KINE 386  Final Study Guide
Formulas
Attack Rate
Attack Rate = (# of people at risk who develop disease / # total at risk) (100%)
Case-Fatality Rate
Case-Fatality Rate = (# of people who will die from a disease in a specified time
range / # of people with the disease) (100%)
Incidence Rate
Incidence Rate = (# of new disease cases / # of total at risk for disease) (1,000)
Prevalence
Prevalence = (# of disease cases in a given population/time / population) (1,000)
Mortality Rate
Mortality Rate = (# of deaths from all causes OR a specific disease in one year in
a given population / total population) (100, 000)
Relative Risk
RR = exposed / unexposed
Odds Ratio
OR = ad / bc
Confidence Interval
For RR = Ln (RR) +/- Z x (b/a) / (b+a) + (d/c) / (d+c)
For OR = Ln (OR) +/- Z x (1/a) +(1/b) + (1/c) / (1/d)
Confidence Interval Correlations
RR or OR = 1  exposure has no association
RR or OR > 1  exposure is causal
RR or OR < 1  exposure is negative, or possibly protective
Standard Error (SE)
SE = (b/a) / (b+a) + (d/c) / (d+c)
Z-Value (Z Score)  Z = always 1.96 for a standard distribution
Limits
Lower Limit = (Ln of RR/OR) – (SE x 1.96/Z)  inverse log (ex) of this value = LL
Upper Limit = (Ln of RR/OR) + (SE x 1.96/Z)  inverse log (ex) of this value = UL
Example Calculations
In the school cafeteria, 265 students ate hamburger with tainted meat and
another 135 ate pizza with some of the same meat on it. By the next day 200
students were ill with food poisoning. What was the attack rate?
Attack Rate = (# who get disease / # at risk) (100%)
Total Ill = 200 people (given)
Total at Risk = 265 + 135 = 400 people
Attack Rate = (200 / 400) (100%)
Attack Rate = (0.5) (100%) = 50.000%
Attack Rate = 50.0%
There were 87,000 people who participated in the Nurses’ Health Study and
eight years into the program 1300 of them had developed Type II Diabetes.
What was the incident rate for TTD?
Incidence Rate = (# of new disease cases / # of those at risk for disease) (1,000)
Incidence Rate = (1,300 / 87,000) (1,000)
Incidence Rate = (0.01494253) (1,000) = 14.94253
Incidence Rate = 14.9 out of every 1,000 nurses developed diabetes
In Nevada, there are 2,839,099 people according to the 2014 census. There
are 160,056 cases of diabetes. What is the prevalence?
Prevalence = (# of disease cases in a population / total population) (1,000)
Prevalence = (160,056 / 2,839,099) (1,000)
Prevalence = (0.05637563) (1,000) = 56.37653
Prevalence = 56.4 out of every 1,000 Nevadans are diabetic
There are 321,368,864 estimated to be living in the U.S. as of July 2015.
There is estimated to be 1,400,000 cases of diabetes with an expected
78,000 deaths from diabetes to occur by the end of the year. What are the
rates of case-fatality and mortality?
Mortality Rate = (# of total deaths / total population) (100,000)
Mortality Rate = (78,000 / 321,368,864) (100,000)
Mortality Rate = (0.0002427118) (100,000) = 24.27118
Mortality Rate = 24.3 of every 100,000 people will die from diabetes in the US
Case-Fatality Rate = (# deaths from a specific disease in a specific time / # of those at
risk during specific time) (100%)
Case-Fatality Rate = (78,000 / 1,400,000) (100%)
Case-Fatality Rate = (0.055714286) (100%)
Case-Fatality Rate = 5.6% of those in the US with diabetes will die this year
Cohort studies are for gathering information about diseases developing,
therefore we compare those exposed to unexposed to measure risk level.
Calculate all rates and fill in the 2x2 table with the answers. Then calculate
the CI.
Subjects over 10 years study Diabetes No disease Totals Incidence rate Relative risk
> 40 g sugar per day
856
2,144
3,000 285.3
10.6
< 40 g sugar per day
108
3892
4,000 27.0
***************
Incidence Rate for Exposed (>40g) = (a / a+b) (1,000)
Incidence Rate >40g = (856 / 856 + 2,144)(1,000)
Incidence Rate >40g = (0.2853333)(1,000) = 285.3333
Incidence Rate >40g = 285.3 out of every 1,000 develops diabetes
Incidence Rate for Unexposed (<40g) = (c / c+d)(1,000)
Incidence Rate <40g = (108 / 108 + 3892)(1,000)
Incidence Rate <40g = (0.027)(1,000) = 27.000
Incidence Rate <40g = 27.0 out of every 1,000 develops diabetes
Relative Risk (RR) = exposed / unexposed
RR = 285.3 / 27.0 = 10.56667
RR = 10.6
Confidence Interval
Ln (RR)
Ln (10.6) = 2.3608540
Ln (RR) = 2.361
SE = (b/a) / (b+a) + (d/c) / (d+c)
SE = ([2144 / 856] / [2144 + 856) + [3892 / 108] / [3892 + 108])
SE = 0.00834891 + 0.00900259 = 0.099217
SE = 0.1
CI = Ln(RR) +/- (Z)(SE)
Lower Limit = Ln(10.6) – ([1.96][0.1])
Upper Limit = Ln(10.6) + ([1.96][0.1])
Lower Limit = 2.361 – 0.196 = 2.165
Upper Limit = 2.361 + 0.196 = 2.557
Lower Limit = e(2.165)
Upper Limit = e(2.557)
Lower Limit = 8. 71460192
Upper Limit = 12.897068
Lower Limit = 8.71
Upper Limit = 12.9
Since the RR is above 1, and the CI is between 8.71 and 12.9, we can determine that
eating more than 40 grams of sugar a day is causal to diabetes.
Case-Control studies are to identify what the sick subjects were exposed to
that may have a causal effect. The cases are compared to controls (healthy
subjects) who were also exposed. However, there will always be cases and
controls who were not exposed as part of the comparison because one risk
factor does not generally make everyone ill.
Calculate all rates of exposure, the odds that the exposure lead to the
disease, and explain your answer.
Subjects
Cases of High Cholesterol Controls
Red meat 5x week 800
2,000
Red meat 1x week 60
800
Proportion Exposed = (a / a+c) (100%)
Proportion Exposed = (800 / 800 + 60) (100%)
Proportion Exposed = (0.93023256) (100%) = 93.023256%
Proportion Exposed = 93.0% of cases ate red meat 5x a week and developed high
cholesterol.
Proportion Unexposed = (b / b+d)(100%)
Proportion Unexposed = (2000 / 2000 + 800) (100%)
Proportion Unexposed = (0.714286)(100%) = 71.4286%
Proportion Unexposed = 71.4% of controls ate red meat 5x a week but did not develop
high cholesterol.
Odds Ratio (OR) = ad / bc
OR = (800 x 800) / (2,000 x 60)
OR = 640,000 / 120,000 = 5.3333
OR = 5.3
Confidence Interval
Ln (OR)
Ln (5.3) = 1.6677068
Ln (RR) = 1.6677
SE = (1/a) + (1/b) + (1/c) + (1/d)
SE = (1/800) + (1/2000) + (1/60) + (1/800)
SE = 0.00125 + 0.0005 + 0.00125 + 0.01667
SE = 0.01967 = 0.14024978
SE = 0.14
CI = Ln(OR) +/- (Z)(SE)
Lower Limit = Ln(5.3)– ([1.96][0.14])
Upper Limit = Ln(5.3) + ([1.96][0.14])
Lower Limit = 1.6677 – 0.2744 = 1.3933
Upper Limit = 1.6677 + 0.2744 = 1.9421
Lower Limit = e(1.3933)
Upper Limit = e(1.9421)
Lower Limit = 4.0281209
Upper Limit = 6.973379
Lower Limit = 4.03
Upper Limit = 6.97
We can say that since the OR is 5.3 and the CI is 4.03 and 7 and all are above 1, that
there is a causal relationship between eating red meat 5x/week and the development of
high cholesterol. However there were a large number of controls who also ate red meat
5x per week and did not have cholesterol issues, so there must be some other effect
occurring that must be considered, for example the controls may be more active, there
may be an inherited factor or perhaps the cases have a secondary risk factor that
together with the red meat is highly causal such as smoking or high alcohol use.
Things to Know
What mental disorders are common by age 18?
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Phobias and ADD  developed by 1st grade
Age 12  50% of anxiety and impulse control cases are developed
o Age 11-12  median age of self-medication experimentation for these
conditions
Age 14  50% of mentally ill individuals are substance abusers
o Age 24  75% of mentally ill individuals are substance abusers
High School Age  depression
What mental health issues are more common in women vs. men?
Women
Men
Anxiety (29.9% of all women)
Impulse Control (25% of all men)
Mood Disorders (21.4% of all women)
Substance Abuse (35.5% of all men)
What are some genetic markers of elite athletes?
 Athlete/Exercise Considerations
o 45% of muscle fiber type is genetic
o ACTN3 capability
 Promote formation of fast-twitch muscle
 Alter glucose metabolism in response to training
 Minimize damage from eccentric contractions
o ACE Activity
 ACE I (Insertion)  lower ACE activity = slow twitch muscle fibers
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= endurance athletes
 ACE D (Deletion)  higher ACE activity = fast twitch muscle fibers
= power athletes and/or sprinters
Criteria for Choosing Athletes
o Parents height and muscularity
o Blood and muscle type
 More white blood cells  power
 More red blood cells  endurance
o Overall general strength
o How the skin pulls away from the muscle
o Jumping height, distance, reversal speed, and extreme flexibility
o Psychological attitude
o Physical reproductive characteristics
 Girls  small breasts, square jaw
Boys  testicle size
o High IQ
o Kinesthetic ability
What are the most common injury sites?
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Most Common Injury Sites
Women
Men
KNEE
22.3% (specifically higher ACL)
23.2%
FOOT
15.7%
12.9%
BACK
10.3%
10.6%
Common Injuries during Physical Activity
o Fractures/dislocations
o Sprains/strains
 Sprain: injury at muscle or ligament
Strain: injury at tendon
o Cuts/bruises
1.5% of the population is injured via physical activity
o Sport related injuries make up 23-25% of all emergency room visits for
people between ages 5-24
What practices reduce Delayed Onset Muscle Soreness?
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Cryotherapy  3 minute whole body immersion in very cold environment
Stretching to release muscle tension
Massage
o Reduces pain during stretch, sensitivity, and pain perception
Chiropractic Care
o Pain and disability significantly reduced compared to MD visits
Complimentary Medicine (however, no evidence when compared with placebos)
o Arnica
o Vitamin C  200mg twice a day for 2 days
o Vitamin C and E  30 days prior taking 500mg daily
 Allowed for greater power and less fatigue, but not full DOMS
reduction
o Chiropractic Care
 Pain and disability significantly reduced compared to MD visits
Understand the phases of the immune system.
 Phases of Immune Response
o First  Innate
 Skin
 Resident phagocytes
 Killer blood proteins
o Secondary  Induced Responses (occurs in a loop)
 Inflammation
 Phagocytes and natural killer cells migrate
 Cytokine hormone released
 When abnormal, cytokine storm can occur (cytokine
hormone will not shut off, causing congested cells and
tissue damage)
o Third Phase  Normal
 Adaptive response of immunity
What are the jobs of various hormones and proteins that fight
bacteria and viruses from where they initiate?
 Skin and Intestinal Tract
o Phagocytic cells find, attack, and kill antigens
o Blood proteins that kill bacteria (e.g. E. coli)
 Blood
o Antibodies produced after antigen exposure to attempt to prevent
pathogen growth
o Leukocytes (WBCs): attack and kill antigens, tumors, and viruses
 Come from bone marrow and circulate in blood, lymph, and
spleen
 Leukocytosis: increase in number of WBCs when sick, indicating
infection
o B-Cells: produce antibodies during initial infection
o T-Cells: help kill or suppress growth of ‘bad’ cells
o Monocytes: multi-functioned cells that travel in the blood and attach
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themselves to tissues
 Absorb wastes and harmful microorganisms
 Regulate clotting and antigen cells
 Become macrophages (giant infection eaters)
o Granulocytes: migrate to sources of inflammation and ingest bacteria
 Abundant in the blood; is what makes up the contents of pus
Spleen
o Natural Killer (Granular Cells): kill tumor cells and viruses without prior
exposure
Cytokines: multi-functioned proteins produced by the cells
o Communicate, escort, bind to, and activate cells in the body
o Boost resistance of immunity
o Impede division of unhealthy or abnormal cells
o Induce acute-phase response: fever, fatigue, loss of appetite, nausea,
etc., when sick
Benefits of Being a Trained Athlete
o Natural killer cells increase in the blood even weeks after training
o Trained subjects have higher levels of leukocytes, granulocytes, and
natural killer cells
o C-reactive proteins reduced in endurance athletes (inflammatory marker)
 Levels are higher in over-fat or sedentary subjects