The Power of Quantitative Data: How Numbers Illuminate Policy Problems and Solutions
October 17, 2018, Biostatistics, Environmental Health Sciences, Faculty, Health Management and Policy, Advocacy, Biostatistics, Chronic disease, Environmental Health, Health Care Policy, Health Communication, Policy, Research, Statistics
In the 1950s, most cars didn't come with seatbelts standard. Now, your car won't let you drive across the parking lot without dinging until you buckle up. In that persistent alarm is a small reminder of just how important empirical data about public health risks is in shaping public policy.
Public health researchers have an important role to play in providing the quantitative, empirical data necessary to identify where policy could be improved. But for these insights to translate to actual policy change, researchers must share data in a way that allows policymakers to make informed decisions about public health policy. Metrics that demonstrate what works in our health care system, what doesn't, and what it costs can become the crucible in which many policy changes live or die.
The Importance of Economic Impact
David Hutton, associate professor of Health Management and Policy, produces strict economic measures of public health interventions, which can be effective in arguing for policy change. "We quantify the benefits of public health efforts in terms of two major categories: costs and health outcomes," says Hutton. "When I say costs, we are talking about total lifetime societal costs. It's not just the immediate cost of a new public health program but also potential cost increases (and savings) induced by the policy. This includes costs to the health care system, like future nursing home costs for those who have had a stroke, and other costs to society, like lost wages from blindness."
"We quantify the benefits of public health efforts in terms of two major categories:
costs and health outcomes. When I say costs, we are talking about total lifetime societal
Measuring these extended costs, and providing policymakers with a dollar figure, can give them a clearer, empirically rooted idea of the true cost/benefit considerations of a particular policy. That can help translate quantitative public health data into real policy change, as it did for Hutton's recent study of Hepatitis B screenings. "In the US, we showed that screening and treatment was cost-effective for high-risk immigrant populations. This led to CDC screening guideline changes," says Hutton. "In my experience, these types of analyses can have policy impact."
The focus on quantifying the dollar impact of various health interventions pervades discussions of public health policy far beyond health care settings. Rick Neitzel, associate professor of Environmental Health Sciences, studies the toll that noise exposure in the workplace and elsewhere takes on our health. And he, too, often frames public health policy in economic terms. "If you can show, for example, that making a policy change could decrease the amount we spend in worker's compensation each year by a certain amount, that often motivates people more than demonstrating the public health impact alone," he says.
Neitzel has also found that focusing on an outcome that people feel strongly about can be an important part of sharing public health insights in a way that can influence policy. For example, Neitzel and others have shown that, in addition to well-established risks like hearing loss, noise exposure can lead to less commonly known and sometimes more serious effects, such as increased risk of cardiovascular disease or injury. "It's not that hearing loss isn't a big deal, because it is, but people just don't seem to care about it quite as much," says Neitzel. "But people do care about injuries. If I knew that increased noise could increase my risk of dying on the job, that might get my attention, more than just knowing that in ten years, I might have hearing loss."
New Metrics, New Perspectives
For over 120,000 people currently waiting for an organ transplant in the US, the decision of who gets an organ and who doesn't is as high-stakes as it gets. Doug Schaubel, professor of Biostatistics, researches how kidneys and livers are allocated to patients on organ transplant waitlists. "The allocation rules for both liver and kidney transplantation are sub-optimal. Donor organs are a scarce commodity. Thousands of patients die on the waitlist each year," he says.
"One might expect that allocation rules would aim to maximize the gain in life years by the patient population. In fact, that is explicitly not the case for either kidney or liver transplantation," says Schaubel. "For liver, the concern is allocating to very sick patients who have poor prognosis with or without a liver transplant. For kidney allocation, patients that are given high priority for transplantation are expected to do well with or without a transplant."
When policymakers do not have the time or the expertise to translate the data for themselves, a simple metric summarizing a more complex system can provide important empirical data in a digestible format.
Seeing this inefficiency in the system, Schaubel and his research group derived a new metric to optimize organ allocation based on the expected years of life potential recipients would gain from a transplant. This new measure includes factors that aren't currently considered, such as prior liver transplants and hospitalization status, to give a more accurate idea of who would gain the most life years from a transplant. While his recommendations have not yet been incorporated into organ transplant policy, Schaubel's sophisticated, multifactor index provides the kind of objective measure that has the potential to resonate with policymakers while staying rooted in empirical data.
Hutton also uses life-year metrics, in his work comparing the effectiveness of different therapies and drugs. "Health outcomes are usually quantified in terms of quality-adjusted life-years (QALYs) gained," says Hutton. "QALYs sound complicated, but they really are just a combination of both length of life and quality of life. Quality of life is quantified on a scale of 0 to 1 where 0 represents death and 1 represents perfect health."
When policymakers do not have the time or the expertise to translate the data for themselves, a simple metric summarizing a more complex system—like a QALY—can provide important empirical data in a digestible format. For Hutton, the hope is that these translational resources would support the development of better policy around drug therapies.
Bridges between Data and Policy
Programs like Michigan Public Health's new Environmental Health Promotion and Policy (EHPP) curriculum are training the next generation of health policy advocates to bridge the gap between scientists producing quantitative data on health risks and policymakers who could turn these insights into real action for public health and safety. "We're trying to train students to be a liaison between environmental health, health policy, and health behavior. We want them to be able to understand the science of it, then sit across from a congressperson and say 'this is why you should care' and also be able talk to communities and say 'here's what you can do about it,'" says Neitzel, who directs the EHPP program. "The students entering the program are totally passionate about this and really savvy."
As public health researchers build increasingly sophisticated quantitative understandings of what impacts health, we will all have access to more information and metrics and can organize our advocacy work more transparently around data-driven discoveries. This way, policy structures can be informed by the measurable—and measured—influence of the factors that make the biggest difference to our health.