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A Case Study Example using Data from the N3C

A Case Study Example using Data from the N3C

This student project is intended to demonstrate proper reporting of N3C results in accordance with N3C policies and guidelines. It also aims to highlight the potential to use nationally harmonized, electronic health record data from the NIH National COVID Cohort Collaborative (N3C) to investigate complex clinical questions in underrepresented populations.

Case Study Abstract

This abstract is an an approved COVID–19–related project that was first submitted as a Data Use Request (DUR) and subsequently reviewed by the Publication Committee to assess its relevance to COVID-19, the submitted DUR, and compliance with the N3C Code of Conduct and Attribution and Publication Principles.

Mental Health Disorder Diagnoses and changes in BMI following the COVID-19 Pandemic Lockdown Among Transitional Age Youth using N3C Data

Celine D. Tran, B.S.1,2; Maryam Abdullah, B.S.1,3; Julie Cederbaum Ph.D.4, on behalf of the National COVID Cohort Collaborative Consortium

1 Southern California Clinical and Translational Science Institute, Los Angeles, CA, USA

2 University of Southern California, Leonard Davis School of Gerontology, Los Angeles, CA, USA

3 Keck Medicine of USC, University of Southern California, Los Angeles, CA, USA

4 Suzanne Dworak-Peck School of Social Work, University of Southern California, Los Angeles, CA, USA

Background: COVID-19 lockdown experiences have been linked to adverse physical and mental health outcomes, where mental health challenges often coincided with weight gain and maladaptive health behaviors. Transitional-age youth (TAY; ages 18–25) with autism spectrum disorder (ASD) or attention-deficit/hyperactivity disorder (ADHD) may have been particularly affected, given their elevated risks for psychiatric disorders and overweight/obesity.

Methods: Using Level 2, de-identified electronic health record (EHR) data from the NIH National COVID Cohort Collaborative (N3C), logistic regression models examined (1) associations between BMI category changes and post-lockdown mental health diagnoses—major depressive disorder (MDD), generalized anxiety disorder (GAD), eating disorders (EDs), and substance use disorders (SUDs)—and (2) moderation by ASD and ADHD in a sample of 96,00+ TAY (Table 1).

Results: A one-level increase in BMI category significantly increased the odds of MDD (OR=1.18, 95%CI=1.13–1.23, p<0.001) and GAD (OR=1.08, 95%CI=1.06–1.11, p<0.001), but lowered the odds of SUDs (OR=0.91, 95%CI=0.88–0.95, p<0.001). Only the BMI-ADHD interaction was significant, where ADHD attenuated the effect of BMI category change on SUDs (OR=1.12, 95%CI=1.03–1.22, p<0.01, Figure 1). Independently, ASD significantly increased odds of GAD (OR=1.48, 95%CI=1.34–1.65, p<0.001) and EDs (OR=1.77, 95%CI=1.19–2.64, p<0.001) but reduced the odds of SUDs (OR=0.65, 95%CI=0.55–0.78, p<0.001); while ADHD significantly increased the odds of all outcomes (MDD: OR=1.50, 95%CI=1.38–1.63, p<0.001; GAD: OR=1.76, 95%CI=1.67–1.86, p<0.001; EDs: OR=1.44, 95%CI=1.15–1.80, p<0.001; SUDs: OR=1.40, 95%CI=1.29–1.51, p<0.001).

Discussion: Findings are preliminary and limited by inconsistent EHR documentation/data modeling (e.g., uncoded diagnoses or shifted dates/times) across N3C contributing sites and systematic exclusion of TAY who did not access care during the timeframe. Nonetheless, results suggest elevated post-lockdown mental health risks among TAY with neurodevelopmental disorders and clinically relevant BMI increases, highlighting the importance of targeted support and equitable policies in response to public health crises.

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Figure 1: The Linear Interaction of ADHD Status and Change in BMI Category on the Log-Odds of any Substance Use Disorder Diagnoses Following the COVID-19 Pandemic Lockdown Response among Transitional Age Youth

 

Table 1. An Example of Approved formatting for reporting Descriptives Tables using data from N3C.

 

Supplement A. Link to the enclave project and a list of Concept Sets used to derive the analysis that may be provided to reviewers.

 

Supplement B. This is an extended abstract that includes all required N3C attestations and acknowledgements, as expected for a manuscript using N3C data.

N3C Attributions (The following are attributions required for abstracts; see Suppliment B for all attestations/attributions required for manuscripts)

The analyses in this abstract were conducted using the NCATS N3C Data Enclave supported by NCATS U24 TR002306 and made possible because of the patients whose data was contributed by partner organizations (covid.cd2h.org/dtas). We gratefully acknowledge the individuals who have contributed to the on-going development of this community resource (covid.cd2h.org/acknowledgements) [1]

  1. Haendel MA, Chute CG, Bennett TD, Eichmann DA, Guinney J, Kibbe WA, Payne PRO, Pfaff ER, Robinson PN, Saltz JH, Spratt H, Suver C, Wilbanks J, Wilcox AB, Williams AE, Wu C, Blacketer C, Bradford RL, Cimino JJ, Clark M, Colmenares EW, Francis PA, Gabriel D, Graves A, Hemadri R, Hong SS, Hripscak G, Jiao D, Klann JG, Kostka K, Lee AM, Lehmann HP, Lingrey L, Miller RT, Morris M, Murphy SN, Natarajan K, Palchuk MB, Sheikh U, Solbrig H, Visweswaran S, Walden A, Walters KM, Weber GM, Zhang XT, Zhu RL, Amor B, Girvin AT, Manna A, Qureshi N, Kurilla MG, Michael SG, Portilla LM, Rutter JL, Austin CP, Gersing KR, N3C Consortium. The National COVID Cohort Collaborative (N3C): Rationale, design, infrastructure, and deployment. J Am Med Inform Assoc. 2021 Mar 1;28(3):427–443. http://dx.doi.org/10.1093/jamia/ocaa196 PMCID: PMC7454687