Invisible Populations
The US Census Bureau conducted an international study in 2019, identifying 16 groups as frequently HTC (Hard-To-Count).
It is important to note that “populations discussed here do not include every group that may be HTC within a national context, nor will each group be HTC within all contexts. Instead, they represent an overview of groups - identified over a range of contexts - that present challenges for accurate enumeration.” Invisible, or HTC, populations are segments we possess the least knowledge of. Therefore, we have the least understanding of how these particularly vulnerable groups may be experiencing different aspects of their environment.
(Above) Breakdown of HTC groups identified by US Census Bureau, including challenges & responses Source
What are HTC Groups?
We know a great deal about the people and livelihoods that make up our society as a collective. By collecting and organizing information into databases, such as a census, we have constructed a system of tools to represent the composition and state of society. Our understanding of a given person's experience of the world is produced by the analysis of available information pertaining to individual experiences. Consequently, our understanding of society’s present state is limited to our tools to create and interpret data. Which begs the question, what population segments pass unseen to our data? How many invisible population segments are out there?
To investigate, RUNWITHIT Synthetics has created half a billion data points that allow us to observe over 12 million simulated individuals interacting in a synthetic replica of the Greater Los Angeles area. These customizable “Living Laboratories” are constructed from open source data pools, using machine learning and other techniques. They correct information disparities between databases and create one enriched data lake more complete than any of the individual data sources used. This allows synthetic environments to create
At-scale geospatially accurate environments, and
“Hypothetical” populations of simulated individuals that are more representative, inclusive, and complete than the census.
Imagine synthetic people interacting throughout their simulated world in real-time, with contextual data describing characteristics such as their preferences, career, living situation, family, behavior, and emotional state. Synthetic environments facilitate a means to incorporate aggregated data, synthesizing it with higher fidelity. Care is taken to isolate any intersections of identity with the environment and protect data privacy by developing our understanding using a hypothetical representative population we call “Synthetic Population.”
Population characteristics, especially those of invisible population segments, encompass several factors that interact and confer vulnerability. Age, for example, regarded as primarily significant in terms of physiology, also relates to exposure through behaviors and activities that can be more amenable to prevention. Children are recognized as a high-risk group, but their vulnerability may differ by childhood stage. At the same time, pregnant women are not explicitly identified as a vulnerable group despite growing evidence for reproductive risks. Finally, social and economic factors of population characteristics have received little attention, although they can affect coping capacity and interact with susceptibility.
(Above) Population vulnerabilities across characteristic & behavior aspects Source
Aspects of the environment that would be experienced differently by HTC groups are a function of population segment enumeration challenges and vulnerability characteristics as identified and measured by data. Therefore, attempts to calculate the extent of disproportionate impact to invisible populations should integrate evidence from vulnerability components of different fields of study. For example, aspects of vulnerability rooted in physiological health, socio-economic circumstance, geographical location, or exposure to air pollution and natural disasters could play significant roles in contributing to adverse environmental factors disproportionately affecting invisible populations. HTC groups studied in an integrated manner will provide a better understanding of population vulnerability, its associated societal costs, and improve the scientific bases to assess risks and develop policies and other health protection initiatives.
Invisible Populations Case: Californians without Housing
California has the highest population of people experiencing homelessness in the United States (United States Interagency Council on Homelessness, 2020). As of 2020, Los Angeles County is home to 66,436 unhoused people, a 13% increase since 2019 (Los Angeles Homeless Services Authority [LAHSA], 2020). Of this population, around 38% of people are unsheltered, and over 80% of unsheltered individuals have been living in Los Angeles for more than five years. Marginalized communities, such as LGBTQIA2S+ people, Black/African American people, and those with mental illness, are overrepresented in people experiencing homelessness (Wilson et al., 2020; LAHSA, 2011, 2020). These communities face structural inequalities that result in sustained homelessness, such as inaccessible social services where they do not feel safe and suffer unaffordable housing. California ranks as the most unaffordable state for housing, where people would have to work for 112 hours per week at minimum wage to afford a two-bedroom rental (National Low Income Housing Coalition, 2021).
Cost of a 1-2 bedroom apartment relative to minimum wage income Source
Many unhoused individuals are left out of traditional data sources such as the census. Furthermore, data sources do not adequately account for unhoused people because they do not have a permanent address and have limited access to the internet (Centre of Poverty and Inequality, 2020). In addition, unsheltered youth often distrust authority figures (Hudson et al., 2010). Therefore, data sources seldom have enough information to identify issues surrounding people living without shelter or home. For example, the Los Angeles Homeless Services Authority (2020) found a 45.7% increase in unhoused families and a 54% increase in those experiencing chronic homelessness after improving their methods. Without sufficient data, people experiencing homelessness are left without support, and the costs of unsheltered and unhoused people remain hidden.
Maintaining homelessness presents a costly challenge to society. LA County reportedly spent approximately $965 million on unhoused & unsheltered in the 2014-2015 fiscal year (Wu & Stevens, 2016). These costs include law enforcement, healthcare expenses, and social services. Expenses increase as individuals grow older and have more healthcare needs. These costs range between $406 per month and $5038 per month in Los Angeles for each person, with an average of $2876 per month (Flaming, Burns, & Matsunaga, 2019). Comparing this to an average of $605 per month to provide supportive housing, tackling unhoused person conditions offers significant savings. Nationally, supportive housing would decrease costs associated with chronic homelessness by an average of 49.5% (National Alliance to End Homelessness, 2017).
(Above) Direct average social costs to support unhoused persons Source
The largest expense when supporting unhoused people in LA County is healthcare (Wu & Stevens, 2016). Continued climate change threatens to increase the costs of homelessness by increasing the number of extreme heat days and other adverse weather events. During extreme heat days, the risk of mortality increases by 3.61 times for people with psychiatric illness, 2.48 times for people with cardiovascular disease, and 1.61 times for people with pulmonary disease (Bouchama et al., 2007). These conditions are more prevalent among unsheltered and unhoused people. Further, people experiencing homelessness are also more exposed to air pollution and extreme heat because of their time spent outdoors. These factors combine to create disproportionate impacts for the unhoused, and unsheltered. In addition to these effects, people experiencing homelessness have fewer resources to cope with illness due to less access to preventative health care measures, chronic stress, and discrimination (Hajat, Hsia, & O’Neill, 20).
The Invisible Burden
A consistent trend found across RUNWITHIT Synthetics’ analysis of invisible populations is the recurring impact that climate change has on these segments. Specifically, the effects of climate change as measured by contribution from and exposure to air pollution. People in low socioeconomic neighbourhoods and communities may be more vulnerable to air pollution because of many factors, such as proximity to industrial sources of air pollution, underlying health problems, poor nutrition, or stress. The triple jeopardy hypothesis states that low socioeconomic status (SES) communities are exposed to higher concentrations of criteria air pollution by A) Higher exposure to air pollutants and other environmental hazards, and B) Increased susceptibility to poor health due to more psychosocial stressors. These factors result in health disparities driven by environmental factors.
Meta analysis of SES group exposure to air pollutants Source
Average pollution exposure of indoor vs outdoor workers Citation
Several studies have confirmed the ill effects of air pollutants on the lung function of outdoor workers, another invisible population segment. According to a government report, each year in the UK around 36,000 deaths are attributable to exposure to outdoor air pollution. In addition, it is associated with lung cancer, heart disease, strokes, reductions in cognition, and it can endanger unborn babies. In 2013, the International Agency for Research on Cancer (IARC) concluded that outdoor air pollution is carcinogenic to humans.
Breathing air with a high concentration of NO2 can irritate airways in the human respiratory system. Such exposures over short periods can aggravate respiratory diseases, particularly asthma, leading to respiratory symptoms (such as coughing, wheezing, or difficulty breathing), hospital admissions, and visits to emergency rooms. Longer exposures to elevated concentrations of NO2 may contribute to the development of asthma and potentially increase susceptibility to respiratory infections. People with asthma, as well as children and the elderly, are generally at greater risk for the health effects of NO2. Citation
NO2 and NOx react with other chemicals in the air to form particulate matter and ozone. Both of these are harmful when inhaled due to their effects on the respiratory system. Depending on the level of exposure, ozone can:
Cause coughing and sore or scratchy throat
Make it more difficult to breathe deeply and vigorously and cause pain when taking a deep breath
Inflame and damage the airways
Make the lungs more susceptible to infection
Aggravate lung diseases such as asthma, emphysema, and chronic bronchitis
Increase the frequency of asthma attacks
Some of these effects have been found even in healthy people, but effects can be more serious in people with lung diseases such as asthma. They may lead to increased school absences, medication use, visits to doctors and emergency rooms, and hospital admissions. In addition, long-term exposure to ozone is linked to aggravation of asthma and is likely to be one of many causes of asthma development. Studies in locations with elevated concentrations also report associations of ozone with deaths from respiratory causes. Source Source
Children and Exposure to Air Pollution
Children are particularly vulnerable to the ill effects of air pollution, especially when correlated to the early development of asthma. Because children are shorter than adults, they are closer to the ground, and therefore to vehicles’ exhaust pipes. Additionally, young children breathe faster, meaning they take in more air relative to their body weight. Studies found that children walking on busy roads may be exposed to up to a third more air pollution compared with adults. This is a significant finding, especially for children worldwide who walk to school every day. In the figure below, several peaks occur. Two of the most dramatic peaks correspond with the journey to and from school; the other major peaks are likely to be linked to exposure to cooking in the home and exposure to pollutants while outside on break time at school.
Navigating The Way Forward
Existing inequities related to environmental hazards are the result of multiple complex factors, both historical and contemporary. These include land-use decisions that predominantly place low-income communities of color close to polluting industries and patterns of commerce and transportation corridors. These multiple sources of pollution then trigger cumulative and synergistic exposures, which exacerbate asthma and disproportionately impact health. Communities face additional risks when regulatory agencies fail to put the populations’ health at the center of their decision-making and fail to provide sufficient regulatory oversight.
To account for geographical aspects of climate change’s impact on invisible population segments, RUNWITHIT Synthetics created a metric indicating the relationship between regional environmental pollution burden and the region’s population characteristics. This analysis method uses the CalEnviroScreen, a static heatmap of regional emissions, and the California Healthy Places Index (HPI), a mapped basket of socioeconomic metrics. By integrating the HPI with the CalEnviroScreen, we have transformed a static tool into a dynamic indicator capable of identifying core problems, contributing factors, and potential solutions.
To facilitate genuine and lasting change for members of invisible population segments, we must understand the data and recognize the bias and gaps. Decision-makers enact change and implement plans based on their best understanding of the available information. For the appropriate and effective actions to mitigate population vulnerability to be taken, decision-makers need to be supported with accurate data, tools, and analysis to make informed decisions on policy, programming, and investments. This support is exactly what RUNWITHIT is striving to accomplish by highlighting the invisible segments of our population in Single Synthetic Environments.
References
Bouchama, A., Dehbi, M., Mohamed, G., Matthies, F., Shoukri, M., & Menne, B. (2007). Prognostic factors in heat wave–related deaths: A meta-analysis. Archives of Internal Medicine, 167, 2170–2176.
Flaming, D., Burns, P., & Matsunaga, M. (2009). Where we sleep: Costs when homeless and housed in Los Angeles. Economic Roundtable Research Report. https://papers.ssrn.com/abstract=2772796
Hajat, A., Hsia, C., & O’Neill, M. S. (2015). Socioeconomic disparities and air pollution exposure: A global review. Current Environmental Health Reports, 2, 440–450.
Hudson, A. L., Nyamathi, A., Greengold, B., Slagle, A., Koniak-Griffin, D., Khalilifard, F., & Getzoff, D. (2010). Health-seeking challenges among homeless youth. Nursing Research, 59, 212–218.
Los Angeles Homeless Services Authority. (2011). 2011 Greater Los Angeles homeless count report. http://documents.lahsa.org/planning/homelesscount/2011/hc11-detailed-geography-report.pdf
Los Angeles Homeless Services Authority. (2020). 2020 Greater Los Angeles Homeless Count. https://www.lahsa.org/documents?id=4558-2020-greater-los-angeles-homeless-count-presentation
National Alliance to End Homelessness. (2017). Ending chronic homelessness saves taxpayers money. http://endhomelessness.org/wp-content/uploads/2017/06/Cost-Savings-from-PSH.pdf
National Low Income Housing Coalition. (2021). Out of reach: The high cost of housing. National Low Income Housing Coalition. https://nlihc.org/sites/default/files/oor/2021/Out-of-Reach_2021.pdf
United States Interagency Council on Homelessness. (2020, January). California homelessness statistics. United States Interagency Council on Homelessness. https://www.usich.gov/homelessness-statistics/ca/
Centre on Poverty and Inequality. (2018, July 1). Will you count? People experiencing homelessness in the 2020 census. Census counts. https://censuscounts.org/whats-at-stake/will-you-count-people-experiencing-homelessness-in-the-2020-census/
Wilson, B. D. M., Choi, S. K., Harper, G. W., Lightfoot, M., Russell S., & Meyer, I. H. (2020). Homelessness among LGBT adults in the US. Williams Institute. https://williamsinstitute.law.ucla.edu/publications/lgbt-homelessness-us/
Wu, F., & Stevens, M. (2016). The services homeless single adults use and their associated costs: An examination of utilization patterns and expenditures in Los Angeles County over one fiscal year. Chief Executive Office. https://homeless.lacounty.gov/wp-content/uploads/2019/02/homeless-costs-final.pdf