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10 posts from April 2016


What is Big Data and how will it revolutionize the health industry? Part II

Big Data is poised to revolutionize the healthcare industry. The revolution goes beyond just analyzing text based notes. It is being used in predictive analytics, prescriptive analytics, genomics, and in many other ways.

You may have heard the term “Internet of Things.” This refers to the fact that many devices are now connected to the Internet, from your phone to your car to wearables like the Apple Watch and FitBit. It is estimated that by 2020, there will be 25 billion connected devices.  These devices capture real time data, and allow for real-time alerts. They produce tons of data on the individual. In combination, they can provide us even more information on entire populations.

Big Data can fill in the blanks for predictive analytics, “the use of data, statistical algorithms and machine-learning techniques to identify the likelihood of future outcomes based on historical data.” Electronic Medical Records can be reviewed and analyzed. An individual patient generates much data which can be analyzed to make predictions on whether or not they will comply with their doctor’s recommendations. For example, one hospital found that patients who live in certain neighborhoods are likely to miss appointments. They concluded that it was actually cheaper to send them a taxi to bring them to the appointment than it was to deal with a missed appointment. This was determined by utilizing multiple data sources: patient data, neighborhood data, and administrative data.

Remember, these data are not all being collected by the researcher. They are being collected independently, and the researcher is able to query the different sources to make a prediction.

Prescriptive analytics are a goal of Big Data in healthcare–to be able to identify and predict the path of a patient, then intervene to set them on the right path. For example, if a patient is supposed to walk a certain number of minutes a day, their phone or wearable would be able to see, in real time, if they choose to do so. If the patient allows these data to be shared with their physician, the physician can connect with the patient and determine why they are not complying. This would allow for immediate interventions that were not possible before.

When a person uses their cell phone late at night, it may indicate they are having trouble sleeping, which their physician can then address. These are very simple examples, but they demonstrate how real-time data can be captured and used to nudge patients in the proper direction.

Genomics research is a third area of opportunity in Big Data. The cost of mapping out an individual’s genome has plummeted since the completion of the human genome project. The individual’s genome itself is a massive dataset. When you can compare the genomes of millions of people, you can gain insight into the effectiveness of medicines. We are already seeing a move towards personalized medicine, which will only be strengthened by the Big Data revolution.

Traditionally, an oncologist might find that patients of European descent respond differently from non-Europeans to a particular treatment, which can then be used to determine the first-line or second-line treatment for those subpopulations. Now, with genomic testing, oncologists can see that those with a particular genetic marker respond very well or not at all to a particular treatment. With rapid genomic testing, the oncologist can then use a patient’s genomic information to recommend the treatment most likely to be effective. We are now able to identify patient sub-populations based on genetic markers, which allows for targeted gene therapy, Think of the advances this will bring us in treating cancer or other devastating diseases.

As more genomic data are captured and compared, we will be able to make insights that were nearly impossible to make before. We can begin to see what was once invisible. The more data there are, the more insights we can glean.

When enough Big Data are available, the insights we will be able to make are beyond comprehension.   It is already transforming how we think of health and public health and it will continue to revolutionize healthcare for years to come.


Magdi Stino, Health Policy PhD Candidate


The Reference Product Strikes Back

A lot of consideration has gone into biosimilars and what they mean for the biologics drug market. The idea behind biosimilars is that they will reduce costs by providing cheaper alternatives to existing biologic drugs. As mentioned in previous blogs however, biosimilars are by no means a cheap and simple cure to high drug costs. There are several road blocks like high production costs and long periods of development biosimilars must face on the way to the market place. These road blocks cut into the discounts biosimilars could offer. This leaves a unique opening for reference products. Reference products could lower their prices slightly and make the discounts offered by biosimilars not worth the risks of adoption.

Imagine a biologic called Reference Product 1 which costs patients and insurers $10,000 a year. Now imagine a biosimilar called Biosimilar 1 comes along and costs patients only $8,500 a year. This is a 15% reduction in cost which is a fair estimate of the discount offered by biosimilars. Depending on the patient or insurer, this reduction in cost may be enough for Biosimilar 1 to be preferred over Reference Product 1. Now imagine that in response to Biosimilar 1, the manufacturer of Reference Product 1 lowers the price by $1000, resulting in a sales price of $9,000This means that patients and insurers are only saving slightly about 5% over what they would spend with the Reference Product 1 if they use Biosimilar 1. In this situation, a comparison must be made between saving 5% or sticking with an original product that has been both studied and used more. Sure some patients will elect to use the cheapest possible alternative but others will pay the higher price for the tried and true product. This could create a situation where the manufacturers of the biosimilar is forced to raise prices or take their product off the market.

Manufacturers of biosimilars must rely on an economic principle known as Economies of Scale in order to compete with the manufacturers of the reference product. The idea of Economies of Scale is that increasing the quantity of the product will decrease the per unit cost of making it. Biosimilar 1 has fixed costs for producing it like the costs of factory and equipment. These fixed costs are spread out more by producing a larger quantity of Biosimilar 1. Once enough patients use Biosimilar 1, the costs of production lowers enough to make the biosimilar profitable for the manufacturer. If the manufacturer of the reference product lowers the price and keeps patients from using the Biosimilar 1, then the sale of the Biosimilar 1 will not be profitable. This is particularly true for biosimilars targeted at small patient populations. In this environment, a smaller number of patients would make up a larger percentage of the   population. Therefore, the manufacturers of the reference product would not need to keep as many patients in order to hurt the profitability of the biosimilar.

This does not mean that there is no room for biosimilars in the biologics market however. Some biosimilars will be priced at a greater discount than the 15% mentioned earlier. These biosimilars may bring prices down low enough that reference products could not compete. Also, biosimilars do not have to be made by competing manufacturers. It may be that manufacturers of reference products will also make biosimilars to their own reference product. These manufacturers already have the equipment and expertise necessary which means production costs would not be as great. In this scenario the biosimilar would not have to deal with competition from a reference product since it is being made from the same manufacturer. In conclusion, biosimilars are not necessarily going to wipe out reference products once they hit the market. In some instances, the reference product may still have a competitive advantage over the biosimilar despite higher costs. Biosimilars will almost guarantee that prices for reference products will come down if only just a little.


Robert Bond, PharmD '18


More Faculty, Staff, Student and Alumni Achievements

Student Mathematics Papers Presented at Section Meeting

Faculty sponsor and Professor of Mathematics Dr. Salar Alsardary led a group of USciences students to present papers at the Eastern Pennsylvania and Delaware Section of the Mathematical Association of America at Muhlenberg College on April 2, 2016. The undergraduates presented on three topics.

The History of Algorithms: Deion Floyd PhC’17, Eli Halpern C’17

Sudoko: Matt Marshall BI’19, Tori Lugiano C’19

The History of Knight's Tour: Brianna Mengini PharmD’20, Brielle Okulicz PharmD’20, Jaden Daubert PharmD’20


Student Life’s Deena Smith, a Residence Director for Goodman and Wilson Halls, was recently accepted into the Regional Entry Level Institute (RELI), to be held on June 1-3, 2016, at Rowan University. RELI is an intensive professional development seminar featuring a range of activities for entry-level professionals who aspire to mid-level positions in residence life and beyond. RELI is funded, coordinated, and hosted by the Mid-Atlantic Association of College and University Housing Officers (MACUHO).


On April 14, the Greek community participated in a community event called Sandwich Wars. They made 445 PB&J sandwiches, which were donated to the Bethesda Project in South Philly.

Amanpreet Kaur PhD'17 had an article that was published in Nature: "sFRP2 in the aged microenvironment drives melanoma metastasis and therapy resistance: Read it here: http://www.nature.com/nature/journal/vaop/ncurrent/full/nature17392.html

College of Physicians of Philadelphia News

Professor of Health Policy and Public Health Dr. Amalia Issa has been re-elected to serve another two year term on the Steering Committee.

Amy Jessop won the Individual Public Health Recognition Award for her work at Prevention Point Philadelphia.


Biosimilars and P&T Committees

Imagine that you are on an APPE rotation at a local managed care organization (MCOs) and your preceptor asks you to research a medication that is being considered on the Pharmacy and Therapeutics Committee (P&T Committee). Because P&T Committees are inter-professional committees designed to consider whether to add a new medication to a formulary, you as a student pharmacist are expected to find information to help make that decision. Since pharmacists are expected to speak the language of business and science, your research must cover a broad area. As you begin to research the medication, you notice that it is a biosimilar to a reference drug the hospital already carries. With biosimilars still new to the United States and only a one or two examples to learn from, your research becomes more challenging than you had expected. This scenario will be a reality for many student pharmacists as the number of biosimilar continue to increase. This blog will address some of the new considerations health care providers need in order to appropriately integrate biosimilars to their practice.

The first of these considerations is the savings that a biosimilars will bring to the MCO. The way to go about understanding this is to see where it will stack up in the Medicare Part D Formulary Tiers. This system is used by Medicare to represent how much the beneficiary will pay for a drug. Drugs in the lowest tier are preferred generics which are the least costly. Drugs in the highest tier are typically specialty drugs which are the most expensive. Since biosimilars cannot go through the more direct pathway that generics use to get to the market, they will most likely be placed in a higher tier than generics. With that being said, biosimilars are not starting completely from scratch like reference drugs meaning that they will be on or below the reference drug’s tier. It is therefore reasonable to assume that the lowest tier a biosimilar will reach is the second tier. This is preferred brand tier which is in the middle of the pack in terms of cost. Since reference products could be placed in tiers two through five, the saving a biosimilar could offer will vary.

The next step in determining whether or not to add a biosimilar to a formulary is to anticipate what challenges the biosimilar could face. Suppose the biosimilar you are researching has fewer indications than the reference product. This means that while some of your patients may be placed on the lower cost biosimilar, other will still require the reference product. If that is the case, then the formulary will still require both the biosimilar and reference product be available in the pharmacies which will increase costs. The costs of still carrying the reference drug in the inventory may offset any savings. Another consideration is whether or not the biosimilar will face shortages. Biosimilars are not equivalent to one another in the same sense as generics. This means that if there are two biosimilars to the same reference product and one of them becomes available, a pharmacy cannot automatically switch to the other. P&T committees must account for this when they select a biosimilar and understand that shortages may create costs which again offset savings. These are just two examples of unique issues that must be thought about with biosimilars.

Of course, biosimilars will still have many similarities to other drugs in the context of a P&T committee. Considerations like available dosage forms, medication safety, pharmacokinetic profile will be examined like with any drug. As medication experts who are knowledgeable about the science and business of drugs, responsibility is likely to fall to the pharmacist to bring these new considerations to the table.

Robert Bond, PharmD '18


PCP Student Receives Travel Grant for National Conference

Christina Ly- Professional PictureChristina Ly PharmD’17 was one of only four students selected nationwide to receive a travel grant worth up to $2,500 from the American College of Apothecaries (ACA), International Academy of Compounding Pharmacists (IACP) and American College of Veterinary Pharmacists (ACVP) Foundations. Ly received the award at the 2016 ACA/IACP/ACVP Educational Conference held from February 24-27, 2016, in Coronado, CA.

The Educational Conference is a joint endeavor by the three hosting organizations, ACA, IACP, and ACVP. The conference provides continuing education sessions to pharmacists and pharmacy technicians that are focused on topics related to independent pharmacy, pharmaceutical compounding, pharmacy law, and veterinary pharmacy. In addition to attending educational sessions, travel grant winners have the opportunity to attend an association board meeting and network with working professionals from across the country. Information on the Educational Conference can be found at www.educationalconference.org.


Introduction to Biosimilars

As clinical guidelines are published and the pharmaceutical industry innovates, the practice of pharmacy changes. At the fore front of innovation is the biologic. These medications are capable of achieving clinical outcomes that traditional small chemical medications cannot. Biologics are not without disadvantages. In addition to being more challenging to create, these medications are also orders of magnitude more expensive. This cost has kept biologics as an alternative or not even an option to patients who would greatly benefit from their use. In order to solve this issue, the pharmaceutical industry is developing biosimilars, or medications that are similar to existing biologics and are offered at a lower cost.

Most medications that are found in a pharmacy are small molecule products. These drugs are synthesized chemically and have been relatively cheap to produce. When a small molecule drug is first offered on the market by a single proprietary manufacturer, they are known as brands drugs. The costs of these products are typically high for a regulated period of time, that proprietor is the only entity legally allowed to produce that drug. This creates a temporary monopoly, allowing the proprietor to sell at a price with no competition. After that period of time is up, other manufacturers are allowed to create what is known as generics. This system of proprietor creation and then generic competition is regulated by the Hatch-Waxman Act. This legislation has two goals. The first is to create an incentive for new drug creation by allowing innovator manufacturers enough of a monopoly to make a profit despite high research and design cost. The second is to lay down a framework where generics can come in and make prices reasonable for patients. When both goals are met, a balance is struck between continuing innovation and low drug costs. Hatch-Waxman has created a model that works well for the brand-generic model which for the time being describes the inventory of most pharmacies.

For biologics, this model cannot be applied. The reason for this difference is due to the method of how biologics are synthesized. Biologics are made from genetically engineered cells. These cells then create proteins which are then isolated. As you can imagine, this process is much more complex and difficult. The process for engineering these cells may be trade secrets which means non-proprietary manufacturers must come up with a different way to arrive at a similar protein. The fact that the active pharmaceutical ingredient is a protein makes replication more challenging for non-proprietary manufacturers. A protein’s function is in part derived from its tertiary structure, the way in which the protein is folded. Slight alterations in the amino acid chains which make up the protein could alter tertiary structure and therefore alter is function. In other words, the creation of generics for biologic proprietary medications are nearly impossible.

This is where biosimilars come into the picture. Biosimilars are highly similar medications to a reference product. A reference product is like the brand product of the original model. The sponsors of the biosimilar must go through a new legal route before they can market the product. Thus far, that legal pathway has been through the Biologics Price Competition and Innovation (BCPI) Act. This act is designed to work similarly to the Hatch-Waxman Act. The BCPI is not the complete story however, leaving the path biosimilars must go through unclear. The amount of research needed for a biosimilar and the time that it will take to develop a biosimilar for example are still unknown.

In conclusion, biosimilars are medications that are clinically similar to proprietary biologics and can be produced and sold at a lower cost. Due to the complex nature of biologics themselves, biosimilars will not be the new generics but could lower health care costs and bring innovative pharmacotherapy to more patients.

Robert Bond, PharmD '18


What is Big Data and How Will it Revolutionize the Health Industry? - PART I

Big Data is one of those new terms that has been getting a lot of media coverage. If you’re like me, you have been confused by what it even means. The short answer is that Big Data is a new approach for organizing and analyzing the massive amounts of data being generated each day. Big Data allows for insights that were practically impossible under traditional approaches. We are at the doorstep of a revolution, yet we still haven’t maximized our potential with old techniques and approaches.

Before we dive into the future of Big Data, it helps to first realize how much data modern society is producing each day. Eric Schmidt of Google noted that “from the dawn of civilization until 2003, humankind generated five exabytes of data. Now we produce five exabytes every two days…and the pace is accelerating.” (How much is an exabyte?).  More recently, it was estimated that we produce two and a half quintillion terabytes of data every day as of 2012. These include everything from your credit card purchases, to the photos you take on your phone, to your social media posts. Everything is being digitized and ubiquitously captured and more data are being produced constantly. Every phone call you make is recorded. Every song you play in iTunes is documented somewhere.

We have an astounding volume, variety, and velocity of data–“the three Vs.” This is where Big Data comes in. Big Data is a new approach to storing, reading, and analyzing these data, which are distributed over many different platforms, and are not standardized. This differs from the traditional mode of data analysis.  The traditional approach has been to organize and build what are called “relational databases,” then apply statistical analysis methods to answer specific questions–often the databases are built for the purpose of answering those specific questions.

A relational database is simply a set of data tables, each made up of rows and columns, which are joined together by one or more columns used as an identifier. For example, if you have a student ID card, then the university has a table of all student IDs, with personal information about each student. Then, there would be another table, say one with course registrations by student. Every time you register for a course, a new row is created with your student ID and the course number. Because each table makes use of your student ID as an identifier, an analyst can find your information from each table–to create a class roster, say, or to print out your schedule for this semester. We can find all the student ID numbers registered for a particular course, then find information on each student from the other table.

A shopper rewards program works the same way. One table records your reward number and all your personal information. Companies can use what are called data mining techniques on this database to encourage more sales. For example, retailers already send catalogs and specials to their customers. If they know your shopping history, they can customize the mailers they send you to highlight items you are more likely to buy. Even just knowing the gender of the customer allows them to segment their advertisements, and get a better return on investment. The more they know about your preferences, and the preferences of people like you, the better they can customize their engagement with you.

But, even with sophisticated techniques like data mining, and with massive transaction databases, we are still not in the world of Big Data. The examples I just gave are part of the traditional approach. The tables are organized in advance, data are captured and recorded neatly in the tables, and normal methods of analysis are used. This is not Big Data–this is just lots of data.

Big Data, unlike this traditional approach, does not need to use relational databases in its analyses. The data are not “collected” in the same way. Oftentimes, the data are being collected (or archived) without the intent of analyzing them later.  Big Data does not have any structure. Data do not have to be neatly organized in tables with rows and columns like relational databases.

Nearly everything we do in modern society leaves a digital footprint. Big Data allows us to use and analyze these data by applying specific techniques.  Primarily, Big Data makes use of Hadoop for faster file storage and data retrieval. Hadoop, an open source architecture developed by Yahoo, based on research conducted by Google, is the primary Big Data tool. Hadoop uses a distributed filing system where raw data are saved across multiple nodes, using a single hierarchy of directories, usually saved in 64 MB chunks. The data are not cleaned or organized in any way, and no business rules are applied. The data are not transformed. Big Data, using Hadoop, allows users to query those data and gain meaningful insights. Facebook, as an example, uses Hadoop to store the massive data generated by its users every single second.

Practitioners of Big Data believe in the “sushi principle”; that is, data should be raw, fresh, and ready to consume. Don’t cook the data! Keep it in its raw form. 

Because Hadoop is open source, and runs on commodity hardware rather than specialized hardware, it is much cheaper and simpler to store data than traditional methods. However, the difficulty arises in later querying and analyzing the data.

Whereas before, specialists were required to build the data sets, create the schema, and capture the data in a consistent way, Big Data eliminates these required skills at the front end, since Hadoop standardizes the approach to storage

Big Data requires expertise and creativity in the querying end. Querying can be complicated, since the data are being retrieved from multiple sources, which are not organized in a standardized way. SQL is becoming the standard querying language in Big Data, as it has been in traditional relational databases.

Because it is so new, it has been said that the only people with 10 years experience in Hadoop, are the men who developed it in the first place.

This provides a huge opportunity for data scientists in the future, and Big Data will surely create a huge demand for analysts who can work within the architecture.

Magdi Stino

Health Policy PhD Candidate


Lowering Drug Costs and the 340b Drug Pricing Program

In order to protect patients from high drug costs, the Centers for Medicare and Medicaid Services (CMS) offers several plans qualifying patients can sign up for (more information available here). There are patients however, who are in need of federal assistance on drug costs but do not qualify for one of these plans. In order to reach some of these patients, the congress created a policy under section 340B of the Public Health Services Act. This policy is managed by the Health Resources and Services Administration (HRSA) and is known today simply as the 340b drug pricing program.

This program is aimed at the hospitals and medical centers who likely treat these underserved patients populations. These organizations are known as , covered entities.   Examples of covered entities include, but are not limited to Federally Qualified Health Centers, Ryan White HIV/AIDS program grantees, children’s hospitals and sexually transmitted disease clinics (full list found here). Patients at these facilities who qualify benefit from access to 340b covered drugs because their drug costs will be significantly less than patients not eligible for 340b drugs. These facilities will benefit because the manufacturer and wholesaler are obligated to sell these drugs at the low 340b price. These savings are typically 23.1% for brand products and 13% for generic products. Covered entities also have the ability to negotiate lower prices from those discounts which would result in further savings. These entities are then reimbursed by Medicaid at slightly higher prices which fall somewhere above the actual acquisition cost or AAC. AAC is what the pharmacy pays for drugs. It therefore factors in all sales and discounts the pharmacy may receive from a wholesaler. Medicaid may pay pharmacies at slightly higher than AAC in order to incentivize pharmacies to participate in the 340b drug pricing program.

The 340b pricing program is still a work in progress. One of the principle issues has been compliance with the program. It can be difficult for covered entities to follow the rules HRSA has established for the 340b program. Imagine that there is a pharmacy with 10 stock bottles of a maintenance drug. One of those bottle was purchased for a 340b patient at a 340b price. The challenge is ensuring that only the 340b patient receives medication from the 340b stock bottle. This becomes incredibly difficult when you factor in many 340b patients on multiple medications. If patients who are covered by Medicare or Medicaid received 340b drugs at typical prices (AWP minus a negotiated percentage) then federal resources would be spread too thin to help the underprivileged.

Fortunately, more changes are expected to occur. One change is in regard to better defining qualifying patients. With new changes, patients must have in-person medical visits with a 340b covered entity to qualify. Another example of a potential change is with manufacturers. Before any changes, the HRSA did not have the power to audit manufacturers and ensure they were proving drugs to covered entities at a discounted price. With the changes, the HRSA would be given the power to both audit manufacturers as well as impose penalties on those who do not comply with 340b regulations. These changes, when implemented, will improve 340b but more is needed for 340b to achieve its original goal of spreading federal resources to the underprivileged.

Robert Bond, PharmD '18

Policies to Help Stabilize Rising Drug Costs

In the last blog I reviewed drug pricing terminology between the wholesaler and pharmacies. In this blog I will review how this process can lead to increasing drug costs. I will also two discuss public policies that have been implemented to try to stabilize that trend.

As mentioned previously, pharmacies are reimbursed at a discounted Average Wholesale Price (AWP). Pharmacies can seek deals with wholesalers to buy drugs at the Wholesale Acquisition Cost, WAC, or Average Manufacturer Price, AMP. Pharmacies will then make a profit by selling at around AWP. In other words, a pharmacy’s profit can be represented as AWP minus either WAC or AMP. Pharmacies can increase their profits by selling drugs with higher AWPs. Knowing this, manufacturers may attempt to set a higher AWP.  Since pharmacies are drawn to higher AWPs and purchase from wholesalers, wholesalers will carry the highest AWP drugs they can to satisfy the pharmacies they sell to. This relationship is similar to that of the manufacturer and the wholesaler. The manufacturer will create the highest AWP possible to attract the wholesalers who are going to buy at lower price like AMP anyway. Because wholesalers do not purchase drugs at AWP, and instead purchase drugs at a lower price, an increase in the AWP will not cause them to search for other manufacturers. This means that manufacturers can increase AWP while simultaneously satisfying the needs of pharmacies, making their product more attractive to wholesalers without losing business to competing manufacturers. AWP has a natural tendency then to increase because pharmacies want it higher and manufacturers can increase it without the risk of losing business from wholesalers.

Increasing AWP does increase costs to insurers and taxpayers. Since the government does serve as an insurer, it has put policies in place to prevent increases in AWP from bankrupting them. The federal government does this by imposing a Federal Upper Limit, FUL. This limit is the maximum price at which Medicaid will reimburse a pharmacy for a drug. In order for a drug to qualify for an FUL price, it must have at least three equivalent products made by three different manufacturers. To put is simply, if “Drug X” had an FUL price, it must have three different therapeutically equivalent generics that are made by at least three competing manufacturers. The FUL prices is set at 150% of the cost for the cheapest equivalent drug. If “Drug X” is made by manufacturers 1, 2 and 3, and the cheapest price is from manufacturer 1 at $100 dollars, then “Drug X”’s FUL price is $150. These strict qualifications means that some drug do not have an FUL price and of those that do, paying 150% for the cheapest generic may not produce any savings. In order to make up for these limitations, some states have created Maximum Allowable Costs or MAC (more information available here). MAC was designed to operate as a continuation of FUL to further increase savings but at a state level. MAC prices are uniquely set by each state and do not have strict rules for establishing what drugs qualify for MAC and what the price ceiling should be. This has created variation between states with some states achieving more drugs that qualify and more aggressive price ceilings than others. Whether or not these MACs were worth the resources put into their creation is something that remains to be seen.

In conclusion, both state and the federal government have created policies to the curb the natural tendency for AWP to rise. The federal government first created the Federal Upper Limit, or FUL, and states later created Maximum Allowable Costs, or MAC based up the FUL. The FUL has severe limitations in the form of drug qualifications that are too strict and a 150% price ceiling that can be ineffective. The MAC on the other hand may be a step in the right direction. Since the MAC is based off of and shares similar limitations to the FUL, its effectiveness remains to be seen. Moreover, while FUL and MAC may be effective in some situations, they alone are not enough to prevent increasing drug costs.

Robert Bond, PharmD '18


Usciences Research Gains Traction in Men's Health

USciences’ motto is “proven everywhere.” One reason why the “proven everywhere” motto makes sense for USciences is because we teach students, and professors themselves use scientific research as the basis for teaching and scholarship. One such area is the Health Policy Program at Mayes College of Healthcare Business & Policy. Health policy is the investigation of problems in health (not just healthcare and its delivery) in its broadest sense using scientific methods of study to develop evidence-based recommendations for changes and innovations in policy. One challenge is that policymakers sometimes eschew data and evidence when making policy; rather, they are sometimes drawn to its opposite – anecdotes – heart wrenching stories from constituents.

When data and evidence alone fail to inform policy, another option that is available is to make the best possible case for particular policies using the force of ethical argumentation. In this regard, evidence and data receive bolstering through analysis of the very values that undergird health and provide exhortation for particular policy approaches. This is the case with some recent work undertaken by Health Policy Ph.D. candidate Janna Manjelievskaia, MPH and Visiting Assistant Professor, David Perlman, Ph.D.

Janna was working with colleagues on a paper examining the policy issues associated with the current U.S. Preventive Services Task Force (USPSTF) recommendations against testicular cancer screening. She suggested to her colleagues that perhaps the paper could be enhanced with an ethical angle. She asked Dr. Perlman, one of her professors who focuses on ethics in health policy and public health, to join in writing the paper, which was recently published in the American Journal of Men’s Health and presented at their conference. The lead author of the paper, Michael Rovito, Ph.D., an Assistant Professor at the University of Central Florida, was recently was interviewed by STAT about the importance of testicular self-examination. The paper, and the power of its ethical argument and coupled with careful, scientific examination of policy, are gaining traction with policymakers, which should hopefully result in a policy change by the USPSTF to change its current recommendation against testicular cancer screening. When that happens, it will be yet another instance of how USciences research and students are “proven everywhere.”

David Perlman, PhD

Janna Manjelievskaia, MPH

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