Event Recap | Opportunities for Data to Drive Solutions for COVID-19 and Beyond | August 12, 2020

On August 12, HC3 (the Health Care Council of Chicago) hosted a virtual collaborative discussion with our partners regarding the implications of using current COVID-19 data and how the intersection of race and ethnicity, specifically the inequities in access and quality of care for vulnerable communities, can be measured to inform interventions and impact. The panel of experts also discussed how tracking current and retrospective data trends may influence the development of sustainable and effective interventions the city can implement to care for populations experiencing homelessness and housing insecurity.


Featured Speakers

Angie Grover, Co-Founder, Metopio


Sage Kim, PhD, Associate Professor in the Department of Health Policy and Administration, UIC, School of Public Health


Steven Brown, MSW LCSW, Preventive Emergency Medicine, UI Health


Moderator

Meghan Phillipp, Executive Director, HC3 (Health Care Council of Chicago)


News Coverage

EXPERTS DISCUSS IMPORTANCE OF DATA COLLECTED DURING COVID-19 PANDEMIC

Health News Illinois, August 19, 2020 | Link to Article


Watch the Recap | Link to YouTube


EVENT RECAP

Exploration of the Importance of Data, presented by Angie Grover

  • There is a large variance in data gathering and analysis practices across the United States. COVID-19 brought to light importance of data, but data is still imperfect and incomplete.

  • Discrepancies in COVID-19-related data:

  • Data collection and standardization has been challenging due to shifting policies, a politically-charged environment, and a de-centralized public health system.

  • Testing was limited earlier on and prioritized for those who were already sick and hospitalized, meaning the population had increased chance of adverse outcomes.

  • Prior to the FDA loosening regulations on the development of COVID-19 tests (February 29), all tests had to be performed by the CDC to be counted as positive.

  • There is not a simultaneous mandate to report positive tests.

  • The number of tests available and who is getting tested can skew the data.

  • States did not immediately track race and ethnicity data in relation to COVID-19.

  • The definition of a COVID-19-related death varies by jurisdiction (e.g., Illinois reports those who died WITH the disease, other states report those who died FROM the disease).

  • Race and ethnicity data must be collected to better understand the disproportionate impact of COVID-19.

  • Fourteen states are still not collecting race and ethnicity for COVID-19 deaths.

  • Forty-seven states are collecting race/ethnicity for cases.

  • Illinois is one of the leaders in collecting race and ethnicity in both areas, but the national dataset is still incomplete.

  • Only 13 states provide data at a state-level on a machine-readable format so data collection is inefficient and time-consuming.

  • For analysis best practices, total COVID-19 cases should be based on population density rather than total number comparisons.

  • Zip codes are a useful way to report data and inform decisions.

  • In a comparison of 60629 vs. 60633 (Chicago), the numbers may look different in total sum, but the variant per capita shows a different story.

COVID-19: Social Vulnerabilities & Risk Factors, presented by Dr. Sage Kim

  • There is and has been a general lack of data, especially in minority communities in Chicago. Racial/ethnic disparity distribution of COVID-19 infection and death has changed throughout the pandemic.

  • March: African Americans represented 50 percent of cases, followed by white and Latinx

  • Mid-April: Latinx cases exceeded white cases and has continued to rise

  • Now: Latinx account for 48 percent of cases and African Americans account for 30 percent of cases

  • Because co-morbidities were not tracked, eviction rates are not tracked. Public health officials understand that better data is absolutely necessary to deal with COVID-19 and other disaster events, and that the social structure introduces inequality.

  • Chicago COVID deaths

  • Latinx death cases continue to increase and account for 30 percent of deaths

  • African Americans account for over 40 percent

  • Social Vulnerability Index (SVI) = composite score is comprised of socioeconomic measures (e.g., poverty, education, household composition, median income, employment)

  • Removed race/ethnicity to be able to measure SVI and compare among race/ethnicity. “There is no reason to believe certain racial/ethnic groups are more inherently more vulnerable than others, so race/ethnicity is used as a proxy for social disadvantages.”

  • Health risk data is very difficult to find outside of Chicago. High levels of existing health risks predominantly in the south & west sides, which is where cases/deaths are more prevalent.

  • Infection rates in Chicago: Lawndale, Little Village, Humboldt Park have the highest rates on the north and west sides. South Shore and Englewood have the highest rates on the south side.

  • Death rates in Chicago: South Shore has the highest death rate, followed by Englewood, Austin, and Roseland.

  • Much of IDPH data is county-level health data which is much broader and too general to analyze. Having a baseline for health data can be exponentially helpful is preparing for incoming pandemics. Better data on existing co-morbidities would have allowed for a more proper response to COVID-19, given the increased risk for those with ongoing conditions.

  • Low-wage job loss seemed to happen largely in middle class areas. The majority of those workers were Latin-x, followed by African Americans.

  • Paycheck Protection Program (PPP) loans were predominately distributed to north side businesses, possibly because there are more businesses in north; however, the data needs to be examined with business distribution.

  • There is no current eviction rate data, but looking at previous patterns, we can assume who will be more vulnerable. Historically, eviction rate has been significantly higher on the southside, and we can expect potential housing issues to be severe in those communities.

  • COVID-19 exposed vulnerability of our system, as well as the need for better data. “Data cannot sit in academic settings; community engagement is critical.”

Addressing Risk Factors: Homeless Population and Potential Housing Crisis, presented by Stephen Brown

  • UI Health partnered with many large health care providers in Chicago to address the pandemic at shelters and camps, which were potential hotspots for COVID-19.

  • UI Health has their own housing program which has housed 75 individuals to date (Better Health for Housing).

  • Access to housing issues emphasize the need for aggregating large amounts of data to inform public policy and clinical integration.

  • The Chicago Homelessness and Health Response Group for Equity (CHHRG) was formed out of fear that the pace of COVID-19 transmission would greatly impact shelters and that shelters will become “super-spreader sites.”

  • The program resulted in a 50 percent reduction in shelter bed capacity almost immediately; within three weeks it created logistical supply chain, screening/testing protocols, and staff education that mitigated outbreaks in five of the city’s largest shelters.

  • Sixty-two percent of the homeless population that at UI Health in 2019 (1,700 people) were considered high risk, in hindsight, by the CDC.

  • Homeless population vulnerabilities:

  • Life expectancies are 27 years shorter than averag

  • High rates of illness and substance use

  • Many have comorbidity conditions, of which most are risk factors for COVID-19

  • Seventy-five percent are black in Chicago

  • Among 60 individuals in the Better Health & Housing program:

  • Sixty-five percent have hypertension

  • Forty percent have chronic kidney disease

  • Twenty-five percent have diabetes

  • Twenty percent have cancer

  • Among 1,700 homeless individuals: 62.7 percent of homeless individuals had CDC risk factors for COVID mortality

  • Substance abuse/mental health issues:

  • Sixty-eight percent with psychiatric disorder

  • Fifty-seven percent with SUD

  • At least double what is reported nationally for the homeless

  • Mortality rate is very high:

  • Thirty-eight among 26 patients

  • There is no way to report homeless mortality yet

  • In partnership with the Illinois Department of Public Health, one of CHHRG’s initiatives is to track homeless mortality rate and access data.

  • Financial disparities and wage and wealth gaps were increasing greatly even before COVID-19. Now, nearly half of renters are burdened in Chicago. There is a huge need for actual affordable housing. A housing crisis is ongoing and forthcoming.

  • Mortgage delinquencies hit an all-time high in April

  • There is potential for 28 million vulnerable individuals to become homeless

  • Prior to crisis, we had a gap of 186,000 affordable housing units needed

  • Greatest concern is that of family homelessness rather than individuals, because there is already a shortage of family shelters.

Open Discussion/Questions from Participants

M: Why is the percentage (%) of female head of households part of the social vulnerability index?

S: The percentage of female-headed households with dependent children is a known indicator for burden: economic burden and difficulty during disaster events.

M: What can we use from these indexes to prepare and improve outcomes?

A: Metopio Is driven by making data transparent so that people can use it to inform decisions in areas of importance to them.

S: SVI allows people to look at big picture of impacts, but through her work Dr. Sage has also connected with organizations, community members, and local decisionmakers that are trying to reallocate resources to where they are most needed.

M: What is the data collection and analysis process? How is the fax machine affecting data availability?

A: The silver lining of COVID-19 is that it has highlighted the importance of data in all regards - collection, analysis, etc. Local data gathering is important, incentivizing local communities to be involved. At Metopio, COVID-19 has been the first data set updated in real-time. “We shouldn’t be using fax machines anymore!” People are really paying attention to how data is collected and what needs to be collected. Dr. Sage’s work is really important for real-time, local data, because government data lags and it will be a while until it catches up with COVID.

S: Homeless individuals can go from ER to ER. One homeless person went to 12 different ERs with 61 transports and was COVID-19 positive, but there was no reporting even though he/she had contact with many people during this time. We recognize the need to be able to look up results and respond accordingly to individuals, and the need for real-time data and reporting both positive and negative test. Communication between providers is difficult. PatientPing was an option, but they only report negative tests.

M: During COVID, what are the closed-loop referral services looking like, and how is that being managed and tracked to address social determinants of health (SDOH)?

S: There have been some tangible studies surrounding SDOH, but they have questionable results. There is large gap between health care and human services. Accountable health communities are trying to bridge the gap and create the data to figure out if social services received had an impact on health, but until we can offer value-based payment to social services, there is no way for human services to work with healthcare organizations (grants vs. reimbursement).

A CBO analysis would be a great solution. Some efforts going on right now could address this. The Illinois Public Health Institute is working on a cross-segregation exchange to pull health record data out of siloes and into central depository for the data to become actionable. We need to be able to see if an individual is receiving social services from people that may be able to address certain needs better. There needs to be communication between all points of care and we need vendors to hand health care organizations this solution.

M: How did you measure where food pantries are and the service area of some of these resources?

A: Metopio was able to take data and aggregate it to the local level to get data like food pantries and other services. Many resources are outside of the depository, many can be in locations like church basements or other locations less-traditional facilities that may have closed or you could not get into contact with them, which raised the question of, “How do we fill these gaps?” because there is no access to that data.

S: Technology might help solve this problem. The innovation center [at UIC] has an idea for an app that provides access to social services like finding where food pantries are or open housing areas.

M: What challenges will we see with data (e.g., lack of data or incompleteness), and who is going to pay for it?

A: Incomplete census data is very real and scary. We really need to be cognizant of the census

and form partnerships that look at data in a very public and transparent way, because we need to fill these gaps. Depending on how decisions are made at the federal level, we may need to redo census because an incomplete census could impact the next decade of policies.

S: And, there would likely be certain groups of people skewed out of the data.

M: Is there any predictability on what spending will be allotted for the affordable housing spending pool?

S: The city issued a five-year plan for housing recommending some pretty innovative reforms. In the past, any new development needed to have 10 percent set aside, so most developers would opt to put it into a public trust where money can be set aside for affordable units. The money was used generally in under-resourced communities and not the most affluent, so it contributed to lack of diversity in high-income neighborhoods. Adding some stipulations that the money needs to be used within a certain distance to address diversification.

Economic and racial diversity is important to balance when thinking about affordable housing. The data is decent. A total of $8 million has been funded right now and $12 million is the goal. LA has a similar model and they build about 3,000 new units a year. Collective impact is needed among government, health care, private philanthropy entities. Has to be a collaborative effort.

Concluding remarks

Because there is a general lack of key data in Chicago, increased data collection at the community level is vital. There is a huge need for collaboration (from the local level up to the federal level) and advocacy for efficient, real-time sharing of actionable data in order to improve evidence-based policy. When we can track eviction rates, food deserts, health records, and so on in one easy-to-access place, then there will be a rise in living standards regardless of other factors.


Resources

  • Event Slides presented by Dr. Kim (UIC) and Angie (Metopio) | Access the Slides

  • Metopio's COVID-19 Insights can be accessed at: https://metop.io/covid-19/

  • Health Risk Factor | Link

  • Social Vulnerability | Link

  • Why Rate Matters | Link

  • Chicago Homelessness and Health Response Group for Equity Report: Building an effective cross-sector partnership to address COVID-19 among vulnerably housed populations of Chicago | Read the Report

  • LinkedIn

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