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Planetary Health in Costa Rica Indiana University Bloomington

The Road to Environmental Justice: The Impact of Environmental Exposures on Life Expectancies Across SES Communities

Posted on by Victoria Crevecoeur

Abstract 

This research explores the pathway of environmental exposures and its potential influence on socioeconomic disparities in health. Exposure to environmental pollutants has been linked to various health conditions including respiratory, cardiovascular, and neurocognitive diseases.[1] Many studies have found that people in lower-income communities are exposed to more concentrations of pollution than people in higher-income communities, resulting in higher mortality rates. A comparative approach was used to examine environmental risk factors in the cities of Bloomington and Fort Wayne Indiana to see how they may contribute to gaps in life expectancies across socioeconomic class communities. Results showed that lower-income communities in Bloomington were exposed to greater levels of particulate matter than higher-income communities and were often associated with lower life-expectancies. Additional findings also suggest that environmental exposures within SES communities may not always be the leading determinant for life expectancy gaps in different settings.

“INJUSTICE ANYWHERE IS A THREAT TO JUSTICE EVERYWHERE. WE ARE CAUGHT IN AN INESCAPABLE NETWORK OF MUTUALITY, TIED IN A SINGLE GARMENT OF DESTINY. WHATEVER AFFECTS ONE DIRECTLY, AFFECTS ALL INDIRECTLY.”

-Reverend Martin Luther King Jr.

Introduction

The link between socioeconomic status (SES) and health has become a growing topic of interest in the recent decades. SES is often referred to as one’s social position, or rank in society and is typically measured according to one’s level of income, education, and occupation. However, there have been many studies showing that inequalities within SES are strongly associated with health disparities, and seem to have a greater burden upon individuals who fall in the low-SES category. It is apparent that people with a lower-SES are more likely to suffer from poorer health outcomes than people with a higher-SES.[2] There are several factors that can provoke health risks including one’s social environment, accessibility to healthcare, lifestyle behaviors, and environmental exposures.[3] However, there is limited knowledge as in how these pathways potentially play a role in the SES disparities in health. One of the main objectives of this research study is to obtain a clearer understanding of SES and its influence on health outcomes, particularly by focusing on environmental risk factors.

In the last several decades, the rate of human consumption on natural resources has risen tremendously and has resulted in high levels of pollution which can trigger all various kinds of health issues such as hypertension, asthma, cancer, as well as neurological disorders. Pollution is considered to be one the largest causes of premature deaths worldwide and is disproportionately affecting low-income individuals.[4] During the 1980s in the United States, the Environmental Justice Movement had emerged to shine light on the injustices that low-income and minority communities were facing in regard to the conditions of the environment they were living in. Low-income areas were essentially neglected by the government’s unequal distribution of environmental policies which caused a disproportionate number of low-SES communities to be exposed to high levels of toxic chemical pollutants. Another concern that many activists had called out on was the fact that many industrial factories, landfills, and power plants, all of which contribute to pollution, were frequently positioned next to poorer communities.[3] Many people have speculated that this could be the reason why people with a lower-SES have a higher mortality rate than people with a higher-SES.

As of 2020, Indiana is ranked number 12 in the US for having one of the lowest life expectancies (77.6 years).[5] While there are several factors that contribute to this statistic such as certain lifestyles and food diets, there is lack of information on how environmental exposures could correlate to life expectancy across SES communities, which is the main question for this research study. Therefore, the second and third objectives of this study are to examine how environmental exposures might vary across different SES communities and to find out if there’s a possible gradient in the distribution of pollution. It was hypothesized that low SES communities would contain higher levels of pollutants than high SES which would suggest that low SES is associated with low life expectancies.

Methods

Study Design

To assess environmental exposure and life expectancy across the lower, middle, and upper SES communities in Indiana, a comparative approach was applied. The purpose of this approach was to build a broader perspective on how this pathway influences the relationship between SES and health in more than one type of setting.[6] The chosen areas to conduct this comparative analysis were the cities of Bloomington and Fort Wayne.

Bloomington’s average life expectancy is 78.3 years while Fort Wayne is 76.5 years.[7] To understand the cause behind the differences in life expectancies, there are several characteristics about each city that must be taken into account. One of these characteristics include population size. Fort Wayne is the second most populous city in Indiana with approximately 262,907 people whereas Bloomington’s population is just 1/3 of that.[7]This factor could influence the concentrations of environmental exposures since greater volumes people in a given area often correspond to higher levels of energy consumption which leads to more pollution in the environment. However, at the same time, Bloomington’s population density is much larger compared Fort Wayne given the fact that its city size spans to about 23 miles while Fort Wayne is 110 miles (4 times the amount). Having a small area of land to accommodate a large population could compromise the ability to manage and dispose wastes in a safe manner. These characteristics along with demographic patterns, and infrastructural complexity all can have an influence on how environmental exposures are distributed on a city-scale.

For the basis of this study, the communities within each city were arranged in the format of census tracts. This was purposely done to have a definitive unit of a community. Data from the CDC was used to obtain estimates of life expectancies for each census tract, which were recorded from the period of 2010-2015.[8] Figures 1 and 2 show the maps of the Bloomington and Fort Wayne and their respective census tracts which are colored-coded to display a distribution of life expectancies throughout each city (year values are provided in Life Expectancy Key). This research design was then used measure the following variables examined in this study: SES, life expectancy, and environmental exposures (air quality and water quality).

Figure 1 – Bloomington, Indiana Life Expectancy Map

Figure 2 – Fort Wayne, Indiana Life Expectancy Map

 

Measuring Socioeconomic Status

In this study, SES was measured according to household income. The census tracts examined in Bloomington and Fort Wayne were assigned SES ranks, using the data from Census Reporter which provided household income estimates from the year 2018.[7] The ranks were classified as lower-class, middle-class, and upper class. Based 2018 US household income reports by Pew Research, households that made less than 48,500 were ranked as lower-class, households that made between $48,500-$145,000 were ranked as middle-class, and households that made more than $145,5000 were ranked as upper- class.[9] These income range values were applied to the census tracts, however the values had to be slightly adjusted to fit with the standard household range that Census Reporter used when estimating the average household income for the census tract. Table 1 provides a key of the adjusted income ranges to rank the tracts examined in this study.

Table 1 – Socioeconomic Status Ranks By Income

Measuring Air Quality

To examine environmental exposures across each census tract, air quality was one of the variables explored. This was assessed by measuring particulate matter (PM) from an air quality detector. Particulates refer to the minute particles of matter (solid or liquid) that are found in the atmosphere. These particles range in various sizes which are usually classified as either PM2.5 (fine particles that are 2.5 micrometers or less) or PM10 (coarse particles that are 10 micrometers or more). Even the smallest quantities of particulates are sufficient to irritate the nose or throat, however frequent exposure can result in respiratory diseases such as asthma.[10] While both exposures to PM2.5 and PM10 can be deleterious to human health, a research study that examined SES and particulate air pollution in Italy found that PM10 was responsible to most of pollutant-related mortalities.[11]

Measuring Water Quality

In this study, water quality was also examined. Water quality reports from 2015 through 2019 (provided by Bloomington and Fort Wayne Utilities) were used to compare lead and atrazine levels.[12][13] Both of the contaminants have been shown to have negative effects on health. Lead is a toxic chemical agent that can increase the risk of hypertension, cardiovascular disease and lower fertility if exposed to it for long periods of time.[14] Atrazine is a compound found in herbicides to target weeds. However, recent studies indicate that atrazine can be also an endocrine disruptor in the human body which can lead to irregularities in the production of hormones and can pose major threat to the health especially individuals who are pregnant.[15]

Results

Table 2 shows the PM2.5 and PM10 measurements collected from 12 out of the 23 census tracts in Bloomington including their corresponding SES ranks and life expectancies (LE). When observing the relationship between SES and life expectancy, there is a gradient indicating that life expectancy increases as SES rank transitions from lower-class to middle-class. However, there are a few variances with tract 8 having the highest life expectancy despite its lower-class rank and tract 9.03 (lower-class) having a slightly higher life expectancy than tract 5.01 (middle-class). The air quality index for the majority of the census tracts were rated as “Good”, with the exception of census tracts 4.01 and 6.01 which had a “Moderate” index. There is also a gradient present when observing the concentration of particulate matter alongside the SES ranks. Middle-class tracts relatively show a smaller concentration in both PM2.5 and PM10 compared to lower-class census tracts. Tract 5.02 is the exception from the lower-class which practically had the same particulate concentrations as tract 5.01. Statistical analysis (t-test) overall showed that there was a significant difference in concentration levels of PM across lower and middle-class tracts (P < 0.05).

Table 2 – Particulate Matter (PM) in Bloomington, Indiana

Statistical Significance (T-Test):

PM2.5: p = 0.01

PM10: p = 0.008

Table 3 shows the PM measurements collected from 19 out of the 77 census tracts in Fort Wayne, along with their respective SES ranks and life expectancies. There is a visible gradient in the relationship between life expectancy and SES, with life expectancy increasing as SES transitions from lower-class to middle-class. This trend suggests that high-income is linked to good health outcomes and that low-income is linked to poor health outcomes. However, the PM measurements do not seem to influence this observed pattern. All of the census tracts had an air quality index of “Good” and there wasn’t a gradient between PM levels and SES rank. Statistical analysis showed that there was not a significant difference in the concentrations of PM between lower-class and middle-class tracts (P > 0.05). This contradicts the hypothesis that low-income communities have greater levels in pollutants than high-income communities.

Table 3 – Particulate Matter (PM) in Fort Wayne, Indiana

Statistical Significance (T-Test):

PM2.5: p = 0.16

PM10: p = 0.25

Table 4 shows the lead and atrazine levels in drinking water recorded from Bloomington’s and Fort Wayne’s annual water quality reports from the years 2015-2019. It appears that Bloomington’s lead levels have gradually declined while Fort Wayne’s have fluctuated. Fort Wayne also showed the highest concentrations of lead from 2017-2019, particularly in 2018, which apparently violated the EPA’s water quality standards.[13] Although the lead concentration in Fort Wayne’s water dropped significantly that following year, it still remained higher than Bloomington’s lead concentrations. For the atrazine levels, both cities show a relatively stable concentration.

Table 4 – Lead and Atrazine Levels from Bloomington and Fort Wayne’s Annual Drinking Water Reports[12][13]

* The last testing period for lead was back in 2014.

Discussion:

The air quality results in Bloomington suggest that SES does play a factor in the differences in environmental exposures in the communities. People living in lower-SES communities are exposed to relatively more concentrations of particulates than people living higher-SES communities. Out of all the tracts that were measured, Tracts  4.01 and 6.01 had the highest PM levels. In addition, a very significant observation was made when measuring PM in tract 6.01, which could provide clear evidence as to why its PM was very high. It was observed that Rev. Ernest Butler Park (a community park within tract 6.01 and also the site where PM was recorded) was just less than 200 feet from an active landfill that recycled auto parts and less than 100 feet from a railroad system. Figure 3 provides a visual of the park next to the landfill and railroad sites. According to additional statistics, census tract 6.01 had the highest poverty rate in Bloomington. Approximately 45.9% people were below poverty which is 25 percent higher than the city’s and about double in the county (Monroe).[7] Majority of the people in poverty were children.[7] Not only does this observation provides evidence to the tract’s high PM concentrations, but it also seems to highlight the injustice of lower-SES communities often close to industrial facilities that contribute to air pollution.

Figure 3* – Observation at Census Tract 6.01 – Reverend Ernest Butler Park Adjacent to Landfill Site and Railroad

*Location Map (Courtesy of TomTom)

On the contrary, there was no significant difference in PM concentrations between the lower-class and middle-class tracts in Fort Wayne. This suggests that SES does not influence the distribution of environmental exposures. Both the lower and middle-class tracts had very similar PM concentrations, despite the clear indication of the direct relationship between life expectancy and SES rank presented in the data. This lack of congruence could essentially mean that environmental exposures is not the dominant factor to Fort Wayne’s low life expectancies. There may be other pathways such as healthcare, social behavior, or occupation that are influencing the link between SES and health outcomes to a larger degree. Nonetheless, it could have been possible that the PM levels simply were low for a just that specific occasion. Air quality in Fort Wayne was measured during the weekend after Thanksgiving which is usually a time when there’s little to no human activity taking place in the environment.

Based on the water quality results in Bloomington and Fort Wayne, it was difficult to assess the link between SES and health. There was very limited data in knowing how each SES community varied in the quality of drinking water. Although water quality was not emphasized as much in this research study, it was still taken into account to see if it made any possible contribution to the life expectancies of each city as a whole. However, there were many irregularities in the data that made it complex to link it to the correlation. For instance, in the years 2016 and 2017, atrazine levels were not listed in Bloomington’s water quality report. This made it difficult to establish a general pattern in atrazine levels across each city.  In addition, the lead concentrations reported in the 2015 and 2016 for Fort Wayne were based off of lead concentrations previously measured in 2014. It is not very clear as to why some of the water contaminants were not reported on an annual basis. Overall, the methodology for assessing water quality in this research was not established properly to correlate SES disparities in health and therefore it needs to be restructured for future approaches.

Limitations/Future Approaches

Throughout this study, there were several limitations that should be taken into consideration. One of the limitations was insufficient sample size. In the study, not all of the census tracts were assessed for air quality. In Bloomington, 12 out of 23 census tracts were visited and this was partly due to the lack of data in eight census tracts. There weren’t any reported life expectancies for those eight tracts which ultimately made them unfit for the study since life expectancy was one of the key variables examined. As for the sample size in Fort Wayne, 19 out of 77 census tracts were visited. This is a very small sample size considering that Fort Wayne is a bigger city than Bloomington. However, there were many time constraints that compromised the opportunity to visit additional census tracts in the area. This limitation could be improved by collecting air samples in more than one location spot within a census tract and also having multiple trials.

In addition, it is important to note that the data used to determine life expectancies for the census tracts were based on statistical measurements from the 2010-2015 period and therefore may not reflect the values in the present times. Similarly, the household income values used to assign SES ranking for census tracts were based on 2018 data reports. Both limitations could have created discrepancies in the SES ranks and life expectancies for the census tracts. Another aspect about the average household income in the census tracts was that some of the tracts were very close to the borderline that separates the lower-class from the middle-class. It is likely that a census tract could have flipped to either a lower or higher SES rank than the previous as the years progressed.

Moreover, there were also some limitations pertaining to the accuracy of the PM results. It was difficult obtaining a stable reading of PM, especially for the Fort Wayne sector of the research study, because the PM values from the air quality detector would often fluctuate at a large degree. One minute the detector would give a PM value that is within the air quality index of “Good”, and the next minute the detector would provide a value that fits in the “Unhealthy” category of the air quality index. There might have been traces of dust particles from the detector case that got stuck in between sensors and thus affecting the air quality measurements.

Another limitation in this study was the fact that air quality is subject to change during short and long periods of time. This could be often due to changes in human activity throughout seasons but also from changes produced by the natural environment itself. It was observed in this study that PM values could sometimes change drastically after a precipitation event. Depending on the severity of air pollution, precipitation can reduce the number of particulates in the atmosphere since the water drops will normally attract the particles to the bottom surface, a process known as coagulation.[16] This might explain why PM in Tracts 5.01 and 5.02 were reported very low since they were measured after a recent rainfall in Bloomington. In the study, not all of the PM measurements were recorded on the same days. In a future approach, the air quality should be monitored frequently for an extended period of time (preferably a year).

Of course, this research is subject to biases that might have influenced the way the results were interpreted. It sounds promising to assume that lower-class is always associated with greater exposures of pollution, but new evidence suggests that this pattern has not always been the case in other settings. The same study from Italy that examined SES and air pollution found a direct relationship between high-SES and high exposure to traffic emissions.[11] In the modern times, there’s a growing demand of affluent people wanting to live in urban environments, which normally have greater concentrations of pollution compared to suburban and rural communities. Nonetheless, the trend that poorer people have worse health outcomes than wealthier people has remained fairly consistent. This is probably because wealthier people often have a better advantage in accessing adequate healthcare and are more likely to address health concerns much early on before it worsens.

Conclusion

In conclusion, SES does seem to have an effect in the distribution of particulate matter and life expectancy. Although it has been understood that low-SES communities often have greater exposure to pollution it has also been learned that this gradient has evolved to where even some of the higher-class communities experience the same quantity of environmental risk factors. Regardless, this finding does not lessen the fact that low-SES individuals have more severe health problems than high-SES individuals. The purpose of this study was to understand the role of environmental exposures on SES and health. There are other variables in addition to particulate air pollution that may contribute to a person’s health outcomes such as housing conditions, work conditions, accessibility to healthcare, food diets, and even noise pollution. Another major factor that was not examined but should be considered in future studies is racial disparities. Racial discrimination is still a huge problem today and has been shown to also influence on the distribution of environmental exposures. Specifically, it was found that black Americans with high-income were more at risk of being exposed to toxic pollutants than white Americans with low-income.[17] This overall shows how extremely multifaceted the disparities in health truly are. Some factors may be more prominent than others in exacerbating health risks, depending on the social structure of the city or state. This is why it is relevant to have continuous studies on the pathways that provoke injustices in order to devise strategies that will help communities that are most vulnerable and ensure that each person regardless of SES can live life to the highest potential.

 

 

 

References

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