There are only two absolutes in life: death and taxes, according to the Founding Father, Benjamin Franklin. We, as humans, will experience death, whether it be through the loss of a pet, friend, or family member. Understanding that it is inevitable, we still ponder how we can prolong our lives. There are certain aspects we can control, but there are also aspects we cannot. Some of the aspects that our within our control are substance use, dietary choices, and where we live. However, there are often things beyond our control, such as mental health and primary care accessibility, race, gender, and the crime surrounding our communities. The project explores the factors within and beyond our control that influence when we die, using premature death as a lens to understand what shapes our mortality. The intention of the project is to define facets that individuals can and cannot control, and a combination of factors that individuals can and cannot control, and how they interact with society.

County.Name Premature.Death Total.Population Population.Density..per.sq.mile. Gender.Dominance Dominant.Race Household.Income Primary.Care.Physicians Mental.Health.Providers Uninsured.Adults Food.Environment.Index Drinking.Adults..Percent. Adult.Smokers..Percent. Total.Violent.Crimes Drug.Overdose.Death.Rate
Accomack County 676 33413 74.36396 Slightly Female Majority 78365.40 14 41 2715 High 15.14 21.3 Low 3.1
Albemarle County 925 112395 156.00186 Strongly Female Majority 146820.10 151 203 6720 High 17.23 12.3 Low 4.5
Alexandria City 1022 159467 34.08855 Slightly Female Plurality 161240.10 124 650 9922 18.14 10.9 2.5
Alleghany County 300 15223 37.32829 Slightly Female Majority 74435.94 6 6 886 High 18.18 22.1 Low 6.8
Amelia County 247 13265 66.05388 Strongly Female Majority 87453.50 1 10 1001 High 17.57 20.5 Low 0.0
Amherst County 523 31307 48.22820 Slightly Female Majority 86901.80 8 12 2214 High 16.76 19.7 Low 3.2

Goal One: Within Our Control

The first graph displays the household income and premature death. The second graph shows income, insurance availability, and premature death. Individuals can modify their income and insurance. While there may be limitations to this adjustment, depending on their socioeconomic status and available resources, these are relatively changeable. These changes can incur positive effects on their lifespan, such as income creating more selections for premium insurance. With more insurance coverage, the individual is relieved of the burden of paying for out-of-pocket health care. Because the United States does not have socialized medicine, many individuals decline to seek out medical attention due to the costs of medical treatments. Unfortunately, many individuals who face medical debt avoid the care they need because of fear of increasing their debt (Aborode et al., 2025). These graphs accentuate the importance of financial freedom and its consequences on premature death.

Virginia Household Income Across Virginia

The “Virginia Household Income by County” displays a map of Virginia, sectioned by counties. The gradient represents the household income, ranging from $54,147 to $214,791, with an average of $99,161. The gradient starts lighter, while it becomes darker, as the household income increases. The household income is calculated by the Average Household Income (In 2024 inflation-adjusted dollars). In addition to the mapping, the outline indicates counties that are in the top 25% of the highest premature death rates. The highlighted counties typically had a lighter color, displaying the relationship between counties with the highest premature death rate and relative income. The latter indicates the importance of financial freedom. With more disposable income, individuals have more opportunities to access better insurance, healthcare, and treatments. The relationship demonstrates a foundation for comprehending how income can contribute to premature death.

Building on the baseline in “Poverty Cycle”, the graph explores the relationship between household income, death per 100,000, expanding on the original graph of the state of Virginia by also adding in uninsured adults. Relating the uninsurance rate to income shows how access to insurance translates into premature death. Often, individuals do not feel comfortable going to the doctor if they do not have health insurance, as many would have to pay out of pocket, dissuading them from getting the care they need - possibly leading to loss of life. Many individuals do not have health insurance because they cannot afford it, or rather the insurance would not cover the care they need, relating to the importance of income and financial freedom.

The graph displays household income on the x-axis, with the death rate per 100,000 individuals on the y-axis. The plot creates circle outlines; the uninsured rate determines the width, and the different colors represent the income groups, where the darker the color, the higher the income. Counties that are outliers within these trends are labeled, drawing the attention closer. Hovering over individual counties populates additional information, including the county name, household income, uninsured, and premature death count. Providing more details enables a more granular lens.

Looking at the granular lens, many of the counties that are outlined have relatively lower average salaries, except for Fairfax County. The Poverty Cycle illustrates how underlying factors, such as income and insurance coverage, can reinforce one another, ultimately limiting healthcare access and shortening life spans. The two plots work together to reveal the importance of how financial status influences the role of insurance as well as premature death, intertwining the factors we can control, which highlights the importance of fiscal responsibility.

Goal Two: Beyond Our Control

There are simply elements of our lives that are out of our control. The first graph explores the availability of mental health and primary care providers across counties. The second graph explores how the population density of the county relates to premature death. The third graph displays that specific gender dominant and race-dominant areas are affected by premature deaths. Lastly, the shinyApp uses drug overdose information, mental health providers, and death to show the unfortunate impact of a resources in high substance-use areas.

An individual cannot control the accessibility of providers around them. However, medical attention can impact the premature death rate, as receiving proper medical care can ensure that their symptoms subside or stabilize. While individuals may have a preference for areas to live, such as a city or a neighborhood, geographic density should not equate to premature death. Your hometown should not equate to a death sentence. Individuals, regardless of location, should have access to proper healthcare and equal access.

Regarding gender and race, many would assume that these factors do not influence premature death; however, there was an experiment completed by the NIH, which displayed health disparities among racial minorities, suggesting that ultimately, death does impact groups differently. “The United States healthcare system distributes services inefficiently and unevenly across populations. Some Americans receive worse care than others” (National Healthcare Quality and Disparities Report, 2021). In addition to geographic information, substance abuse surrounding the area cannot be controlled by one individual; however, substance abuse is also linked to higher usage among minority groups, causing communities to become divided based on opioid usage (CDC, 2022). Certain factors are simply beyond our control, and individuals must navigate the realities of our communities.

Mental Health and Primary Care Providers Across Virginia

Population Density and Premature Death Across Virginia

Premature Death by Racial Concentration and Gender Across Virginia

In “Mental Health and Primary Care Providers Across Virginia,” the top and bottom 5 counties are for the variables, respectively. The animated plot starts with revealing the bottom 5 counties, and works it way to the top counties. Using the animation brings to light the major discrepancies between the top and bottom 5 counties. The light green color indicates mental health providers, which is typically the color associated with the field, symbolizing growth, renewal, and hope, reflecting the journey of recovery. The light blue color represents primary care providers, which symbolizes calmness, trustworthiness, and cleanliness associated with the healthcare field. The graph displays the relative gaps in mental health and primary care providers, per 100,00 individuals. Scaling the information to the county population ensures proper reading of the data. Within the top counties, it shows a relative gap between mental health providers and primary care providers, showing an abundance of mental health providers; however, there is a disparity of primary care providers. The counties with a lower rank depict a relative lack of primary care and mental health providers, scaled to the population. Within the graph, it is evident that certain counties have better access. The graph reveals that a patient’s home location can shape their access to mental health and primary care, reflecting the unequal distribution of healthcare resources across communities. Ideally, healthcare staffing would reflect the geographic distribution of the population; however, this is rarely the reality. Rural communities bear the burden of these disparities, facing greater challenges in accessing adequate care.

Rural residents experience major disparities in mental health outcomes, even though the prevalence of mental illness in rural and metropolitan areas is similar. Mental health access is a persistent issue that requires innovative approaches to resolve. One-fifth of the United States population is rural, and one-fifth of individuals in that area have a mental illness, showing the prevalence of the topic (Morales et al., 2020). Mental health is also a significant aspect of premature death, especially for individuals who struggle with a serious mental illness (SMI), such as bipolar disorder, schizophrenia, and major depressive disorder. They have an increased likelihood of having a reduced life expectancy of approximately 10–25 years compared to the general population (Sippel et al., 2022). Showing that mental health access relates to premature death, as well as access to health care depending on location, ties into population density as well as premature death.

In “Population Density and Premature Death Across Virginia,” the graph is faceted into three subsections based on the county type: rural, suburban, and urban. Each observation within the plot represents a county within Virginia. Depending on the population density, the county is either rural, suburban, or urban. Counties with a population density greater than 1,000 are urban, those with a density of 100 or more are suburban, and the remaining are rural. Population density represents the number of individuals per land area. The counties are plotted based on their respective county, colored by the panel, and the y-axis represents premature death, scaled per 100,000 individuals. If the county is higher on the y-axis, then it has a higher premature death rate. The county names in the outliers are displayed to emphasize the differences in these counties. A regression line is added to each panel, showing a slight upward trend between population density and premature death rate in the rural areas, a slightly decreasing rate for suburban areas, and a relatively horizontal relationship for urban areas. The graph supports the notion that individuals who face certain crises, such as mental illness, would have disproportionate opportunities, as this cements that geographic location determines access, thus access is limited. Premature death is on the rise where there are limits to accessibility. The graph supports the notion that depending on which county the individual lives in, it impacts their health, not only in their access to PCP or MH providers.

The “Premature Death by Racial Concentration and Gender Across Virginia” shows similar information through a different lens. Rather than focusing on mental health, it focuses on another aspect that many individuals are affected by each day - their race and gender. The plot examines whether premature death affects each county similarly, relative to its racial and gender concentration. While it is true that all of us will eventually have the same fate, the graph suggests that death may affect the groups differently. The facet graph divides into two racial concentration groups: majority and plurality. Counties where a single racial group comprises 60% or more of the population are classified as Majority, those where a single group represents 40–59% are labeled Plurality, and all remaining counties are categorized as Diverse. However, none of the Virginia counties fell into the “Diverse” category. Within each faceted graph, the x-axis represents the gender composition, broken down into balanced, slightly female, slightly male, strongly female, and strongly male. Each dot represents a county within the plot. The horizontal bar displays the group means, while the dashed line shows an overall Virginia average. The faceted plot displays the relationship between gender, race, and premature death. The graph indicates the relationship between gender and race and premature death, and that death may not affect everyone the same, depending on demographic features. In the Majority plot, many of the counties are slightly female. The strongly male category has a much higher group mean than the Virginia average. In the Plurality plot, there are fewer counties represented in the group. The balanced category exceeded the Virginia average; however, the remaining categories largely aligned with the state average, except for the slightly male group. Note that the group consisted of only one observation, suggesting that the county could be an outlier. The graph indicates that there are slight deviations in how we are affected by death, which is unfortunately perpetuated by the healthcare inequality in society. A study states that healthcare delivery is not experienced equitably by all populations and that there is a significant disparity in healthcare. There is a noticeable difference between population groups in the way they access, experience, and receive healthcare (National Healthcare Quality and Disparities Report, 2021). These unchangeable aspects of our lives impact our future.

Goal Three: The Intersection

While most of us wish that we could control all the factors to prevent premature death, unfortunately, that is not the case. In fact, many of the factors that we can control and cannot control are relatively intertwined, such as those explored in the first graph, it explores the relationship between health risks (self-induced, such as alcohol and smoking usage) and access to primary care providers. Individuals who smoke and use alcohol more have more health complications, and thus need access to healthcare.

The second visual explores the disparities between rural and urban areas, while investigating insurance and death rates, and also considering drug overdose and other substance usage. While it would be nice to categorize the items that we can and cannot control, ultimately, different facets of life interact in ways that are as easy to distinguish between as they are to categorize into factors that we can and cannot control. Within the first plot, individuals who drink and smoke are at an increased risk of chronic health conditions, requiring more medical attention. The choices we make can then result in a larger issue that exposes us to the inequalities around us, such as healthcare access (Moore et al., 2022). In addition, learning about the interdependence of drug overdose in rural and urban areas is critical. Understanding how these values are interlinked offers a more nuanced understanding of how society shapes mortality. There has been an increase in prescription opioid misuse, and the issue concentrates in areas with large rural populations, such as Kentucky, West Virginia, Alaska, and Oklahoma. Researchers noted that there are greater opioid prescriptions in rural areas, creating availability from which illegal markets can arise (Keyes et al., 2014).

Factors that we can and cannot control are heavily interlinked. While understanding certain choices in life can help reduce the risk of premature death, such as limiting substance use, these factors cannot be easily divided into what can and cannot be controlled. Our lens of mortality is much more limited in comparison to the reality of our certain death.

In “Food Access and Health Risks,” the plot represents the food index scores of individual counties, as well as risk factors that could influence their health, such as drinking and smoking, and the context of the care they have surrounding them, with the primary care variable. Each county is either a dark green or light blue dot, representing whether it has a high, low/medium food index score. Food index scores measure how well an individual’s diet aligns with dietary guidelines. Lower scores suggest dietary improvement. The scores consider the individual’s intake of total fruits, total vegetables, protein foods, refined grains, added sugars, and the like. The individual can hover over the county for additional information, such as alcohol, smoking, and access to primary care. The stacked charts show the counts of adults (18+) who use the substances and the count of primary care physicians practicing medicine in the county. Relating the factors that could cause individuals to go to a primary care provider, such as substance use and poor diets, shows how certain individuals’ choices can impact premature death. Understanding the decisions that influence lifespans allows us to anticipate and interpret likely outcomes.

Conclusion

The project explores mortality with premature death in three varying lenses: within our control, beyond our control, and the intersection. Within our control, we emphasized the importance of financial freedom through the availability of income and insurance premiums. Beyond our control, noted the important division between gender, race, and population density, drug usage. While these discrepancies are significant, organizations, such as the WHO, are working every day to make healthcare more accessible, hoping to limit premature death by creating access for the most vulnerable and marginalized people in society (World Health Organization, 2024). While our death is inevitable, there are factors we can control and should consider each day we are given to avoid premature death.

Glossary

Diversity (Racial Concentration): Counties where a single racial group makes up 0-40% of the population.

Gender Concentration: Counties are divided into slightly female/male, strongly female/male, or balanced. Slightly is determined if the group has 1-2% more of the population, whereas strongly is determined by more than 2% of the population. Balanced is around 49.00-50.1%.

Household Income: The average household income in 2024 inflation-adjusted dollars.

Majority (Racial Concentration): Counties where a single racial group makes up 60% or more of the population.

Plurality (Racial Concentration): Counties where a single racial group makes up 40-59% of the population.

Premature Death: Death occurring before an average age, typically under 75 or 70 years, representing a loss of potential life.

Population Density: Represents the average number of individuals living in the specific area, and in this case, county in Virginia.

Citations

Aborode, A. T., Oginni, O., Abacheng, M., Edima, O., Lamunu, E., Folorunso, T. N., Oko, C. I., Iretiayo, A. R., Lawal, L., Amarachi, R., Badri, R., Bamigbade, G. B., Olanrewaju, O. F., Agwuna, F. O., & Adesola, R. O. (2025). Healthcare debts in the United States: A silent fight. Annals of Medicine and Surgery (2012), 87(2), 663–672. https://doi.org/10.1097/MS9.0000000000002865.

Agency for Healthcare Research and Quality. (2021, December). Disparities in Healthcare. In 2021 National Healthcare Quality and Disparities Report. https://www.ncbi.nlm.nih.gov/ books/NBK578532/.

Centers for Disease Control and Prevention. (2022, July 19). Overdose Deaths Rise, Disparities Widen. https://www.cdc.gov/vitalsigns/overdose-death-disparities/index.html.

County Health Rankings & Roadmaps. (2023). Health Data 2023 Release [Data dictionary]. https://www.socialexplorer.com/tables/HD2023/R50101550.

Federal Bureau of Investigation. (2019). Crime in the United States, 2019 [Uniform Crime Report]. U.S. Department of Justice. https://www.socialexplorer.com/reports/social explorer/en/report/91d69e2e-379b-11f1-b5a6-47f847d70b80.

Keyes, K. M., Cerdá, M., Brady, J. E., Havens, J. R., & Galea, S. (2014). Understanding The Rural-Urban Differences in Nonmedical Prescription Opioid Use and Abuse in the United States. American Journal of Public Health, 104(2), e52–e59. https://doi.org/10.2105/ AJPH.2013.301709.

Moore, J. R., Castro, Y., Cubbin, C., & von Sternberg, K. (2022). Associations of At-risk
Drinking, Current Smoking, and Their Co-occurrence With Primary Care Service Utilization. American Journal of Health Promotion, 36(3), 429–439. https://doi.org/ 10.1177/08901171211056130.

Morales, D. A., Barksdale, C. L., & Beckel-Mitchener, A. C. (2020). A Call to Action to Address Rural Mental Health Disparities. Journal of Clinical and Translational Science, 4(5), 463–467. https://doi.org/10.1017/cts.2020.42e.

National Institute on Minority Health and Health Disparities. (2023). Population (Hispanic) for Virginia by County: Both Sexes, All ages, 2019–2023 [Data table]. HDPulse Data Portal. https://hdpulse.nimhd.nih.gov/data-portal/social/table?socialtopic=070&socialtopic_opti ons=social_6&demo=00026&demo_options=pop_12&race=00&race_options=raceall_ 1&sex=0&sex_options=sex_3&age=001&age_options=ageall_1&statefips=51&statefip s_options=area_states.

Sippel, L. M., Myers, A. L., Brooks, J. M., Storm, M., Mois, G., & Fortuna, K. L. (2022). Risk And Protective Factors in Relation to Early Mortality Among People with Serious Mental Illness: Perspectives of Peer Support Specialists and Service Users. Psychiatric Rehabilitation Journal, 45(4), 343–351. https://doi.org/10.1037/prj0000522.

U.S. Census Bureau. (2020). 2020 Census of population and housing [Documentation]. https://www.socialexplorer.com/tables/CDHC2020/R50101542.

U.S. Census Bureau. (2024). ACS 2024 5-year estimates [Data dictionary]. https://www.socialexplorer.com/reports/socialexplorer/en/report/aea15bee-0c7a-11f1-b6 82-97a3c8c53700.

Virginia Department of Health. (2026, March 10). Drug Overdose Deaths [Data dashboard]. https://www.vdh.virginia.gov/drug-overdose-data/overdose-deaths/.

World Health Organization. (2024). Improving service access and quality. https://www.who.int/ activities/improving-service-access-and-quality.