Proactive Pandemic Response: The Critical Role of Local Data Insights

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By Peter J. Plantes, M.D.

Demand for greater transparency into COVID-19 data is growing daily, and the fluid nature of public health reporting creates significant challenges to informed local decision-making. Communities are simultaneously overwhelmed with data and lacking both timely information and critical insights that can ensure optimal decisions around re-opening.

The reality is that many public health officials rely on state and national data, which offers limited proactive insight into how the pandemic is advancing at the local level. Consider the state of Florida. On July 24, the state was seeing COVID-19 cases increase by as much as 10,000 per day. Yet the differences at the county level state-wide could be stark: Okaloosa county in Northwest Florida reported 3 positive new cases that day, while Polk County in Central Florida reported 55 and Broward County in Southeast Florida reported 299, each requiring very different local responses.

When armed with the right county and sub-county data, local public health officials can issue guidance, such as mask mandates and small-crowd compliance measures, possibly stopping an outbreak before it starts. 

Empowering Local Communities

In May, many states began easing the lockdown restrictions imposed in March and April, opening the door for large gatherings as Memorial Day weekend approached. By early July, data such as ER visits and hospitalizations were skyrocketing in regions within these same states. By that time, however, it was too late to enact the proactive measures that could have prevented the upward trajectory that put many communities well beyond the 1% baseline detected rate threshold. 

The CV19 Lab Testing Dashboard™ powered by hc1 , a, free resource for public health, healthcare and policy decision-makers that provides hyperlocal COVID-19 insights down to the county and PUMA level, began showing an upward trend in early June, indicating that South Florida, Arizona, Texas and California would soon emerge as the newest coronavirus hot spots. Had local officials used these critical insights, they would have better understood local risk levels and the rate at which risk was rising and falling over time. 

The CV19 Lab Testing Dashboard was created by hc1 with a coalition of commercial and health system laboratories and technology partners. The collaborative effort aims to equip public health agencies and healthcare organizations on the front lines with detailed lab testing insights drawn from over 2,000 lab testing sites in the U.S. and more than 20 billion laboratory transactions to track a significant portion of COVID-19 test results. 

The Dashboard’s exclusive hc1 Local Risk Index™ (LRI) provides an “earliest indicator” of the risk of being exposed to another person with COVID-19 (symptomatic or asymptomatic) down to the granular level of county and sub-county geography. The graphing of LRI levels over time enables users to track the trend in risk levels and the rate at which risk is rising or falling over time. The LRI reports the percent of those tested by nasal swab for the COVID-19 virus in the current week that show a detected test and then calculates the LRI from this percent (%) detected rate. Coupled with other geographical risk metrics, the LRI serves as an indicator of the current level of active viral infection in an area and how the level of active infection has changed over time. A county or PUMA with a high LRI likely has an elevated number of infected residents, increasing the risk associated with infection and the eventual spread of COVID-19. 

Understanding LRI

The hc1 Local Risk Index is a ratio of the percent of positive COVID-19 viral swab tests during the current week over the course of one week divided by the baseline percent detected where the infection rate can be controlled within a particular region by containment (quarantine) efforts alone. 

In addition to LRI, public health officials should consider on a local level:

  • Current week percent (%) detected: Communities will want to measure the percent of patients who tested positive during the current week. This measure is calculated by dividing the number of positive tests over a four-day period of complete data by the total count of patient test results (Detected Results / Total Results = % Detected).
  • 1% baseline detected rate:  A 1% prevalence is the epidemiological threshold below which outbreaks of respiratory viruses like SARS-CoV-2 can be controlled by containment measures alone (identify, quarantine and trace contacts). Once a community moves above this detected rate, much broader mitigation measures (social distancing and other non-pharmaceutical interventions) will be necessary. 
  • Public Use Micro Area (PUMA) Trends: PUMAs are geographic units used by the U.S. Census for providing statistical and demographic information. Each PUMA contains at least 100,000 people. Especially in large cities, PUMA trends are important for understanding risk.  
  • The Rolling Trend in % Detected (7/7): This metric is calculated by dividing the average detected rate for the past seven days by the average detected rate for the seven days prior to that period (i.e., days 8–14 or “week-over-week”) and can show day-over-day trends when graphed out.

Following the first week of September 2020, the CV10 Lab Testing Dashboard had identified the following emerging hotspots based on the following criteria:

  • a population of greater than 100,000
  • more than 100 patients tested in the past week
  • LRI scores greater than 7
  • rolling acceleration trends of greater than 50% for the percent of positive tests in the current week vs. the previous week 

Effective response to the current pandemic starts at the local level. Community leaders must understand their region’s unique risk in order to make the best decisions. Going forward, the type of insights provided by LRI will be critical to flattening the curve and saving lives.

Peter J. Plantes, M.D., is a Physician Executive with hc1.

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