BIOL 4254 LAB REPORT - CASEY NOYES

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Louisiana State University *

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4254

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Health Science

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Feb 20, 2024

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Name : Casey Noyes Section Number : 1 Pattern: Due to several socioeconomic factors, specifically health care gaps, individuals living in rural areas tend to be less healthy than their urban-living counterparts. Overall research question: Does density-based area classification affect mean life span and survivorship in Louisiana? Research Hypothesis 1 : Living in a rural area with poor healthcare access decreases mean life span in Louisiana. Reasoning: Individuals living in rural areas have poor access to health care, causing individuals to die from preventable causes at an earlier age than those who have proper access. Null Hypothesis 1 : Mean life span will not differ in urban versus rural areas. • Analytical Approach and Appropriate Graph type: t-test and bar graph • Independent Variable: urban and rural areas • Dependent Variable: mean life span Research Hypothesis 2 : Living in a rural area with poor healthcare access decreases age specific survivorship in Louisiana. Reasoning: Individuals living in rural areas have poor access to health care, causing individuals to die from preventable causes at an earlier age than those who have proper access. Null Hypothesis 2 : Age specific survivorship will not differ in urban versus rural areas. • Analytical Approach and Appropriate Graph type: survivorship curve, no stats • Independent Variable: age at midpoint • Dependent Variable: survivorship Academic integrity statement By signing, I acknowledge that I have completed this coversheet and the attached report according to the Code of Student Conduct and the syllabus for this course (i.e., I certify that I wrote this report independently and that the material within is my intellectual property and not anyone else’s): __Casey Noyes _______________________ Date _25 September 2023 _______________

Abstract: When choosing a location to live out one’s life, many factors come into play including school systems, communities, and family locations. However, choosing a certain location to reside in may have even deeper impacts, including how long an individual may live. Within this study, our main goal was finding how population density of an area affects the human lifespan and providing reasonable explanations for this phenomenon. To complete this task, we gathered data from several cemeteries in two separate parishes in Louisiana and input the collected information into Microsoft Excel. We then used the data to construct life tables, calculate mean life span, and survivorship curves using JMP version 16. The information obtained showed no significant relationship between population density and mean life span but did show some minor differences in middle-age survivorship. Although our findings were not outstandingly significant, we were able to use the information gathered to suggest further studies on how the different factors of populations affect the human life span. Introduction: Studying populations in an ecological context enables us to gather data about characteristics, relationships, and evolution over time in multiple species. Life tables are an important ecological tool used to analyze and predict this gathered data. Using data from a closed population’s life table, one can develop a survivorship curve by plotting the number of individuals alive at each age class. In ecology, there are three types of survivorship curves, each indicating different rates of mortality throughout the population’s lifespan. In this study, we use humans as our model organism, who show a Type I survivorship curve with a large portion of individuals surviving through adolescence and the majority or mortalities occurring in old age. With high levels of intelligence, humans are a complex model organism to study since they possess the constant ability for higher level thinking, innovation, and ability to communicate. In just the last 200 years, life expectancy has doubled from forty years to about eighty years in most countries (Roser et al. 2013). A large contributor of this increased lifespan is the technological and medical advances, however, not all human populations have easy access to these resources. In rural areas, individuals are less likely to have medical resources readily available due to increased distance between treatment centers. In this paper, we use data collected from cemeteries to create life tables of the different geographical regions of Louisiana to see the effect of population density on life expectancy and survivorship. According to the 2014 RUPRI Health Panel report, physical barriers to healthcare result in unaddressed medical needs, including a lack of preventive and screening services and treatment of illnesses (MacKinney et al. 2014). Based on this information, our expectations are as follows: People living in rural areas will have lower mean lifespans than those in urban areas, and age-specific survivorship will be decreased in all age groups within these rural areas. Methods: We collected the sex, age at death, and year of death from 240 individuals buried in Louisiana from six cemetery databases in two different parishes: Baton Rouge National Cemetery (40), Jewish Cemetery (40), and Magnolia Cemetery (40), Bastrop City Cemetery (40), Beekman Cemetery (40), and Christ Church Cemetery (40). Using findagrave.com, we were able to choose individuals in alphabetical order based on their birth year (1900 ± 5 years). For treatment purposes, we combined the data from Baton Rouge National, Jewish, and Magnolia cemeteries to create our urban population (East Baton Rouge Parish), and our rural

population (Morehouse Parish) was created with the data from Bastrop City, Beekman, and Christ Church cemeteries. We entered this data into Microsoft Excel to calculate Mean Life Span using the following equation: MLS=[Σ(age interval midpoint)(d x )]/n 0 , where d x is the number of individuals dying in an age class, and n 0 is the number of individuals alive at the start of age class 0. We also used the excel sheet to create survivorship curves for the urban and rural populations. We began by calculating survivorship using the following equation: l x =n x /n 0 , where n x is the number of individuals alive in an age class, and n 0 is the same as in the MLS equation. By calculating l x , we can now find N x (1000), which will be used as the dependent variable in our survivorship curve. To calculate N x (1000), we multiplied l x by 1000. The values derived from the MLS calculation were transferred to JMP version 16 and used to run a t-test and generate a bar graph to view the relationship between MLS and population density. JMP was also used to build survivorship curves for the two different population density classifications using the data from the survivorship calculations. The statistics from JMP were then used to determine if the data was significant. Results: Figure 1. The life span of humans buried in areas with low population densities (Rural) and high population densities (Urban). Each bar represents the mean life span (± SE) calculated by life table statistics. There was not a significant difference in the mean life spans of those buried in rural versus urban areas (t 4 = 0.569, p = 0.5995). Average life span of individuals buried in rural areas was 58.0 ± 3.726 (mean ± SE) and the average life span of individuals buried in urban areas was 61.0 ± 3.726. Although there is a slight numerical increase in mean life span for individuals buried in

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urban areas, the lack of statistical significance (p > 0.05, overlapping error bars) does not allow us to reject the null hypothesis. Figure 2. Survivorship curves of individuals buried in rural versus urban areas. There was no significant difference in age specific survivorship for the age classes between 0-19 and 65-109, but survivorship was statistically increased in urban areas from ages 20-64. Both urban and rural areas exhibit a Type I survivorship curve, indicating that mortality in humans is persistently increased in old age. Discussion: Based on our findings, mean life span for Louisiana residents was not significantly affected by residing in rural versus urban areas. Thus, we were unable to reject null hypothesis 1 . Additionally, the age specific survivorship data indicates that survivorship was not significantly affected by living in rural versus urban areas for the ages 0-19 and 65-109. However, we were able to note significant increases in survivorship of those living in urban areas from the ages of 20-64. We were able to reject null hypothesis 2 . Therefore, although MLS was unaffected, there is data to show that rural living does negatively impact survivorship in certain age groups. The negative impact on age-specific survivorship in middle-aged rural individuals could be due to preventable diseases occurring during these age classifications which are untreated in the medically underserved communities. A recent study performed by the CDC points out that the increased distance between healthcare facilities and trauma centers in rural areas leads to a 50 percent increase in unintentional injury deaths in rural areas compared to their urban counterparts (CDC 2017).

Additional causes of premature death could be due to higher rates of cigarette smoking, obesity, and decreased seatbelt use in rural areas (CDC 2023). Although our research did highlight some discrepancies in rural versus urban lifespans, it is important to note that we were unable to see several crucial aspects that could affect these individuals’ lifespans. For example, since we did focus on cemetery data, we were unable to determine where each individual spent the majority of their life or where they actually died. Future research on this topic could use hospital records of rural versus urban areas in order to combat this issue. Other factors that could affect human lifespans that we did not address include genetics, gender, race, diet, exercise, lifestyle, and many more (Disabled World 2022). Additional studies performed on this topic should include more in-depth analysis of individuals’ lifestyles, long-term housing, and other confounding variables. Overall, our study proved that while there is some relation between population density and human survivorship, it is nearly impossible to determine causation due to the countless other factors that contribute to human mortality. Attempting to define human lifespans based on one factor alone is extremely difficult due to the intertwining of so many elements within the human life. References: Centers for Disease Control and Prevention. 2017, January 12. Rural Americans at higher risk of death from five leading causes. Centers for Disease Control and Prevention. https://www.cdc.gov/media/releases/2017/p0112-rural-death-risk.html Centers for Disease Control and Prevention. 2023, April 21. Leading causes of death in rural America. Centers for Disease Control and Prevention. https://www.cdc.gov/ruralhealth/cause-of-death.html#:~:text=Residents%20of%20rural %20areas%20in,use%20than%20their%20urban%20counterparts. Disabled World. 2022, April 14. . Longevity: Extending Life Span Expectancy. Disabled World. www.disabled-world.com/fitness/longevity/ MacKinney, C., Coburn, A. F., Lundblad, J. P., McBride, T. D., Mueller, K. J., & Watson, S. D. 2014, August. Access to Rural Health Care – A Literature Review and New Synthesis. RUPRI. https://rupri.org/wp-content/uploads/20140810-Access-to-Rural-Health-Care-A-Literature- Review-and-New-Synthesis.-RUPRI-Health-Panel.-August-2014-1.pdf Roser, Max, Esteban Ortiz-Ospina, and Hannah Ritchie. 2013, May 23. Life Expectancy. Our World in Data. https://ourworldindata.org/life-expectancy#twice-as-long-life-expectancy- around-the-world.

BIOL 4254 LAB REPORT - CASEY NOYES

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