Understanding the impact of “long COVID.”
Jonah Leshin¹ and Dustin D. French, PhD²³⁴
Introduction
There is growing interest in understanding the patient impact of COVID-19 not just during an acute case, but long afterwards. The mounting evidence of longer-term mental and physical effects suggests a need for better understanding of care requirements beyond the emergency room.
The implications of “long COVID” have had a tangible impact on primary care, where patients often receive follow-up treatment. The situation has been particularly acute considering that primary care was already under-resourced and facing a myriad of challenges prior to the pandemic.
This study analyzes the volume of primary care follow ups to ER visits introduced by COVID-19, illustrating the burden on a fragile system after an initial emergency room visit.
Data Sources:
We used Medical Claims Data that contain patient demographics, diagnoses, procedures, providers, service dates, and billing information (e.g., doctor, facility, and insurance carrier). The data set used contains > 3 billion claims on > 100 million unique patients over the last 7 years. The data set indicates whether a patient was diagnosed as COVID-19 positive. The data comes from Office Ally, a full-service medical claims clearinghouse that provides numerous software products to providers and patients.
Additionally, we used the National Plan and Provider Enumeration System (NPPES) National Provider Identifier (NPI) registry in combination with the National Uniform Claim Code (NUCC) to identify primary care providers.
Methodology:
Our base population studied consisted of patients with a record containing a COVID-19 diagnosis and emergency room visit. We then analyzed the volume of primary care follow ups for these patients. We obtained primary care follow up counts by totaling the number of primary care visits with a COVID-19 diagnosis, where the visit occurred between 1 and 60 days after the date of the initial emergency room visit. We counted multiple visits for the same patient as distinct visits.
Results:
Figure 1 shows the total number of unique monthly patients in the dataset who had an emergency room visit with a Covid diagnosis. It contains COVID-19 ER use by age bands, and shows age 55+ with the most pronounced ER utilization, up to 10 times greater than those ages 0—18, up to 4 times those ages 19—35, and more than double those ages 36—54. We saw spikes in all age groups for July and December 2020, with the December spike significantly larger.
Figures 2, 3, 4 and 5 show the number of Covid related follow up visits for primary care and primary care plus specialty care apart from ER visits expressed as a ratio (follow up visits/initial ER patients), limited to within 60 days of the initial ER visit. Months in these figures represent the month of the initial ER visit. These metrics can be thought of as an average number of follow up visits per ER patient.
As one might expect, our results show that the volume of follow up care required increases with age.
Both health care follow up types paralleled a spike in ER use, suggesting importance of subsequent non ER care after infection. Notably, however, although the second spike in ER visits is larger by absolute count (demonstrated in Figure 1), the spike in the average amount of follow up care required is actually smaller than the initial spike of average follow up care required in July 2020 across all age bands. This may be attributable to more effective initial ER treatment or home self-care as more about the disease was learned.
Finally, Figure 6 breaks down follow up care by the time interval between the initial ER visit and the follow up. It shows a positive correlation between age and follow up time intervals – that is, follow ups from younger patients occurred within less time of the initial ER visit.
Conclusions:
The COVID-19 pandemic resulted in surges of cases and emergency room use that crippled the U.S. healthcare system. The results of this study suggest that primary care utilization mirrors emergency room use, and likely off-sets a fragile hospital system that was on the brink of collapse at different points in time. Future public health policies for pandemic and crisis response should go beyond the hospital safety net to consider the importance of primary care practitioners.
Figures:
AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).
AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.
"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ.
“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."
As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.
As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health.
Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.
Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:
Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.
Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:
By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.
Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:
By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.
While accessing SDOH data offers significant advantages, challenges can arise from:
To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.
With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.
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