How Can We Improve the Blood Donation Process in the United States?

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1 How Can We Improve the Blood Donation Process in the United States? By H. H. Modeling Team: H. H. and Hilary Johnson Wilson High School Math Modeling and System Dynamics May 8, 009 System Dynamics Advisor: Diana Fisher Mathematics Department Educator Wilson High School Portland, Oregon Outside Advisor: Daphne Mathew Communications Manager American Red Cross-Pacific Northwest Blood Services Region Portland, Oregon

2 Introduction This model is one that demonstrates the blood donation process in the United States. Currently, the problem with blood donation is that the supply of donated blood available is not large enough to accommodate all the sick or injured people who need blood transfusions. According to the 007 National Blood Collection and Utilization Survey Report, 8,000 donations are needed daily in the United States; this translates into over 4.5 million patients who need blood transfusions each year in the United States. However, less than one third of the U.S. population that is eligible to donate blood actually does so each year. Therefore, the goal of this model is to figure out the best way to improve the blood donation process so that more people will donate blood and thus more blood will be available in life-threatening situations which require blood transfusions. The main components of this model are the donors, the blood that they donors donate, and those receiving blood transfusion. Figure : Reference Behavior Graph of Actual Donors This graph shows what has happened in the past, the current level of donors, and both the hope and fear for the future level of donors. The fear is that the number of donors will continue to drop while the hope is that the number of donors will increase until it reaches a steady, high level.

3 Figure : Behavior over Time Graph of Actual and Potential Donors This graph shows in further detail what we hope the future donor level will look like. The number of potential donors will decrease respectively with an increase in actual donors since those who are no longer potential donors are assumed to have become actual donors. The Process of Model Building The first thing we did was sketch a model diagram that included a donor stock, a donation aging chain, and a connection of the stocks through several contact converters between those who donate and those who receive blood. The contact converters resemble closely a segment of the epidemic model. We already knew that our model was going to have a reinforcing feedback loop through education; however, we didn t know exactly how to include this education component and whether it would involve an informational delay for the time it takes before a person acts on the education they have received. Thus, our first model looked like this:

4 Percentage Eligible Average Delay Time of Education Gap Birth Rate Death Rate Potential Donors Total Population Actual Donors Becoming a Donor Births minus Deaths Probability of Becoming an Actual Donor Probability of Meeting a Potential Donor Number of Contacts with Potential Donors Contacts per Affected Person per year Total Contacts by Affected Population per Year People Who Receive Blood People Affected By Blood Donation Amount of Blood per Transfusion Percentage of Donated Blood that is unusable Number of Donations Per Person Per Year Blood Bank Actual Donation Units Per Donation Amount of Blood Transfused Transfusions to People Transfer to Banks New Donations Dwell Time of Blood Discarded Testing Difference in Blood Bank Target Figure : Early Model; 50 percent completed

5 We realized we were missing a very important component that was necessary for the correct functioning of our model. We had a numerical difference between the target supply of blood needed to accommodate all needed blood transfusions, but we didn t include a way in which that difference would affect the rest of our model. Thus, we lacked an Effect of Difference on Education graphical converter, which would affect the probability of a potential donor becoming an actual donor and the number of contacts made by those affected from blood transfusions to the potential donors. Additionally, we made slight adjustments in the blood donation aging chain; we added outflows from the stocks to account for the small percentage of donated blood that is deemed unusable due to poor testing results or expiration. Also, we made the number of new donations more accurate by adding in the number of donations per person per year rather than just assuming each donor donates once annually. Finally, we removed the informational delay for education and instead added a probability of becoming a donor component to our model. Our intermediate model was the following:

6 Normal Probability of Percentage Eligible Becoming an Actual Donor Actual Probability of Becoming an Actual Donor Effect of Difference on Education ~ Birth Rate Death Rate Potential Donors Becoming a Donor Actual Donors Total Population Births minus Deaths Number of Contacts with Potential Donors Normal Contacts per Affected Person Per year Probability of Meeting a Potential Donor Total Contacts by Affected Population per Year Actual Contacts per Affected Person per year ~ Effect of Difference People Who on Education Receive Blood Average Number of donations per person per year Amount of Blood per Transfusion People Affected By Blood Donation Percentage of Blood in Banks that Expires per year Percentage of Donated Blood that is unusable per year Average Number of DonationsPer Year Units Per Donation Transfusions to People Blood Bank Transfer to Banks Actual Donation Amount of Blood Transfused New Donations Discarded after expired Discarded after testing Difference Time to Discard Time to discard Target Figure : Intermediate Model; 70 percent complete

7 The Finished Model and How It Works Rate of Discontinuing Donation Discontinuing donors Normal Probability of Becoming an Actual Donor New Percentage Eligible Potential Donors Actual Probability of Becoming an Actual Donor New Potential Donors Effect of Ratio on Education Becoming a Donor Actual Donors Figure 4a: Donor Component to Final Model Diagram The component in figure 4a is the donor component in the final model diagram. The stocks are potential donors and actual donors, and both stocks are in people units. The potential donors consist of those within the population who are technically able to donate but do not necessarily do so. When potential donors begin to donate blood, they flow into the actual donors stock. The small percentage of actual donors who stop donating at any point in their lives (while they are still eligible to donate) flow back to the potential donors stock. There are constantly new people flowing into the potential donors stock because as the population grows and changes, new people are eligible to donate blood. The effect of difference on education will affect the probability of a potential donor becoming an actual donor. Percentage of Blood in Banks that Expires per year Percentage of Donated Blood that is unusable per year Effect of Ratio on Education Amount of Blood Transfusions to Transfer to Transfused Blood Bank Actual Donation People Banks Units Per Donation New Donations Used Blood per Year Discarded after expired Discarded after testing Ratio of Actual Donations to Target Time to Discard Time to discard Target Figure 4b: Blood Component to Final Model Diagram

8 The component shown in figure 4b is the blood component in the final model diagram. The stocks are actual donation, blood bank, and amount of blood transfused, and all stocks are in units of blood. This segment is like an aging chain; it follows the path of blood from the donation from one person to the transfusion into another person. When blood is donated, it begins in the actual donation stock. It is then transferred to the blood banks, yet a small percentage of the blood is discarded after is it deemed unusable through the tests that are performed on each unit of donated blood. From the blood bank, the blood is transfused into people with life-threatening emergencies, yet another small percentage of that blood is discarded because it spent over 4 days in the banks and is therefore expired. Once the blood is transfused into a human, it remains there. Actual Donors Number of Contacts with Potential Donors Probability of Meeting a Potential Donor Actual Contacts per Affected Person per year Normal Contacts per Affected Person Per year Total Contacts by Affected Population per Year Effect of Ratio on Education Amount of Blood per Transfusion People Affected By Blood Donation People Who Receive Blood Figure 4c: Contact Component to Final Model Diagram This segment is the connection between the blood units and the donors. It is part of the education component to the model and shows how the number of actual donors can be increased through word of mouth. The people who receive blood through transfusions combine with the actual donors to become the people who have been affected, in some way or another, by the blood donation process. Each of those affected people makes contact (through verbal communication and education) with the potential donors. These contacts make the potential donors more informed about the blood donation cause, and thus the total number of contacts made with the potential donors affects the becoming donors flow and the number of potential donors who become actual donors. This connection also serves as the main feedback loop in our model, as will be explained in the next section. The complete, final model diagram is shown in figure 5, below.

9 Rate of Discontinuing Donation Discontinuing donors Birth Rate Death Rate Percentage Eligible Normal Probability of Becoming an Actual Donor Births minus Deaths US Total Population Actual Probability of Becoming an Actual Donor Potential Donors Effect of Ratio on Education Becoming a Donor Age Delay New Percentage Eligible New Potential Donors Actual Donors Probability of Meeting a Potential Donor Age Delay Normal Contacts per Affected Person Per year Number of Contacts with Potential Donors Actual Contacts per Affected Person per year Total Contacts by Affected Population per Year Effect of Ratio on Education Amount of Blood per Transfusion People Affected By Blood Donation Average Number of donations per person per year People Who Receive Blood Average Number of DonationsPer Year Percentage of Blood in Banks that Expires per year Percentage of Donated Blood that is unusable per year Effect of Ratio on Education Amount of Blood Transfusions to Transfer to Transfused Blood Bank Actual Donation People Banks Units Per Donation New Donations Used Blood per Year Discarded after expired Discarded after testing Ratio of Actual Donations to Target Time to Discard Time to discard Target Figure 5: Finished Model Diagram

10 The Model Feedback and Loop Story Becoming a Donor Actual Donors Actual Donation Number of Contacts with Potential Donors Total Contacts by Affected Population per Year People Affected by Blood Donation R People Who Receive blood Blood Bank Amount of Blood Transfused Figure 6a: Actual Donors Feedback Loop One An initial increase in actual donors will cause an obvious increase in actual donations. An increase in actual donations leads to more blood units available in the blood bank, which also leads to an increase in the amount of blood transfused (since more supply is available to accommodate the large demand for blood). If the amount of blood transfused increases, the number of people who receive blood should increase respectively. The more people receive blood, the more people are affected by blood donation. For example, a person whose blood transfusion saved his life is more affected by blood donation than a person who has no relation to the blood donation cause. If the number of people affected by blood donation increases, more affected people will communicate with the general public, and thus the total contacts made by the affected population will increase. If the total contacts value increases, the number of contacts by the affected population specifically with the potential donor population will also increase. Finally, more potential

11 donors who are informed of the blood donation cause will choose to donate, and as the number of potential donors who become actual donors increases, the entire actual donor population will increase as well. Since every component increases (or decreases) in the same direction as the next component, and increases (or decreases) in one component have a compounding effect on the loop, this feedback is reinforcing. The previous feedback affects the whole system by creating a continual increase in all factors of the model. The whole model will experience a compounding change in the same direction because a change in one factor reinforces a change in the same direction with the next factor. Actual Donors Becoming a Donor New Donations Number of Contacts with Potential Donors C Actual Donation Actual Contacts Per Affected Person per Year Effect of Ratio on Education _ Ratio of Actual Donations to Target Figure 6b: Actual Donors Feedback Loop Two Describing another important feedback, an initial increase in the number of actual donors will create an increase in the number of new donations and thus also an increase in actual donations. As more blood has been donated, the ratio of actual donations to the target will decrease. Therefore, a change in actual donation in one direction causes a change in the ratio in the other direction. If the ratio decreases, the effect of difference on education will also decrease because if more blood is available, then the need to educate is not as

12 important. As the effect of the ratio on education decreases, the actual contacts per affected person per year will also decrease. A decrease in the actual contacts per affected person per year will lead to a decrease in the number of contacts with potential donors because less over contacts means less with each specific group within the entire population. Finally, as the number of contacts with potential donors decreases, fewer potential donors will become actual donors, therefore decreasing the number of actual donors. This loop is counteracting because it has one factor that changes in the opposite direction from the previous factor. This feedback affects the whole system by balancing it. Since the feedback is counteracting, it helps to stop the entire system from increasing (or decreasing) continually and instead helps to put the system into equilibrium. When not enough blood is being donated, this feedback loop helps to create an increase in that factor. Yet, when enough blood is being donated, this feedback loop helps to create a decrease in that factor. In short, this feedback helps to make sure that the whole system does not compound in one direction continually. The Model Boundaries The most important factor that we left out of the model is the separation of blood into different blood types (A, AB, and O) and different parts of blood (platelets, white blood cells, etc.) in the blood banks. We left this factor out of our model because it would complicate things in an unnecessary way; we knew the final numbers and statistics for the amount of blood donated, the amount of blood in blood banks, and the amount of blood transfused, and those statistics weren t separated into different blood types or parts. Therefore, it wasn t relevant to our model and would have made our model less comprehensible. Choice of Time We chose to use years in our model for time specs because all the data we collected from research was given for respective years. The simulation time is 0 years. We chose this because the average age of the population can shift over this much time (and thus the number of eligible donors will also change). We considered doing a longer simulation time at first, but then decided against it because we predict that within 50 or more years, there will be new technology that will change the blood donation process completely yet we cannot predict how it will affect this process. Model Testing After building a rough model, the main segment, the blood component, was not in equilibrium. Thus, we separated that segment from the rest of the model and performed a number of tests on it to make sure each piece was in equilibrium.

13 When we ran our final model, the segments we were looking at were actual donors and potential donors. The first time we ran it, the number of actual donors was steadily decreasing so we knew something was wrong. We realized that what was previously the difference converter needed to be changed to a ratio converter so that it could correspond with the effect of ratio on education converter since the latter converter was a graphical function with an x-axis with values 0-. Therefore, a difference would be too large to correspond with the graphical converter, and a ratio was needed instead. Once we made that change, we knew our model was behaving as it should because our graph looked as follows: : : : Actual Donors : Potential Donors : : : : Page Years :6 AM Fri, May 5, 009 Actual Donors and Potential Donors Figure 7: Actual Donors and Potential Donors This graph demonstrates what we hope will happen in the future; the number of actual donors increase. However, this model doesn t agree with our reference behavior graph in one area: we predicted that the potential donors and actual donors would have an inverse relationship and that as actual donors increased, potential donors would have to decrease respectively. Instead, what happens is that the potential donors still continues to increase and this is because we now take into account that the population continues to increase as well. There are constantly more people becoming eligible donors as the average population age shifts.

14 Actual Donors: : : : Page Years Actual Donors Sensitivity Graph (Normal Contacts, =0, =4, =8, 4=, 5=6) :4 AM Fri, May 5, 009 Figure 8: Actual Donors Sensitivity Graph (Normal Contacts, =0, -4, =8, 4=, 5=6) For the above sensitivity graph, we changed the normal contacts per affected person per year by increments of four with the first parameter value at 0 contacts per year and the fifth parameter value at 6 contacts per year. The main effect that this has on the actual donors is that it makes the initial increase of the donors less gradual and the donor level remains slightly higher. Therefore, it would be beneficial to increase the number of normal contacts per affected person per year when the need for blood transfusions is urgent and consistently high. : Actual Donors : Potential Donors : : : : : : Page Years Actual Donors and Potential Donors with Increase in Normal Contacts to 8 in year 0 :5 AM Tue, May 9, 009 Figure 9: Actual and Potential Donors with an Immediate Increase in the Number of Normal Contacts per Year in Year 0 of the Simulation

15 The above graph demonstrates what would occur if there was an immediate increase in the number of normal contacts per year from 0 contacts per year to 8 contacts per year in the 0 th year of the simulation. Therefore, at the 0-year mark on the graph, the actual donors curve begins to increase at a faster rate than in it did previously. After the simulation is complete, the actual donor curve is at a higher level at the 0-year mark than it would be without the increase in the number of normal contacts per year (as seen in Figure 7). This occurs because of the reinforcing feedback involving the education component (see figure 6a). The increase in the number of normal contacts per year does not have a significant affect on the level of potential donors. : Actual Donors : Potential Donors : : : : : : Page Years Actual Donors and Potential Donors with Decrease in Normal Contacts to in year 0 0:44 AM Thu, May, 009 Figure 0: Actual and Potential Donors with an Immediate Decrease in the Number of Normal Contacts per Year in Year 0 of the Simulation The above graph demonstrates what would occur if there was an immediate decrease in the number of normal contacts per year from 0 contacts per year to contacts per year in the tenth year of the simulation. This is essentially demonstrating what would happen with almost no education at all. The reinforcing feedback loop in the model (see figure 6a) is disrupted at year 0, and with almost no education to continue the compounding increase in the level of the donors, the actual donor level drops significantly. It continues to drop for the rest of the simulation. This test proves how important education is in relation to the actual donor level.

16 : : : Actual Donors : Potential Donors : : : : Page Years Actual Donors and Potential Donors with an Increase in the Target by 5% in Year 0 :6 AM Thu, May, 009 Figure : Actual Donors and Potential Donors with an Immediate Increase in the Target by 5% in Year 0 (from to 77500) The above graph demonstrates what would occur if there was an immediate increase in the target (the amount of blood ideally available) by 5 percent in the tenth year. Thus, the target would go from units of blood to units of blood in tenth year. An example of a realistic situation in which this could occur is if the United States entered a war in the tenth year of the simulation, and the need for blood transfusions increased in accordance with the number of injured soldiers. This graph demonstrates the counteracting feedback loop involving the effect of ratio on education converter (see figure 6b). When the ratio of the actual donations to the target decreases dramatically in the tenth year, the effect of the ratio on education stimulates a major increase in the education component of the model, thus also increasing the number of actual donors to reflect that. Therefore, with the policies that our model involves, the actual donor level will change in accordance with the target (the need for blood). The Results of Modeling and Thinking The Results Our outside expert, Daphne Mathew, is the current communications manager for the Pacific Northwest Blood Services Region with the American Red Cross. When we communicated, she indicated that she has learned through her experience with this organization that education is a major influence in people s decision to donate. This was the primary reason we made sure to include several policies involving education in our model.

17 The graphs and tables also demonstrate the influence that education has on the number of actual donors in the United States. The more people inform others about the blood donation cause and process, the more likely it is that potential donors will decide to donate. Examples of ways that affected people can make contact with potential donors are through educational speeches about blood donation, media technology, and word of mouth. When we raised the number of contacts affected people make per year, it permanently increased the number of actual donors. This shows that the problem we studied, the blood donation process, can easily be improved through stronger educational efforts. The Final Graph Actual donors, actual donation, and people affected by blood donation are the components shown on the final graph. Actual donors is the most important component to graph because as long as that factor continues to increase, all the other major components, such as the blood supply, will increase respectively. Actual donation is shown on the graph because it demonstrates an increase in the blood supply that will be available for life-saving transfusions. From the actual donation curve, we are also able to see that actual donors often donate more than once annually, which accounts for an actual donation curve being much higher than the actual donor curve. Finally, people affected by blood donation is shown on the graph because it is a main connection in the feedback. As the number of people affected by blood donation (either through donating blood and saving a life or by receiving donated blood to have one s life saved), more education will occur naturally. This graph shows that all three of the components explained above increase due to the policies we came up with and it shows the potential success that education can have on the number of people who donate. : : : : Actual Donors : Actual Donation : People Affec y Blood Donation : : : : : : Page Years :7 AM Fri, May, 009 Actual Donros, Actual Donation, People Affected by Blood Donation Figure : The increase of Actual Donors, Actual Donation, and People Affected by Blood Donation

18 The Key Learning from the Modeling Process I learned that the process of model building is a multi-step process; we continually add on components to our model and change it until it reflects what we believe to be realistic behavior. I learned how serious the issue blood donation is; there are hundreds of people who are waiting for blood transfusions and die before the blood supply is large enough to accommodate them. I learned about the main reasons why people do and do not choose to donate. The major reason people choose not to donate is because they don t realize that their blood is needed. This was important to learn because it proved to us that it will be easier to convince someone to donate through education. I learned that one donation can save three lives. I learned how to think critically and determine what policies and ideas could lead to success in improving the blood donation process in the United States. In response to the question posed as the title of this paper, the best way to improve the blood donation process in the United States is to increase awareness and education about the blood donation cause. This can be done through formal lectures in hospitals and classrooms, through word of mouth by those affected by blood donation, and through media technology. New adaptations could be made in order to make this model reflect other issues as well. For example, one similar issue is the organ donation process in the United States (either general or with specific organs). Organ donation is currently facing the same problem as blood donation: shortages in supply in comparison to the need. Therefore, simple changes could be made on the current model to make it demonstrate the trend with kidney donation, liver donation, bone marrow donation, lung donation, etc. More complicated changes could be made to reflect the donation trend for organs that can only be donated once the donor is no longer living, such as heart and cornea donation. Another use for this model could be to show the monetary donations that individuals make to charities. Changing certain policies within that model would help show how nonprofit organizations that are suffering from lack of monetary funds could spread the word about their cause through education and thus increase their annual budgets.

19 Bibliography American Red Cross. 07 May 009 < "FAQ About Donating Blood." Give Life American Red Cross. 07 May 009 < "56 Fast Facts About Blood." Memorial Blood Centers. Memorial Blood Centers. Apr. 009 < "Information Concerning the Blood Donation Process." interview. Daphne Mathew. Apr "United States." CIA The World Factbook. Apr CIA. 6 Apr. 009 < Whitaker, Barbee I., James Green, Melissa R. King, Linda L. Leibeg, Sunitha M. Mathew, Karen S. Schlumpf, and George B. Schreiber. "The 007 National Blood Collection and Utilization Survey." 007. US Department of Health and Human Services. 6 Apr. 009 <hhs.gov>. Survey published every two years

20 Appendix Equations with units and Documentation - Omitted The Final Model Rate of Discontinuing Donation Discontinuing donors Birth Rate Death Rate Percentage Eligible Normal Probability of Becoming an Actual Donor Births minus Deaths US Total Population Actual Probability of Becoming an Actual Donor Potential Donors Effect of Ratio on Education Becoming a Donor Age Delay New Percentage Eligible New Potential Donors Actual Donors Probability of Meeting a Potential Donor Age Delay Normal Contacts per Affected Person Per year Number of Contacts with Potential Donors Actual Contacts per Affected Person per year Total Contacts by Affected Population per Year Effect of Ratio on Education Amount of Blood per Transfusion People Affected By Blood Donation Average Number of donations per person per year People Who Receive Blood Average Number of DonationsPer Year Percentage of Blood in Banks that Expires per year Percentage of Donated Blood that is unusable per year Effect of Ratio on Education Amount of Blood Transfusions to Transfer to Transfused Blood Bank Actual Donation People Banks Units Per Donation New Donations Used Blood per Year Discarded after expired Discarded after testing Ratio of Actual Donations to Target Time to Discard Figure 5 Time to discard Target

21 The Graphs : : : Actual Donors : Potential Donors : : : : Page Years :7 AM Fri, May 5, 009 Actual Donors and Potential Donors Figure 7

22 Actual Donors: : : : Page Years :4 AM Fri, May 5, 009 Actual Donors Sensitivity Graph (Normal Contacts, =0, =4, =8, 4=, 5=6) Figure 8 : Actual Donors : Potential Donors : : : : : : Page Years :5 AM Tue, May 9, 009 Actual Donors and Potential Donors with Increase in Normal Contacts to 8 in year 0 Figure 9

23 : Actual Donors : Potential Donors : : : : : : Page Years 0:44 AM Thu, May, 009 Actual Donors and Potential Donors with Decrease in Normal Contacts to in year 0 Figure 0 : Actual Donors : Potential Donors : : : : : : Page Years :6 AM Thu, May, 009 Actual Donors and Potential Donors with an Increase in the Target by 5% Figure

24 : : : : Actual Donors : Actual Donation : People Affect By Blood Donation : : : : : : Page Years :7 AM Fri, May, 009 Actual Donros, Actual Donation, People Affected by Blood Donation Figure

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