a non-Hispanic-white figure of only 73.7 years. Lamar, a county of roughly 50,000 residents, is 8.8 percent Hispanic (4,412 persons in 2020). I have a difficult time believing that a population this large could really have a life expectancy of over 100 years. The same table also lists non-Hispanic whites in Starr County in far south Texas as having a life expectancy of over 100 years. Non-Hispanic whites only make up 1.78 percent of this county’s population. Intriguingly, Starr County’s non-Hispanic white population plummeted from 2,449 in 2010 to 1,171 in 2020. These patterns are difficult to explain and deserve further investigation.

The Geography of Health and Longevity in Montana (and the Rest of the U.S.)

Article-Grid, County Atlas of Montana, Health, Montana Atlas, Northern California / By Martin W. Lewis

Maps of health and longevity show many of the same patterns seen on earlier maps posted in this GeoCurrents sequence. In the United States as a whole, several county-clusters of relatively low life expectancy stand out. The most prominent is in eastern Kentucky, southern West Virginia, and southern Ohio, an area mostly inhabited by Euro-Americans. Several areas demographically dominated by African Americans also post low longevity figures, including the southern Mississippi Valley and a few of the large cities that are visible on this county-level map, such as Baltimore and Saint Louis. Almost all counties with large Native American populations also have relatively low figures. Counties with Hispanic majorities, in contrast, generally have average or high levels of longevity, as is clearly visible in southern Texas. Although life expectancy tends to correlate with income, the correlation collapses in many of these areas. Hidalgo County, Texas is over 91 percent Hispanic and has a per capita income of only $12,130, making it “one of the poorest counties in the United States,” but it ranks in the highest longevity category on this map. In Camp County, in northeast Texas, the average life expectancy of white residents is only 74 years, whereas that of Hispanics is 92 years. In most Texas countries, Hispanics outlive whites. Presumably, diet and activity levels are major factors.

Life expectancy varies significantly across Montana, with some counties falling in the highest category and others in the lowest. In Montana, low figures are found in counties with large Native American populations and in the former mining and smelting counties of Silver Bow and Deer Lodge. The relatively wealthier counties of south-central Montana post high longevity figures.

The map of heart-disease deaths in the United States shows some stark geographically patterns. Rates are highest in the south-central part of the country but are also elevated across most of the eastern Midwest. Heart disease death rates tend to be lower in major metropolitan counties as well as in rural countries across much of the West and western Midwest. Many of the patterns seen on this map are difficult to interpret. Why, for example, would heart-disease death rates be much lower in western North Carolina than in eastern Tennessee? (Perhaps the obesity map, posted below, offers a partial explanation, although only by begging the question.) And why would some counties that are demographically dominated by Native Americans have very high rates whereas others, especially those in New Mexico and Arizona, have very low rates? In Montana heart disease tends to be elevated in Native American counties – and in Silver Bow (Butte). South-central Montana has low rates.

On the U.S. cancer death-rate map of 2014, high rates are clearly evident in areas demographically dominated by poor white people (central Appalachia) and poor Black people (the inland delta of the Mississippi in western Mississippi and eastern Arkansas). Low rates tend to be found in the Rocky Mountains, south Florida, and parts of the northern Great Plains. No clear patterns are evident in Montana. Gallatin and Liberty counties fall in the lowest category found in the state, yet have almost nothing in common. The small population of Liberty County, however, makes comparison difficult.

Finally, the patterns seen on the U.S. obesity map are similar to those seen on the preceding maps. The low rates found in the Rocky Mountains, extending from northern New Mexico to northwest Montana, are notable. The northern Great Plains have a higher obesity rate than might be expected based on other health indicators. Some odd juxtapositions are found on this map, with several neighboring counties of similar demographic characteristics posting very different figures. Why, for example, would Pecos County in West Texas have such higher rates than its neighbors? In Montana, it is not surprising that Gallatin County, with its youthful, outdoor focused population, has the lowest obesity rate.

Mapping Crime and Substance Abuse in Russia

Article-Grid, Asya Pereltsvaig, Geography of Crime and Punishment, Guest Posts, Health, Russia, Ukraine, and Caucasus / By Asya Pereltsvaig

In the previous post, I examined regional differences in demographic issues across Russia. As many sources note, alcoholism is one of the biggest factors contributing to low life expectancy and high rate of death from non-natural causes. In fact, Russia ranks at the top in terms of both alcohol consumption (especially by men), as discussed in detail in my earlier post. Russia and the neighboring FSU countries also top the charts in percentage of deaths attributable to alcohol. According to the World Health Organization report, such a high level of alcohol-related deaths results not only from what and how much people drink (e.g. drinking spirits causes more alcoholism and alcohol-related deaths than drinking wine or beer), but also how they drink. The WHO report includes data on what the authors refer to as “Patterns of Drinking Score” (PDS), which is “based on an array of drinking attributes, which are weighted differentially in order to provide the PDS on a scale from 1 to 5”; the attributes include the usual quantity of alcohol consumed per occasion; festive drinking; proportion of drinking events, when drinkers get drunk; proportion of drinkers who drink daily or nearly daily; and drinking with meals and drinking in public places. Some countries with high per capita alcohol consumption have low PDS scores (particularly, countries in southern and western Europe), while Russia has one of the highest PDS scores worldwide.

A combination of what, how much, and how people drink—as well as who is doing the drinking—creates the observed patterns of alcoholism within Russia as well. (The data on alcoholism and drug abuse, discussed below, comes from the Wikipedia.) The prevalence of alcoholism differs from region to region even more than other social indicators considered in previous posts. The range between the highest and lowest alcoholism levels is one of three orders of magnitude, from less than one case reported per 100,000 population in Ingushetia to nearly 590 cases per 100,000 in Chukotka. Overall geographical patterns are quite clear: the highest levels of alcoholism are registered mostly across the Far North (and some of the regions of the Far East, which despite their relatively low latitude qualify legally as the “Far North”), while the lowest levels of alcoholism are found in the Caucasus. According to the alcoholizm.com website, alcohol consumption is also the lowest in the north Caucasus, especially in Chechnya, Ingushetia, and Dagestan, and the highest in the harshest areas of the Far North/Far East (particularly in the Jewish Autonomous oblast, Nenets Autonomous Okrug, Magadan, and Kamchatka). However, they do not list the specific figures of alcohol consumption in various regions and I could not find them elsewhere, but I suspect that the quantity of alcohol consumed per capita in the northeast Caucasus and in the Far North does not differ by three orders of magnitude. Much more significant is the type of alcoholic drink being consumed: vodka is the most common choice in the Far North, whereas the Caucasus region has a long history of viniculture going back 8,000 years. Besides viniculture, the Caucasus is well-known for its venerable tradition of wine-drinking with meals, accompanied by elaborate toasts, which slows down the consumption and metabolism of alcohol. Another important factor is genetically based difficulty that people from many indigenous groups have in breaking down alcohol. This genetic propensity causes particularly high levels of alcoholism in such areas of northern Russia as Nenets Autonomous Okrug, Sakha Republic, and Chukotka.

Unlike alcoholism, drug abuse is considerably less common than alcoholism across the Russian Federation: all but four federal subjects register over 50 cases of alcoholism per 100,000, whereas none reports more than 50 cases of drug abuse per 100,000, and fewer than a dozen register over 30 cases. Geographical patterns of drug abuse are less obvious and harder to explain than those of alcoholism. Particularly high levels of drug abuse (over 30 cases per 100,000) are found in Murmansk, Chelyabinsk, Sverdlovsk, Kurgan, Novosibirsk, Kemero, Amur, and Sakhalin oblasts, and in Primorsky Krai. One common denominator is that all of these regions had a high level of industrial urban development during the Soviet period but have experienced significant economic stagnation since the fall of the USSR. One area that does not fit this generalization is Sakhalin, which is still a major area of natural resource extraction and processing. It would be interesting to know if any of our readers can think of another explanation for these patterns of drug abuse.

Interestingly, neither alcoholism nor drug abuse correlate very closely with crime rates, although there is a connection. Of the areas where drug abuse is most prevalent, only Sakhalin and Kemerovo oblasts also exhibit high crime rates (over 2500 cases per 100,000), while several others—Murmansk, Sverdlovsk, and Novosibirsk—have an average level of crime. Alcoholism correlates with crime even less, as only Magadan and Sakhalin oblasts exhibits high levels of both. A number of regions with a high level of alcoholism, particularly Nenets Autonomous Okrug and Sakha Republic, which have a large percentage of indigenous population, exhibit little crime. Other areas with high proportions of indigenous peoples, such as the Caucasus and the Middle Volga, also have particularly low levels of crime. The low level of reported crime in Tula, Ryazan, and Belgorod oblasts is perplexing, however.

When it comes to murder, eastern and especially southern Siberia is clearly more dangerous than European Russia or western Siberia. The “murder capital” of Russia is Tuva, which registered over 35 homicides per 100,000 population. Neighboring Altai Republic has over 25 murders per 100,000 population, as does Zabaikalsky Krai. Curiously, high rates of alcoholism may contribute to the elevated murder rate in such areas as Sakha Republic, Chukotka, and Jewish Autonomous oblast, but other areas where alcoholism is prevalent, such as Karelia and Sakhalin, register only slightly above-average murder rates. Similarly, the prevalence of drug abuse does not correlate with murder rates. Nor do crime rates or murder rates correlate with the level of urbanization, unemployment level, or regional GDP.

Mapping Regional Differences in Economic and Social Development in Russia—A GeoCurrents Mini-Atlas

Article-Grid, Asya Pereltsvaig, Guest Posts, Health, Regionalism, Russia, Ukraine, and Caucasus / By Asya Pereltsvaig

Generalized indicators of economic and social/human development, such as GDP per capita or HDI, typically place Russia into a medium-high category. However, such ratings overlook regional differences in economic and social development, which are highly pronounced in Russia. To examine these regional patterns, GeoCurrents has created a mini-atlas of Russia, designed using GeoCurrents customizable maps, which are available for free download. These maps examine a wide range of topics, from food consumption to alcoholism, and from crime rate to healthcare; additional maps cover issues that help explain regional patterns in development, such as the age structure and ethnic composition of the population. Unless indicated otherwise, the data comes from the Federal State Statistics Service, and refers to the year 2013. Since the data offered by the FSSS is presented in 83 Word files, one for each federal subject, we have re-organized the data into one Excel file (available for download here: Rosstat_data); some of the measures, such as the percentage of working age adults or of pensioners and sex ratios, have been calculated based on the FSSS data. Additional data comes from the “Children in Russia” publication by the FSSS, available (in Russian) here; this document, published in 2009, contains data from the preceding year. Some other data come from Wikipedia and refer to 2010 or 2013. Unfortunately, we have not been able to obtain data from a more recent date, particularly from after the annexation of Crimea in March 2014; if any of our readers know of such publicly available data, in English or Russian, please let us know.

We’ll begin by looking at two rather unusual measures of the standard of living: the availability of living space and food consumption. Although Russia is a large and sparsely populated country, the availability of residential housing has long been a problem. As can be seen from the map on the left, residents of central Russian oblasts have more living space per capita than average, with inhabitants of Tver oblast enjoying an average of 29 sq. meters (312 sq. feet) per person. The only exception here is Moscow City, where an average resident has only 19.2 sq. meters (207 sq. feet) of living space, reminding one of Mikhail Bulgakov’s lament about Moscovites written some 75 years ago: “mercy sometimes knocks at their hearts…ordinary people… only the housing problem has corrupted them…” (Master and Margarita). While residents of Northern European Russia, the Volga region, and the Far East (Chukotka, Kamchatka, Sakhalin) have fairly ample living space, the North Caucasus and most of Siberia offer an average citizen more crowded housing. Dagestan, Kabardino-Balkaria, and Chechnya have less than 20 sq. meters (215 sq. feet) of living space per capita, while Ingushetia posted the second-lowest figure in all of Russia: 13.5 sq. meters (145 sq. feet). A notable exceptions here is North Ossetia-Alania, with the figure of 26.9 sq. meters (290 sq. feet) of living space.

Similarly, several Siberian regions, such as Khanty-Mansiysk and Yamalo-Nenets Autonomous Okrugs, and Altai Republic (not to be confused with Altai Krai), have less than 20 sq. meters (215 sq. feet) of living space per capita. Particularly striking is the situation in Tuva: 12.9 sq. meters (139 sq. feet) per capita. As we shall see in subsequent posts, Tuva is found at the bottom of many development rankings. As mentioned above, the Far East overall has more residential housing per capita, although differences between, on the one hand, Primorsky Krai and Jewish Autonomous oblast, with less than 22 sq. meters (237 sq. feet) per capita, and Magadan oblast, with its ample 29 sq. meters (312 sq. feet) per person, is striking. However, in the case of Magadan, the higher availability of residential housing may be a symptom not of a higher standard of living, as one might think, but actually of a lower standard of living: since the dissolution of the Soviet Union, Magadan oblast has a significant depopulation trend, and as we shall see in subsequent posts, many other indicators of human development there paint a grim picture, which helps explains this trend.

Russia_Meat_consumption_2013

Consumption of different foodstuffs, particularly meat and dairy, which tend to be the pricier components of the Russian diet, is another interesting topic. According to Rosstat data cited in an article in Kommersant.ru, the type of food consumed in largest per capita quantity is dairy: an average Russian consumes over 200 kg (440 lbs) of it a year. (The most popular type of dairy is 3.2% milk and yoghurts.) Meat, however, takes the third place in the Russian diet: an average Russian citizen consumes 75-80 kg (165-176 lbs) of meat annually, which is less than the average annual consumption of bread and other grain-based foods. However, there are significant differences in the amount of meat and dairy consumed in different regions. For example, residents of Kalmykia consumed more than twice as much meat per capita as residents of Dagestan (114 kg vs. 40 kg). As for dairy, per capita consumption in Tatarstan is more than 3.5 times greater than Chukotka.

The geographical patterns of meat and dairy consumption can be explained only in part by economic factors, as they seem to correlate more closely with culinary traditions. For example, higher meat consumption correlates well with the presence of traditionally semi-nomadic, Turkic- and Mongolic-speaking peoples: Kalmyks (Mongolic), Sakha (Turkic), and smaller Turkic-speaking groups in Altai Republic. As can be seen from the map of ethnic composition, these regions have substantial populations of their titular ethnicities and lower percentages of ethnic Russians. But this pattern does not work elsewhere; thus, Chuvashia, Tatarstan, and Tuva also feature a significant Turkic population yet have much lower figures for meat consumption. Economic factors may play a more prominent role here. But economics does not tell the whole story either, as such high GDP areas as the three Autonomous Okrugs (Nenets, Yamalo-Nenets, and Khanty-Mansiysk AOs) and Tyumen oblast have some of the lowest meat consumption figures. I find especially perplexing the low figure in Chukotka—merely 51 kg (112 lbs) per person per year, less than half of the amount of meat consumed by an average Kalmykian—because Chukotka is both economically productive and has a substantial indigenous population, which traditionally lives on reindeer and seal.* It is much easier to explain similarly low meat consumption in Northeastern Caucasus—Dagestan (40 kg), Ingushetia (54 kg), and Chechnya (58 kg)—a region of both low GDP and a culinary tradition of supplementing meat (mostly lamb and goat, as well as poultry) with a lot of fruits and vegetables (for more on the cuisine of different parts of the Caucasus, see here and here).

As for dairy, one finds higher levels of milk consumption in (some of the) steppe regions, including Tatarstan (364 kg per capita per year), Bashkortostan (312 kg), Orenburg oblast (308 kg), parts of southwestern Siberia (esp. Altai Krai, 335 kg, and Omsk oblast, 301 kg), as well as in Sakha Republic (281 kg)—all areas where reliance on milk has been an important feature of traditional cuisine of cattle- and horse-raising semi-nomadic indigenous groups. However, milk has not been a staple for reindeer pastoralist groups: Evens, Evenkis, Nenets, Chukchi—so even today dairy consumption in their traditional areas remains fairly low. Another area which registers higher-than-average dairy consumption is St. Petersburg (315 kg) and the surrounding Leningrad oblast (293 kg), which probably goes back to the high number of dairy-producing sovkhoz (state-owned farms) during the Soviet era.

More perplexing are the relatively low figures of dairy consumption in four neighboring oblasts in north-central Russia: Yaroslavl (246 kg), Tver (243 kg), Vologda (236 kg), and Kostroma (194 kg). These regions are traditionally renowned for their specialty butter (Vologda) and cheeses (Yaroslavl, Tver, and Kostroma), so one might expect higher dairy-consumption figures. Historically, Russians produced and consumed “white” or “farmer’s cheese” but not “yellow” or “hard cheeses”, which first came to Russia from Holland with Peter the Great. According to moloko.cc website, the first cheese-making facility in Russia was opened in 1795 in Tver gubernia (now, oblast) in the estate of Prince Meschersky. The first large-scale cheese-making factory was also opened in Tver gubernia in 1866 by Nikolai Vereschagin (brother of famous artist). Cheese-making then spread to Yaroslavl gubernia, where local specialty cheeses were developed: Yaroslavsky, Uglichsky, Poshekhonsky cheeses (the latter two are named after the towns where they were first made: Uglich and Poshekhonye). In 1878, a first cheese-making facility opened in Kostroma by Vladimir Blandov; according to the Wikipedia, by 1912 Kostroma gubernia boasted 120 cheese-making factories in which a variety of cheeses, including the specialty Kostromskoy cheese, were being produced. Kostroma became an unofficial “cheese-making capital of Russia”, notes Vkusnoblog.net. What, then, explains the decline in local cheese-making and dairy consumption in this area? The answer seems to be Soviet food policy. During the communist era, regional specialty cheeses were turned into standardized recipes, mass-produced in factories all around the country, undermining the local specialization. Since the 1990s, some local artisanal cheese making has been revived, but most small local producers have not been able to complete with larger domestic factories and foreign imports. Kostromskoy and Poshekhonsky cheeses, for example, gave way to imported brie and camembert. Russia has imposed sanctions on the importation of many foreign foodstuffs, but it remains to be seen what effect these measures would have on local cheese production. (I thank Sonia Melnikova-Raich for a helpful discussion of this topic.)

The rest of this post examines figures and maps concerning healthcare infrastructure. Overall, Russia ranks very high in physician density and the number of hospital beds per capita, but quite low in nurse density. Regional differences in these indicators are quite pronounced, however, and some of the geographical patterns are rather baffling. For example, unsurprisingly, Saint Petersburg boasts the highest physician density (81.2 physicians per 10,000 population), whereas Vladimir, Tambov, Tula, and Vologda oblasts in central Russia are served by fewer than 35 physicians per 10,000. One might expect Saint Petersburg to lag behind Moscow in this measure, but the figure in Moscow City is actually much lower (68.6). This contrast is probably related to the rapid population expansion in Moscow in the last two decades, something that did not occur in Saint Petersburg (for illustrative population graphs, see here); Moscow’s health infrastructure simply could not keep up with that demands of the growing population. (Saint Petersburg also has more nurses per capita and substantially more hospital beds per capita than Moscow.) Overall, Siberia’s population is served by more physicians per capita than that of European Russia and the southern Urals, although there are exceptions: Khakassia and Jewish Autonomous oblast have fewer than 40 physicians per 10,000, and Kurgan oblast is served by merely 30.2 physicians per 10,000. Another geographical pattern that stands out is the disparity in the concentration of physicians between major cities and their surrounding oblasts (Saint Petersburg: 81.2; Leningrad oblast: 34.5; Moscow City: 68.6; Moscow oblast: 39). Sharp contrasts between neighboring federal subjects are found elsewhere as well: Vladimir oblast (33.9) and Yaroslavl oblast (58), Vologda oblast (34.7) and Arkhangelsk oblast (54.5), Volgograd oblast (48.2) and Astrakhan oblast (65.8), Jewish Autonomous oblast (37.7) and Amur oblast (60.6), North Ossetia-Alania (71.7) and Ingushetia (37.7). The high physician density in Astrakhan oblast and North Ossetia-Alania is perplexing in and of itself.

As for nursing personnel, higher nurse density (over than 130 nurses per 10,000 population) is found across the Russian Far North and in parts of the Altai region, which are generally areas of lower population density. The highest figures are found in Magadan oblast (151.3), Chukotka (151.1), and Komi Republic (146.6). In contrast, lower figures (fewer than 100 nurses per 10,000 population) characterize most of European Russia, the North Caucasus region, southwestern Siberia, and the southern part of the Far East. The shortage of nurses is experienced in federal cities (Moscow City: 97.9; Saint Petersburg: 98.4) and even more acutely in the surrounding oblasts (Moscow oblast: 76.7; Leningrad oblast: 73); Leningrad oblast has the lowest figure in all of Russia. Another area where nurses are in short supply is the North Caucasus economic region (Krasnodar Krai: 88.1; Dagestan: 82.1; Ingushetia: 77.1; Chechnya: 73.2). As with physician density, sharp contrasts are observed in some cases between neighboring federal subjects: Leningrad oblast (73) and Karelia (123.6), Samara oblast (91.7) and Ulyanovsk oblast (127.7), Zabaikalsky Krai (114.4) and Magadan oblast (151.3).

Finally, Russia ranks 3rd in the world (after Japan and Korea) with respect to the availability of hospital beds per capita; unsurprisingly, these three countries top the charts in terms of average length of hospital stays (an average Russian patient stays in hospital for 13.6 days; compare to 4.9 days in the United States). But yet again, regional variation in Russia is quite pronounced, with 149 beds per 10,000 population in Chukotka, but only 46 beds per 10,000 population in Ingushetia. The availability of hospital beds correlates somewhat with nurse density, though far from perfectly. There are more hospital beds per capita (over 120 per 10,000 population) in Siberia (especially, in Eastern Siberia and the Far East) and in parts of the European North (especially, in Nenets Autonomous Okrug and Murmansk oblast). Besides Chukotka, the highest figures are found in Magadan oblast and Tuva (both 136), and Kamchatka and Sakhalin (both 129). Lower figures (fewer than 90 hospital beds per 10,000 population) characterize much of European Russia, the Mid-Volga region and southern Urals, parts of Western and Southern Siberia, and the North Caucasus region. As with physician density, federal cities have higher figures than the surrounding oblasts (Moscow City: 85; Moscow oblast: 79; Saint Petersburg: 92; Leningrad oblast: 69); however, even the two cities do not boast particularly high figures. As with the other healthcare indicators, sharp contrasts are found between neighboring regions, such as Lipetsk oblast (79) and Oryol oblast (101), or Altai Republic (80) and Tuva (136).

Overall, it should be noted that the per-capita healthcare infrastructure does not correlate with the region’s GDP.** For example, physician density is expectedly high in richer federal cities and in Chukotka, but it is fairly low in other high GDP areas, especially in Nenets Autonomous Okrug. Similarly, nurse density is predictably high in Chukotka, Sakhalin, Yamalo-Nenets and Khanty-Mansiysk Autonomous Okrugs, but surprisingly low in other high GDP areas, particularly in the federal cities and in Tyumen oblast. Likewise, the availability of hospital beds per capita is unsurprisingly high in such rich regions as Chukotka, Sakhalin, and Nenets Autonomous Okrug, but low in others, especially in Tyumen oblast and Khanty-Mansiysk Autonomous Okrug. Nor is there a close correlation between these three indicators. For instance, federal cities are characterized by a high level of physicians per capita but few nurses; conversely, there are few physicians but many nurses in Kurgan oblast. Similarly, there is no correlation between the numbers of nurses and hospital beds per capita: for example, Khanty-Mansiysk Autonomous Okrug and Altai Republic have more nurses than hospital beds (1.83 and 1.69 nurses per hospital bed, respectively), whereas in Primorsky Krai and Tomsk oblast there are fewer nurses than hospital beds (0.83 and 0.93 nurses per hospital bed, respectively).

____________

*Figures for meat consumption refer to “meat and meat products, including offal of category II and raw animal fat”. According to the Wikipedia, “offal of category II” includes heads (without tongues), feet, lungs, ears, pigs’ tails, lips, larynxes, thyroid glands, esophagus meat, and stomachs. Tongues, livers, kidneys, brains, hearts, beef udders, diaphragms, and beef and mutton tails are considered “offal of category I”.

**Of course, quantitative measures of health infrastructure say nothing about its quality. Much has been written (especially, in Russian-language blogosphere) about the pitiful state of many Russian hospitals. Recently, two lethal incidents that happened in the 2nd city hospital in Belgorod have brought this point home. In the first incident, a doctor pounded a patient to death; a video of the incident caught on security camera is rather difficult to watch. Two weeks later, an 84-year old patient fell from a 4th floor window of the same hospital; whether he committed suicide, or was pushed, or whether it was an unfortunate accident remains to be seen.

Ultimate Hypocrisy?: Indoor Marijuana Growing and the Environmental Movement

Article-Grid, Economic Geography, Environmental Geography, Health, Northern California / By Martin W. Lewis

Imagine if you will an alternative world in which the leaders of one of our most reviled industries – say tobacco – had just figured out a new way to marginally enhance the quality of their product while significantly boosting their profits, but at a gargantuan cost to the environment. In this hypothetical universe, tobacco researchers discovered that they could produce slightly more refined smoking material in high-tech growing factories than they could in outdoor fields buffeted by unpredictable weather events. This new production system proved additionally profitable by abolishing the traditional cropping cycle in favor of monthly harvests, eliminating the headaches associated with long-term storage and annual planning. But the key issue was that of leaf quality and aesthetics, as in this universe the social stigma against tobacco use was rapidly declining, and well-heeled, trendy smokers and tobacco retailers had become obsessed with excellence, wanting to deal only with the most premium grades. As it turned out, the tobacco harvested in the new antiseptic grow-ops was slightly smoother, slightly more potent, slightly less contaminated by organic impurities, and significantly more uniform than that grown under the sun. In this parallel world, even the finest Dominican cigars were now occasionally reviled as dirt-grown trash, scorned by the most posh smoke shops.

Imagine as well that a number of U.S. state governments were encouraging this development, in part to bolster their own coffers. Indoor tobacco could be grown in states that were climatically marginal or inappropriate for outdoor cultivation, and it could be overseen and taxed at every stage of the operation, from the cloning of tobacco plants, to the processing of cigarettes and other nicotine products, to the final retail sale in a limited number of closely regulated, state-sanctioned shops. And to protect their revenue streams, such states were even able to ban the importation of outdoor tobacco from both other parts of the country and foreign lands.

In this alternative world, as in ours, the environmental consequences of such a tobacco transformation would be huge. The growing facilities would have to replace sunlight with high-intensity artificial illumination, sucking energy with abandon and generating in the process a mammoth carbon footprint. And lighting would be only one of several energy demands in this brave new world of high tech farming. Extensive ventilation and dehumidification systems would be needed as well, as would air conditioning in the summer months. Many tobacco growers would even artificially ramp-up carbon dioxide concentrations to enhance plant growth, with much of the added gas leaking into the atmosphere. These agro-factories would be as far removed from organic faming as possible, with virtually all plant nutrients supplied through chemical means. And although clean-room status would be the goal, insects and other pests would sometimes get through the defenses and would then multiply geometrically, given the absence of predators. As a result, heavy applications of biocides would be periodically necessary.

The resulting production system would of course produce expensive tobacco, unaffordable by the financially disadvantaged. As result, a market for a lower-grade product would persist. But recall that in this imagined scenario, a number of states had essentially outlawed cheaper tobacco grades through regulations, prohibitions, and rigorous taxation regimes. Illegal production would therefore spring up to meet the low-end demand. Mexican drug cartels, noted for their brutality and environmental disregard, would step into the resulting gap. Some low-quality tobacco would be smuggled across the southern border of the U.S., in operations that went hand-in-hand with heroin and cocaine trafficking as well as with extortion, kidnapping, and mass-murder. The same cartels would also establish clandestine tobacco farms in the U.S., tucked away in national forests, private timberlands, and other remote locales. Here they would be joined by a number of local, renegade mass-producers. Worked in part by exploited, undocumented immigrants, these outdoor tobacco “grows” would pollute streams with agricultural chemicals and human waste, and litter the landscape with plastic tubing, growing containers, and the basic garbage of human existence. As a result of these growers’ paranoia and vigilance, merely hiking through these areas would become a dangerous and potentially deadly activity. More troubling still, these farms would use copious amounts of rodenticide to extirpate tobacco-gnawing wood rats, which would in turn devastate the populations of small carnivores, pushing some, such as the fisher (Martes pennanti), to the brink of local extinction.

The environmental consequences of this tobacco transformation would be fairly obvious, but not to their full extent. Imagine, however, investigative journalists from publications such as Mother Jones running damning exposés (see here and here as well) that outlined in some detail the damage imparted by both indoor and large-scale, illicit, outdoor tobacco growing. One report demonstrated that in California alone, the factory-farming of tobacco accounted for nine percent of the state’s household electricity consumption in early 2014, and that nationwide the industry used the output equivalent to that of seven large coal-burning or nuclear power plants. Imagine as well that this industry was steadily expanding not just in California but in other states as well, many of which were climatically unsuitable for outdoor tobacco cultivation. As a result, state energy planners were beginning to wonder where all of the extra electricity would come from, and were therefore contemplating the construction of new power facilities.

In such a world, one could well image the resulting outrage of not just the environmental community, but also that of all advocates of responsible government and rational public policy. 4,600 pounds of carbon dioxide released for every pound of tobacco produced, and for what end? So that tobacco connoisseurs could enjoy a slightly more refined smoking experience? So that tobacco companies could avoid the need for annual planning? For this we would be willing to devote the entire output of seven—and counting—major power plants?

This entire scenario is, of course, ludicrous beyond all measure. As a result, indoor tobacco cultivation would be a non-starter, and even if it were somehow able to gain traction, it would arouse the immediate and overwhelming opposition of every green organization in existence, as well as that of a great many other powerful pressure groups. The alternative reality that I have sketched out above, in short, makes no sense, and thus would thus seem to be unimaginable.

Or is it? As my title indicates, all that one has to do is substitute “marijuana” for “tobacco,” and the bulk of this post describes the actual situation currently existing in California, Washington, and several other U.S. states that have partially or fully legalized the consumption and sale of cannabis. There are, of course, limits to this comparison. The legal environments of tobacco and marijuana remain distinctive across the country, and I do not intend to imply that the two products are in any way equivalent. The existing evidence, for example, indicates that cannabis does indeed have a variety of legitimate medical uses, whereas the idea of “medical tobacco” is hard to take seriously. (To be sure, tobacco can have therapeutic and perhaps even prophylactic effects for such diseases as Parkinson’s, but almost all medical authorities insist that the product’s harm greatly outweighs any of its potential benefits.) I could have constructed my hypothetical alternative around any other highly valued crop, particularly those that have a major snob-appeal factor. I picked tobacco largely for its shock value; where I live, there is a significantly greater social stigma attached to tobacco smoking than there is to cannabis consumption, and as result the absurdity of my little thought experiment is duly intensified.

The widespread antipathy to tobacco in U.S. environmental circles and among the political left more generally would ensure that any major expansion of its carbon footprint would generate massive opposition. But when it comes to marijuana, the situation could hardly be more different. Over the past several years, I have noticed no evidence of any concerted resistance among major environmental groups to the burgeoning indoor marijuana industry, and very little to environmentally destructive, large-scale cultivation carried out on remote lands.

To see if such seeming lack of concern is indeed the case, I examined the websites of a number of well-known environmental groups, searching under such terms as “indoor marijuana,” and “cannabis.” Some of my findings are available in the screenshot images posted here. As can be seen, no results were returned from the Audubon or the Greenpeace sites. The Natural Resources Defense Council highlighted an article about eco-friendly hemp clothing, as well as several warning about the dangers of indoor pollution stemming from marijuana smoking at home. 350.org, an organization wholly devoted to fighting greenhouse gas emissions, would almost appear to advocate indoor cannabis cultivation; although its website contains no articles on the subject, it does run a number of search-linked advertisements for “grow lights” and “professional grow rooms.” The Sierra Club website, on the other hand, does bring up a significant number of articles. But as can be seen from the screenshot posted here, most of them concern weeds and pots rather than weed or pot, and indoor toilets rather than indoor grow-ops. The one result that does appear pertinent on first glance, marked with a red arrow on the image, turns out to be a red herring, as the article in question is actually about Massey Energy in West Virginia, a much more conventional Sierra Club target. When it comes to the massive energy consumption and colossal carbon footprint of indoor marijuana growing, an article in the Seattle Times sums up the situation nicely: “Leaders at other environmental groups such as the Sierra Club and Conservation Northwest say they have other priorities.”

The one prominent environmental organization that does appear to be concerned about the negative effects of certain forms of cannabis cultivation is the Center for Biological Diversity, as can be seen in another screenshot posted here. It appears that the Center worries only about those problems associated with outdoor cultivation, but that focus seems appropriate, given its mandate. Some local branches of the Sierra Club have also taken up this issue, with the Redwood Chapter describing large-scale illegal cultivation as “an Environmental Plague on the North Coast.” The Humboldt branch of Earth First!, on the other hand, appears to be completely unconcerned, despite the fact that it is situated at the epicenter of environmentally destructive, large-scale, outdoor grows, and despite the fact that the organization as a whole claims to brook “no compromise in the defense of Mother Earth.”

None of this, it is essential to note, should be taken as an indictment of marijuana growing per se. Cannabis is a hardy plant that thrives in a wide array of climatic conditions, although the most premium grades do require relatively low humidity levels during the crucial September-October maturation period. Most importantly, almost all the power that is needed for marijuana growing flows naturally from the sun. The small-scale growers whom I have interviewed never use insecticides, rodenticides, or any other toxic chemicals, and they strive to keep their footprints, carbon and otherwise, as small as possible (more on that in a later post). But they get no credit whatsoever for any of these efforts, either from the marijuana market itself or from the environmental community and its political allies. Here the paradoxes run deep indeed.

The burning question, of course, is that of why: why would green organizations turn a blind eye to this huge, rapidly expanding, and entirely unnecessary source of environmental degradation? Anti-environmentalists would likely respond by claiming that this is yet more evidence that the environmental movement is not what it claims to be, as its true goal is the dismantling of global capitalism rather than the protection of the atmosphere or of nature more generally. I do not, however, think that this is the case, as will be explained in the next GeoCurrents post.

Notes: In Washington, one of the first U.S. states to legalize recreational marijuana, legal growing can be done outdoors, but all sources that I have found maintain that the vast bulk of the legal crop is cultivated indoors. For the source of the garbage photo posted above, see this LA Times article. Note also that the statistics cited by Mother Jones and other sources are debated, but whatever the actual numbers are, it is clear that they are far from trivial.

Circumcision Quandries in Zambia

Article-Grid, Gender, Gender News, Health, News Map, Sub-Saharan Africa / By Martin W. Lewis

Public health officials have been urging circumcision on men in sub-Saharan Africa, arguing that the universal application of the practice could prevent two million HIV cases a year. A recent study in Zambia, however, shows that roughly a quarter of newly circumcised men resume sexual activities before they have fully healed, facilitating the spread of the virus. As a result, health experts fear that the spread of circumcision in the region could actually increase HIV infection rates.

And the Suicide Capital of the World Is … The Republic of Mari El

Article-Grid, Cultural Geography, Health, Russia, Ukraine, and Caucasus / By Martin W. Lewis

Suicide rates vary greatly from one part of the world to another. As the first map indicates, self-killing reaches its peak over most of the former Soviet Union, and is common across Europe north of the Alps as well as in southern and eastern Asia. Suicide rates are low across most of the Muslim world (with notable exceptions in Somalia, Kazakhstan, and Iraq) and much of Latin America. But one must regard such official data with some skepticism. Where suicide is highly disparaged by religious beliefs, questionable deaths are often classified as accidental. I thus find the World Health Organization’s “0.0” figure for the female suicide rate in Egypt hard to swallow.

At the top of the suicide chart is either Lithuania or Belarus, depending on the source. South Korea, Japan, Kazakhstan, and Russia chart near the top, exceeding twenty-four self-inflicted deaths per 100,000 people per year. Japan’s rate is more than twice that of the United States, and that of Belarus is three times higher. As the U.S. has plenty of suicides itself, the elevated figures of Russia and Japan signal to many observers serious social and psychological problems.

But while the world map of suicide distribution shows significant geographical variation, it actually under-reports it by at least a factor of two. If gargantuan Russia were divided into its first-order constituent units, we would see figures as high as sixty-six per 100,000. The world’s “suicide capital” is not Lithuania or Belarus, but rather the Russian internal republic of Mari El (population 728,000). Mari-El’s neighboring republic of Chuvashia also suffers extremely high rates of self-killing. Nowhere else in the world, evidently, is suicide as common as it is in this part the Volga basin, a fascinatingly diverse and historically significant region that tends to vanish into the undifferentiated Russian vastness.

High suicide rates in the middle Volga seem to be rooted in the region’s cultural background. In regard to the Mari, religious practices are often blamed. Mari El is the last redoubt of animism in Europe, and Christian critics have linked the prevalence of suicide to “pagan” beliefs and practices. As Geraldine Fagan reported in 2002,

According to local Baptist pastor Timothy Gerega, Mari-El has the highest suicide rate in the CIS — up to 17 a week — which he ascribes to the strength of local paganism. “There are usually two rival groupings, each with their own kart [pagan priest], in every village,” he says. “The karts are constantly putting curses upon the other faction.” In addition to prayer gatherings, [Mari anthropologist Nikandr] Popov admits, traditional Mari pagan practices include magic healing and witchcraft.

Such explanations, whatever their merit, cannot hold for the Chuvash, who are have been largely Christian for generations. According to Mark Ames, non-religious cultural traditions provide the key:

Historically, suicide has always played a key role in Chuvash culture. Until a century or so ago, the ultimate form of revenge a Chuvash could take on his enemy was to go into his enemy’s courtyard and hang himself on his doorstep. In the morning, said enemy would open the door and see the avenging Chuvash hanging there, neck snapped, tongue hanging out, eyes bulging. The living lose. Game over: … the surviving Chuvash would never recover from the shame, while the dead, suicidal Chuvash would live on as a man of honor and integrity, a real fighter.

The Chuvashian practice of killing one’s self to spite one’s enemies might seem culturally incomprehensible to most readers. Still, there is a universally recognized power in self-destructive protest. Egypt may have one of the lowest rates of suicide in the world, but recent weeks have seen a spate of Egyptian self-immolations, as young men follow the example of Mohammed Bouazizi, the Tunisian youth whose flaming end sparked demonstrations that took down a regime. As a result, Muslim clerics across Egypt have been denouncing self-sacrifice as un-Islamic, and one hard-core fundamentalist (Salafiyya) leader has gone so far as to assert that, “Whoever tries to commit suicide ‘Tunisian style’ to motivate Egyptians to revolt is a heretic,” destined to Hell. Such religiously inspired denunciations, however, have not yet put an end to politically motivated self-burning across the Arab world.

Vaccination, HIV Awareness, Contraception, and Literacy in India

Article-Grid, Cultural Geography, Economic Geography, Education, Gender, Health, South Asia / By Martin W. Lewis

Our final post on social development in India takes on a miscellany of indicators. The first map, showing vaccination, is notable for extreme variability, with the rate varying from 81 percent in Tamil Nadu to 21 percent in Nagaland. As expected, the center-north lags well behind the south and far north. Low rates of vaccination here are a concern, as the area is one of the world’s few remaining reservoirs of the polio virus. New immunization campaigns, however, are underway. Also notable are the very high rates of vaccination in the southeast (Andhra Pradesh and especially Tamil Nadu), and the fact that West Bengal for once outpaces Punjab, Maharashtra, and Himachal Pradesh. Clearly, the various aspects of social development advance unevenly across the states of India.

The second map, charting women’s awareness of the HIV virus, also shows pronounced variability while conforming more closely to the typical pattern of development. Of particular note are the high levels of awareness in the northeastern states of Nagaland, Manipur, and Mizoram, generally poor areas hampered by insurgency and underdeveloped infrastructure, yet nonetheless undergoing pronounced cultural modernization. Owing to widespread outreach programs, HIV awareness has been increasing across India over the past five years. In July of 2010, a train dedicated to AIDS education streamed across northern India. According to one report, “Thousands of people from villages and towns in Assam turned up to see what the seven-coach ‘Red Ribbon Express’ train had to offer, as it chugged across the remote north eastern state earlier this month.   The train, which has counseling and medical services, and a troupe of artists on board, is traveling across India to sensitize people about HIV.”

The third map, depicting modern contraceptive use, yields a few real oddities. Note the relatively low rates of contraception in Kerala and Goa, which are well known for their below-replacement fertility levels and strikingly high levels of general social development. The fact that roughly a third of Goa’s inhabitants are Catholic may influence this figure. In general, however, religion is not a good predictor of contraceptive use. India’s three predominately Christian (Protestant) states – Mizoram, Nagaland, and Meghalaya – have some of the highest and lowest rates of modern contraceptive use.

The final map, depicting literacy, is perhaps the most important of all. Here Kerala and Mizoram really shine, as does Himachal Pradesh in the northern Himalayan belt. Assam and Madhya Pradesh have surprisingly high figures, but the most unexpected feature of this map is the low showing of both Karnataka and Andhra Pradesh, seats of India’s most important information technology (IT) hubs, Bangalore and Hyderabad. Despite major investments, both states contain pockets of entrenched poverty and illiteracy, lagging well behind Tamil Nadu and Kerala in across-the-board social development. Some of the IT magnates of southern India, along with the country’s Human Resources Development Ministry, think that a soon-to-be-released $35 computer will help address the problem. “The hope is that an affordable computer will allow more students of all ages to engage in today’s digital world, increasing the country’s standards in education and also spurring economic stimulation.”

Tomorrow’s post will conclude our exploration of Indian development.

The Indo-European Controversy: Facts and Fallacies in Historical Linguistics	Diversity Amid Globalization: World Regions, Environment, Development (6th Edition)
The Myth of Continents: A Critique of Metageography	Green Delusions