HIV Activity Packet

What you will find in this packet:

  1. Explanation of packet (page 1)
  2. Instructions (page 1)
  3. Activity (begins page 2)

Part I: Explanation of packet

Before completing this assignment, students should first learn about HIV and AIDS – specifically the life cycle and treatment options, along with important concepts in genetics and immunology. The purpose of this packet is to reinforce biological concepts while practicing statistical principles that will be tested on the AP Exam. In this packet students will an in-class exercise and a homework assignment. Additionally, students will get to take on the role of doctors, which is sure to capture their attention.

Part III: Instructions

To set up: Print off copies of all worksheets. Place “Pathology Lab Results” at the front of the classroom. Students should work in groups of 4. Give one copy of both the “Patient Information Worksheet” and the “Medicine Data Table” to each group of 4. In this activity, students will be analyzing HIV+ patient histories, performing t-tests and chi square tests, and prescribing treatment options. After completing the assignment, students should complete thehomework assignment to each student.

Part IV: Activity

Worksheet list:

  1. Pathology Lab Results (place a few copies at the front of the classroom)
  2. Patient Information Worksheet (one copy per group of 4)
  3. Medicine Data Table (one copy per group of four)
  4. HIV and Data Analysis Activity (one copy per student)
  5. Homework Assignment (one copy per student)

Pathology Lab Results: simulating lab at doctor’s office

Patient Information: #48392 CJ Blue

Tested for: / Amount:
Platelets / 100,000/mL
White Blood Cells (WBCs) / 5700 cells/µL
T-cell count / 431 cells/mm3
Red Blood Cells (RBCs) / 5.1 mil cells/µL
Glucose / 98 mg/dL
Hemoglobin / 13 gm/dL

Patient Information: #277948 Naomi Green

Tested for: / Amount:
T-cell count / 130 cells/mm3
Platelets / 234,000/mL
Red Blood Cells (WBCs) / 5.7 mil cells/µL
White Blood Cells (RBCs) / 8,957 cells/µL
Hemoglobin / 17 gm/dL
Glucose / 130 mg/dL

Patient Information: #920938 Cynthia Orange

Tested for: / Amount:
Red Blood Cells (RBCs) / 6.1 mil cells/µL
Glucose / 77 mg/dL
T-cell count / 395 cells/mm3
White Blood Cells (RBCs) / 7,723 cells/µL
Platelets / 309,000/mL
Hemoglobin / 9 gm/dL

Patient Information: #156309 John Red

Tested for: / Amount:
Platelets / 276,835/mL
T-cell count / 175 cells/mm3
Hemoglobin / 10 gm/dL
White Blood Cells (RBCs) / 4,714 cells/µL
Red Blood Cells / 6.1 mil cells/µL
Glucose / 99 mg/dL

Patient InformationWorksheet (one copy per group)

Patient #1 Information

The patient’s name is John Red. He was born on July 8, 1960 in North Carolina. He is 6’2” and weighs 180 pounds. He had a tonsillectomy (tonsils removed) when he was 8 years old. His grandfather had lung cancer. His dad has heart disease and had a heart attack when he was 60, but survived. His twin brother had a stroke when he was 33. His blood pressure today was 120/80 mm Hg. His heart rate is 75 bpm. He is an accountant who liked to play sports and hike in his free time. He is currently suffering from Kaposi’s Sarcoma, which is an opportunistic infection that comes when someone’s immune system is lowered due to HIV/AIDS. Kaposi’s is a type of cancer that causes legions on the body. Today he has legions in his mouth.

Patient #2 Information

The patient is CJ Blue. He is 5’7” and weighs 198 pounds. He was born on December 29, 1989 in New York. His dad suffers from anxiety. His mom has arthritis. His grandfather has heart disease. He previously had an appendectomy (appendix removed). He takes Flonase for asthma. His blood pressure today was 120/80 mm Hg. His heart rate is 100 bpm. He is a graduate student at a university, who enjoys video games and web design. Today he has a flu-like illness. He had been taking Nyquil for a few days because he thought he had the flu. However, his HIV test just came back positive, so he knows it is not the flu.

Patient #3 Information

The patient’s name is Naomi Green. She was born on January 1, 1981 in California. She is 5’3” and weighs 90 pounds. Her grandmother had colon cancer. Her dad and sister have diabetes. Her mom has heart disease. Her blood pressure today was 140/90 mm Hg. Her heart rate is 100 bpm. She is a stay at home mom, who enjoys planning philanthropic events and school fundraising. She is suffering from wasting syndrome, which means she is losing weight and muscle mass. Wasting syndrome is common with HIV and AIDS.

Patient # 4 Information

The patient is Cynthia Orange. She is 5’10” and weighs 180 pounds. She was born on September 13, 1981 in Texas. Her grandmother had breast cancer. Her dad has type II diabetes. Her younger sister had leukemia but survived. She previously had surgery for a torn ACL while playing basketball. Her blood pressure today was 140/100 mm Hg. Her heart rate is 68 bpm. She is a scientist, who enjoys bird watching in her free time. Today she is suffering from joint pain and swollen lymph nodes. She had been taking ibuprofen because she did not know she had HIV until her test came back positive today.

Medicine Data Tables: (one copy per group)

Medicine A:NRTI (nucleoside reverse transcriptase inhibitor)

Gender / D.O.B. / T-cell count before treatment (cells/mm3) / Follow-up T-cell count after 3 months (cells/mm3)
Patient 1 / M / 1989 / 302 / 306
Patient 2 / F / 1957 / 417 / 417
Patient 3 / F / 1978 / 212 / 213

Medicine B:PI (protease inhibitor)

Gender / D.O.B. / T-Cell count before treatment (cells/mm3) / Follow-up T-cell count after 3 months (cells/mm3)
Patient 1 / F / 1963 / 436 / 440
Patient 2 / M / 1993 / 315 / 315
Patient 3 / M / 1984 / 281 / 282

Medicine C: HAART therapy (2 NRTIs and 1 PI)

Gender / D.O.B. / T-cell count before treatment (cells/mm3) / Follow-up T-cell count after 3 months (cells/mm3)
Patient 1 / M / 1999 / 404 / 409
Patient 2 / M / 1977 / 213 / 219
Patient 3 / F / 1950 / 340 / 347

Medicine D: Antibiotic

Gender / D.O.B. / Before treatment (cells/mm3) / Follow-up T-cell count after 3 months (cells/mm3)
Patient 1 / M / 1998 / 206 / 209
Patient 2 / F / 1968 / 300 / 300
Patient 3 / F / 1940 / 447 / 447

Name: ______

Date: ______

HIV and Data Analysis Activity

Part I: Patient Medical Record

  1. Congratulations, you are now doctors! Working in groups of 4, you must analyze your patient’s histories and diagnose them with the appropriate conditions. Additionally, you must also determine their current symptoms. Use the following charts to gather all the necessary information.
  1. Everyone in the group is responsible for filling in the medical record for 1 patient. Do not duplicate patients so that they all get the help they need.
  1. Review: HIV infects ______
  1. Heart cells
  2. T-cells
  3. Red Blood Cells
  1. What are potential consequences of having a weakened immune system?

Patient Medical Record

PATIENT INFORMATION

First M.I. Last
Name:
Date of Birth / Month / Day / Year

GENERAL INFORMATION

Temperature / Normal: 98.6 °F
Weight / Normal: Varies
Heart Rate / Normal: 60-100 bpm
Blood Pressure / Normal : 120/80 mm Hg

FAMILY HISTORY

PREVIOUS SURGERIES

CURRENT MEDICATIONS

CURRENT SYMPTOMS/OPPORTUNISTIC INFECTIONS

BLOOD TEST RESULTS (go to Pathology Lab to obtain results)

Date: / / / Normal Range / Normal? Y/N
Glucose / 70 - 110 mg/dL
Hemoglobin / 12 - 18 gm/dL
White Blood Cells (WBCs) / 4,300 - 10,800 cells/µL
Red Blood Cells (RBCs) / 4.2 - 6.9 million cells/µL
Platelets / 150,000 - 350,000/mL
T-cell count / 500-1000 cells/mm3

CURRENT CLASSIFICATION (T-cells/mm3)

Healthy >500 HIV+ 500-200 AIDS <200

Review: What are a few differences between HIV and AIDS?

Part II:Prescribing Medication

As you can see, patients diagnosed with HIV and AIDS have a broad range of symptoms. Now it is time to prescribe a treatment option.

Dr. ______

Prescription Form

Medicine A: NRTI (nucleoside reverse transcriptase inhibitor) – this medicine inhibits reverse

transcription, so that viral RNA cannot create cDNA within the host cell.

Medicine B: PI (protease inhibitor) – This medicine prevents an enzyme (called protease) from

cleaving long HIV protein strands into smaller proteins, which are sent off in vesicles

to infect other cells.

Medicine C: HAART therapy “cocktail” (2 NRTIS and 1 PI) – a combination therapy.

Medicine D: Antibiotic – kills bacterial infections in the body.

  1. Based on the descriptions of the medicines, which do you think will be most effective? Why?
  1. Clinical trials were run for each of the 4 medicines listed above. The results are listed in the “Medicine Data Tables” which have been provided to your group.
  1. Divide up the 4 medicines among each of the 4 group members.
  1. In order to determine which medicine is most effective in treating HIV/AIDS, a data analysis test must be performed. In this case, we will use a t-test. Why is a t-test the appropriate test?
  1. Perform the t-test for your assigned medicine. *** use p = 0.05***

H0 (null hypothesis):

(In this example, the Specified mean difference (µ) is 0. )

Ha (alternative hypothesis):

Total difference (add up differences for all patients between ‘ before’ and ‘after’ treatment): ______

Mean difference: ______(total difference/number of patients)

Standard Deviation:

Standard Error:

T – value = (mean difference) – (specified mean difference)

Standard Error

T –value =

D F= n-1 = ______

Now find the α-value. α = ______

How does α compare to the p-value of 0.05? ______(hint: greater or less than)

  1. Share the results with your group.

Medicine A- t= ______α = ______is the α – value greater or less than p?

Medicine B - t= ______α = ______is the α – value greater or less than p?

Medicine C - t=______α = ______is the α – value greater or less than p?

Medicine D- t=______α = ______is the α – value greater or less than p?

  1. Which medicine has statistically significant results (meaning it was an effective treatment)?
  1. Did you predict correctly? If not, why do you think your medicine was not as effective?

Part III: HIV treatments throughout the world

Studies done in the United States show that HAART therapy is effective in the treatment of HIV. However, a group of researchers is trying to determine if HAART therapy will be effective in HIV+ patients in other countries. To test this, researchers collected the average t-cell counts after 3 months of HAART therapy for men and women in Scienceville, USA. These are the results they found:

Location / Gender / Avg T-cell count after 3 months of HAART Therapy (cells/mm3)
Scienceville, USA / Male / 440
Scienceville, USA / Female / 451

Next, the researchers went to Medicineville, World. They tested the HAART therapy in 14 patients.

Results:

Location / Gender / Avg T-cell count after 3 months of HAART Therapy
(cells/mm3)
Medicineville, World / Female / 490
Medicineville, World / Female / 501
Medicineville, World / Female / 477
Medicineville, World / Male / 460
Medicineville, World / Female / 470
Medicineville, World / Male / 455
Medicineville, World / Male / 478
Medicineville, World / Female / 471
Medicineville, World / Male / 457
Medicineville, World / Male / 463
Medicineville, World / Male / 460
Medicineville, World / Female / 480
Medicineville, World / Male / 468
Medicineville, World / Female / 471
  1. A Chi Square Goodness of Fit test should be used to determine if the results observed in Medicineville, World are statistically similar to the results obtained in Scienceville, USA (expected results). Why would you use a Chi Square test and not a t-test?
  1. Perform the Chi Square test.

Fill in the chart:

Gender / Observed / Expected / (O-E) / (O-E)2 / ((O-E)2)/E)

X2 =

DF = n-1 = ______α = ______

How does the α-value compare to the p-value?

  1. Was there a statistically significant difference between the two populations? If so, what reasons can you think of for why HAART therapy affected the patients in Medicineville, World differently than the patients in Scienceville, USA?

Name: ______

Homework Assignment: HIV Statistics

The goal of this homework assignment is to learn more about HIV prevalence in the US and the World.

Part I: HIV by Age in the US

Source:

Using this graph, answer the following questions.

  1. What is the mean age of diagnosis?
  1. What is the median age of diagnosis?
  1. What is the mode age of diagnosis?
  1. Describe the distribution of the graph (ex: skewed, bimodal, etc).

Part II: HIV throughout the US

State/Dependent Area / Number of Diagnoses of HIV Infection, 2011
California / 5,973
Florida / 5,403
Texas / 5,065
New York / 4,960
Georgia / 2,522
Illinois / 2,142
Maryland / 1,783
North Carolina / 1,672
New Jersey / 1,567
Pennsylvania / 1,545

Source:

  1. Use the following information to construct an appropriate graph that accurately displays the data.
  1. Explain why you chose this type of graph.
  1. Do you notice any trends? (For ex: geographic regions with high rates of HIV infection?)

Part III: HIV throughout the world

Geographical region / Number of children (0-14 years) receiving antiretroviral therapy / Estimated number of children needing antiretroviral therapy / Percentage of children receiving coverage?
Sub-Saharan Africa / 495 700 / 1 830 000
Eastern and southern Africa / 426 800 / 1 310 000
West and central Africa / 68 900 / 520 000
Latin America and the Caribbean / 17 000 / 39 000
Latin America / 13 500 / 29 000
The Caribbean / 3 500 / 10 200
East, South and South-East Asia / 44 400 / 111 000
Europe and Central Asia / 8 200 / 7 800
North Africa and the Middle East / 900 / 6 500
Total / 566 000 / 1 990 000

Source:

  1. Determine the percentage of children receiving treatment in each area of the world and complete the chart.
  1. What are reasons you can think of for why children in some world regions do not receive the treatment they need?

1