By the early 1900s, the modern diet had long been a growing concern, as it already was a topic of public debate going back a century, such as obesity and conditions like ‘nerves’. This public health issue became a moral panic with tuberculosis and neurasthenia that was linked to diet. Much of the focus was scientific study. Many vitamins and micronutrients were being discovered and researched.
Also, the industrial seed oils were being linked to ill health right from the start; although not yet understood as oxidative, inflammatory, and mutagenic. The initial observations were being made on farm animals being fed “on by-products from margarine factories”, as advised by feeding experts. It would be decades later that a mass experiment would be initiated on humans when, in the 1930s, industrial seed oils replaced animal fats as the main source of fatty acids in the American diet.
The following decades after that in the post-war period would begin the public health crisis of skyrocketing rates of metabolic syndrome: obesity, heart disease, strokes, diabetes, etc. But long before that, the health decline was already becoming apparent to many, such as Dr. Weston A. Price and Dr. Francis M. Pottenger Jr, and even earlier with Dr. Claude Bernard, Dr. William Harvey, Dr. James H. Salisbury, etc. Another example of someone on the leading edge was Dr. M. J. Rowlands.
My clinical investigations began as far back as 1912, when I installed an X-ray apparatus with the idea of trying to find out what similarity there was in the lesions amongst my cases. In the war during 1914 and 1915 stationed at Netley. The blood-cultures and joint punctures I carried out proved sterile.
Owing to ill-health I had to relinquish the Service for some time; I returned to it again in 1916 and was given the pathological charge of three hospitals of some 2,000 patients, where I could place as many rheumatoid patients for whom I could find beds, an order being posted in the London area that all true rheumatoids were to be sent to one of my hospitals. In this way I was able to accumulate some 200 rheumatoids and keep them for investigation. But with all this opportunity and all the advantages of able assistance and cordial help for over three years, until May, 1919, nothing of great value was discoverable. In 1916 I wrote a paper which was published in the Lancet1 giving the results of my investigations up to that time.
After the war I again took up the investigation of this disease chiefly owing to my farming instinct. The question of vitamins and the work of Hopkins, Funk, Plimmer and Drummond, was being published. I began to experiment with pigs, as I found that a large number of my pigs which were bred on the open-air system were from time to time suffering from marked stiffness and swollen joints. I began to feed my animals on a full vitamin diet and the result of these experiments was marvellous. There was a complete change in the condition of my herd and I decided to show my experimental animals at the largest Fat Stock Show in the world-namely, Smithfield. The result of the first time of showing was every possible prize that I could have won as well as the Cup. This gave me ample proof that in animals’ malnutrition lay the seat of investigation. In 1921 I read a paper before the Farmers’ Club at the Surveyors’ Institute discussing my experiments. Professor T. B. Wood, of Cambridge, and Dr. Crowther, Principal of the Harper Adams College, who opened the discussion, ridiculed all my experiments, and the whole idea of vitamins, and, in fact, the only member of the audience who agreed was Lord Bledisloe. To-day I think both Professor Wood and Dr. Crowther are aware of the value of vitamins and now admit their use to the British farmer. […]
I had by me all the notes of an experiment I had carried out a few years previously. Feeding experts were constantly advising farmers-and are doing so to-day-to feed their pigs on by-products from margarine factories, such as palm kernels, coco-nut, earth-nut, soya beans, etc. So I placed three pens of pigs on these foods as a test, using against them a food containing meat, yeast, cod-liver oil and a salt mixture, the carbohydrate content of the diet being the same in all the pens. Within a few weeks it became apparent that the pigs on a diet of palm-kernel and coco-nut were rapidly going downhill; and at the end of the test the pigs fed on my mixture had increased by 143 lb., and for every 1 lb. of increase in weight had consumed 2 * 62 lb., whereas the ” palm kernel pigs ” had increased only 40 lb., and for every 1 lb. of increased weight they had consumed 5 lb. The palm kernel pigs showed a vitamin B deficiency.[…]
In dealing with the deficiency of vitamin B in cases of rheumatism, Dr. Rowlands’ paper was convincing and dramatic, but the relationship between this deficiency and the various forms of rheumatism was not clearly shown. Whereas it was probably a factor in rheumatoid arthritis, the co-relation was not evident in either osteo-arthritis, with its prevailing characteristic of robustness, or in the climacteric type associated with thyroid deficiency. Possibly there were other vitamin deficiencies-an “A” deficiency and probably a “D” deficiency-concerned in the control of phosphates, […]
Rheumatoid arthritis was certainly a deficiency disease, and the deficiency was connected with the assimilation or utilization of phosphoric acid and other phosphates, so that probably vitamins B and D were often associated with it. Rheumatoid arthritis never attacked the bon viveur or the alcoholic, but was the disease of the total abstainer, the vegetarian and the careful liver. […]
An important point which none of the discussers had mentioned was the great change in our diet, not so much in our own choice of food, but in the food of the animals on which we depended so much for our own. For instance, cows used to be fed on ground oats, ground wheat, ground barley, ground rye; all these contained the essential vitamin B. To-day very few farmers gave such food to their cattle; instead, they gave cotton-seed cake, linseed cake, and all kinds of patent foods which were deficient in vitamin B, and therefore. milk was not now so good as in former days. Chickens, again, were now fed on all sorts of material, and were the subjects of intensive culture, with the result that the egg-yolk was not of the same value as formerly. Vitamin B was not an animal product, it must be supplied to the animal from some outside source.
Related to the high-fat vs low-fat debate, there is an interesting article to shake up our thinking: Study of Alaska Natives confirms salmon-rich diet prevents diabetes, heart disease. It states that, “A diet of Alaska salmon rich in Omega-3 fatty acids appears to protect Yup’ik people from diabetes and heart disease — even when the individuals in question have become obese, according to a recent study that examined eating habits and health in the Yukon Kuskokwim Delta region. […] Something was different, and it didn’t appear to be genetics. […] “Interestingly, we found that obese persons with high blood levels of Omega-3 fats had triglyceride and CRP concentrations that did not differ from those of normal-weight persons,” Makhoul concluded.” Now that is fascinating. There could be a lot going on with this population, but they do make for a useful comparison.
To begin, it should be noted that these Inuit/Eskimos are on average overweight, similar to other Americans. Yet they have some of the lowest rates in the world of metabolic syndrome and obesity-related diseases like diabetes. This is in spite of their no longer being entirely on a traditional diet. They are getting plenty of crappy processed and packaged foods, in line with the industrialized Standard American Diet (refined grains, high fructose corn syrup, seed oils, etc). And these native Alaskans are unhealthy in other ways, as obesity isn’t a good thing. But those large doses of healthy unoxidized Omega-3s from wild whole foods seem to be their saving grace. It is true that most Americans are getting too many inflammatory Omega-6s and increasing Omega-3s is already known to decrease inflammation. That is all the more reason to eat fresh cold water fish, assuming it’s wild-caught in clean waters (it’s too bad we’re overfishing the oceans). Or, failing that, supplements might be beneficial; including algae-based sources.
That might go against the argument of those like Dr. Paul Saladino who speculate all polyunsaturated fats (PUFAs) are problematic at high intake; whether Omega-6s or Omega-3s, industrial or whole, oxidized or fresh; and no matter the PUFA ratio. The argument is all PUFAs will oxidize, even in the body after consumption because the unsaturated carbon bonds are unstable in being able to pick up oxygen atoms and the body can only handle so much oxidization using its limited supply of self-produced antioxidants and dietary antioxidants. The system overwhelmed by oxidized PUFAs is unable to contain the free radicals that wreak havoc with oxidative stress. But is that excess PUFA theory true? The jury is still out on that. Even if too many PUFAs overall might still be harmful in other ways, the recent Inuit study indicates certain PUFAs maybe can’t be blamed for metabolic syndrome and such.
It would be useful to look at these Inuits’ total PUFA intake and Omega-6 to Omega-3 ratio, which determines inflammation levels. And one might wonder about a causal link between inflammation and insulin resistance. Of course, as Dr. Saladino would argue, it might be simpler to just remove all the processed carbs and industrial seed oils; rather than try to counteract the harm with more Omega-3s. But if your (carb-caused, stress-induced, etc) cravings or other factors beyond your control have compelled you to eat a health-destroying diet that has made you fat or otherwise metabolically unfit, not to mention inflamed and maybe with high LDL (a response to inflammation), then by all means glug down some Omega-3s as medicine. It is known to have numerous health benefits, at least for those on an unhealthy diet, including this other evidence for possibly preventing/reversing insulin resistance and diabetes. You might slowly die of some other dietary-related disease, but at least you’ll lessen a large swath of health problems and feel relatively better.
Dietary details and confounders aside, this study blows the anti-fat crowd out of the water, including those like Ted Naiman who argue for low-carb, high-protein, and moderate fat. This seriously challenges the claim that the carbohydrate-insulin hypothesis is dead and that it’s simply about energy excess, either carbs or fat (or both). Ben Bikman, a leading insulin expert and active researcher, still thinks the carbohydrate-insulin hypothesis is valid and his view appears to be supported or not contradicted, according to this data. But, if nothing else, this new evidence clearly keeps the debate undeniably alive and even more compelling, however it might remain unresolved in continuing disagreements. What is refuted is the sweeping declaration that all energy excess, though surely sometimes a valid factor, can apply to every form of dietary energy under all conditions and in all diets.
It really does matter what kind of fat one is eating. Then again, it also matters what kind of carbs (Dr. Saladino thinks honey might be metabolically different, a whole other contentious debate). Talking about macronutrients as general abstract categories may not always be helpful. Sure, many people can lose fat by restricting calories or particular macronutrients. Both low-carb, high-fat diets and low-fat, high-carb diets can cause some people to naturally reduce calorie intake because there is nothing that causes overconsumption like the fattening powerhouse of carb-fat combo. And no doubt one could choose to increase protein, instead. But even if one eats high-carb, high-fat diet and so unsurprisingly becomes obese, it doesn’t follow that metabolic syndrome is inevitable. In that case, the healthy fats might protect one against metabolic syndrome, even on an industrial diet. If this is confirmed, Omega-3s not only balance excess Omega-6s but also excess simple carbs.
This seems to imply the unoxidized Omega-3s from fresh wild-caught whole foods is maintaining insulin sensitivity, despite the fact that all those carbs typically would be causing insulin resistance. That is the really interesting part. The whole point of the carbohydrate-insulin hypothesis is that excess glucose in the blood eventually overtaxes the body’s capacity and throws off the hormonal system, specifically the hormone insulin but also possibly involving insulin-glucagon ratio. The hormonal system acts as locus of messaging and control for multiple other systems, including metabolism. With insulin resistance, fat simply gets stuck in fat cells and can’t be accessed. So, the individual gets hungry and eats more. Interestingly, long-term fasting can sometimes kick insulin sensitivity back in gear and so the body will start burning the fat. That mechanism described is what the carbohydrate-insulin hypothesis is all about. That is the theory that supposedly down for the count.
Maybe we need another theory. As countering the harm described by the carbohydrate-insulin hypothesis, we could call it the fat-insulin hypothesis or, to be more specific, the Omega3s-insulin hypothesis. This might relate to how certain fats promote fat-burning, specifically in terms of Stearic fat (in tallow) which is a saturated fat, the supposedly worst fat. It apparently means eating energy as this kind of fat not only increases metabolism but encourages the release of the bodies energy stored as fat. This presumably would have to include a role of insulin sensitivity, the opposite of insulin resistance. It’s true that eating lots of Stearic acid on a high-carb industrial diet while obese and metabolically unfit might not be all that helpful. As another factor, consider that wild-caught fish would be higher in fat-soluble vitamins and micronutrients. The fat-soluble vitamins play a powerful role similar to hormones. In that case, it might be a fat-soluble-vitamin-insulin hypothesis, but that is getting a bit wordy. Context, as always, is king. Obviously, we need to get away from overly simplistic generalizations. The macronutrient model is as unhelpful as the caloric model, if not combined with more detailed knowledge.
In reference to the below COVID-19 graph of loss of life and jobs (per capita), someone wrote to us that the, “Lower left would appear better [i.e., more people alive and working. BDS]. Iowa was slightly lower left, but mostly in the center of all states. Hawaii had lowest excess death rate (negative), but highest job loss. West Virginia, Maine, and Indiana were well balanced.” The graph is from Hamilton Place Strategies. It is included with their brief data analysis as presented in the recent (4/18/21) article, 50 States, 50 Pandemic Responses: An Analysis Of Jobs Lost And Lives Lost, co-authored by Matt McDonald, Stratton Kirton, Matisse Rogers, and Johnny Luo. The time period for the data is unstated, which could make a difference. That aside, most of the states clump near the center; although more states tended toward higher death toll; but, of course, it’s the outliers in the four quadrants that grab one’s attention.
We didn’t initially give it much careful thought, even though such data does make one curious about what it represents, beyond some seemingly obvious observations. Here was our initial off-the-cuff response: “It maybe should be unsurprising that the most populated states struggled the most with finding a balance or, in some cases, keeping either low.” That was tossed out as a casual comment and it was assumed no explanation was necessary. But apparently it was perceived as surprising (or speculative or something) to our collocutor who asked, “Why?” This seems to happen to us a lot, in that we are so used to looking at data that we assume background knowledge and understanding that others don’t always share. It genuinely was not surprising to us, in that ‘populated’ clearly signifies particular kinds of factors and conditions. Once committed to the dialogue, we felt compelled to answer and explain. Continue further down, if you wish to see the unpacking of background info and social context that, once known, makes the graphed data appear well within the range of what might be expected.
It seemed unsurprising to us, as we’ve looked at a lot of analysis of (demographic, economic, and social science) data like this over the years. So, we’re familiar with the kinds of patterns that tend to show up and probable explanations for those patterns. But maybe it seems less intuitively obvious to others (or maybe we’re biased in our views; you can be the judge). In the original article, the authors do note some relevant correlations indicating causal factors: “States with major hospitality and tourism sectors were hit hard in terms of job loss, with the impact falling unevenly across sectors. And states that were in the first wave of infections—when the healthcare system was still learning how to treat COVID-19—fared comparatively worse on their death tolls. New York, which falls into both categories, had the worst overall outcome, with both high excess deaths and high job losses.”
The authors go on to say, “The states that emerged in the best position were Idaho, Utah, and West Virginia, all with some combination of low loss of life and low loss of employment.” Others that did reasonably well were North Carolina, Nebraska, Maine, West Virginia, Indiana, and Wyoming. I don’t recall any of these being hit early by COVID-19 outbreaks nor are they major tourist and travel destinations, other than NC to some extent. It could also be noted that all are largely rural states, if not as rural as they were last century, but still way more rurally populated (or rather less urbanized with fewer big cities and metropolitan areas) than states that had it rough in soaring death and jobless rates: New York, New Jersey, Louisiana, etc. It comes down to a divide between more and less urbanized, and hence more and less populated and dense. That has much to do with the historical economic base that determined how many people, over the generations, have moved to a state and determined their residential location.
As for the really obvious observations, there is the typical clear divide between North and South. Many liberty-minded Southern states, with historically high rates of total mortality and work-related mortality (along with historically overlapping classism and racism), were tolerant of sacrificing the lives of disproportionately non-white workers during a pandemic, particularly when it kept the economy going and maintained corporate profits for a mostly white capitalist class (see: Their Liberty and Your Death). ln general, all of the Deep South and Southwest states, along with most of the Upper South states, had above average death tolls (with MS, AL, AZ, and SC leading the pack); whether or not they kept job losses low, although they did mostly keep them down. All of the states that sacrificed jobs to save lives are in the North (AK, RI, MN, MA, etc) or otherwise not in the South (HI), be it caused by intentional policy prioritization or other uncontrollable factors (e.g., reduced tourism). Northern industrial states, as expected, took the biggest economic hit.
As for the initial point we made, larger populations that are more concentrated create the perfect storm of conditions for promoting the spread of contagious diseases. This represents numerous factors that, though any single factor might not be problematic, when all factors are taken together could overwhelm the system during a large-scale and/or long-term crisis. That typically describes states with large cities and metropolitan areas. Look at all of the highly populated and urbanized states and, no matter what region they’re in, they are all near the top of excess deaths per capita. None of them managed to balance keeping people alive and employed, though some did relatively less worse. And it is apparent that the worst among them had the highest population density. That last factor might be the most central.
For comparison, here is the land area, population, and population density of the top 6 largest US cities, all in different states: New York City (301.5 sq mi; 8,336,817; 28,317/sq mi), Los Angeles (468.7 sq mi; 3,979,576; 8,484/sq mi), Chicago (227.3 sq mi; 2,693,976; 11,900/sq mi), Houston (637.5 sq mi; 2,320,268; 3,613/sq mi), Phoenix (517.6 sq mi; 1,680,992; 3,120/sq mi), and Philadelphia (134.2 sq mi; 1,584,064; 11,683/sq mi). New York City has about half the land as Houston and Phoenix, but has about four times the population of Houston and about seven times the population of Phoenix. So, even among the largest cities in the US and the world, there are immense differences in population density. States like Texas and Arizona have encouraged urban sprawl which, though horrible for environmental health, does ease the pressure of contagious disease spread.
This particular pattern of public health problems is seen all the way back to the first era of urbanization with the agricultural revolution when populations were concentrating, not sprawling. It wasn’t merely the nutritional deficiencies and such from change in the agricultural diet. The close proximity of humans to each other and to non-human animals allowed diseases to mutate more quickly and spread more easily (a similar probable reason for COVID-19 having originated in China with wilderness encroachment, habitat destruction, and wild meat markets). Many new diseases appeared with the rise of agricultural civilizations. Even diseases like malaria are suspected to have originated in farming populations before having spread out into wild mosquitoes and hunter-gatherer tribal populations. Even in modern urbanization, humans continue to live closely to and even cohabitate with non-human animals. This is why populations in New England, where indoor cats are common, have high rates of toxoplasmosis parasitism, despite a generally healthy population.
Plus, at least in the US, these heavily urbanized conditions tend to correlate with high rates of poverty, homelessness, and inequality (partly because most of the poor left rural areas to look for work in cities where they became concentrated) — these high rates all strongly correlated to lower health outcomes, particularly the last, inequality. Of the only four states with above average economic inequality in the US, three of them (NY, LA, CA) had all around bad COVID-19 outcomes, with only high inequality Connecticut escaping this pattern by remaining moderate on job losses and excess deaths. As expected, the states that did the best in keeping both low were mostly low inequality. Other than two in the mid-range (WV, NC), all of the other cases of COVID-19 success are among the lowest inequality states in the country — according to ranking: 1) UT, 4) WY, 7) NE, 12) ID, 13) ME, and 15) IN. All of the top 10 low inequality states were low in COVID-related mortality and/or unemployment. That result, by the way, is completely predictable as it matches decades of data on economic inequality and health outcomes. It would be shocking if this present data defied the longstanding connection.
By the way, rural farm and natural resource states tend to be low inequality, whether or not they are low poverty, but research shows that even poverty is far less problematic with less inequality — as economic inequality, besides being a cause or an indicator of divisiveness and stress, correlates to disparities in general: power, representation, legacies, privileges, opportunities, resources, education, healthy food, healthcare, etc (probably entrenched not only in economic, political, and social systems but also epigenetics; maybe even genetics since toxins and other substances, such as oxidized seed oils in cheap processed foods, can act as mutagens which can permanently alter inherited genes; and so inequality gets built into biology, individually and collectively, immediately and transgenerationally). Certain economic sectors tend toward such greater or lesser inequities, and this generally corresponds to residential patterns. But the correlation is hardly causally deterministic, considering the immense variance of inequality among advanced Western countries with more similar cultural and political traditions (party-based representative democracies, individualistic civil rights, and relatively open market economies).
The economic pattern is far different between rural states and urban states, specifically mass urbanization as it’s taken shape over the generations, and it has much to do with historical changes (e.g., factories closed in inner cities and relocated to suburbs and overseas). In big cities, many large populations of the poor (disproportionately non-white) have become economically segregated and concentrated together in ghettoes, old housing, and abandoned industrial areas (because of generations of racist redlining, covenants, loan practices, and employment). These are the least healthy people living in the least healthy conditions (limited healthcare, lack of parks and green spaces, lead toxicity, air pollution, high stress, food deserts, malnutrition, processed foods, etc), all strongly tied to COVID-19 comorbidities. In these population dense and impoverished areas, there is also a lack of healthcare infrastructure and staffing that is especially needed during a public health crisis, and what healthcare exists is deficient and underfunded.
To complicate things, such densely populated areas of mass urbanization make public health difficult because there are so many other factors as well. Particularly in American cities with immigrant and ethnic residents historically and increasingly attracted to big cities, additional factors include diverse sub-populations, neighborhoods, housing conditions, living arrangements, places of employment, social activities, etc. And all of these factors are overlapping, interacting, and compounding in ways not entirely predictable. This might be exacerbated by cultural diversity, since each culture would have varying ways of relating to issues of health, healthcare, and authority figures; such as related to mask mandates, vaccination programs, etc. It would be challenging to successfully plan and effectively implement a single statewide or citywide public health policy and message; as compared to a mostly homogeneous small population in a small rural state (or even a mostly homogeneous small population in a small urban country).
Also, disease outbreaks in big cites and metropolitan areas are much harder to contain using isolation and quarantines, as many people live so close together in apartment buildings and high-rises, particularly the poor where larger numbers of people might be packed into single apartments and/or multiple generations in a single household, and that is combined with more use of mass public transit. This came up as an issue in some countries such as in Southern Europe. Italians tend to live together in multigenerational households and tend to take in family members when unemployed. Combined with poverty, inequality, and policies of economic austerity, the Italian government’s struggle to contain the COVID-19 pandemic made it stand out among Western countries, such that it early on showed potential risks to failing to quickly contain the pandemic. But, in many ways, it might have been as much or more of a sociocultural challenge than a political failure.
On the completely opposite extreme, the Swedish have the highest rate in the world of people living alone, but also some of the lowest poverty and inequality in the world. So, even though Sweden is heavily urbanized (88.2%), contagious disease control is easier; particularly with an already healthy population, universal healthcare, and a well-funded public health system (no economic austerity to be found in Swedish social services). Indeed, they only had to implement moderate public measures and, with a high trust culture, most of the citizenry willingly and effectively complied without it becoming a politicized and polarized debate involving a partisan battle for power and control. By the way, Sweden has a national population only slightly above NYC but less than the NYC metro. Of Nordic cities, Stockholm is the largest in area and the most population dense: total density (13,000/sq mi), urban density (11,000/sq mi), and metro density (950/sq mi). New York City has about two and a half times that urban density.
Then again, all of that isolated urbanization takes it’s toll in other ways, such as a higher suicide rate (is suicide contagious?). It is one of the most common causes of death in Sweden and the highest rate in the West; in the context of Europe being one of the most suicidal continents in the world, although it’s Eastern Europe that is really bad. Among 182 countries, Sweden is 32nd highest in the world with 13.8 suicides per 100,000; compared to Italy at 142nd place with 5.5 suicides per 100,000. That is two and half times as high. But, on a positive note, COVID-19 seems to have had no negative impact in worsening the Swedish suicide epidemic (Christian Rück et al, Will the COVID-19 pandemic lead to a tsunami of suicides? A Swedish nationwide analysis of historical and 2020 data), as presumably being socially isolated or at least residentially isolated is already normalized. If anything, suicidal inclinations might become less compelling or at least suicide attempts no more likely with the apparently successful response of the Swedish government to COVID-19, especially combined with the Swedish culture of trust. Not that global pandemic panic and local pandemic shutdown would be a net gain for Swedish mental health (Lance M. McCracken et al, Psychological impact of COVID-19 in the Swedish population: Depression, anxiety, and insomnia and their associations to risk and vulnerability factors).
So, theoretically, public health during pandemics doesn’t necessarily have to be worse in large dense urban areas, as other factors might supersed. But, unfortunately, it apparently was worse in the US under present (social, economic, and political) conditions, however those conditions came about (a whole other discussion barely touched upon here). Many of the states that fared badly are massively larger than Sweden. As seen with New York City, the US has cities and metros that are larger than many countries in the world. These unique conditions of not merely mass urbanization but vast urbanization have never before existed in global history. The US population now in the COVID-19 outbreak is more than three times larger than during the 1918 Flu. The five boroughs of NYC have almost doubled in population over the past century with Queens almost five times as populated, and surely the NYC metro area has increased far more.
Places like Houston, Los Angeles, Chicago, and New York City are hubs in immense systems of commerce, transport, and travel with heavily used airports and sea ports, interstate highways and railways, a constant flow of people and products from all over the country and the world (the rise of mass world travel and troop transport was a key factor in the 1918 Flu, helping it to mutate and spread in the deadly second and third waves). Systems thinking and complexity theory have come up in our studies and readings over the years, including in discussions with our father whose expertise directly involves systems used in businesses and markets, particularly factory production, warehousing, and supply chains. Those are relatively simple systems that can to varying degrees be analyzed, predicted, planned, and controlled. But massive and dense populations in highly connected urban areas are unimaginably complex systems with numerous confounding factors and uncontrolled variables, unintended consequences and emergent properties. Add a pandemic to all of that and we are largely in unknown territory, as the last pandemic in the US was over a century ago when the world was far different.
Also, there is there is the issue of how systems differ according to locations and concentrations of various demographics, specifically in contrasting the privileged and underprivileged. That goes back to the issue of poverty, inequality much else. A major reason we’ve had so many problems is because most politicians, lobbyists, media figures, public intellectuals, and social influencers involved in the ‘mainstream’ debate that gets heard and televized are living in separate comfortable, safe, and healthy communities, as separate from both the rural and urban masses, particularly separate from minorities, the poor, and the working class (see: Mental Pandemic and Ideological Lockdown). We could note that the individual who originally showed us the graphed data, as mentioned at the beginning of the post, is of this typical demographic of wealthier urban white who has never personally experienced impoverished population density (AKA slums or ghettoes). And even though urban, like us, he lives in this same rural state with clean air surrounded by open greenspace of parks, woods, and farms; not to mention being smack dab in the middle of the complete opposite of a food desert. This could be why our reference to ‘populated’ states could gain no purchase in his mind and imagination.
Obviously, as complex systems, the densely populated big cities and metros described above aren’t isolated and insular units, contained and controlled experiments. Their populations and economies are inseparable from the rest of the global society, even more true in this age of neoliberal globalization. That would complicate pandemic response in dealing alone with either excess deaths per capita or job loss per capita, but that would exacerbate further the even greater complexity of finding a balance between the two. When these major centers of industrial production, service industry, commerce, trade, transportation, marketing, and finance get shut down (for any reason) and/or when other closely linked major centers get shut down, it severely cripples the entire economy and employment of the state, even ignoring the potential and unpredictable pandemic threat of overwhelmed hospitals, death toll, and long-term health consequences. Economic and public health effects could ripple out and in with secondary and tertiary effects.
It’s not anything like less populated rural farm states and natural resource states where, no matter what is going on in the rest of the country and world, the local population is more isolated and the local economy usually keeps trucking along. The Iowa economy and housing, for example, was barely affected by the 2008 Recession. Indeed, for all its failed state leadership in dealing with COVID-19, low inequality and low poverty Iowa was below average on both job losses and excess deaths. So, if Iowa could do better than most states, in spite of horrible leadership by the Trump-aligned Governor Kim Reynolds (even our Republican parents despise her handling of the crisis), maybe governments in other states also don’t necessarily deserve as much of the blame or credit they are given, at least not in terms of the immediate pandemic response, although long-term public health planning and preparation (over years and decades) would still be important.
That is to say, the situation is complicated. Yet we seem to know what are some of the key complications, however entangled they may be as potentially causal or contributing. It’s a large web of factors, but strong correlations can be discerned, all of it mostly following already known patterns, but of course we are biased in what we notice according to our focus. The data gathered and analyzed this past year, as far as we can tell, is not fundamentally different in nature than any other data gathered and analyzed over the past century. So, even though COVID-19 is a highly unusual event, what is seen in the data isn’t likely to be surprising, even if requiring multiple layers and angles of interpretation. Still, unexpected results would be welcome in possibly indicating something new and interesting. Serious study of this pandemic has barely begun. The data will keep rolling in. Then decades of debate and theorizing will follow. Some of the observations offered here might to varying degrees stand the test of time, such as the well-established inequality links, but surely much of it might prove false, dubious, misleading, or partial. Many questions remain unanswered and, in some cases, unasked.