We must make a serious commitment
to increase financial resources and provide better analytics for real world
evidence/real time data in support of public health.
John Halamka, M.D., president, Mayo
Clinic Platform, and Paul Cerrato, senior research analyst and communications
specialist, Mayo Clinic Platform, wrote this article.
Public health has been underfunded
for decades. That neglect has had a profound impact since the COVID-19 pandemic
has taken hold, and awakened policy makers and thought leaders to the need for
more investment.
Consider the statistics: The U.S.
spends about $3.6 trillion each year on health but less
than 3% of that amount on public health and prevention. A 2020
Forbes report likewise pointed out that “From the late 1960s to the 2010s,
the federal share of total health expenditure for public health dropped from 45
percent to 15 percent.” This relative indifference to public health is partly
responsible for the nation’s mixed response to the SARS-CoV-2 pandemic. A recent
McKinsey & Company analysis concluded: “Government leaders remain
focused on navigating the current crisis, but making smart investments now can
both enhance the ongoing COVID-19 response and strengthen public-health systems
to reduce the chance of future pandemics. Investments in public health and
other public goods are sorely undervalued; investments in preventive measures,
whose success is invisible, even more so.”
Among the other “public goods” that
require more investment is population health management and analytics. Although
experts continue to debate the differences between public health and population
health, most are unimportant. For our purposes, population health refers to the
status of a specific group of individuals, whether they reside in a specific
city, state, or country. Public health usually casts a wider net, concerned
about the status of the entire population. Managing the health of these
subgroups requires an analytical approach that can take into account a long
list of variables, including social determinants of health (SDoH), the content
of their medical records, and much more. SDoH data from Change Health care, for
instance, has demonstrated that economic stability index (ESI) is a strong
predictor of health care utilization. ESI is a cluster model that uses market
behavior and financial attitudes o group individuals into one of 30 categories,
with category 1 representing persons most likely to be economically stable and
category 30 least likely to be stable. The figure, which links race, ESI and health
care utilization in Kentucky, suggests that Blacks/African Americans are far
less likely to be economically stable (category 1). The same analysis found
that Blacks/African Americans were almost twice as likely to use the ED
compared to Whites (30.5% vs 18.1%). A growing number of health care
organizations are starting to see the value of such population health metrics
and are incorporating these statistics into their decision making.
Among the valuable sources of data
that can inform population health are patient surveys, clinical registries, and
EHRs. Several traditional analytics tools are available to extract actionable
insights from these data sources, including logistic regression. Over the
decades, several major studies have also generated risk scoring systems to
improve public health. The Framingham heart health risk score has been used for
many years to assess the likelihood of developing cardiovascular disease over a
10-year period. Because the scoring system can help predict the onset of heart
disease, it can also serve as a useful tool in creating population-based
preventive programs to reduce that risk. The tool requires patients to provide
their age, gender, smoking status, total cholesterol, HDL cholesterol, systolic
blood pressure, and whether they are taking antihypertensive medication. The
American Diabetes Association has developed its own risk scoring method to
assess the likelihood of type 2 diabetes in the population. The tool takes into
account age, gender, history of gestational diabetes, physical activity level,
family history of diabetes, hypertension, height and weight. Another analytics
methodology that has value in population health is the LACE Index. The acronym
stands for length of stay, acuity of admission, Charlson comorbidity index (CCI),
and number of emergency department visits in the preceding 6 months. More
recently, there are several AI-based analytic tools currently being used to
improve population health. A review of ML-related
analytic methods found that neural networks based algorithms are the most
commonly used (41%) in this context, compared to 25.5% for support vector
machines, and 21% for random forest modeling.
There is no way of knowing how the
world would have coped with COVID-19 had policy makers fully invested in public
and population health programs and analytics. But there’s little doubt that we’ll
all fare much better during the next health crisis if we put more time, energy,
and resources into these initiatives.
