Precision Public Health, Race, and Social Determinants During the COVID-19 Pandemic

As the COVID-19 pandemic continues to spread across the
US there are emerging patterns for the disease burden coming to light.
African-American and Latinx populations are suffering a disproportionate amount
of the disease burden and deaths. It is often said that pandemics are a window
into society and amplify health disparities and social suffering patterns that
have been present prior to the outbreak. In this blog post we take a look at
some of the analytical tools and challenges that are emerging to help evaluate
the impact of the pandemic as well as shed light on the policy responses that
will be required going forward. The stimulus efforts to date have not done
enough to directly impact communities bearing a disproportionate amount of
suffering. Any future healthcare stimulus funding, or HITECH Act 2.0, efforts
need to focus on tools and resources that can build a bridge from health IT to
public health and precision public health efforts targeted at the communities
hardest hit.

Key Takeaways

African-American and LatinX populations are bearing a
disproportionate COVID-19 disease burden. Health disparities, environmental
inequalities and lack of insurance are exacerbating health inequalities through
risk profiles, access to care and treatment.

Missing Data. CDC has not released disaggregated data
that can be used in data analytics efforts.  Much more work needs to be done to acquire
better data and fill in the holes in datasets so that accurate analytics of the
burden of disease and overall epidemiology are understood better.

Environmental factors are rising in importance.
Preliminary research is revealing that air quality can dramatically impact risk
of death from COVID-19. Exposures to PM 2.5 is cumulative over the lifespan and
many low-income communities are made more vulnerable due to the damage already
done.

Future stimulus funding for health IT, a HITECH 2.0,
needs to take into account social determinants and broader approaches to
strengthen health systems.  The
pandemic exposes and exacerbates existing inequalities. Our ability to contain
outbreaks and resume economically active lives means that health equity needs
to be front and center. Gaps in the system impact the whole.

In recent days an increasing amount of data on the racial
disparities in mortality rates has come to light as the pandemic proceeds
across the country. Preliminary data from Florida indicate that minority
populations are getting hit harder than white populations, in Michigan where
African-Americans make up 15% of the population they constitute 33% of cases
and 41% of deaths. In Louisiana the initial data are indicating that 70% of
deaths are African-Americans who comprise only 32.4% of the overall population.

Many states and cities are not releasing data on the ethnic
and racial outcomes, however, we know that there is great variation in hospital
quality across the country and low-income communities and minorities will
assume the burden of poorer quality of care. A large number of social
determinants ranging from employment and housing to underinsurance and closer
living quarters for extended families come into play as well. Furthermore,
there is still a lot of missing data on the disaggregated population impact of
the pandemic.

Machine learning tools are rapidly being configured and
retooled to serve the needs of hospitals, public health and social services as
the pandemic proceeds. We will see even more reliance on some of these tools should
we head into a second, and possible third, COVID wave later this year through
winter. The pandemic is putting more pressure on the system to innovate around
precision public health, or the ability to customize interventions at the
community level given their distinct risk profiles, vulnerabilities and case-loads.

Open Data For AI-Driven Pandemic Tools

One of the first major tasks in developing tools that can
help us understand the contribution of race and socio-economic status to the
disease burden of coronavirus will be access to data. The White House Office of
Science and Technology Policy has created the COVID-19 Open Research Data Set
for data scientists to analyze with the goal of creating tools useful for
clinicians and public health professionals. Kaggle, MIT SOLVE and the Allen
Institute for AI are all offering either data sets or data challenge platforms
to contribute to the pandemic response.

One of the biggest challenges is obtaining race and
ethnicity disaggregated data on coronavirus cases. To date, the CDC has not
provided this data so it has been left to states and local public authorities
to provide these data. Even the New
York Times is contributing to the cause through their county-level data
tools. Black
Demographics is providing race disaggregated data where available. It is a
very urgent issue that better data are collected on who is impacted and where
so that better tools can be developed to address this long-term issue. Massachusetts
has recently announced they will be publishing disaggregated data very soon as
part of their daily case count reports and we expect others to follow soon.

Figure 1: Racial Disparities
in COVID-19 Pandemic (Source: BlackDemographics.com)

Most of the drivers of the racial disparities in health
outcomes are the product of decades of social policies and systemic racism.
However, as stimulus packages are passed by Congress it is imperative that the
health-centric AI and the data analytics tools we have are used to inform
policies and direct resources to communities in the most effective way to drive
better outcomes.

Air Quality as Risk Factor and AI Solutions

Where you live has a huge impact on exposure to air
pollution and we can see clear race and income gradients linked to such
exposures. However, Air pollution has been a somewhat neglected aspect in
public health despite efforts such as the Clean Air Act. Unfortunately, early
data from the COVID19 pandemic are beginning to shed light on the likely effect
of air pollution in creating a pool of immuno-compromised individuals and
damage to the respiratory and circulatory systems that can increase the risk of
acquiring or dying from the virus.

Researchers at Aarhus University (Denmark) and the
University of Siena (Italy) have found
an association of higher air pollution exposures to higher rates of mortality
in Lombardy and Emilio Romagna where death rates have reached 12%. Even
economic historians have found similar associations between proximity to coal
powered electrical plants during the 1918 Spanish Flu Epidemic and
differentials in mortality rates across cities. Researchers at Harvard just
published a study
demonstrating that even very small increases in PM 2.5 exposures as low as one
microgram per cubic liter can contribute to a 15% increase in death rates.

Jvion’s COVID Community
Vulnerability Map has become quite useful for public health researchers in
identifying risk factors at the community level. Air quality in areas such as
Louisiana’s “Cancer
Alley” where racial disparities in air pollution exposures and COVID
risks currently appear to be quite pronounced. The Jvion tool is one
example of a tool that could be used for creating the ground truth that community
organizations and data analysts need for resource allocation efforts and
programs.

Other Social Determinants and Steps Toward Re-thinking A “HITECH ACT 2.0”

Public health practitioners, data scientists and social
service organizations will need to bring localized analytics of social
determinants and COVID-19 interactions to drive policy changes going forward. For
example, density of population, mobility and lack of insurance are key drivers
of who gets infected by COVID-19. These factors come together when many people
lack jobs that allow them to stay at home for mitigation efforts. Reuters has
been publishing maps showing mobility rates over time as the pandemic proceeded
that offer another window into WHO can adopt social distancing measures (Figure
2). Lower income individuals were more likely to continue working outside of
the home after mitigation efforts began because many do not have jobs that can
be accomplished remotely. This means additional social welfare policies may
need to be targeted to enable these households to survive under lockdown
conditions.

Figure 2: Mobility and Income (Source: https://graphics.reuters.com/HEALTH-CORONAVIRUS/USA/qmypmkmwpra/
)

Chronic Disease Burden and Race, Gender and Income

The American Heart
Association Center for Health Metrics and Evaluation offer a large number
of  visualization tools that illustrate
the associations between heart disease and COVID-19 outcomes as well as data on
hospitals and health system capacity. Bringing together environmental data,
mobility and co-morbidity data may provide more granular understandings of
COVID-19 outcomes and resources that enable a more precision public health
approach to the pandemic.

Access to diagnostics and population screening will need to
have a social justice component (eg. insured vs. uninsured) to ensure that we
can equitably identify hotspots and allocate resources. This will enable all
citizens to return to work more quickly.  AI developers will need to be extremely
careful in rooting out bias
that has resulted in algorithms that adversely impact women and minorities as
we have seen in the recent past.

At Chilmark Research we are increasingly discussing the need
for a new HITECT ACT 2.0 that moves beyond EHRs for providers and build a
health technology infrastructure that includes post-acute care, assisted
living, public health and other health centric organizations. Surveillance
systems, investment in community and social services, databases on community
resources, geographic information systems, and AI can all play a role. We also
need the human element of cooperation that moves beyond health IT systems in
isolation to urban and regional planning, transportation, local economic
development, the Environmental Protection Agency, and beyond. Intersectoral
interoperability, albeit challenging, will be necessary for future pandemics.

Yes, there will be more.

The post Precision Public Health, Race, and Social Determinants During the COVID-19 Pandemic appeared first on Chilmark Research.

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