How healthy people are in a country, a state, or neighborhood is both critical and complicated. There are various measures of well-being called health metrics, that one could use to compare the physical and/or emotional well-being of persons and/or groups. A health metric that combines several measures known as a health index is probably the most useful type of health metric for a group of people. Experts disagree though on which factors are most important to include in a health index, who should be included in a health index, and what sort of other factors (like poverty or war) should be included in a health index.
Infant Mortality Rate
One of the most useful health metrics is Infant Mortality Rate (IMR), which is a count of the number of children who die during their first year of life per 1,000 live births in that region.
Figure 4-3: World Map - Infant Mortality Rate. Sub-Saharan Africa has rates ten times that found in the developed world. Source: World Bank. ** Interactive Map
Global disparities in IMR are substantial. Much of the variation in infant mortality can be traced to poverty, and the various problems associated with being poor, especially malnutrition. Other factors, including disease, lack of access to quality health care, and unsanitary living conditions also contribute to poor survival rates for infants. For these reasons, the infant mortality rate is an excellent indicator of the overall health of a population.
Figure 4-4: US Map - The Infant Mortality Rate in the United States is twice as high in Mississippi than it is in New Hampshire. Source: CDC Interactive Map.
Infant mortality rates in the United States are low on average. They should be in the country that spends more on health care by a wide margin than any other country in the world. However, despite the extremely high medical costs in the US, the IMR for American babies is three times higher (6 vs 2 per 1000) than it is for babies born in Finland or Japan. Shockingly, the American IMR is also higher than it is in Botswana, Lebanon, and Cuba. The reason for the high IMR in the US is complex, but poverty and the peculiar American health care system are major factors. The role of women in society also affects a country’s IMR. That piece of the puzzle is explored later in the chapter on gender.
The map of IMR by American states makes it evident that poverty, ethnicity, and the local political climate are key predictors of infant health in the US. Babies born in liberal, white and prosperous Vermont are half as likely to die as those born in states with many poor, minority and politically conservative voters – like Mississippi and Alabama. Studies show that the problem lies not so much in the hospital care provided babies but in the 10 months or so after babies come home from the hospital. It is during these later months that the health care system breaks down for infants from poor families, especially if their parents are African-American. The world map of infant mortality shows this is not just a matter of poverty or ethnicity. Cuba, for example, has much less wealth than the US, and a higher percentage of people of color, but the Cuban government’s willingness to deliver quality, cheap, accessible healthcare for everyone keeps their IMR lower than United States’ IMR.
Life Expectancy
Like IMR, life expectancy is a useful indicator of the overall health of a population. This metric is an estimate of how long a person is expected to live on the day they were born. Calculations are largely based on observed death rates, plus additional considerations. The life expectancy of any group of people can be greatly affected by things like war, or the outbreak of diseases. In Africa, for example, after years of improvement, the life expectancy recently stagnated as AIDS swept across the continent. Iraq and Syria have seen life expectancy downgraded in recent years as wars in the region continue indefinitely.
Figure 4-5: World Map - Life expectancy ranges from a low of less than 45 years in parts of Africa to 82.5 in Japan. Source: World Bank. Interactive Map
In the United States, babies born today can expect to live to be about 79 years old. Again, this isn’t great considering that life expectancy generally is a product of national wealth and health care expenditure. Almost every developed country in the world and even some developing nations (Chile, Cuba, Costa Rica, e.g.) better life expectancies than Americans. Depressingly, in recent years, life expectancy in the US has gone down which indicates troubling trends in our society. Rising poverty and associated lifestyles are generally cited as reasons for the shrinking life span in the US.
The geography of poverty, government policies, and cultural practices all affect longevity. In McDowell County, West Virginia, where Americans have the shortest life expectancy (76.5 males; 81.2 females), you’ll find that their median income is around $25,000 a year, about 20% don’t have health insurance, few people complete college, over 16% suffer from diabetes, and over 35% of adults smoke. Even the homicide rate is exceptionally high McDowell County. As a result, babies born in wealthy and health-conscious Marin County, California can expect to live nearly 15 years longer than babies born in McDowell County, West Virginia.
Figure 4-6: US Map - Life expectancy in Mississippi is 75 years, but in Hawaii, it is over 81. Substantial variation exists within states too. Poverty and ethnicity are key causal variables in the differences. Source: Measure of America/CDCInteractive Map
Ethnicity plays an important role in health too, but it appears to be less important than geography. For example, Asians, perhaps because of their dietary practices, better access to health care, and maybe genetics, can expect to live about 13 years longer than African-Americans in general. When you combine ethnicity, gender, and location, the difference becomes even greater. For example, African American boys born in Washington DC today have a life expectancy of only 66.5 years, whereas Asian-American girls born in Boston are expected to live, on average, to almost 92! On the other hand, African-American boys born today in Minnesota may expect to live to be about 80 years old on average, which is about the same Asian-American boys born in Hawaii. The point is that lifestyles, dietary practices are affected by both ethnicity and geography. Indeed, what may seem a common “ethnic behavior” in one part of the US may not be so common among the same ethnicity in another part of the country.
Physical and Mental Health
Another way of gathering data about the overall health and well-being of an individual or a group of people is to survey them. Geographers use survey methods to gather information about a wide range of topics. Well done surveys are complex to plan, perform, and analyze; so,researchers must exercise extreme caution when using survey data, especially when the survey data was collected by others. The world’s largest telephone survey is done by the Centers for Disease Control and Prevention (CDC) with the aid of local health departments. This survey is called the Behavioral Risk Factor Surveillance System (BRFSS) and it provides a substantial amount of quality data about the health and health care of Americans. Several questions are useful in measuring the quality of life of people around the US. The CDC makes this data available in a variety of formats, including format useable in a GIS, allowing health geographers easy access to exceptionally high-quality data sets necessary to solve numerous health-related problems.
Healthy Days
A couple of the most basic questions asked by the CDC on the BRFSS are “Would you say that in general your health is _______? (Excellent, very good, good, fair, poor) and “How many days in the past 30 days was your physical health poor?” (numeric answer, none, not sure, refuse to answer). Similar questions are asked about mental health. The answers to these questions can be mapped at various scales (county, city, state, etc.) to paint a compelling picture of a region’s health. Hundreds of researchers, and dozens of organizations working to improve the health and well-being of communities, use this data.
Figure 4-7: US Map - Eastern Appalachia and parts of the South report five times as many poor health days as some counties in the Upper Midwest. What are the costs to employers and taxpayers? Source: County Health Ranking – Interactive map
Survey results indicate a wide variation in the number of days people are sick in the US. In some places, people on average have less than two “sick days” per month. In other places, especially the Deep South and Appalachia, people are sick, on average, about seven days per month. While a few days difference may not seem noteworthy, multiplied by millions of people that live in most states, it is a huge difference. Chronic illness has significant implications for the economy of a region at the very least. Imagine for a moment how a company looking to open a factory in Appalachia would evaluate the health indicator data for a county where people are sick about three months out of every year? How much money would the factory stand to lose in a location like this? The unhealthy conditions of Americans living in poverty are not only a humanitarian concern but a significant economic drain on the entire US economy because the poor health of Americans in other regions of the country is often passed on to the rest of the country via external costs -like extra taxes and increased health insurance costs.
Disability
One of the key outcomes of poor health is disability. Over 10 million Americans were receiving disability payments at the end of 2017. On average, the monthly benefit paid to claimants was around $1,200. The program began in 1957, but expanded rapidly in the 1990s after cuts to other welfare payments eliminated cash payments to the able-bodied poor, many of whom were economically struggling parents of small children.
Figure 4-8: US Map - Many poor counties have over 10% of their population receiving disability payments from the US government. Age is partly a factor, but unhealthy lifestyles cost American taxpayers billions annually, while contributing to a cycle of poverty. Source: Social Security
Unhealthy lifestyles, dangerous working conditions, risky cultural behaviors, and bad luck all increase the likelihood of individuals becoming dependent on the government for support. By mapping these individuals as groups, we can see very uneven patterns of disability across the US, which strongly suggests that both cultural practices and economic conditions are important causal variables in the creation of a disability crisis in the United States. Employment in mining and factory work seems one predictor of worker disability, which makes sense because those jobs are often physically demanding and sometimes dangerous. A lack of economic diversity in many of these same locations means that few other job opportunities are available for those with only a physical disability. This means, that even if you were injured while working in one job, in some parts of the country, you could find another job where your physical condition didn’t matter. In some parts of the US, because the types of jobs are limited to hard physical labor, an inability to lift heavy objects (for example) would keep you from finding almost any job.
The geographic pattern evident in the map of disability welfare differs wildly from media stereotypes about persons receiving government welfare. Mapping disability coverage offers a counterbalance to a common, misguided stereotype of the urban welfare queen, the politically charged symbol of those who abuse government assistance, generally assigned to women of color. The map of disability payment hotspots shows that, in reality, welfare payments go to communities that are overwhelmingly rural, and predominantly white. While it is difficult to estimate the percent of fraudulent disability claims, the intense clustering visible on the map invites further research into why some counties have so many disabled people. It is statistically unlikely that nearly one-third of any region’s total population could be physically disabled by workplace injuries, even though the demographic profiles of poor, rural counties skew toward the elderly and ill-prepared to survive with a disability. To account for this reality, geographers age-adjust data to help account for the fact that older people are more likely to suffer a workplace injury from which they cannot recover. People without a high school diploma may also be declared disabled by an injury that would not qualify as an injury for a person with a college degree. It is reasonable to assume serious injuries should occur in a somewhat random pattern around the US, producing a somewhat random pattern to the disability map as well. Instead, there is a definite clustering pattern to disability claims in the US, which suggests fraud -which is very difficult to prove. Closely associated with the disability epidemic in the United States is burgeoning opioid drug addiction. Many of the same regions of the US where physical disabilities are very common also suffer from widespread opioid addiction. This crisis is explored more fully in the chapter on crime and punishment.
Autism
One of the disabilities recognized by the US government is Autism, which is, in reality, a
group of related conditions characterized by a range of cognitive and behavioral
impairment levels, more properly known as Autism Spectrum Disorders (ASDs). ASDs are
among the fastest-growing health concerns worldwide. The cause or causes of ASDs is the subject of exceptionally intense debate and millions of hours of research. Nobody yet
knows for sure what causes ASD.
Figure 4-9: Map: Autism Cluster in wealthy, northwestern Los Angeles, County. Source: UC Davis Health System
Generally, researchers think that genetics is the primary factor in ASD, but determining causality has proven to be very complex. Partly this is because the symptoms themselves are hard to identify, but it’s also because of the geography of autism. Medical geographers and spatial epidemiologists are heavily involved in autism research because autism clusters are reasonably easy to identify on a map. In greater Los Angeles, for example, unusually high rates of autism appear in Torrance, Beverly Hills, Van Nuys, Calabasas, Laguna Beach, and Mission Viejo. Of course, Los Angeles has a well-earned reputation for air pollution, leading some to believe that exposure to airborne toxins is a causal variable. Indeed, there is some evidence to suggest that environmental exposures to various pollutants may function as triggers for the condition, but definitive answers have proven elusive. What is clear is the effect of the neighborhood on the diagnosis of ASD. Most of the autism clusters in greater Los Angeles are in wealthy neighborhoods; so geographers suspect that the disorder’s dramatic rise in upscale areas is likely a product of improving diagnostic capabilities among medical professionals serving the upper-middle class, rather than evidence of a real increase in ASD. In poorer areas, where environmental conditions are generally far worse, parents, families, and school officials appear to misdiagnose ASD or overlook symptoms that are commonly noticed in wealthier communities. Ethnicity and economics may also affect the likelihood that parents will acknowledge or accept a diagnosis of ASD for their child. The uneven spatial pattern of diagnoses makes the task of identifying the root causes harder because the known pool of persons with diagnosed with ASD is an unrepresentative sample of the true ASD population.
Vaccinations
The most controversial topic surrounding ASD has been the popular, but the scientifically unproven, belief that vaccinations cause ASDs. These unfounded fears keep many parents from vaccinating children against common, and sometimes deadly, diseases. As a result, a handful of diseases thought extinct have re-established themselves in the US.
Figure 4-10: Infographic - In recent years, measles, a disease that was once declared eliminated in 2000 has re-established itself. Most people who get measles were not vaccinated, so exposure from outside sources spreads rapidly. Source: CDC
Measles is a classic example. In the year 2000, the CDC declared measles “eliminated” from the US because no Americans had the disease. In the 1960s, millions were infected with measles and hundreds died every year. Today, Americans not vaccinated against measles remain at risk when traveling internationally, or when exposed to international visitors, or immigrants from affected regions. A significant outbreak of measles occurred in Southern California during early 2015, after a tourist with measles visited Disneyland in Anaheim and it spread among unvaccinated children in the region. In 2019, the worst outbreak in many years sent hundreds to hospitals across the country. The worst outbreak was probably in New York City’s Orthodox Jewish community, where exposure to people from Israel is high and vaccination rates are relatively low.
Pertussis, better known as “whooping cough” is another disease that has re-emerged in recent years thanks to vaccination paranoia. In recent years, the number of pertussis cases in the US has risen to levels not seen since the 1940s. By mapping pertussis rates by California’s county shows an interesting pattern. Higher rates of pertussis are evident in parts of California with many Latin American migrants. Latinos had by far the highest rate of whooping cough at 174 cases per 100,000 in 2014. This high rate is likely caused by poor access to affordable, quality medical care for pregnant women and infants. Language barriers between patients and health care providers may exacerbate the problem. Extended families who live together in crowded housing, especially if adults recently arrived from Latin America without updated vaccinations, can put at risk the health of infants and small children living within the household.
Figure 4-11: California Counties - Pertussis infection rates vary considerably across California in 2014. Areas with large immigrant populations and large anti-vaccine populations show high infection rates. Source: CHHS – downloadable data
The map of pertussis in California also shows outbreaks in some of the wealthiest, best-served counties. Many wealthier families purposefully opt-out of vaccination programs, thereby fueling the pertussis epidemic in otherwise wealthy, healthy, and medically well-served communities. In parts of upscale Sonoma County, where pertussis rates were exceptionally high in 2014, several schools had vaccination rates well below the rates considered safe. In some schools, more than half of the children were not vaccinated. Their parents had signed “personal belief exemptions”, which excused children from being vaccinated based on religious or moral grounds. Still, they sent their children to school not immunized against common contagious diseases. Though pertussis is unlikely to kill healthy children in upscale neighborhoods, it is nevertheless highly contagious and spreads into other neighborhoods, or even countries, where infants from poor families are at serious risk from the disease. California eliminated most exemptions for children attending public school in 2015.
Children who are not immunized against disease take advantage of what is called, herd immunity, a condition that characterizes groups of people in which around 90% of the group have developed immunity, generally through vaccinations, to an infectious disease. The group immunity critically lowers the chances of infection for those without vaccinations and/or immunity. This behavior is sometimes used as an example of the free-rider problem, that occurs when individuals take advantage of a community resource without contributing to the maintenance of the shared resource. The free-rider problem echoes the “tragedy of the commons” scenario, discussed in the Political Geography chapter. Those people with compromised immune systems who cannot be vaccinated must rely on herd immunity to keep from getting sick, making it imperative that those with otherwise healthy immune systems get vaccinated for the good of others.
Figure 4-4: US Map - The Infant Mortality Rate in the United States is twice as high in Mississippi than it is in New Hampshire. Source: CDC Interactive Map.
