Why epidemiology matters

I became a nurse, like many other nurses I know, to do heroic things: bathe feverish foreheads, stitch wounds, save lives and all that. I loved being a nurse for the same reasons. Clara Barton will always be my #1 hero (at least, after God, my husband, my parents, and Anne Shirley). Today I'm working on my PhD, and occasionally I lose that clarity in a haze of late-night writing, combing statistics, and endless literature searches. I wrote about epidemiology today (dry topic, I know), and felt a little of the old flame for nursing coursing through my intellectual veins. As I am too tired to come up with anything more creative or personal, I'm going to share a glimpse of my work at school here. Because it matters to me, personally, as a mother of a child with a sick brain and a cancer patient myself. And it really should matter to everyone in our nation at the moment, too. Try not to fall asleep now.

From my writing today on the subject why should nurses care about epidemiology:

I have a poignant memory of an "a-ha" moment I had regarding epidemiology, during my first residency week in 2007. I listened to a professor speaking eloquently about how we can define various populations of patients based on the statistics that describe them. In his presentation, he was focusing on the the fact that over 30% of health care expenditures are generated by less than 3% of the patient population, the "outliers" - the sickest of the sick. Epidemiology, as the study of frequency, risk, and cause of disease (British Medical Journal, n.d.), is concerned with disease both when it occurs in a majority, and when it occurs in a minority, like that infamous 3%. It is important to nurses, first and foremost, because being armed with information makes us more capable as we care for sick or hurting individuals. Yet it is important in other, more subtle ways, too: understanding disease and the statistics that surround it may help us identify those most at risk - either so we can eliminate them from a system governed entirely by cost, or so we can help them in a system governed by human compassion. We stand on a perilous balance beam between capitalism and socialism: should we provide services only to reduce risk, or compensate victims of risk with social insurance (Devarajan & Jack, 2007)?

Being identified as "high risk" could become a deadly game in a health care environment that is run by third-party payers (either private or public). We can no longer afford to walk into a doctor's office to obtain a diagnosis, nor can we afford to pay a nurse to come to our home to administer I.V. medications for our year of cancer treatment, any more than we can afford to buy a share in an MRI machine so we can have our brain scan or a cath lab so we can have a stent placed. Our ability to purchase health care is but a distant memory. Therefore, we are at the whim of the third party responsible for guarding - and financing - our health. Physicians face the conundrum of balancing the Hippocratic oath with a patient who cannot afford to pay for a service that is not truly within the power of the physician to prescribe or administer. Nurses balance beneficence and justice with hosts of minority, unemployed, or uninsured patients whose vast health needs build up over decades of missed preventative care opportunities and explode in a multi-million dollar inpatient care extravaganza that may do little to improve the patient's quality of life in the end. Insurers are left holding massive bills, and are faced with three unappealing options: increase the price, dilute the quality, or lessen the quantity. Who wins? Nobody. Who loses? Usually the patient. We, as health care providers, lose, too - as we throw up our hands and struggle to maintain our grip on our ethics, our dignity, and our show of compassion.

What happens to the "high risk" patient, and how does this relate to epidemiology? Epidemiology is at the crux of the ongoing debate about who should pay for health care, how much should be paid, what limitations are necessary and/or reasonable, and what happens to the 3% that generate such a huge percentage of annual health care costs. Epidemiology is how we established the 3%. It has the potential to identify the 3% long before they ever get sick, perhaps even before they are born, if the science of pre-implantation genetic diagnosis continues to explode (Verlinksy et al, 2002; Verlinsky et al, 2004). This could mean we rush in to find solutions to the problems that place this group at such high risk, or it could mean we abandon them even before they get sick, denying them coverage based on their risk (Davenport, 2009). Epidemiology has provided statistics that have allowed a business loophole for private insurers for decades when it comes to high-cost patients such as those with certain cancers or cardiac conditions (Schwartz, Claxton, Martin & Schmidt, 2009).

As nurses, this may affect us in two ways: first, it may tell us who requires the most intervention from a physical care perspective. It may help us identify populations in which our current preventative care methodologies are not effective and point our most innovative scientists in a direction for inquiry that will aid those most in need of new ideas and new approaches. Second, it may tell us who we must rush to protect. If health care becomes a statistics game based on an epidemiological formula, that 3% is in danger of becoming a severely marginalized, underserved, and vulnerable group. We need to design solutions that create a healthcare system that serves, protects, and rescues those most at risk, rather than a healthcare system that denies benefits, coverage, treatments, or help to those most vulnerable. The information is being gathered daily, globally, in electronic health records that are scrutinized by analysts and risk adjusters: the information is just information, not inherently good or bad, dangerous or ambiguous. As always, ethics is what we do with the information we gather in epidemiological study. The debate is the history of nursing concentrated down to a statistical percentage: epidemiology must be our call to arms, not our excuse to retreat.

British Medical Journal (not dated). What is epidemiology? Collections. Accessed online January 12, 2010 at http://www.bmj.com/epidem/epid.1.html.

Davenport, T. (2009). Why health care reform is vulnerable to smart analytics. Harvard Business Review: Information & technology blog. Accessed online January 12, 2010 at http://blogs.hbr.org/davenport/2009/11/how_how_analytics_and_could_di_html.

Devarajan, S. & Jack, W. (2007). Protecting the vulnerable: the tradeoff between risk reduction and public insurance. The World Bank Economic Review, 21 (1), 73-91. Accessed online January 12, 2010 at http://www9.georgetown.edu/faculty/wgj/papers/Devarajan-Jack-WBER.pdf.

Schwartz, K., Claxton, G., Martin, K. & Schmidt, C. (2009). Spending to survive: Cancer patients confront holes in the health insurance system. Washington, D.C.: The Henry J. Kaiser Family Foundation and the American Cancer Society. Accessed online January 12, 2010 at http://www.cancer.org/downloads/accesstocare/Spending_to_Survive.pdf.

Verlinsky, Y., Cohen, J., Munne, S., Gianaroli, L., Simpson, J., Ferraretti, A. et al (2004). Over a decade of experience with preimplantation genetic diagnosis: A multicenter report. Fertility and Sterility, 82 (2), 292-294.

Verlinsky, Y., Rechitsky, S., Verlinsky, O., Masciangelo, C., Lederer, K. & Kuliev, A. (2002). Preimplantation diagnosis for early-onset alzheimer disease caused by the V717L mutation. JAMA, 287, 1018-1021.