Research Needs Robust Data: The CDC Utilizes the PINC AI™ Healthcare Database

Key takeaways:

• The Centers for Disease Control and Prevention (CDC) is utilizing the PINC AI^™ Healthcare Database (PHD) to further COVID-19 public health science.
• Managing an infectious disease outbreak or a viral pandemic requires massive amounts of standardized data.
• PHD data can help generate evidence-based, sustainable solutions to help public health officials, clinicians and health systems prepare for the next COVID-19 surge and future disease outbreaks.

Three years into the COVID-19 pandemic, and the "new normal" continues to see a steady stream of variants and subvariants. Currently, the more contagious BA.5 omicron subvariant is causing a spike in infections, and, according to the CDC, COVID-19 hospital admissions are on the rise, putting a strain on those impacted hospital staff and resources across the nation.

Public health and clinical researchers are continuously seeking more information about COVID-19, its variants, patterns of infection, vaccine and booster efficacy, impacts on health outcomes, antimicrobial resistance, costs and healthcare resource utilization (HRU). Such information may allow researchers and clinicians to analyze surges, allowing communities and health systems to prepare with appropriate staffing, personal protective equipment (PPE) supplies, treatment options and access to care.

Amid this environment, our researchers are turning to actionable insights derived from the PINC AI™ Healthcare Database (PHD).

COVID-19 Data in the PHD

As of July 20, 2022, the PHD has captured more than 5 million confirmed COVID-19 patients who were treated in inpatient and outpatient settings across 956 U.S. health systems and more than 3.5 million patients who received a COVID-19 vaccine in these hospitals and health systems.

With a lag time of approximately 60-90 days and detailed clinical data on hospital visits that include treatments, comorbidities, diagnoses, procedures, microbiology, general labs, vital signs and imaging, the PHD is one of the most comprehensive and timely databases driving research and providing answers to the toughest COVID-19 related questions.

The PHD in Action

Managing an infectious disease outbreak or viral pandemic has proven to be enormously challenging and requires massive amounts of data. Basic-level data can enable researchers to track the number of infections, infection severity, and where and to whom infections are occurring. But standardized, comprehensive data, like that contained within the PHD, provides a deeper set of insights into the disease — allowing researchers to run stronger analyses and help to predict future trends and outbreaks.

Recently, the PHD helped fuel the CDC’s public health science on all aspects of COVID-19 including antimicrobial utilization and this resulting report indicated an increase in healthcare-associated infections (HAIs) and antimicrobial resistance – halting progress in antimicrobial stewardship efforts. PINC AI^™ Clinical Intelligence and PAS continue to provide resources that help both the CDC and health systems monitor and investigate HAIs and COVID-19 infections to generate evidence-based sustainable solutions to help prevent increases in antimicrobial resistance.

As of July 2022, the CDC has published 27 COVID-19 related research studies using PHD data in CDC’s Morbidity and Mortality Weekly Report and peer-reviewed journals. Some of these studies are described below.

1. Is COVID-19 linked to a higher risk of myocarditis?
   Viral infections are a common cause of myocarditis, an inflammation of the heart muscle that can result in hospitalization, heart failure and sudden death. During the pandemic, the CDC sought to understand if COVID-19 was associated with a higher risk of myocarditis. In their study using PHD data, they found:

   
   

   • The occurrence of myocarditis inpatient encounters in 2020 was 42 percent higher than pre-pandemic (in 2019).
   • The risk for myocarditis among patients with COVID-19 was nearly 16 times higher than patients without COVID-19 during the pandemic.
   • The association between COVID-19 and myocarditis was more pronounced among children (younger than 16 years old) and older adults (50 years old and older).
   
   They found that while myocarditis was uncommon overall, a diagnosis of COVID-19 significantly increased a patients’ risk of myocarditis.
   
   
2. What were the trends around antibiotic use during the pandemic?
   Antimicrobial resistance is a major concern, killing at least 1.27 million people worldwide with more than 2.8 million antimicrobial-resistant infections occurring in the U.S. each year. In a recent retrospective study using PHD data, the CDC sought to understand antibiotic use trends during the pandemic.
   
   Observing data from 716 hospitals that reported at least 100 antibiotic days of therapy per 1000 patient days from March to October 2020, they found that 77 percent of inpatients hospitalized with COVID-19 received antibiotics and 81 percent of them were started on admission — even when data showed limited reported evidence for bacterial coinfections among COVID-19 patients.
   
   Antibiotic stewardship programs are an essential component in antimicrobial resistance efforts and health systems can leverage their infrastructure to address challenges that COVID-19 presented. Even during a pandemic, antibiotics should be used responsibly and sparingly to help avoid unintended long-term consequences associated with overuse.
   
   
3. Were patients with Type 1 diabetes at a higher risk for serious complications?
   In one CDC study, researchers investigated whether patients hospitalized with COVID-19 and Type 1 diabetes mellitus (T1DM) were at a higher risk for severe outcomes.
   
   
   • Utilizing PHD data, they found that COVID-19 patients with T1DM had a 21 percent higher risk of intensive care unit (ICU) admission and invasive mechanical ventilation (IMV) use compared to COVID-19 patients without T1DM.
   • Although the risk of ICU/IMV was 9 percent higher among patients with T1DM than those with T2DM, the difference attenuated after accounting for diabetic ketoacidosis. Patients with T1DM had a 5 percent higher risk of mortality than patients without diabetes.
   
   
4. Did body mass index (BMI) play a role in severe COVID-19 outcomes?
   
   Obesity affects 42.4 percent of U.S. adults and is a recognized risk factor for severe COVID-19, impaired lung function and other chronic diseases.
   
   One CDC study using data from the PHD assessed the association between BMI and severe COVID-19 outcomes. The findings included:

   
   

   • Among patients with a COVID-19 diagnosis and in emergency department or inpatient care, 28.3 percent were overweight and 50.8 percent had obesity.
   • Obesity was a risk factor for hospitalization and death, and risks increased with increase in BMI categories.
   
   
   
5. Did COVID-19 adversely affect pregnancy outcomes during the pandemic?
   
   In a cross-sectional study of U.S. delivery hospitalizations, the CDC leveraged PHD data to assess differences in select maternal and pregnancy outcomes between April through December in 2019 and 2020.
   
   In-hospital maternal death increased from 2019 to 2020, while maternal ICU admission and preterm births decreased. However, there was no significant difference in maternal death after excluding deliveries with a COVID-19 diagnosis. The proportion of cesarean deliveries with pre-labor rupture of membranes (PROM), prolonged labor, and attempted forceps or vacuum slightly increased, as did high-risk cesarean deliveries in 2020 compared to 2019.
   
   Researchers saw an increase in gestational diabetes from 8.4 percent in 2019 to 9.8 percent in 2020.
   
   Overall delivery length of stay (LOS) was shorter in 2020 compared to 2019.
   
   
6. Was a COVID-19 diagnosis common among newborns?
   
   During the pandemic, little was known about the effects of COVID-19 on newborns. Some studies indicate that maternal transmission to neonates appears rare but is still possible — and there is a lack of data and evidence on this topic.
   
   To help understand COVID-19’s impact on newborns, the CDC utilized PHD data to conduct a retrospective cohort study between March and December 2020.
   
   They found just 209 newborns (0.03%) with a COVD-19 diagnosis out of more than 700,000 birth hospitalizations. Late preterm/term newborns with COVID-19 had increased ICU admission and sepsis risk, and early preterm newborns with COVID-19 had increased risk for IMV. Overall, diagnosis of COVID-19 was rare among newborns and most late preterm/term newborns with COVID-19 did not require ventilatory support.
   
   
7. Were underlying medical conditions associated with severe COVID-19 illness?
   
   Anytime clinicians are treating patients, they take into consideration how the patients’ underlying medical conditions impact their current illness. This was no different during the pandemic, and the CDC conducted a study with data using from the PHD to understand the association between underlying medical conditions and severe COVID-19 illness.
   
   The study found:
   

   
   

   • 9 underlying medical conditions were associated with severe COVID-19 illness. These 9 conditions were prevalent and found in 81.9 percent of all hospitalized COVID-19 patients.
   • The highest risk factors for death among hospitalized COVID-19 patients were obesity, anxiety and fear-related disorders, diabetes with complication, chronic kidney disease (CKD) and neurocognitive disorders.
   
   
   The number of underlying medical conditions itself was a risk factor for severe outcomes of COVID-19 (death, IMV and ICU admission) among hospitalized patients.
   These results reinforced previous study findings of higher risk of severe illness associated with diabetes with complication, obesity , coronary atherosclerosis and other heart disease, chronic obstructive pulmonary disease, and neurocognitive disorders.
   
   
8. What were the risk factors for severe COVID-19 outcomes among vaccinated adults?
   
   While it is already known that COVID-19 vaccines are effective in preventing COVID-19-related hospitalizations and death, understanding the risk factors for severe outcomes among the vaccinated is important for clinicians and will allow them to plan targeted interventions.
   
   An NIH-led study that the CDC co-authored used data from the PHD and found that risk factors for severe outcomes included age ≥65 years, immunosuppressed, and six other underlying conditions. All persons with severe outcomes had at least one risk factor; 78% of persons who died had at least four.
   
   
9. Did COVID-19 caseload surges have an impact on survival rates?
   
   U.S. hospitals saw surge after surge in COVID-19 caseloads during the height of the pandemic. NIH-led and CDC co-authored a retrospective cohort study leveraging PHD data to better understand the impact caseload surges in hospitals had on risk of mortality.
   
   
   • They found that nearly 1 in 4 COVID-19 deaths (or 23 percent) was potentially attributable to hospitals strained by surging caseload between August and October 2020.
   • Hospital COVID-19 case surges were detrimental to survival and potentially eroded benefits gained from emerging treatments.
   
   
10. What were the trends associated with severe COVID-19 illness?
    
    Clinical severity of COVID-19 ranged widely from asymptomatic infection to multiorgan failure and varied over time as new variants emerged. One PHD data-informed CDC study analyzed the severity of acute illness trends over time among hospitalized patients with COVID-19 in the U.S.
    
    Findings demonstrated that clinical severity of hospitalized COVID-19 patients fluctuated over time, but no evidence was found for consistent worsening of COVID-19 severity between April 2020 and April 2021.
    
    Severity in COVID-19 illness tended to be lower among women, younger adults and those with fewer comorbidities compared to their counterparts. In addition, severity across racial and ethnic groups tended to be similar.
    
    
11. How substantial was the cost for COVID-19 inpatient care during the pandemic?
    
    A recent CDC study, using PHD data from more than 800 hospitals covering all payers in the U.S., assessed the average per-patient cost of inpatient care for hospitalized adult COVID-19 patients, overall, and by severity, age, sex, underlying medical conditions and acute complications.

    
    

    • The study found that overall cost among 654,673 patients hospitalized with COVID-19 was $16.2 billion between March 2020 and July 2021.
      
    • Estimated per-patient hospitalization cost was $24,826.
    • Among surviving patients, estimated per-patient cost was $13,090 without ICU admission or IMV, $21,222 with ICU admission alone, and $59,742 with IMV.
    • The estimated cost among patients who died was $27,017 per-patient and costs ticked higher for patients with underlying medical conditions and acute complications.

Efforts to navigate and manage effectively through the pandemic depend on data insights – and those generated through PHD data have proven invaluable for federal agencies and for public health.

Research, greater knowledge and a willingness to take evidence-based actions can help relieve strain on healthcare systems and improve patient care and outcomes.

PHD insights like those found in the aforementioned CDC scientific studies have provided processes and vital information that is helping communities, policymakers, health systems, payers and clinicians make effective changes and make meaningful strides in the battle against COVID-19.

Take a page from the CDC playbook and learn how you can partner with PAS to utilize the PHD as part of your research and product development strategy.

For more on this topic:

• Read more about PINC AI™ Applied Sciences’ (PAS) efforts in COVID-19 research
• Browse the PAS Publication Database to see more research leveraging the PHD.
• Check out the capabilities of the PHD as they relate to COVID-19 data.
• Explore Premier’s applied research and analytics solutions.

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