News & Insights

Metopio’s Network Effect

Sharing recent Metopio news and insights from leaders in the field, recent research and interesting observations driven by bringing diverse datasets and smart people together.  

May is Mental Health Awareness Month

But where is the data to understand the depth and impact of mental illness across America?

Simply put it is sparse, uneven and detached from typical physical indicators we use to measure health.  There are many reasons for this, including the stigma that has long been attached to any suggestion of mental deficiency.

So what is mental health? 

According to the World Health Organization (WHO), mental health is a state of wellbeing in which an individual “can cope with the normal stresses of life, can work productively and is able to make a contribution to his or her community. Mental health is fundamental to our collective and individual ability as humans to think, emote, interact with each other, earn a living and enjoy life.”

Mental health is more than the “absence of a mental illness”—it is an integral part of overall health. In fact, it can positively or negatively impact a person’s physical health, including their life expectancy.  See for yourself by clicking here or the scatterplot on the right.

Who is at risk?

Even before the pandemic, more than 51 million adults in the U.S. experienced some form of mental illness — that’s one out of every 5 adults — according to the Substance Abuse and Mental Health Services Administration (SAMHSA). 

Daily life stressors can increase risk of experiencing mental health problems.  Worrying about personal stability such as employment, access to healthcare, paying bills and having enough food can all contribute.

COVID-19 and racial unrest over the past year caused many to confront violence, discrimination or racism like never before.  Individuals have found they lacked enough social and community support, or simply did not have access to adequate mental healthcare.

Understanding the data

The Health Insurance Portability and Accountability Act of 1996 (HIPAA) and the Privacy Rule published in 2002 make up the backbone of how an individual’s physical and mental health data can be shared.

This was a critical step forward and created a way to holistically look at patients, but did not significantly increase the data that was collected and shared publicly.

Publicly available data around mental health and specific mental illnesses is hard to come by.  Most of what is available is at the state or county level, based on sparse surveys, and demographic stratifications are rarely provided.

One way to tackle this challenge is to take a layered, environmental approach looking at the limited health conditions available along with some of those daily stressors.   

Start with data available about mental health conditions. 

The Centers for Disease Control and Prevention (CDC) provides data on self-reported poor mental health, poor mental health days and low social emotional support, while the National Vital Statistics System provides data around suicide deaths and drug overdose mortality.

For this analysis we’ve prepared a series of insights using Metopio’s scatterplot to identify correlations between diverse data.  Then we visualize these related topics on a filtered map to examine places where they occur together in a meaningful way.  

You can see the complete set of insights in Metopio’s Mental Health Analysis project.

Starting with poor self-reported mental health and drug overdose mortality, we find highly significant relationships with unemployment and sleeping less than 7 hours.  Remember, this is not causation but correlation, although we are controlling for demographics and socioeconomics like income and education.

Taking these correlations and layering them on a map along with mental health providers per-capita helps us understand where environmental stressors are prevalent and there are fewer mental health resources to meet the need.  

We can also see how poor self-reported mental health has a highly significant relationship with chronic diseases such as diagnosed diabetes, obesity and hypertension using the scatterplot. The data does not let us determine whether poor mental health influences chronic disease, or vice-versa, but the reality is that the relationship is likely bi-directional.

Data can help understand the interrelated nature of physical and mental health, but only to the extent that the research is done and the data are available. 

Finally, if you or someone you know is in need of mental health resources, there are many organizations like the National Association of Mental Illness (NAMI) or the National Association for Suicide Prevention at 800-273-8255 that are available to listen and provide assistance.  Don’t delay, reach out.

Do you have data that would enhance this analysis?

Metopio aggregates high-quality, verified data to understand places and populations.  We are always seeking  data that can help policy-makers, strategists, healthcare leaders and advocates make better, more informed decisions. 

Metopio technology supports new Chicago Health Atlas

New enhanced atlas offers improved data and analytics capabilities to understand communities.

Today, Chicago community members, organizations, researchers, and the general public will be able to access data across Chicago’s 77 community areas, 58 ZIP codes, and 800 Census tracts using the new Chicago Health Atlas.

The new health atlas is innovative in providing a platform that allows any stakeholder to access and use data to understand health disparities and drive real change regardless of their data science experience.

The School of Public Health recently launched the Population Health Analytics, Metrics, and Evaluation, or PHAME, Center, will spearhead updates and oversee the Atlas going forward alongside the CDPH Office of Epidemiology. Updates will include increased community engagement opportunities as well as advanced analytics capabilities and enhanced data visualizations.

At launch, the Health Atlas has over 200 indicators that can be visualized in maps, charts, graphs and tables for any place in Chicago.

“You should not have to spend hours curating data to solve problems,” said Will Snyder, Metopio Chief Executive Officer. “The pandemic and recent events have demonstrated that understanding impacts on communities is essential to quickly identifying need and deploying valuable resources.  We are proud to support the work of so many who are using data to drive decisions and tell impactful stories.”

To increase the ability of users to drive meaningful insights, Metopio’s technology delivers improved performance and capabilities such as census tract data where available, margins of error, filters in the map, labeling points in the map, data dictionary download, powerful new map engine, beautiful and easy-to-use chart tool and the ability to download insights.

Metopio provides data, analysis and visualization in a single, tested interface that removes risk and maximizes the work of any user regardless of their data science background to organizations, government and individuals across the country. 

Impact of Air Pollution on Diverse Communities

How our communities are built has a lasting impact on community health. See how air quality impacts health.

Science Magazine recently published a study that found that higher levels of the tiny, dangerous particles in air known as particulate matter, or PM 2.5, have resulted in 85,000 to 200,000 premature deaths per year and untold health complications.

We have replicated this finding with Metopio’s filtered maps.

First, we used particulate matter (PM 2.5) concentration higher than the median nationally. We added the Non-Hispanic White population below the median to account for diverse demographics and found that fifteen states that are significantly impacted.

In fact, we find PM 2.5 concentration, or air pollution, is highly correlated with shorter life expectancy.

With Metopio, you can then dive deeper and examine other health conditions that may be impacted by air pollution using PM 2.5 concentration. For example, using the scatterplot we find pollution and chronic disease data at the county level shows highly significant correlations for each of these conditions. Click each to view the corresponding scatterplot.

These correlations mean that air pollution can be a contributing factor in these chronic diseases – remember, correlation is not causation.

Each trend line in the above four linked scatterplots move up and to the right. This shows an increase in air pollution closely correlates with a chronic disease when holding some variables constant like income, education, and demographics.

When you hover over any of the bubbles that are far away from the trend line to the top right, you will see a correlation with the state analysis above. These places have high PM 2.5 concentration and high chronic disease.

Each step in this analysis creates a clearer and clearer picture of who is at risk and where. The next step is to put the insight to work. For example, you might be a —

  • Policymaker—Ways to offset the amount or impact of air pollution could result in a healthier population. Action – Consider prioritizing parks, green infrastructure, incentives for low emission vehicles or pollution offsets in communities with the highest impacts.
  • Patient Navigator— Are the community conditions exacerbating a chronic condition? Are you missing a contact point to collect important data. Action – Consider outreach to your patients in high-risk locations and make sure they have the resources they need.
  • Insurer—If you have a risk-based contract with a provider, are you modeling what social determinant burdens might exacerbate chronic conditions? Action – Consider matching this data with your own to see if you should be planning for increased need in certain areas.

See all the analyses in this article in the project: Health Impact of Air Pollution.

Do you have data that would enhance this analysis?

Metopio aggregates high-quality, verified data that has some geographic component – think address or any part thereof – to develop insights that inform your business and policy decisions. Let us know: use the contact button below.

Insight: March 4, 2021

With vaccinations rolling out across the country, how do you contextualize the data?

Carefully.

The pandemic brought to light the team of players necessary to make public health work. Unfortunately, the infrastructure to support that team is fractured – and data collection demonstrates this perfectly.

After twelve months of ever-changing policy and evolving pandemic response, data has been reported across cases, testing, test outcomes, fatalities and now vaccines. How and what data is collected has proven to be a challenge.

Race/ethnicity was not collected uniformly or, in some cases, collected at all. Data definitions varied widely. For example, some states reported fatalities as those who died with the disease and others only when the individual died from the disease.

Testing and case data is particularly fickle. The number of tests does not equate to unique individuals as many get multiple tests, and cases are not typically reported by government entities over the weekend or on holidays which creates artificial peaks in the data.

In December 2020, the Centers for Disease Control (CDC) released COVID-related ICD-10 codes for 2021 which will help align reporting across healthcare settings and others needing to work with insurance.

Vaccination Data

The CDC has released state-level data for vaccinations. Metopio has curated two rates.

First, COVIV represents the percentage of people who have completed their required dosage whether that be one vaccination or two. COVIW represents the percentage of people who have had at least one COVID vaccine, even if that was also their last. See Metopio’s earlier article Why Rates Matter.

This data is updated daily and will also evolve over time.

There still isn’t a universal standard or mandate to collect race/ethnicity data. This poses challenges in assessing the equitable distribution of vaccines which is at the forefront of public discussion.

Currently, the CDC is only providing a national snapshot of vaccinations by race/ethnicity which you can access here. As of March 2, data on race/ethnicity was only available for just over half or 53% of the vaccinations.

Another consideration is how each state decided to prioritize various populations for vaccinations. Without national guidelines, each state set their own standards.

By and large, senior citizens and front-line healthcare workers were prioritized even if these categories were defined differently from state to state. Early data will be skewed to reflect these populations and get more noisy as each state moves at a different pace. It is important to note that while these populations were prioritized, it doesn’t mean they were able to easily get registered or were interested in receiving a vaccination. Requirements around temperature and distribution of vaccines have also created opportunities for individuals outside of priority groups to be vaccinated to ensure that the doses do not go unused.

Finally, the first vaccines released from Moderna and Pfizer require two shots to achieve optimum efficacy but now we have Johnson & Johnson’s single dose vaccination as well. Various strains of the virus may require boosters or additional vaccinations may be approved so there is no simple or easy way to encapsulate total vaccination progress in a single number.

What’s Next

New virus strains. Vacillating mask mandates and capacity restrictions. Continued vaccinations. Summer.

Life goes on as we try to understand the impact of COVID-19 on populations and places. The pandemic has provided us clear examples of where we can improve data collection and analysis. While imperfect, the process should continue to evolve and we should be cognizant of context as we collect and analyze what is available to understand its impact.

As your provider of quality, trusted data, Metopio considers all these questions and more when curating public data. We have internal processes in place to ensure consistent reporting and definitions across jurisdictions, account for the impact of reporting issues, estimate the likely bias and skew resulting from reporting and definitional challenges, and provide clearly defined demographic breakdowns where available.

Raw public datasets are often thorny and challenging to interpret; we do this work so you don’t have to. For instance, we can add your local jurisdiction’s vaccination data which is often more granular and mapped to smaller geographies such as county, ZIP code or where available census tract. Check out our Curated Data Library made available to all subscribers for more information on our public data sets, including topics related to COVID-19.

Can we help your organization understand the impact of COVID-19 on populations and places you care about? Let us know by clicking on the contact us button below.

Insight: February 10, 2021

Those who can get registered online for the limited doses, and quickly.

COVID-19 vaccines continue to be distributed across the country and allocated to individuals in phases.  The Biden Administration has set a goal of 100 million vaccines in the first 100 days. Individual states have set up programs to administer the vaccines, and each state’s program is different.

States have even taken to setting their own definition of senior citizens for purposes of vaccinations – some making the first cut off at 65 while others have set it at 75 or even 80. All together, this is a huge group. There are 54 million people in the United States over 65 according to the latest figures from the U.S. Census Bureau.

To get a vaccine, you have to register. There is not much consistency across states in program design or priorities except for one thing – you can register online.

States have offered digital registration for vaccinations hoping to reach as many people as possible. For those who aren’t computer savvy and may not have access to the internet, a computer or a smartphone, online registration is not possible even if they are eligible.

Half of the states have no centralized phone hotline or use the state’s general 211 non-emergency phone line as an alternative means of providing information and access. This has sent advocates, community-based organizations, health systems, providers and family members into action.

To see where these issues might be most acute, we used Metopio’s map filter tool. We looked at four critical components to identify areas where additional outreach could be most impactful. We started by identifying counties that have a large number of households with seniors living alone – not in nursing homes or other group settings – by adjusting the slider above the median. Then we layered three additional topics on the map to reflect access to technology including the internet, computers and smartphones.   We did this analysis at the county level, but it can also be replicated by ZIP code or census tract in your market. A note on interpretation–this map represents where all the criteria are met but does not represent correlations.

As decisions are made about vaccine distribution, understanding how to engage with communities is critical. Whether it is to reach vulnerable populations like seniors or prioritize communities hit the hardest by the pandemic.

Metopio’s curated data allows you to ask a question and quickly get an answer. Our data is constantly updated to offer more opportunities to understand how the places and populations you care about are impacted. Check back to access new data sets and insights.

Metopio aggregates high-quality, verified data that has some geographic component – think address or any part thereof – to develop insights that inform your business and policy decisions. Do you want easy access to curated data to use in your decision -making?

Insight: December 8, 2020

What happens if you have to work 95 hours a week at minimum wage to afford a place to live?

Even before COVID-19, housing affordability was a challenge.

A national moratorium on evictions was extended until March 31, 2021. Even with those protections, over 12 million Americans owe at least $5,000 in back rent according to Moody’s Analytics.

Meanwhile “shelter in place” directives were not feasible for people living in poor-quality, overcrowded, or unstable housing—or without any home at all. Here we examine how these issues of housing affordability can impact health.

There is no place in the United States where someone working a 40-hour work at minimum wage week can afford a 2-bedroom rental at a fair market rate. The new data from the National Low Income Housing Coalition’s Out of Reach 2020 Report shows how this varies across the country.

There is an important distinction between this data, which defines a modest 2-bedroom rental at 30% of your income to rent versus severely rent-burdened, which is defined at 50% of your income to rent.

Type 2 diabetes is considered a lifestyle disease, meaning that you can change behaviors to manage the disease such as altering diet, increasing exercise and eliminating unhealthy lifestyle activities (e.g. smoking, excessive alcohol and insufficient sleep. However, these require time, money and/or the availability of resources.

Examining the usual number of hours worked and exercise also showed a highly significant correlation – the more you work, the less time you have for exercise.

Regardless, stable housing is foundational to good health.

It is hard to afford much else when a significant portion of a person’s income goes to keeping a roof over their head or they have to work 95.3 hours a week at minimum wage, as in our Wisconsin example.

Inevitably, this impacts many other parts of a person’s life.

Lower wages often force people to work more hours. Examining a chronic condition such as diagnosed diabetes, we find a highly significant correlation between the usual number of hours worked and a diabetes diagnosis.

More and more people, impacted by the COVID-19 pandemic, face job losses and eviction notices. Stagnant employment and an uncertain economic future will continue to make stable housing a challenge for many, but most certainly for those who are already working hard for the lowest wages.

Metopio’s curated data allows you to dive deeper and examine other factors that impact the housing and health for populations and places you care about. We constant grow and update our curated data sets to offer opportunities to explore a wide variety of topics. Check back often as we access more data and dig deeper to provide you with these valuable insights.

Do you want easy access to curated data to use in your decision-making?

In places where workers have been hardest hit by the coronavirus.

Understanding the secondary effects of COVID-19—from job loss to evictions to an increased need for social services—is critical for employers, policymakers and community-based organizations. This analysis examines food insecurity.

To start, we need modeled data to predict food insecurity based on projected changes to unemployment and poverty. Feeding America, the nations’ largest domestic hunger relief organization, has done just that with the 2020 Map the Meal Gap study. Read more about Feeding America’s research on this topic.

The Feeding America data examined against the Bureau of Labor Statistics’ industry sectors shows workers impacted by business closures or in industries that could not perform work from home during the pandemic are more at risk for food insecurity.

The most significant correlations are workers in –Retail, Accommodation and food services, Real estate, rental and leasing, Utilities, construction and mining and Government.  Click on any of these links to explore the relationship shown in a scatterplot. You can hover over any bubble to see the corresponding county on the map or use the map to find your geography.

Each scatterplot shows that an increase in the percentage of workers in the industry closely correlates with an increase in food insecurity when holding some variables constant like income, education, and demographics. Metopio provides explanations for you in the What this plot shows or the green Highly Significant drop down.

Metopio then allows you to dive deeper and examine other social factors that impact these workers. For example, we find workers in the accommodation and food services sector also are more likely to be renters, not speak English as their primary language, and are employed but still below the poverty threshold.

Each step in this analysis creates a clearer and clearer picture of who is at risk and where. The next step is to put the insight to work. For example, you might be a —

  • Employer—How are you understanding burdens your associates may be facing at home, like food insecurity? Action – Consider developing an anchor mission strategy and using data like these as a baseline.
  • Policymaker—The food insecurity burden in the US is expected to increase 30% because of COVID-19. Action – Consider supplemental funding, perhaps through the CARES Act, for SNAP and other food access programs.
  • Patient Navigator—Because of COVID-19, many patients are delaying or missing routine visits, which means you are missing a contact point to collect important data. Action – Consider outreach to your patients in high-risk locations and make sure they have the resources they need.
  • Insurer —If you have a risk-based contract with a provider, are you modeling what social determinant burdens might increase because of the pandemic? Action – Consider matching this data with your own to see if you should be planning for increased need in certain areas.

We are constantly updating Metopio’s curated data sets to offer opportunities to understand how the places and populations you care about are impacted. Check back as we access more data and dig deeper to provide you with these valuable insights.

Metopio aggregates high-quality, verified data that has some geographic component – think address or any part thereof – to develop insights that inform your business and policy decisions. Want to know more?

Insight: September 22, 2020

With so much at stake, what do we need to acknowledge with the COVID-19 data set?

At Metopio, we are dedicated to data hygiene.
 
After nearly eight months of regularly updating cases, case rates, testing rates, positive tests and case fatality rates, we wanted to assess where there are gaps and share how you should think about them in your analyses.COVID-19 data is imperfect yet we need to keep collecting and analyzing what is available to understand its impact. Imperfect does not mean unusable. As the pandemic continues, though, we need to understand these gaps and how to improve data collection going forward.

Government Context

As an infectious disease, all positive COVID-19 tests have to be reported to health departments but there isn’t a similar mandate for negative tests. Early in the pandemic, the Centers for Disease Control (CDC) was not collecting data from private labs. In June, they revised that guidance so certified labs could submit test results. This is an example of how government policies impact this data set.

 
The U.S. public health system is made up of a vital network of local agencies that provide care, coordinate, collaborate and roll up to state agencies. The pandemic has exposed antiquated systems and a wide variation of resources, technical skills and staffing from jurisdiction to jurisdiction. In fact, in some places these agencies’ geography overlaps, making tracking even more complicated.
 
Variations in how data is reported adds to the complexity. For example, how deaths are defined vary by jurisdiction. As of September 2020 Oregon state reports COVID-19 deaths as those who died from the disease while Washington state reports them as those who died with the disease. This is not just semantics.
 
Uncertainty at the federal level has exacerbated challenges in data collection. On July 14 the Trump Administration issued guidance that the Department of Health and Human Services would be collecting COVID-19 data rather than the CDC’s National Healthcare Safety Network. Then only a month later, the Administration reversed that decision citing issues of continuity, timeliness and transparency.
 
Shifting policy in a politically charged environment–layered on top of the operational challenges of a decentralized public health system–make data collection and standardization difficult.
 
December 2020 Update
On December 3, the CDC released guidance around ICD-10 codes for COVID-19 related illness and death. Many states have announced that they will adjust their reporting to align with this guidance which should produce more consistent reporting across the country in 2021.
 
Meanwhile, holiday schedules and increased travel among the American public have complicated reporting around cases and testing.  Dr. Anthony Fauci, who will continue to serve as Chief Medical Advisor to President-elect Biden and as part of the COVID-19 Task Force, cautioned that data demonstrating surges post-holiday may be related to reduced staff capacity and government holidays. Outside of daily hospital admissions, this catch-up reporting will show a surge as cases and testing show up in bulk rather than over time.

Impact of Access

Another important variable confounding COVID-19 data collection is the number of tests available and who is able to get tested. This brief timeline demonstrates how testing and collecting results has evolved but continues to be challenged.

  • On February 29, the FDA loosened the regulations on the development of COVID-19 tests. Before this date, all tests had to be conducted by the CDC for a case to be counted as a “confirmed positive”.
  • On March 13, the U.S. declared the coronavirus a national emergency. Prior to this, tests were extremely limited and prioritized for those who were coming to a hospital. That means the population was more likely to be sick and had an increased chance of adverse outcomes.
  • On May 18, HHS announced the CDC would distribute $10.25 billion in funding for state and local jurisdictions for testing.
  • On August 5, the CDC updated its guidance for a “confirmed” case based on a polymerise chain reaction or PCR test. Positive results from antigen tests are considered “probable” cases because they can be less accurate. It is critical for testing to evolve, become more accurate and more available. This Food and Drug Administration (FDA) is a good resource as testing continues to evolve.
  • In late August college and universities began returning to campuses. Many were administering antigen tests because the results are delivered in minutes without needing a lab to process. However, colleges are not typical healthcare providers and they don’t have an easy or uniform way to send data electronically to public health authorities.

If an increase use of antigen tests occurs with a decrease in PCR tests, this could make the count of infected individuals artificially low. As specific populations have more access to testing and science races to keep pace with the pandemic, data collection must also evolve. Meanwhile, we need to recognize it creates a wide variety of results that cannot always be reconciled.

Race/Ethnicity Data

It is essential to collect race/ethnicity data to understand the disproportionate impacts of COVID-19. However, it is extremely variable across government agencies. Several entities are tracking how this data is being collected.

  • In June, the American Medical Association reported that 14 states were still not collecting race/ethnicity data for COVID-19 deaths. As of September, it had decreased from 14 down to 2.
  • The COVID Tracking Project provides updates on how much of each state’s data includes race/ethnicity. Not all states were collecting it from the beginning, and some only collect it for certain data points.

While there are many efforts to provide consistent, national daily updates for researchers and decision-makers–only 13 states provide the data in a machine readable format–making data collection very inefficient and time-consuming when time is of the essence.

Our Curation Process

As your provider of quality, trusted data, Metopio considers all these questions and more when curating public data. We have internal processes in place to ensure consistent reporting and definitions across jurisdictions, account for the impact of reporting issues, estimate the likely bias and skew resulting from reporting and definitional challenges, and provide clearly defined demographic breakdowns where available.

Raw public datasets are often thorny and challenging to interpret; we do this work so you don’t have to. Check out our Curated Data Library made available to all subscribers for more information on our public data sets, including topics related to COVID-19.

Can we help your organization understand the impact of COVID-19 on populations and places you care about? 

Metopio aggregates high-quality, verified data that has some geographic component – think address or any part thereof – to develop insights that inform your business and policy decisions. Do you want easy access to curated data to use in your decision -making?