Aggressive Breast Cancer Vs.Suicide Rates - Term Paper Example

?Marina Musheyeva AGGRESSIVE BREAST CANCER VS.SUICIDE RATES STATISTICS FOR BEHAVIORAL SCIENCES W. Kunz 14 This is a study aimed to compare the relationship of the suicides rates with aggressive breast cancer. Aggressive breast cancer is has been mentioned to lead to depression, therefore I seek to establish where patients of this form of breast cancer are prone to commit suicide. To achieve my goal, I collected data from the American Cancer Society of both the suicidal rates and aggressive breast cancer rate from thirty states in United States randomly. This data is more recent (2010). Table of contents Page 1 - Cover page Page 2 - Abstract Page 3 - Table of contents Page 4 - Introduction Page 5 - Presentation of raw data Page 6 - Presentation of arrayed aggressive breast cancer data Page 7 - Presentation of arrayed suicide rate data Page 8 - Descriptive statistics for aggressive breast cancer data Page 9 - Descriptive statistics for suicide rate data Page 10 - Grouped data for aggressive breast cancer Page 11 - Histogram for aggressive breast cancer Page 12 - Grouped data for suicide rate Page 13 - Histogram for suicide rate Page 14 – Scattergram of both variables Page 15 - Correlation of both variables Page 16 - Linear Regression graph and equation Page 17/18 - Samples using the Regression equation Page 19 - Conclusion regarding original thesis Page 20 - Critique and what’s nest Page 21 - Appendices INTRODUCTION Breast cancer is one of the most fatal and prevalent of all killer diseases and ranks in the top three killers of women in the USA, though it also affects men. Meanwhile there has been an association of the aggressive breast cancer with suicides in the United States. In this study, I will conduct statistical test to verify this relationship of aggressive breast cancer with rates of suicides. My hypothetical theory is that victims of the aggressive breast cancer are more prone to commit suicide. I anchor my theory to the worrying trend of the increasing mental anxiety and depression among the aggressive breast cancer patients. To achieve my objective of linking suicide rates to aggressive breast cancer condition, I collected data for the suicides rates and aggressive breast cancer from thirty states. I subjected this data to statistical analysis to establish a link in these two variables. These states were collected randomly. PRESENTATION OF RAW DATA AND DISCUSSION ARRAY OF ALL DATA FROM THE THIRTY STATES Presentation of data on arrayed form with discussion  Table 1: Female Breast Cancer Incidence and Suicide Rates in thirty states (2002-2006) State Breast Cancer Incidence Suicide Rates Alabama 114.9 23.2 Alaska 128.9 22.0 Arkansas 113.9 22.8 California 128.0 23.9 Colorado 123.7 22.6 Connecticut 137.5 24.3 Delaware 123.8 23.5 Florida 115.9 21.8 Georgia 120.7 22.5 Hawaii 139.1 21.7 Idaho 118.7 22.5 Illinois 124.1 24.2 Indiana 115.1 24.2 Iowa 124.3 22.8 Kansas 125.2 24.2 Kentucky 118.8 24.4 Maine 128.0 23.4 Massachusetts 134.6 24.4 Missouri 122.2 25.3 Montana 118.8 23.0 Nevada 115.1 24.9 New Jersey 132.6 27.3 New Mexico 115.6 23.4 Oklahoma 127.6 25.0 Oregon 130.5 24.2 Pennsylvania 124.1 25.9 Rhode Island 129.6 23.5 South Carolina 121.2 22.8 Virginia 121.4 24.4 Wyoming 118.2 22.9 Source: Source Incidence 4, 2009 Data which is to be subjected to statistical tests for ascertain the relationship of the two variables. The data is listed alphabetically according to states Array of aggressive breast cancer incidence in thirty states data, in descending order State Aggressive Breast Cancer Incidence Suicide Rates 1. Hawaii 139.1 21.7 2. Connecticut 137.5 24.3 3. Massachusetts 134.6 24.4 4. New Jersey 132.6 27.3 5. Oregon 130.5 24.2 6. Rhode Island 129.6 23.5 7. Alaska 128.9 22.0 8. California 128.0 23.9 9. Maine 128.0 23.4 10. Oklahoma 127.6 25.0 11. Kansas 125.2 24.2 12. Iowa 124.3 22.8 13. Illinois 124.1 24.2 14. Pennsylvania 124.1 25.9 15. Delaware 123.8 23.5 16. Colorado 123.7 22.6 17. Missouri 122.2 25.3 18. Virginia 121.4 24.4 19. South Carolina 121.2 22.8 20. Georgia 120.7 22.5 21. Kentucky 118.8 24.4 22. Montana 118.8 23.0 23. Idaho 118.7 22.5 24. Wyoming 118.2 22.9 25. Florida 115.9 21.8 26. New Mexico 115.6 23.4 27. Indiana 115.1 24.2 28. Nevada 115.1 24.9 29. Alabama 114.9 23.2 30. Arkansas 113.9 22.8 Among the data sampled for the thirty samples for the two variables, there existed no relationship between the breast cancer rate and the suicide rates. This can been observed in the table above where a state such as Nevada with a low breast cancer recording a relatively high suicide rate as compared to Hawaii with the highest incidence rate of aggressive breast cancer. Array of suicide rates data in thirty states, in descending order State Suicide Rates Aggressive Breast Cancer Incidence 1. New Jersey 27.3 132.6 2. Pennsylvania 25.9 124.1 3. Missouri 25.3 122.2 4. Oklahoma 25.0 127.6 5. Nevada 24.9 115.1 6. Kentucky 24.4 118.8 7. Massachusetts 24.4 134.6 8. Virginia 24.4 121.4 9. Connecticut 24.3 137.5 10. Illinois 24.2 124.1 11. Indiana 24.2 115.1 12. Kansas 24.2 125.2 13. Oregon 24.2 130.5 14. California 23.9 128.0 15. Delaware 23.5 123.8 16. Rhode Island 23.5 129.6 17. Maine 23.4 128.0 18. New Mexico 23.4 115.6 19. Alabama 23.2 114.9 20. Montana 23.0 118.8 21. Wyoming 22.9 118.2 22. Arkansas 22.8 113.9 23. Iowa 22.8 124.3 24. South Carolina 22.8 121.2 25. Colorado 22.6 123.7 26. Georgia 22.5 120.7 27. Idaho 22.5 118.7 28. Alaska 22.0 128.9 29. Florida 21.8 115.9 30. Hawaii 21.7 139.1 This trend is also repeated when our data is sorted in a descending order with respect to the suicide rate with the state of New Jersey having the highest suicidal rate but not necessarily the highest aggressive breast cancer rate which is held by Hawaii, which apparently has the lowest suicidal rate. Descriptive statistics for aggressive breast cancer rates Mean 123.7367 Median 123.75 Mode 128 Standard Deviation 45.763 In the descriptive statistics for the breast cancer rates, the median of the data falls closely to the mean indicating that most the aggressive breast cancer rates across the 30 states cluster around the mean. Descriptive statistics for suicides rates Mean 23.75333 Median 23.7 Mode 24.2 Standard Deviation 1.168 In the deceptive statistics for the suicides rates the mean and the median are best representative of the rates across all the thirty states since they are close to each other mean= 23.7533 and median 23.7.Measures of central tendency calculated in the descriptive statistics include the mean, median and mode of the two sets of variables. Grouped data for aggressive breast cancer rate and suicide rates   prevalence rate suicide rates 0-100 189.6 101-110 0 111-120 351.4 121-130 449.1 Histogram of aggressive breast cancer Highest cancer rates are recorded at around 125 which is close to the mode in the descriptive statistics for breast cancer. Histogram of suicide rates Suicides rates are high around 25 indicative of the mean of the suicides rates across the states. Overall there is the difference between the states recording the highest suicide rate is large as shown in graph above. Scattergram of aggressive breast cancer and suicide rates In the scatter gram of the two variables I attempted to relate there is apparently no link/relation between the rates of aggressive breast cancer and suicides as seen in the graph above and the implied link may therefore not exist. Correlation of aggressive breast cancer rates and suicide rates correlation Correlation of determination R=.004 R2=0.000 Linear regression graph and equation Regression Model – Linear type The regression model implemented is of the linear type. However, the correlation between the 2 variables – Breast Cancer and Suicide rates have a significance of .022 which is close to zero. The constant is 20.618. The model can therefore be summed as y=mx+c where c= 20.618 and m=0.024 Graphs (histograms) of grouped data and discussions  As seen previously, if there is a lot of data to be analyzed, grouped data can be used to make it easier to work with the data. Furthermore, the problem of outliers is minimized when grouped data is used. Histograms can be drawn from data that has been grouped. From the above two sets of data we can get histograms as follows. Graph of the two fields (scatter gram, two graph-polygons, one polygon with two scales, your choice) with discussion. Scatter grams and polygons will help us further analyze and work with the data. Scatter grams can help us see if there s any pattern in the data one is working with. Any outliers can also be identified using scatter grams. The scatter grams for our information from the two sets of data above are as follows. Correlation of the two variables with discussion  Correlation measures the proportion between variables and how the variables relate. It is usually measured between two variables thought to have some relation to each other. Correlation occurs if the change in one variable affects another variable. This could be either positive or negative. A positive correlation means that both variables are moving in the same direction. For instance, if one variable is increased, the other variable will increase by the same proportion or otherwise. A decrease in either variable will cause a decrease in the other too. A negative correlation means that the variables move in different directions. The movement is most likely to be in the same proportion or in a certain proportion. For instance, if one variable is increased, there will be a proportionate decrease in the other variable. If one variable moves up, then the other must move down; if one variable becomes largely positive, then the other becomes largely negative by the same proportion. The pivot point of correlation is mostly the number zero. The closer a correlation figure is close to zero, the less the two variables are correlated. The further away the correlation figure is from zero, the more correlated the variables are. Our thesis was measuring the correlation between prevalence of breast cancer and suicide rates among people living in the thirty states of United States of America. The correlation between breast cancer occurrence and suicide rates was -0.004. Regression analysis involves studying how an independent variable is affected by another variable called the dependent variable. In regression, a formula is formed to show how the random variable can be adjusted in order to affect the independent variable. The formula usually contains at least one random and unknown variable connected to the independent variable. There is usually a constant in the formula, which sometimes represents a margin of error of the data or formula. The smaller the margin of error is, the more reliable the formula and data are. The bigger the margin of error is, the more unreliable the data is, or there could be many outliers interfering with the data. The constants or margins of errors could be negative or positive, depending on the data being used. Regression analysis can be done using programs in the computer or by drawing graphs and getting information about the x and y intercepts and the nature and shape of the gradient of the line graph. Straight lines in graphs as opposed to curves suggest a linear correlation between the independent and dependent variables. These lines can also have positive or negative gradients, suggesting the presence of positive or negative regression between the two variables. In the data above, a regression analysis can be done to determine how prevalence of breast cancer relates with suicide rates among people of Caucasian origin living in the United States of America. The independent variable is prevalence of breast cancer while the dependent variable is the suicide rates for the data presented above representing the Caucasian race. The margin of error in the regression formula for prevalence of breast cancer relates with suicide rates being the dependent factor, among breast cancer patients who are of Caucasian American descent is -0.00010131, which is very small indeed, suggesting that the data is mostly accurate. Conclusions regarding the original thesis  The original thesis suggested that there might be some correlation between prevalence of breast cancer and suicide rates among victims of breast cancer itself. This paper has used statistical methods and graphs in order to determine the truth in this thesis. The data was collected from Caucasian patients of breast cancer, and the focus was directed to this race because data available has recorded the highest rates of breast cancer and suicide rates among this particular group. Data from thirty states was considered over a period of four years getting the total number of patients from each race. Central measures of tendency like the mean and median were considered. The figures gotten could be compared with means and median for other races to determine whether this figure is high or low. Measures of dispersion like the variance and standard deviation should be treated the same way. However, the variance and standard deviation seems to be on the low side from the mean. If these figures are higher than those of other races, in general, the mean and median for Caucasians will tend to be high for suicide cases among breast cancer patients. There appears to be a weak and negative correlation between breast cancer prevalence and suicide rates among Caucasians, suggesting that an increase in the number of reported cases of breast cancer led to a decrease in the rates of suicide among them. Furthermore, there could be very little relation between breast cancer and suicide rates among people of the Caucasian origin. The regression analysis showed that there was a very small margin of error in the data collected, further showing that there really is a correlation between breast cancer prevalence and suicide rates among patients of Caucasian origin in the United States of America, though it is a very small one, and possibly insignificant in number. Critique and what’s next  In the study above, the only factor that was considered was the race of the patients. Furthermore, there are many more other races living in the United States for who breast cancer, and hence the rates of suicide related to these cases affect. Though these are in a smaller number and are not affected to an extent as the two large races, a factor contributed by the fact that these people are less in numbers than Americans who are Caucasians, the proportion of these patients could be larger when compared to the total numbers of their populations living in the United States of America. Furthermore, only 30 states in the United States were considered, representing a little over a half of the total number of states in the US. As is common, many of these states are occupied by majority of some races and minority of others. Collecting data in these scenarios, therefore, presents some kind of bias in the data. With these kinds of errors, it is not easy to determine whether the findings in this study are representative of the breast cancer prevalence and suicide rates related to these across the whole of the United States of America. Furthermore, some data was not accurately collected, especially those labeled under 16 cases in a state. In order for such a study to be successful, all data collected has to be accurate to reduce errors in the study. Other factors should be considered as well in order to cover most, if not all grounds, including the stage of diagnosis, the socio-economic status of patients and their families, risky habits, family history and occupational exposure to breast cancer. When all these are put into consideration and studied, the main correlation factors can be considered and help in reducing the cases of suicide among breast cancer patients, in order to help them live better and positive lives. More scientific research should also be carried out to determine why race is a significant factor in the prevalence of breast cancer. This could help future generations of all races achieve better qualities of life, armed with such knowledge and information. Works Cited Data sources: Incidence – Cancer in North America, 2002-2006. Volume One: Combined Incidence, NAACCR, 2009. Data are collected by cancer registries participating in the National Cancer Institute’s SEER Program and the Centers for Disease Control and Prevention’s National Program of Cancer Registries. Mortality – National Center for Health Statistics, Centers for Disease Control and Prevention, 2009. Collaborative Group on Hormonal Factors in Breast Cancer. Effects of chemotherapy and hormonal therapy for early breast cancer on recurrence and 15-year survival: an overview of the randomized trials. Lancet. May 14-20 2005; 365(9472):1687-1717. Print. Coombes RC et al. A randomized trial of exemestane after two to three years of tamoxifen therapy in postmenopausal women with primary breast cancer. N Engl J Med. Mar 11 2004; 350(11):1081-1092. Print. Cummings SR et al. Prevention of breast cancer in postmenopausal women: approaches to estimating and reducing risk. J Natl Cancer Inst. Mar 18 2009; 101(6):384-398. Print. Galvao DA. Review of exercise intervention studies in cancer patients. J Clin Oncol. Feb 1 2005; 23(4):899-909. Print. Miller MD et al. The association between exposure to environmental tobacco smoke and breast cancer: a review by the California Environmental Protection Agency. Prev Med. Feb 2007; 44(2):93-106. Print. Read More