Category Archives: MHA 610

UOP MHA 610 Week 1 Assignment U.S. Mortality Rates Histogram latest

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U.S. Mortality Rates. Due by Day 7. Examine the burden of disease in the United States to provide important information on which parameter is to base decisions on public health priorities.
To do this, we will utilize mortality data for the United States. In the first part of this assignment, you will download and examine mortality data for your home state.
Go to USA Causes of Death by Age and Gender
Choose your home state under the Choose State option (panel on left hand side)
Select BOTH under the Choose gender option in the middle panel.
Scroll down to the bottom of the page, and read the fine print to learn for which year the mortality data have been tabulated.

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UOP MHA 610 Week 1 Discussion Hospital Data latest

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Hospital Data. Prior to beginning this discussion, please make sure to watch Screen cast Part 1 and Screen cast Part 2. The MHA610_Week 1_Discussion_Hospital Data Excel file and MHA610_Week 1_Discussion_Hospital Data Stat disk file contains basic demographic information on 250 patients admitted to a community hospital over a two week period. The first row of the worksheet indicates the variable names:
Gender
Male (M) or female (F)
Ethnicity
SevIllnessCode
These are All Patient Refined Diagnosis Related Groups (APR-DRG) categories of severity of illness, ranging from:
SevIllnessDescr
Mild (Category 1) to extreme (Category 4)
Age
In years
Wt
Patient weight in kilograms
Ht
Patient height in centimeters
BMI
Patient body mass index (BMI) where BMI = wt/ht*2, with weight in kilograms and height in meters
APR-DRG
Denotes All Patient Refined Diagnosis Related Group, a widely used inpatient classification system.
For this discussion, describe and summarize the demographic information on these patients. You may use tables or graphs (or both) for this purpose. Your goal is to convey to the reader an accurate snapshot of these patients. Support your response with correct scholarly sources. You initial post must be at least 250-500 words.
Guided Response: Respond to at least two of your peers by Day 7, 11:59PM. Review your colleague’s summary of the data. Did the method of presentation provide you with any latest insights? If so, what are they? If not, what suggestions might you make to your colleague that could improve his or her representation of the data? All initial and peer postings should be at least 250-500 words in APA format supported by scholarly sources.

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UOP MHA 610 Week 2 Assignment Sex Ratios latest

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Sex Ratios. Due by Day 7. The normal male to female live birth sex ratio ranges from about 1.03 to 1.07. The sex ratio is defined as the ratio of male births to female births. You might expect boy and girl births to be equally likely, but in fact, baby boys are somewhat more common than baby girls.
Higher sex ratios are thought to reflect prenatal sex selection, especially among cultures where sons are prized more heavily than daughters. We will review sex ratios in the United States as a whole, as well as in individual states, to determine whether sex ratios vary significantly among various ethnic and racial groups.
To do this analysis, we will utilize natality data for the United States, provided by the Centers for Disease Control.
In the first part of the assignment, we will look at sex ratios for your home state, over the time period 1995 to 2002, by race. To obtain this information

 

UOP MHA 610 Week 2 Discussion Game of Chance latest

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Game of Chance. For this discussion, select a game of chance, explain it briefly if it is likely to be unfamiliar to your classmates, then calculate probabilities of various outcomes like winning or losing in this game. For example, you might choose your state lottery, scratch card game, a card game like poker, or a dice game like Craps or Yahtzee, as your game of chance.
As illustration, read a lottery analysis in Powerball-Methodology.
Guided Response: Respond to at least two of your classmates who chose a different game of chance than you by Day 7 at 11:59PM. Did your colleague provide enough explanation of the game to allow you to understand the analysis? Was the analysis provided by your classmate correct? If so, what optimal strategy for playing that particular game was described? If not, what suggestions would you make to your colleague to amend any issues?

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UOP MHA 610 Week 3 Assignment Immune Responses latest

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Immune Responses. Background: Abnormal immune responses can trigger a range of autoimmune diseases, in which an individual’s immune system is attacking normal tissues in the body. Well- known examples of autoimmune diseases are type 1 diabetes mellitus, lupus, and multiple sclerosis.
Ideally, one would like to harness the immune system to attack abnormal substances or tissues like cancer, while sparing the normal (unaffected) tissue. Many tumor cells produce antigens (proteins) that theoretically ought to trigger an immune response: that is, one’s immune system ought to recognize cancer cells as somehow foreign or abnormal, and thereafter eliminate these cells from the body. The field of cancer immunotherapy is actively pursuing this study.
The levels are given in the columns with headings Ab14, HCC1, IMP1, KOC, MDM2, NPM1, P16, P53, P90, RaIA, and Survivin. (These are the designations of the 12 TAAs, all of which were thought to be potentially predictive of cancer.) Tumor antigens may also be useful for diagnostic tests; high levels of tumor antigens could be taken as markers or indicators of cancer. In this assignment, you will be examining levels of tumor-associated antigens (TAAs) as determined from immunoassays (i.e., biochemical tests that measure the concentrations of the tumor-associated antigens in serum samples). • Download the Excel file MHA610_Week 3_Assignment_Data.xls, and open it. • The spreadsheet contains data on 250 individuals: 90 normal individuals from San Diego (the controls), and 160 individuals from Korea and China, all of whom were diagnosed with hepatocellular carcinoma (HCC). o Serum samples were taken from the controls and from the cases at time of diagnosis of HCC. Levels of a panel of 12 tumor-associated antigens (TAAs) were assessed via immunoassays in all individuals;

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UOP MHA 610 Week 3 Discussion Confidence Intervals latest

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Confidence Intervals. In this discussion, we will investigate confidence intervals for binomial probabilities. The discussion is in two parts.
• Return to the data you had generated in the second part of the Week Two assignment. You should have total numbers of first-born boys and girls in your state between the years 2007 and 2012 separately by racial group: American Indians or Alaska Natives, Asian or Pacific Islanders, Black or African Americans, and Whites. For the first part of this discussion, construct and report the 95% confidence intervals for the proportions of first-born boys, separately for each racial group. (Use the normal approximation to the binomial distribution.) Comment on the confidence intervals: can you infer from the confidence intervals that the proportions of first-born boys differ among the racial groups? Explain what the widths of the confidence intervals tell you.
• Leading up to elections, you often hear results of polls of voters’ preferences, with statements such as: “This poll was taken from a random sample of 600 potential voters, and has an accuracy exceeding 96%.” Please interpret this statement in light of your knowledge of binomial confidence intervals. (Remember, the width of a confidence interval is a measure of the precision of the estimate.)
Guided Response: Respond to at least two of your peers by Day 7, 11:59PM. Consider the 95% confidence intervals your colleague presented. Do all the intervals overlap with those you presented in your initial post? Did the inferences presented by your colleague match with yours? Compare the proportion of boy births in his or her state with those in your state. What statistically significant differences can you note? Do you concur with your colleague’s interpretation of the polling statement? What suggestions might you make to aid your colleague in evaluating this type of polling result? All initial and peer postings should be at least 250-500 words in APA format supported by scholarly sources.

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UOP MHA 610 Week 4 Assignment A Crossover Clinical Trial latest

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A Crossover Clinical Trial. Due by Day 7. Background: Randomized controlled trials are the gold standard for clinical research. Biostatisticians are heavily involved in such trials, from the planning stage (e.g., sample size and power considerations) through the analysis of findings (e.g., estimation of treatment effects). In this assignment, we will examine treatment outcomes in a two treatment, two period (two-by-two) crossover design.

In the two-by-two crossover design, subjects are randomly assigned to one of two groups. The first group initially receives treatment A in the first period of the trial followed by treatment B in the second period of the trial, and the other group initially receives treatment B in the first period of the trial followed by treatment A in the second period. The response, or primary endpoint of the trial, is measured at least twice in each patient, at the end of the first period and again at the end of the second period. Each patient is his or her own control for comparison of treatment A and treatment B.

Crossover designs are used when the treatments alleviate a condition, rather than affect a cure. After the response to the treatment administered in the first period is measured, there is a washout period in which any lingering effect of the treatment administered in the first period dissipates, and then the response to the second treatment is measured. 32

MHA610: Introduction to Biostatistics COURSE GUIDE

An advantage of a crossover design is increased precision afforded by comparison of both treatments on the same subject, compared to a parallel group clinical trial (in which patients are randomized onto different treatment arms). Disadvantages of crossover trials are complex statistical analyses of findings (typically, by complex analyses of variance), potential difficulties in separating the treatment effects from the time effect (patients may respond differently in the first period and the second period), and the carryover effect (the effect of the treatment given in the first period may not totally wash out, but may carry over onto the second period).

We will give a simple example of a two-by-two crossover trial, and undertake analyses of the trial results via t tests. The trial was meant to assess the efficacy of a latest experimental therapy for interstitial cystitis (IC). Interstitial cystitis is a chronic bladder condition affecting primarily women; symptoms include bladder pressure and pain, urgency, and occasionally pelvic pain. The latest experimental therapy was meant to reduce pain and urgency relative to standard therapy. A total of 24 patients were enrolled in the trial; trial results are given in the Excel workbook titled crossover_trial_data.xls.

Open the workbook, and examine the worksheet. The first row contains column headings, and the next 24 rows represent the 24 patients entered into the trial. The group one patients received experimental therapy in the first period of the trial followed by standard therapy in the second period of the trial. The group two patients received standard therapy in the first period of the trial followed by experimental therapy in the second period.

The primary outcome of the trial was an area under the curve (AUC) calculation of relative pain and urgency the patient experienced following therapy: the smaller the AUC, the less severe the patient’s pain and urgency. AUC_period1 denotes each patient’s AUC during the first period of the trial, and AUC_period2 denotes the patient’s AUC during the second period of the trial. The column headed Rx denotes the treatment each patient received during the first period of the trial.

• We will first test for carryover effects.

o The t test formulation for the test for carryover proceeds as follows: calculate the total (sum) of the AUC_period1 and AUC_period2 values for each patient in group one (12 patients) and separately for each patient in group two (12 patients).

 

UOP MHA 610 Week 5 Assignment Brain Size And Intelligence latest

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Rain Size and Intelligence. Due by Day 7. Background: Is brain size a measure of intelligence? Brain size tends to vary with body size: for example, sperm whales and elephants have brains up to five times as massive as human brains. So across species, brain size is not a perfect measure of intelligence. And within species, the underlying organization (complexity of connections) and molecular activity of the brain are likely to be more directly associated with intelligence than mere size.
MHA610: Introduction to Biostatistics COURSE GUIDE
In this assignment, we will investigate relationships between physiological measures of the brain, and intelligence. Download and open the Excel workbook, brain_data.xls. The workbook contains data on 20 youths, in rows two through 21. Eight variables (the columns) were recorded on each individual; the columns headings are given in row one. The column headings are as follows:
IQ the individual’s IQ ORDER the birth order (1 = firstborn, 2 = not firstborn) PAIR marker for genotype SEX gender, 1 = male, 2 = female CCSA corpus callosum surface area (in cm2) HC head circumference (in cm) TOTSA total brain surface area (in cm2) TOTVOL total brain volume (in cm3) WEIGHT body weight (in kg)
The neuroanatomical measures CCSA, TOTSA, and TOTVOL were determined from magnetic resonance imaging (MRI) of the brains, followed by automated image analyses of the scans. The corpus callosum is a bundle of neural fibers beneath the cortex, connecting the left and right cerebral hemispheres of the brain; it is the communication highway between the two hemispheres. (The more lanes to the highway, the faster the traffic ought to flow.)
The following questions can be answered in Excel, Stat Disk, or other statistics software you may have available. • Examine all of the pair wise correlations among the physiological measures CCSA, HC, TOTSA, TOTVOL, and WEIGHT. Which two variables have the strongest correlation? Report the correlation, and plot the scatter gram for these two variables. • Determine whether the physiological parameters CCSA, HC, TOTSA, TOTVOL, and WEIGHT are significant predictors of IQ. That is, run a sequence of univariate regressions, with IQ as the dependent variable, and the physiological parameters as the independent variables. Report the best univariate regression with statistics and a graph of the regression. Describe whether IQ can be accurately predicted from any of these brain measures individually or in combination.
39

MHA610: Introduction to Biostatistics COURSE GUIDE
is via linear regression: take log(CCSA) as the dependent variable and log(TOTVOL) as the independent variable; the fitted regression coefficient (slope) is an estimate of the exponent. (Do you see why this is true?) Perform this linear regression, and report your results. Describe whether the regression coefficient is significantly different from 2/3. (The 2/3rd power law occurs often in nature.)  (k is an unknown constant here), then a simple way of estimating the exponent BONUS. Power law distributions, that is, functional relationships between two variables in which one variable is roughly a power of the other, are often used to model physiological data. One of the oldest power laws, the square-cube law, was introduced by Galileo in the 1600’s: empirically, the square-cube law states that as a shape grows in size, its volume grows faster than its surface area. We shall investigate the square-cube law with two variables from our dataset, CCSA and TOTVOL. If CCSA varies with some power of TOTVOL, for example, CCSA = k * (TOTVOL)

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UOP MHA 610 Week 6 Assignment Final Project latest

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Final Project. In this final assignment, we will revisit datasets that we have utilized in previous assignments, but with latest objectives.

• In the Week One assignment, you looked at mortality in your particular state, with two different metrics: the first was numbers of deaths, and the second was years of life lost. For this question, return to the original dataset, but this time first pool all cancer causes of death together, so that cancer constitutes the only category for cause of death. Then, repeat your analyses from Week One. How do your conclusions change?
• In the Week Two assignment, you looked at sex ratios for births in your state.
o Take the data you have assembled from the second part of your Week Two assignment, namely, numbers of first-born boy and girl births in your state between 2007 and 2012, separately by racial group (i.e., American Indians, Asians, Blacks, and Whites). Form a two-by-four contingency table from these data: the two row categories are female (girl) and male (boy), and the four column categories are the four racial groups. Calculate the chi-square statistic from this contingency table, and interpret the result.
o Return to the CDC Wonder website, and obtain the numbers of births in your state between 2007 and 2012, by month. (Disregard gender, or race, or birth order—you want all births). Calculate a chi-square statistic to assess whether there is any seasonality to births. (Your null hypothesis is that births should be equally likely to occur in any of the 12 months. We are ignoring the varying lengths of the months to simplify calculations.) How would you interpret your findings? Explain in 500 words in APA format supported by scholarly sources.
BONUS: Give a graphical representation of your findings for this portion highlighting what you consider significant.
• In the Week Three assignment, you were given levels of tumor-associated antigens in a sample of 90 normal (non-cancer) individuals, and 160 hepatocellular carcinoma (HCC) patients. Here is a proposed diagnostic test for HCC:
o For each individual, calculate a numerical score:
score = -3.95 + 10.7 * HCC1 – 4.14 * P16 + 13.95 * P53 + 28.92 * P90 + 6.48 * survivin
(This equation was derived from logistic regression.)
o If this score is positive (i.e., > 0), diagnose this individual as an HCC patient; if this score is negative (i.e., <0), diagnose this individual as normal (i.e., non-cancer).

o Apply this rule to the entire cohort of 250 individuals. Report the sensitivity of this rule, the specificity, the false positive rate, the false negative rate, and the overall accuracy. Do you think the score function provides a good diagnostic test for HCC? Explain.
• In the Week Four assignment, we considered a simple two-by-two crossover trial of a latest experimental treatment for interstitial cystitis. We calculated t tests for carryover and treatment effects, but we have not yet considered period effects. It is unlikely that there are any period effects in this trial, but we may want to test this formally. If there were a period effect, then patient responses under either treatment would likely be systematically higher in one period than the other. (Here’s an analogy: Think of taking the same test twice. You would likely perform better on the test the second time, since you have learned from your experience of taking the first test.) Explain how you would devise a t test for assessing a period effect in this trial. (Hint: look at the explanation of the t test for treatment effects given in the Week Four assignment. There, we based the test on the random variable X – Y. Suppose we look instead at X + Y?)
• In the Week Five assignment, you investigated measures of brain size and intelligence in a sample of 20 youths. A potential shortcoming of your prior analyses is that you did not take into account all available information in the dataset, in particular, gender. Answer the following questions and explain your answers:
o Do any of the physiologic variables CCSA, HC, TOTSA, TOTVOL, and WEIGHT differ significantly between males and females?
o Do IQs differ significantly by gender?
o Undertake a paired analysis of IQs, in order to assess whether firstborns have higher IQs than non-firstborns. In this regard, there are 10 pairs of related youths, as denoted by the variable PAIR.
Completing the Final Project
The Final Project:
1. Must include a title page with the following:
a. Title of paper
b. Student’s name
c. Course name and number
d. Instructor’s name
:2. Must begin with an introductory paragraph that has a succinct thesis statement.
3. Must address the topic of the paper with critical thought.
4. Must end with a conclusion that reaffirms your thesis.
5. Must use a minimum of 3-5 scholarly, peer-reviewed sources published within the last five years (not including the course text) or those applicable to the data sets.
6. Must document all sources in APA style, as outlined in the Ashford Writing Center.
7. Must include a separate reference page, formatted according to APA style as outlined in the Ashford Writing Center. The number of pages must be applicable to the specific data sets outlined in the Final Project assignment.

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UOP MHA 610 Week 6 Discussion Health and Nutritional Status latest

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Health and Nutritional Status
Since 1971, the National Center for Health Statistics had been assessing the health and nutritional status of both children and adults in the United States, through periodic National Health and Nutritional Examination Survey (NHANES) surveys. These surveys are an invaluable resource to epidemiological and public health research; the surveys can be used to determine the prevalence of major diseases and risk factors, to assess nutrition and health promotion, and to guide public health policy.
All initial and peer postings should be at least 250-500 words in APA format supported by scholarly sources.
In 2012, the NHANES National Youth Fitness Survey (NNYFS) was conducted in conjunction with NHANES to obtain physical activity and fitness levels of U.S. youths aged 3 through 15. Initial data from the NNYFS were released in 2013 and serve as the basis for this discussion problem.
Begin by downloading the Excel file MHA610_Week 6_Discussion_NNYFS_workingdata.xls. This workbook was created by merging two datasets from the NNYFS: the demographic variables dataset, and the body measures dataset. For the purposes of this discussion, many variables were eliminated from the original datasets, as well as observations with missing data on height and weight. The Excel workbook thus consists of one worksheet, with 1576 rows (the first row contains headers, and the next 1575 rows are observed values for the participants), and 11 columns of variables. The columns in the Excel file are the following:
SEQN the respondent sequence number (index for all the files)
RIAGENDR gender of the participant, 1 = male, 2 = female
RIDRETH1 race/Hispanic origin:
1 = Mexican American
2 = other Hispanic
3 = non-Hispanic white
4 = non-Hispanic black
5 = other
RIDEXAGY age in years at time of physical exam
INDHHIN2 annual household income, categorized
INDFMIN2 annual family income, categorized
INDFMPIR ratio of family income to poverty, 0 to 5
BMXWT weight, in kg
BMXHT height, in cm
BMXBMI body mass index (kg/m^2)
BMDBMIC BMI category:
1 = underweight
2 = normal weight
3 = overweight
4 = obese
. = missing
More detailed descriptions of these variables are given at the data documentation web pages for the NNYFS, at http://www.cdc.gov/nchs/nnyfs/Y_DEMO.htm and at http://www.cdc.gov/nchs/nnyfs/Y_BMX.htm.
For purposes of this discussion, you are asked to answer the three following questions:
• Does BMI vary significantly between boys and girls?
• Does BMI vary significantly among the racial/ethnic groups?
• Is there any trend to BMI with age?
Comments:
There are several ways to address these questions. For example, you might take BMXBMI as your outcome variable of interest: it is continuous, so you could then perform a two-sample t test for (1), a one way analysis of variance for (2), and a simple regression analysis (with age as the predictor variable) for (3).
Alternatively, you might reduce the problem to consideration of binomial probabilities: for example, you could classify everyone as obese or not obese (or maybe, overweight/obese vs underweight/normal), then compare binomial outcomes for (1) and (2) (z tests with the normal approximation or contingency tables), and conduct a t test on ages for (3).
Neither approach is wrong—the key is interpreting your findings!
If you prefer to do the analyses in Stat disk, there is a file, MHA610_Week 6_Discussion_NNYFS_workingdata.csv, ready to be read into Stat disk. (It’s the original Excel workbook, saved as csv.) No need to go through any additional steps, unless you wish to restructure the data in Excel.
Incidentally, the income variables are not needed for these questions, but as a bonus, you might want to investigate whether obesity is related to socioeconomic status (as reflected by family income).
Guided Response: Respond to at least two of your peers who chose a different of analysis that you by Day 7, 11:59PM. Did you arrive at the same conclusions as your colleague even though you chose different methods? If so, which method do you think is preferable and why? If not, which method do you believe produces more credible results and why? (You might consult the text to support your argument.). All initial and peer postings should be at least 250-500 words in APA format supported by scholarly sources.

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