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Transcript: Survivorship Curve Before and After Influenza Vaccine Cady Houghton Procedure Background Experimental Design Methods Research Question 1) A data base of cemeteries was looked up to find death dates and information. 2) From the data base information was put into a google drive document. 3) 40 people dying before the 1945 influenza vaccine were put into one chart showing the ages of death. 4) 40 people dying after the 1945 influenza vaccine were put into another chart also showing ages of death. 5) Both charts of groups of people were converted into a life history table. 6) From each life history table the data was put into another chart to make two survivorship curve charts. 7) The survivorship curve charts were put into one survivorship curve chart to show the difference in curves. A laptop was used for this lab. If the influenza vaccine works then if we compare the survivorship curve of before and after the vaccine it will show people living longer because the vaccine will be decreasing the death rate. The independent variable in this experiment is the influenza vaccine. The dependent variable is the death rate in this experiment. The exponential variables in this experiment are the other disease epidemics during the time period the data was chosen from. The control is the survivorship curve before the influenza vaccine and the experimental is the survivorship curve after the influenza vaccine. The survivorship curves of before and after the influenza vaccine was created are both Type I survivorship curves. They also have the caracteristics for K-selected species. While they are both Type I and K-selected species, the survivorship curve after the influenza vaccine was created had a decrease in death rates between 20 and 70 years of age of about .5 #/1,000 surviving. Then, towards the 75 and 90 years range, the #/1000 surviving leveled out and became extremely similar in curves. Did people die younger before the influenza vaccine was created? In comparing survivorship curves a Log10 scale is used for its ability to show you greater numbers in a more readable way and to show same mortality with the same curve when two scales are put together. In a Log10 scale there are three Types of information. The first, Type 1, are mainly humans. Type I survivorship curves are characterized by high survival in early and middle life, followed by a rapid decline in survivorship in later life. Type 2 is mainly birds and Type 3 is mainly reptiles. Within these Types are two categories of species, K-Selected, or Equilibrium, species and R-Selected, or Opportunist, species. K-Selected species usually have stable resources and a stable environment, and this is usually what humans are categorized as. R-Selected species are usually whose population size tends to fluctuate greatly in reaction to variations in the environment. Comparing the survivorship curves, defined as a graph showing the number or proportion of individuals surviving at each age for a given species or group cohort, before and after the influenza vaccine was created will show if the vaccine helped increase the population. Before the 1940s when the influenza vaccine was created, the epidemic was particularly deadly in US Army training camps, where the death rate was as high as 80% in some camps. Death rate is usually calculated as the number of deaths per one thousand people per year. usually calculated as the number of deaths per one thousand people per year. The US military was getting hit with influenza the most forcing them to developed the first approved vaccine for influenza, which was used in the Second World War. Studying this specific survivorship curve will also help to tell if their were more survival rates in the US military during the time. Results With the original research question in mind, before the influenza vaccine was created people were dying younger. This question was answered by the survivorship curve graph comparing the curves before and after the influenza vaccine. As the graph shows between 20 and 70 years of age there is a significant difference in curves, meaning that the death rate before the vaccine in that time period was increased more than after the vaccine was created. From that specific area of the graph it can be concluded that people were dying younger before the influenza vaccine was created, thus the hypothesis was correct. For future study it would be interesting to see the survivorship curve in the US military before and after the vaccine not just the population in general. Because the US military was the one who invented it because they were the one being impacted with the death rate from the influenza epidemic the most, the survivorship curve would have a chance of being more predominant. Graph Clear Digital Media, Inc. (1997-2011). Cemetery Records Online. October 1, 2013, www.interment.net Genealogical Gleanings. (1997-2004). Plagues and Diseases. October 3, 2013, genealogical-gleanings.com/Plauges.htm Introduction

Medical Research Poster Presentation

Transcript: Background of Type 2 Diabetes Type 2 diabetes is a chronic metabolic disorder characterized by insulin resistance and relative insulin deficiency. It presents significant health risks, including cardiovascular disease, especially in populations with inadequate healthcare access and awareness, such as rural areas in India. Introduction Key Findings on Obesity and Vascular Ageing Methodology and Results The study revealed a significant correlation between increased BMI and arterial stiffness, indicating that even in rural populations with low obesity, vascular health is adversely affected. Findings suggest a need for targeted health interventions in these communities. This section details the study design, population sampling, methodology utilized in pulse wave analysis, and statistical evaluation used to investigate the correlation between obesity measures and vascular ageing in Type 2 diabetics. Key findings highlight critical insights into obesity and vascular health in rural West India. This section outlines the critical aspects of Type 2 diabetes, emphasizing its correlation with obesity and vascular ageing in rural West India, a region with low prevalent obesity. Understanding these components is essential for addressing health disparities in this population. Obesity in Rural West India Study Design and Population Statistical Analysis In rural West India, obesity rates are relatively low compared to urban counterparts; however, the prevalence of undernutrition complicates health outcomes. This contradiction highlights the need for targeted interventions to address both obesity and malnutrition in diabetes management. A cross-sectional study was conducted involving Type 2 diabetics from rural regions of West India. Participants were selected based on specific inclusion criteria, focusing on those with low prevailing obesity levels to assess the correlation with vascular ageing. Statistical analyses were performed using SPSS software, employing correlation coefficients to establish relationships between obesity measures and vascular ageing. Confidence intervals were calculated to validate the significance of findings. Importance of Vascular Ageing Objectives of the Study Pulse Wave Analysis Technique The study aims to investigate the correlation between obesity measures and vascular ageing in Type 2 diabetics in rural West India. By identifying these relationships, the research seeks to inform targeted health interventions and improve patient outcomes in the community. Vascular ageing refers to the progressive deterioration of blood vessels, influencing cardiovascular health. In individuals with Type 2 diabetes, early vascular ageing can lead to increased risk of heart diseases, necessitating comprehensive assessments to mitigate risks in rural populations. Pulse wave analysis (PWA) was utilized to assess arterial stiffness and vascular ageing. This non-invasive method measures the speed of blood pressure waves through arteries, providing insight into overall vascular health among participants. Data Collection Methods Data were collected through structured interviews and clinical examinations. Key variables included obesity indices (BMI, waist circumference) and vascular health markers, ensuring comprehensive data capture for analysis. Discussion and Conclusion This section synthesizes the findings from correlating obesity measures with vascular ageing in type 2 diabetics in rural West India, highlighting the broader implications for health interventions in similar populations. Summary of Findings Interpretation of Results This study reveals a critical link between measures of obesity and vascular ageing in rural type 2 diabetics in West India. It emphasizes the necessity of recognizing and addressing vascular risks associated with diabetes to improve health outcomes in these vulnerable populations. The study found a significant correlation between obesity measures and vascular ageing in type 2 diabetics, indicating that even in low obesity prevalence settings, weight-related health risks are evident. This underscores the importance of monitoring vascular health in all demographics, regardless of obesity status. Recommendations for Future Research Comparison with Existing Literature Future studies should include larger, more diverse populations to further explore the relationship between obesity and vascular ageing. Longitudinal studies examining these correlations over time are essential for identifying intervention points for healthcare practitioners. Current findings align with previous studies indicating a strong relationship between obesity and vascular ageing. Unlike findings from regions with higher obesity, this research highlights that vascular issues can manifest even when obesity prevalence is low, expanding understanding of diabetes complications. Medical Research Poster Presentation Implications for Rural Health The results highlight pressing health challenges in rural regions where diabetes

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