This study seeks to identify demographic and clinical factors prompting clinician prescribing of nirmatrelvir/ritonavir to pediatric patients for management of coronavirus disease 2019 (COVID-19) infection.
Patients aged 12 to 17 years with a COVID-19 infection and nirmatrelvir/ritonavir prescription during an outpatient clinical encounter within a PEDSnet-affiliated institution between January 2022 and August 2023 were identified using electronic health record data. A multivariate logistic regression analysis was used to estimate odds of nirmatrelvir/ritonavir prescription after adjusting for various factors.
A total of 20 959 patients aged 12 to 17 years were diagnosed with a COVID-19 infection on the basis of an electronic health record-documented positive polymerase chain reaction or antigen test or diagnosis during an outpatient clinical visit. Of these patients, 408 received a nirmatrelvir/ritonavir prescription within 5 days of diagnosis. Higher odds of nirmatrelvir/ritonavir treatment were associated with having chronic or complex chronic disease (chronic: odds ratio [OR] 2.50 [95% confidence interval (CI) 1.83–3.38]; complex chronic: OR 2.21 [95% CI 1.58–3.08]). Among patients with chronic disease, each additional body system conferred 1.18 times higher odds of treatment (95% CI 1.10–1.26). Compared with non-Hispanic white patients, Hispanic patients (OR 0.61 [95% CI 0.44–0.83]) had lower odds of treatment.
Children with chronic conditions are more likely than those without to receive nirmatrelvir/ritonavir prescriptions. However, nirmatrelvir/ritonavir prescribing to children with chronic conditions remains infrequent. Pediatric data concerning nirmatrelvir/ritonavir safety and effectiveness in preventing severe disease and hospitalization are critical optimizing clinical decision-making and use among children.
The US Food and Drug Administration (FDA) issued an emergency use authorization (EUA) in December 2021 allowing nirmatrelvir/ritonavir (Paxlovid) administration to children aged ≥12 years, at risk for progression to severe disease, for treatment of mild-to-moderate coronavirus disease 2019 (COVID-19).1 Although the FDA has authorized nirmatrelvir/ritonavir use for adults aged ≥18 years,2 its use in patients aged 12 to 17 years remains restricted to EUA.3,4
Numerous factors increase the risks of severe disease, hospitalization, and complications from COVID-19 among children.5–7 Children with chronic disease, compared with those without, have an eightfold increased risk of COVID-19 hospitalization.5,8–10 COVID-19 viral coinfections, occurring almost 3 times more frequently in children than adults,7 also increase the risks of severe disease and hospitalization. Although nirmatrelvir/ritonavir has a potential role in mitigating COVID-19 disease burden among children,6,11 data on pediatric use, safety, efficacy, and effectiveness remain limited, even 2 years post-FDA EUA issuance.7,12–16 Use of extrapolated adult data to guide pediatric use17 has fueled uncertainty about the relative benefits and burdens of nirmatrelvir/ritonavir among children, including those at highest risk for COVID-19–related hospitalization.18 This study leverages data from PEDSnet, a national pediatric learning health system, to identify pediatric nirmatrelvir/ritonavir prescribing rates and associated factors, with special focus on children at highest risk for COVID-19–related hospitalization.19 This study is among the first to characterize factors associated with pediatric nirmatrelvir/ritonavir prescribing using a national data source.
Methods
PEDSnet is a national network of pediatric health systems that apply collective clinical data to conduct observational research, clinical trials, and population surveillance. The institutional review board of record designated this study as not human subjects research. In this 20-month retrospective cohort study of electronic health record (EHR) data from 8 PEDSnet health systems, collectively providing services to 3.3% of the nation’s children (2.4 million patients) annually, we identified a cohort of patients aged 12 to 17 years with COVID-19 diagnosed during an EHR-documented outpatient visit (ie, ambulatory, synchronous telemedicine, or emergency department) during the study period, January 1, 2022, to August 31, 2023.1 COVID-19 infection criteria included a positive COVID-19 antigen or polymerase chain reaction test and/or diagnosis code (Fig 1). Patients hospitalized ≤30 days before COVID-19 diagnosis were excluded from the cohort to ensure capture of only outpatient-diagnosed COVID-19 infections, an eligibility requirement for nirmatrelvir/ritonavir treatment under FDA EUA guidelines.3,4 Within the outpatient COVID-19 cohort, nirmatrelvir/ritonavir prescriptions initiated ≤5 days of COVID-19 index infection date (aligned with FDA EUA guidelines3,4 ) were included in the analysis.20
Cohort selection flowchart. Figure 1 depicts each step of the cohort selection process, including the numbers of patients excluded based on the eligibility criteria for this analysis. PCR, polymerase chain reaction.
Cohort selection flowchart. Figure 1 depicts each step of the cohort selection process, including the numbers of patients excluded based on the eligibility criteria for this analysis. PCR, polymerase chain reaction.
Descriptive statistics were calculated using PEDSnet contributions to the Researching COVID to Enhance Recovery PCORnet EHR Database Version September 15, 2023, with R version 4.2.0. Unadjusted group comparisons were performed using χ2 tests for categorical variables and Fisher’s exact t tests for continuous variables. The most conservative version of the Pediatric Medical Complexity Algorithm (PMCA) Version 3.0 was used to categorize children by chronic disease comorbidity burden (Supplemental Table 2).20 This PMCA use aligns with calculation and categorization methods used in numerous major studies demonstrating increased risk of COVID-19 hospitalization among children with chronic and complex chronic disease.5,8,10
To identify demographic and clinical features associated with treatment of nirmatrelvir/ritonavir ≤5 days after an outpatient COVID-19 infection, we fitted a multivariable logistic regression to include health system, age group, biological sex (female versus not female), race/ethnicity (Hispanic, non-Hispanic Black or African-American, non-Hispanic multiple race, non-Hispanic white, other, unknown), PMCA complex chronic disease flag, PMCA count of chronic conditions affecting the 17 body systems, obesity (ie, a BMI-for-age-sex z score >1.64) in the year before COVID-19 infection, and vaccination before COVID-19 infection. Self-reported race and ethnicity data were collected by hospitals according to institutional recording practices. We include the social constructs of race and ethnicity in this analysis to explore whether inequities exist in nirmatrelvir/ritonavir use and thereby inform future equity-focused research on COVID-19 therapeutics. We estimated odds ratios (ORs) and used P = .05 as the threshold to identify significant predictors for Wald tests and 95% confidence intervals (CIs).
Results
Figure 1 depicts the cohort selection process. Of the 562 439 individuals aged 12 to 17 years in the PEDSnet Researching COVID to Enhance Recovery database with a clinical encounter(s) during the study period, 23 119 patients (4.1%) had a positive test or diagnosis for COVID-19 infection during an outpatient clinical encounter based on an EHR-documented positive polymerase chain reaction or antigen test and/or diagnostic code. Of these, we excluded 2160 patients hospitalized ≤30 days before their COVID-19 index date. The remaining 20 959 patients with an outpatient COVID-19 positive test or diagnosis comprised 90.7% (20 959 of 23 119) of the original sample’s COVID-19 positive patients aged 12 to 17 years and were the focus of this analysis to align with the FDA nirmatrelvir/ritonavir EUA’s guidelines.3,4 Cohort patients had a mean age of 15.0 (SD 1.7) years and were 51.0% female (Table 1). Most (57.1%) identified with a race/ethnicity other than non-Hispanic white. About half (49.5%) had received a COVID-19 vaccination ≥28 days before their index COVID-19 diagnosis date.
Characteristics of Outpatient COVID-19–Positive Patients: Treated With Paxlovid Within 0–5 Days Versus Not Treated for (Outpatient) Paxlovid Within 0–5 Days
| Characteristica | Nirmatrelvir/Ritonavir Within 0–5 d After Infection, N = 408e | No Nirmatrelvir/Ritonavir Within 0–5 d After Infection, N = 20 551e | Overall, N = 20 959e | Pf |
|---|---|---|---|---|
| Age (y) | 15.3 (1.7) | 15.0 (1.7) | 15.0 (1.7) | .002 |
| Age group (y) | .003 | |||
| 12–13 | 105 (25.7%) | 6730 (32.7%) | 6835 (32.6%) | |
| 14–15 | 140 (34.3%) | 7028 (34.2%) | 7168 (34.2%) | |
| 16–17 | 163 (40.0%) | 6793 (33.1%) | 6956 (33.2%) | |
| Sex | .60 | |||
| Female | 203 (49.8%) | 10 496 (51.1%) | 10 699 (51.0%) | |
| Not female | 205 (50.2%) | 10 055 (48.9%) | 10 260 (49.0%) | |
| Race/ethnicity | .001 | |||
| Non-Hispanic white | 181 (44.4%) | 8845 (43.0%) | 9026 (43.1%) | |
| Hispanic | 65 (15.9%) | 3918 (19.1%) | 3983 (19.0%) | |
| Non-Hispanic Black/African American | 59 (14.5%) | 3927 (19.1%) | 3986 (19.0%) | |
| Non-Hispanic Asian American | 33 (8.1%) | 968 (4.7%) | 1001 (4.8%) | |
| Non-Hispanic multiple race | 13 (3.2%) | 697 (3.4%) | 710 (3.4%) | |
| Other | 37 (9.1%) | 804 (3.9%) | 841 (4.0%) | |
| Unknown | 20 (4.9%) | 1392 (6.8%) | 1412 (6.7%) | |
| Any vaccination 28 or more d before index date | <.001 | |||
| Vaccinated before index date | 279 (68.4%) | 10 100 (49.1%) | 10 379 (49.5%) | |
| No evidence of vaccination before index date | 129 (31.6%) | 10 451 (50.9%) | 10 580 (50.5%) | |
| Hospitalization in 1 to 30 d after index dateb | .70 | |||
| Hospitalized | 17 (4.2%) | 781 (3.8%) | 798 (3.8%) | |
| Not hospitalized | 391 (95.8%) | 19 770 (96.2%) | 20 161 (96.2%) | |
| PMCAc: Complex chronic flag | <.001 | |||
| Complex chronic | 124 (30.4%) | 2113 (10.3%) | 2237 (10.7%) | |
| Chronic | 66 (16.2%) | 1534 (7.5%) | 1600 (7.6%) | |
| No evidence of chronic or complex chronic disease | 218 (53.4%) | 16 904 (82.3%) | 17 122 (81.7%) | |
| PMCA: Number of body systems affected | <.001 | |||
| 1 body system | 131 (32.1%) | 5492 (26.7%) | 5623 (26.8%) | |
| 2 body systems | 85 (20.8%) | 2344 (11.4%) | 2429 (11.6%) | |
| 3–4 body systems | 66 (16.2%) | 1308 (6.4%) | 1374 (6.6%) | |
| 5–17 body systems | 45 (11.0%) | 605 (2.9%) | 650 (3.1%) | |
| No body systems | 81 (19.9%) | 10 802 (52.6%) | 10 883 (51.9%) | |
| PMCA: Number of body systems affected | 1.7 (2.0) | 0.9 (1.4) | 0.9 (1.4) | <.001 |
| Monoclonal antibodies (outpatient prescription only) | .70 | |||
| Monoclonal antibodies 0–7 d after index date | BT | 114 (0.6%) | ∼119 (0.5%) | |
| Monoclonal antibodies at another time | 0 (0.0%) | BT | BT | |
| No monoclonal antibodies | ∼403 (99%) | ∼20 432 (99%) | ∼20 835 (99%) | |
| Remdesivir (outpatient prescription only) | .20 | |||
| Remdesivir 0–10 d after index date | BT | 26 (0.1%) | ∼31 (0.1%) | |
| Remdesivir at another time | 0 (0.0%) | BT | BT | |
| No remdesivir | ∼403 (99%) | ∼20 520 (100%) | ∼20 918 (100%) | |
| Molnupiravir (outpatient prescription only) | >.90 | |||
| Molnupiravir 0–5 d after index date | 0 (0.0%) | BT | BT | |
| Molnupiravir at another time | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | |
| No molnupiravir | 408 (100.0%) | ∼20 546 (100%) | ∼20 954 (100%) | |
| 3-mo period of COVID-19 index date | <.001 | |||
| January–March 2022 | 25 (6.1%) | 10 650 (51.8%) | 10 675 (50.9%) | |
| April–June 2022 | 92 (22.5%) | 3077 (15.0%) | 3169 (15.1%) | |
| July–September 2022 | 118 (28.9%) | 3073 (15.0%) | 3191 (15.2%) | |
| October–December 2022 | 60 (14.7%) | 1645 (8.0%) | 1705 (8.1%) | |
| January–March 2023 | 56 (13.7%) | 1303 (6.3%) | 1359 (6.5%) | |
| April–June 2023 | 23 (5.6%) | 326 (1.6%) | 349 (1.7%) | |
| July–August 2023 | 34 (8.3%) | 477 (2.3%) | 511 (2.4%) | |
| Obesity (measured in the y before index date)d | <.001 | |||
| Obesity | 133 (32.6%) | 3106 (15.1%) | 3239 (15.5%) | |
| No indication of obesity | 275 (67.4%) | 17 445 (84.9%) | 17 720 (84.5%) |
| Characteristica | Nirmatrelvir/Ritonavir Within 0–5 d After Infection, N = 408e | No Nirmatrelvir/Ritonavir Within 0–5 d After Infection, N = 20 551e | Overall, N = 20 959e | Pf |
|---|---|---|---|---|
| Age (y) | 15.3 (1.7) | 15.0 (1.7) | 15.0 (1.7) | .002 |
| Age group (y) | .003 | |||
| 12–13 | 105 (25.7%) | 6730 (32.7%) | 6835 (32.6%) | |
| 14–15 | 140 (34.3%) | 7028 (34.2%) | 7168 (34.2%) | |
| 16–17 | 163 (40.0%) | 6793 (33.1%) | 6956 (33.2%) | |
| Sex | .60 | |||
| Female | 203 (49.8%) | 10 496 (51.1%) | 10 699 (51.0%) | |
| Not female | 205 (50.2%) | 10 055 (48.9%) | 10 260 (49.0%) | |
| Race/ethnicity | .001 | |||
| Non-Hispanic white | 181 (44.4%) | 8845 (43.0%) | 9026 (43.1%) | |
| Hispanic | 65 (15.9%) | 3918 (19.1%) | 3983 (19.0%) | |
| Non-Hispanic Black/African American | 59 (14.5%) | 3927 (19.1%) | 3986 (19.0%) | |
| Non-Hispanic Asian American | 33 (8.1%) | 968 (4.7%) | 1001 (4.8%) | |
| Non-Hispanic multiple race | 13 (3.2%) | 697 (3.4%) | 710 (3.4%) | |
| Other | 37 (9.1%) | 804 (3.9%) | 841 (4.0%) | |
| Unknown | 20 (4.9%) | 1392 (6.8%) | 1412 (6.7%) | |
| Any vaccination 28 or more d before index date | <.001 | |||
| Vaccinated before index date | 279 (68.4%) | 10 100 (49.1%) | 10 379 (49.5%) | |
| No evidence of vaccination before index date | 129 (31.6%) | 10 451 (50.9%) | 10 580 (50.5%) | |
| Hospitalization in 1 to 30 d after index dateb | .70 | |||
| Hospitalized | 17 (4.2%) | 781 (3.8%) | 798 (3.8%) | |
| Not hospitalized | 391 (95.8%) | 19 770 (96.2%) | 20 161 (96.2%) | |
| PMCAc: Complex chronic flag | <.001 | |||
| Complex chronic | 124 (30.4%) | 2113 (10.3%) | 2237 (10.7%) | |
| Chronic | 66 (16.2%) | 1534 (7.5%) | 1600 (7.6%) | |
| No evidence of chronic or complex chronic disease | 218 (53.4%) | 16 904 (82.3%) | 17 122 (81.7%) | |
| PMCA: Number of body systems affected | <.001 | |||
| 1 body system | 131 (32.1%) | 5492 (26.7%) | 5623 (26.8%) | |
| 2 body systems | 85 (20.8%) | 2344 (11.4%) | 2429 (11.6%) | |
| 3–4 body systems | 66 (16.2%) | 1308 (6.4%) | 1374 (6.6%) | |
| 5–17 body systems | 45 (11.0%) | 605 (2.9%) | 650 (3.1%) | |
| No body systems | 81 (19.9%) | 10 802 (52.6%) | 10 883 (51.9%) | |
| PMCA: Number of body systems affected | 1.7 (2.0) | 0.9 (1.4) | 0.9 (1.4) | <.001 |
| Monoclonal antibodies (outpatient prescription only) | .70 | |||
| Monoclonal antibodies 0–7 d after index date | BT | 114 (0.6%) | ∼119 (0.5%) | |
| Monoclonal antibodies at another time | 0 (0.0%) | BT | BT | |
| No monoclonal antibodies | ∼403 (99%) | ∼20 432 (99%) | ∼20 835 (99%) | |
| Remdesivir (outpatient prescription only) | .20 | |||
| Remdesivir 0–10 d after index date | BT | 26 (0.1%) | ∼31 (0.1%) | |
| Remdesivir at another time | 0 (0.0%) | BT | BT | |
| No remdesivir | ∼403 (99%) | ∼20 520 (100%) | ∼20 918 (100%) | |
| Molnupiravir (outpatient prescription only) | >.90 | |||
| Molnupiravir 0–5 d after index date | 0 (0.0%) | BT | BT | |
| Molnupiravir at another time | 0 (0.0%) | 0 (0.0%) | 0 (0.0%) | |
| No molnupiravir | 408 (100.0%) | ∼20 546 (100%) | ∼20 954 (100%) | |
| 3-mo period of COVID-19 index date | <.001 | |||
| January–March 2022 | 25 (6.1%) | 10 650 (51.8%) | 10 675 (50.9%) | |
| April–June 2022 | 92 (22.5%) | 3077 (15.0%) | 3169 (15.1%) | |
| July–September 2022 | 118 (28.9%) | 3073 (15.0%) | 3191 (15.2%) | |
| October–December 2022 | 60 (14.7%) | 1645 (8.0%) | 1705 (8.1%) | |
| January–March 2023 | 56 (13.7%) | 1303 (6.3%) | 1359 (6.5%) | |
| April–June 2023 | 23 (5.6%) | 326 (1.6%) | 349 (1.7%) | |
| July–August 2023 | 34 (8.3%) | 477 (2.3%) | 511 (2.4%) | |
| Obesity (measured in the y before index date)d | <.001 | |||
| Obesity | 133 (32.6%) | 3106 (15.1%) | 3239 (15.5%) | |
| No indication of obesity | 275 (67.4%) | 17 445 (84.9%) | 17 720 (84.5%) |
BT, below threshold.
a Counts >0 and <10 have been suppressed and are labeled as below threshold; approximate (∼) counts and percentages are provided for rows in categories containing suppressed data. To prevent back-calculation, counts and percentages were jittered by assuming counts labeled below threshold equaled standard values of 5.
b COVID-19 patients were not included if they had a diagnosis associated an inpatient visit, or an inpatient visit (inpatient hospital stay; observation stay; emergency department visit resulting in admission for an inpatient hospital stay) within 0 to 30 days before the COVID-19 index date.
c We used the taxonomy from the PMCA, which uses International Statistical Classification of Diseases and Related Health Problems, Ninth Revision, Clinical Modification, and 10th Revision, Clinical Modification, codes to aggregate related chronic diagnoses according to body system, with a separate category for malignant neoplasms. A condition is considered progressive if it is associated with deteriorating health and increased risk of shortened life expectancy in adulthood. We considered only conditions within 3 years before the COVID-19 index date. We removed the PMCA’s sole obesity condition code, morbid obesity (International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Clinical Modification, code E66.01), from all PMCA calculations.
d Obesity indicates whether a person had an anthropometric measurement consistent with obesity within the year before their index event, as indicated by a BMI-for-age-sex z score of >1.64.
e Mean (SD); n (%).
f Fisher’s exact test; Pearson’s χ2 test.
Patients who received nirmatrelvir/ritonavir prescription ≤5 days of COVID-19 diagnosis (“treated”) comprised 1.9% (408 of 20 959) of the analytic cohort (Table 1). The remainder included those not receiving a nirmatrelvir/ritonavir prescription ≤5 days of diagnosis (“untreated”; n = 20 551). Supplemental Figure 3 illustrates quarterly nirmatrelvir/ritonavir prescription counts. Within the analytic cohort, 18.3% (3837 of 20 959) patients were identified as having a chronic or complex chronic disease; of these patients, only 5.0% (190 of 3837) received a nirmatrelvir/ritonavir prescription ≤5 days of COVID-19 infection. The percentage of patients in the treated group who were obese (32.6%) was more than double that in the untreated group (15.1%). We also identified extremely limited use of several alternative COVID-19 therapeutics3,4 : Monoclonal antibodies ≤7 days of index COVID-19 date21 (0.5% [∼119 of 20 959]); remdesivir ≤10 days of index date22 (0.1% [∼31 of 20 959]), and molnupiravir ≤5 days of index date23 (0.0% [below reporting threshold]). Virtually all receipt of these medications occurred among patients untreated with nirmatrelvir/ritonavir.
On the basis of crude unadjusted data, demographic and clinical characteristics reveal differences between treated and untreated patients (Table 1). Treated patients were older, had greater preexisting disease burden, and were more likely to have received vaccination. The percentage of patients hospitalized ≤30 days after index infection appeared similar for treated and untreated patients. Because this current data set does not have the capability to distinguish between hospitalizations related versus unrelated to COVID-19 complications, odds of nirmatrelvir/ritonavir prescription receipt among patients hospitalized ≤30 days after index infection versus not hospitalized were not included in adjusted analyses below.
On the basis of adjusted logistic regression analyses, several clinical and demographic characteristics were associated with nirmatrelvir/ritonavir treatment ≤5 days after index COVID-19 infection (Fig 2, Supplemental Table 3). Compared with non-Hispanic white patients, Hispanic patients (OR 0.61 [95% CI 0.44–0.83]) had lower odds of treatment. Patients with a PMCA-defined chronic or complex chronic condition had higher odds of treatment compared with patients with no evidence of either (chronic: OR 2.50 [95% CI 1.83–3.38]; complex chronic: OR 2.21 [95% CI 1.58–3.08]). For each additional PMCA-classified body system identified in the 3 years before COVID-19 infection, patients had higher odds of treatment (OR 1.18 [95% CI 1.10–1.26]). Patients identified with obesity within the year before COVID-19 infection also had higher odds of treatment compared with patients with no indication of obesity in the previous year (OR 2.19 [95% CI 1.73–2.76]). Patients with COVID-19 vaccination ≥28 days before infection had higher odds of treatment than those without evidence of previous vaccination (OR 1.48 [95% CI 1.19–1.86]). Compared with patients aged 16 to 17, patients aged 12 to 13 had lower odds of treatment (OR 0.75 [95% CI 0.58–0.97]). Odds of treatment did not differ significantly between female and not female children (P > .1).
Forest plot of logistic regression ORs. Figure 2 is a forest plot of ORs (ie, points) and their 95% CIs (ie, lines through each point) generated by multivariate adjusted logistic regression. ORs <1.0 represent lower odds of nirmatrelvir/nitonavir prescription receipt associated with a specific predictor compared with its reference value (see Methods). ORs >1.0 represent higher odds of prescription receipt. NH, non-Hispanic.
Forest plot of logistic regression ORs. Figure 2 is a forest plot of ORs (ie, points) and their 95% CIs (ie, lines through each point) generated by multivariate adjusted logistic regression. ORs <1.0 represent lower odds of nirmatrelvir/nitonavir prescription receipt associated with a specific predictor compared with its reference value (see Methods). ORs >1.0 represent higher odds of prescription receipt. NH, non-Hispanic.
Discussion
Pediatric outpatients with COVID-19 who received a nirmatrelvir/ritonavir prescription during the study period were more likely to have chronic or complex chronic disease, with higher burden of multisystem disease further increasing odds of prescription receipt. Yet, an exceedingly small minority of COVID-19–diagnosed pediatric outpatients with chronic or complex chronic disease (5.0%) received nirmatrelvir/ritonavir prescriptions during the study period, despite established increased risk of hospitalization among these children.5,8,10 This prescribing rate is markedly lower than the eligible adult prescribing rate of 12%, identified as a driver of high US COVID-19 hospitalization rates among adults.24,25 Addressing this inequitable national underprescribing to eligible children after COVID-19 infection may be a strategy for mitigating infection-related pediatric hospitalizations.5,8,10,24,25 Pediatric underprescribing may result from clinical uncertainty because of limited pediatric evidence about the benefits and harms of nirmatrelvir/ritonavir, in addition to other known systemic nirmatrelvir/ritonavir prescribing barriers.5,8,10 For example, nirmatrelvir/ritonavir has many potential drug–drug interactions that can complicate its use among pediatric patients receiving treatment of underlying chronic conditions (eg, immunosuppressive medications like tacrolimus).26 Filling pediatric nirmatrelvir/ritonavir knowledge gaps and understanding pediatric clinician barriers to prescribing remain foundational to addressing pediatric access limitations to nirmatrelvir/ritonavir.26
Children of Hispanic ethnicity (but not non-Hispanic Black children) were less likely to receive a nirmatrelvir/ritonavir prescription than non-Hispanic white children, suggesting presence of racial and/or ethnic disparities in use of this therapeutic. Our finding that children with previous COVID-19 vaccination were more likely to receive a nirmatrelvir/ritonavir prescription than those who did not suggests that health care access factors, which drive many health disparities, may also influence nirmatrelvir/ritonavir prescribing.6
Endemic COVID-19 continues to present risks of severe disease and hospitalization among children, especially those with chronic disease and minoritized children.6 Concurrently, the end of the COVID-19 federal public health emergency has prompted a shift to traditional insurance coverage for COVID-19 therapeutics like nirmatrelvir/ritonavir that may include cost sharing between payer and patient.27 Robust pediatric evidence concerning safety, effectiveness, and equity must accompany payment policy changes to ensure children have equitable access to therapies that may mitigate severe COVID-19 disease.27
Limitations
Several limitations exist within our study, many of which related to EHR data capture. Patients may have received prescriptions, COVID-19 diagnoses, or vaccinations outside PEDSnet health systems. Although most PEDSnet health systems link to state data registries for COVID-19 vaccinations, nirmatrelvir/ritonavir linkage gaps persist, including for pharmacy dispensing of prescriptions. Our analyses were also limited by this current data set’s inability to distinguish COVID-19–related hospitalization from other hospitalization causes after incident COVID-19 infection. However, our study methods and findings are informing the design of ongoing EHR queries evaluating nirmatrelvir/ritonavir effectiveness in reducing pediatric hospitalization risk, duration, and acuity after COVID-19 diagnosis. Finally, the diversity of chronic conditions present in our cohort yielded small sample sizes for specific disease states, precluding disease-specific analysis.28 Despite this limitation, large national networks, like PEDSnet, are uniquely poised to conduct disease-specific studies as nirmatrelvir/ritonavir prescription counts accrue over time.
Conclusions
This study is among the first to report on pediatric nirmatrelvir/ritonavir prescribing patterns using a national data source. Our central finding, identifying very low nirmatrelvir/ritonavir prescribing rates among children at high risk of COVID-19–related hospitalization, highlights the need for increased emphasis on pediatric research evaluating its effectiveness in reducing COVID-19 hospitalization rates and complications, especially among children with chronic diseases. Concurrent research evaluating the role of health care access and use upon pediatric nirmatrelvir/ritonavir prescribing may inform strategies to promote equitable delivery of COVID-19 therapeutics.
Acknowledgments
This study is part of the NIH Researching COVID to Enhance Recovery (RECOVER) Initiative, which seeks to understand, treat, and prevent the post-acute sequelae of SARS-CoV-2 infection (PASC). For more information on RECOVER, visit https://recovercovid.org/. We thank Miranda Higginbotham for her contributions. We would also like to thank the National Community Engagement Group (NCEG), all patient, caregiver, and community representatives, and all the participants enrolled in the RECOVER Initiative.
Dr Bose-Brill conceptualized and designed the study, drafted the initial manuscript, and critically reviewed and revised the manuscript; Ms Hirabayashi designed the data collection instruments, conducted the initial analyses, supported drafting of the initial manuscript, and critically reviewed and revised the manuscript; Dr Schwimmer critically reviewed and revised the manuscript and supported follow-up data analysis and data visualization; Drs Pajor, Rao, Mejias, Jhaveri, Forrest, Bailey, Christakis, Thacker, and Lorman advised on the selection and refinement of study variables, assisted with interpretation of treatment prescribing patterns, and critically reviewed and revised the manuscript; Dr Lee assisted with the development of methods, analyzed the data, and critically reviewed and revised the manuscript; Drs Hanley, Patel, Cogen, Block, and Prahalad critically reviewed and revised the manuscript; and all authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.
Authorship has been determined according to International Committee of Medical Journal Editors recommendations.
FUNDING: Funded by the National Institutes of Health agreement OT2HL161847-01 as part of the Researching COVID to Enhance Recovery program of research. The funder had no role in the design or conduct of this study. The content is solely the responsibility of the authors and does not necessarily represent the official views of the Researching COVID to Enhance Recovery program, the National Institutes of Health, or other funders.
CONFLICT OF INTEREST DISCLOSURES: Dr Brill reports previous funding from Novartis and Regeneron Pharmaceuticals for research support. Dr Lee serves on the Platform for Advanced Scientific Computing advisory board for United Health Group. Dr Patel reports funding from the National Institutes of Health. Dr Rao reports previous grant support from GSK and Biofire. Dr Mejias reports funding from Janssen and Merck for research support, Janssen, Merck, and Sanofi-Pasteur for advisory board participation, and Sanofi-Pasteru and AstraZeneca for continuing medical education lectures. Dr Jhaveri is a consultant for AstraZeneca, Seqirus, and Dynavax, and receives an editorial stipend from Elsevier and the Pediatric Infectious Diseases Society, and royalties from Up To Date/Wolters Kluwer. All other authors have indicated they have no conflicts of interest relevant to this article to disclose.
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