INTRODUCTION
Nirmatrelvir-ritonavir (Paxlovid), an effective treatment for COVID-19, was approved for emergency use authorization (EUA) in December 2021, for individuals at high risk of developing severe COVID-19.1 Nirmatrelvir-ritonavir has been shown to decrease mortality and hospitalization rates, yet between April and August 2022 only 28.4% of eligible patients received prescriptions.2 While the US government has made nirmatrelvir-ritonavir free, it is unclear whether there are disparities in access to the drug. This research examined whether there are demographic/socioeconomic disparities among those who are accessing nirmatrelvir-ritonavir based on eligibility for prescription as outlined in the EUA.
METHODS
We conducted an online cross-sectional survey among a random, representative sample of adult participants in the Los Angeles Pandemic Surveillance Cohort Study between January and March 2023. Participants and procedures are described elsewhere.3 In total, 1504 participants were invited to take the survey via email and text; 1370 completed the survey (91.1% follow-up rate). The Los Angeles County Department of Public Health (LACDPH) Institutional Review Board approved all study procedures.
The survey assessed demographic/socioeconomic characteristics and self-reported health history. Nirmatrelvir-ritonavir eligibility was coded as “yes” if the participant reported any medical conditions that are considered high risk for severe COVID-19.4 We also assessed the relative geographic impact of COVID-19 based on COVID-19 mortality rates in participants’ zip codes using public health data from LACDPH (coded as low, medium, and high impact). The main outcome variable was self-reported nirmatrelvir-ritonavir use, assessed by asking “Did you take Paxlovid when you tested positive for COVID-19?” Bivariate and multivariable logistic regression models were run among participants who reported ever testing positive for COVID-19 after nirmatrelvir-ritonavir was authorized (N = 552).
RESULTS
Of the 552 eligible participants, 55 (9.96%) reported taking nirmatrelvir-ritonavir (of those who took nirmatrelvir-ritonavir, 6.5% reported a rebound). There was no significant difference in the prevalence of nirmatrelvir-ritonavir use between those eligible for a nirmatrelvir-ritonavir prescription (10.07%) and those not eligible (9.66%). In bivariate and multivariable analyses, nirmatrelvir-ritonavir use was not associated with eligibility (bivariate OR = 1.05; 95% CI = 0.55–1.99; multivariable OR = 0.79; 95% CI = 0.36–1.74).
In bivariate and multivariable analyses among participants who were eligible for nirmatrelvir-ritonavir, participants age 65 and older had significantly higher odds of nirmatrelvir-ritonavir use compared to the youngest age group (Table 1). There were no significant associations between demographic/socioeconomic characteristics and nirmatrelvir-ritonavir use among participants who were not eligible for nirmatrelvir-ritonavir (Table 2).
Table 1.
Nirmatrelvir-Ritonavir Use among Those Eligible for Prescription
| No nirmatrelvir-ritonavir use n = 366 (89.93%) |
Nirmatrelvir-ritonavir use n = 41 (10.07%) |
Bivariate OR (95% CI) |
Multivariate OR (95% CI) |
|
|---|---|---|---|---|
| Race | ||||
|
Non-Hispanic White Non-Hispanic Black Non-Hispanic Asian Hispanic Non-Hispanic Other |
144 (87.80%) 30 (93.75%) 44 (97.78%) 143 89.94%) 5 (71.43%) |
20 (12.20%) 2 (6.25%) 1 (2.22%) 16 (10.06%) 2 (28.57%) |
Ref 0.48 (0.11–2.16) 0.16 (0.02–1.25) 0.81 (0.40–1.62) 2.88 (0.52–15.85) |
Ref 0.77 (0.15–3.95) 0.34 (0.04–2.87) 1.23 (0.49–3.14) 7.40 (1.05–51.94) |
| Gender identity | ||||
|
Female Male |
218 (90.08%) 141 (89.81%) |
24 (9.92%) 16 (10.19%) |
Ref 1.03 (0.53–2.01) |
Ref 0.96 (0.45–2.05) |
| Income | ||||
|
Under 50,000 50,000–99,999 100,000 or more |
95 (87.96%) 111 (90.24%) 159 (90.86%) |
13 (12.04%) 12 (9.76%) 16 (9.14%) |
Ref 0.79 (0.34–1.81) 0.74 (0.34–1.60) |
Ref 0.59 (0.21–1.62) 0.40 (0.14–1.13) |
| Insurance | ||||
|
No insurance Private insurance Public insurance |
21 (95.45%) 236 (90.42%) 99 (86.84%) |
1 (4.55%) 25 (9.58%) 15 (13.16%) |
Ref 2.22 (0.29–17.25) 3.18 (0.40–25.43) |
Ref 2.20 (0.24–20.28) 1.56 (0.16–14.95) |
| Education | ||||
|
Some college or less College graduate or more |
110 (91.67%) 254 (89.12%) |
10 (8.33%) 31 (10.88%) |
Ref 1.34 (0.64–2.83) |
Ref 1.74 (0.7–4.33) |
| Age | ||||
|
18–29 30–49 50–64 65 or older |
36 (90.00%) 171 (91.94%) 124 (93.94%) 35 (71.43%) |
4 (10.00%) 15 (8.06%) 8 (6.06%) 14 (28.57%) |
Ref 0.79 (0.25–2.52) 0.58 (0.17–2.04) 3.60 (1.08–12.01) |
Ref 0.75 (0.21–2.6) 0.52 (0.12–2.17) 4.73 (1.10–20.39) |
| CIA | ||||
|
Low Medium High |
63 (90.00%) 152 (87.36%) 138 (93.24%) |
7 (10.00%) 22 (12.64%) 10 (6.76%) |
Ref 1.30 (0.53–3.20) 0.65 (0.24–1.79) |
Ref 1.30 (0.49–3.49) 0.70 (0.23–2.15) |
Values in bold indicate statistically significant results
Table 2.
Nirmatrelvir-Ritonavir Use among Those NOT Eligible for Prescription
| No nirmatrelvir-ritonavir use n = 131 (90.34%) |
Nirmatrelvir-ritonavir use n = 14 (9.66%) |
Bivariate OR (95% CI) |
Multivariate OR (95% CI) |
|
|---|---|---|---|---|
| Race | ||||
| Non-Hispanic White | 42 (93.33%) | 3 (6.67%) | Ref | Ref |
| Non-Hispanic Black | 7 (100%) | 0 (0.00%) | - | - |
| Non-Hispanic Asian | 33 (86.84%) | 5 (13.16%) | 2.12 (0.47–9.53) | 4.44 (0.78–25.09) |
| Hispanic | 47 (88.68%) | 6 (11.32%) | 1.79 (0.42–7.60) | 1.81 (0.34–9.70) |
| Non-Hispanic Other | 1 (100%) | 0 (0.00%) | - | - |
| Gender identity | ||||
| Female | 70 (88.61%) | 9 (11.39%) | Ref | Ref |
| Male | 61 (92.42%) | 5 (7.58%) | 0.64 (0.20–2.01) | 0.66 (0.17–2.54) |
| Income | ||||
| Under 50,000 | 16 (80.00%) | 4 (20.00%) | Ref | Ref |
| 50,000–99,999 | 44 (93.62%) | 3 (6.38%) | 0.27 (0.05–1.35) | 0.23 (0.03–1.69) |
| 100,000 or more | 71 (91.03%) | 7 (8.97%) | 0.39 (0.10–1.51) | 0.38 (0.05–3.11) |
| Insurance | ||||
| No insurance | 6 (85.71%) | 1 (14.29%) | Ref | Ref |
| Private insurance | 94 (92.16%) | 8 (7.84%) | 0.51 (0.05–4.78) | 0.84 (0.05–15.3) |
| Public insurance | 25 (86.21%) | 4 (13.79%) | 0.96 (0.09–10.22) | 1.95 (0.12–30.96) |
| Education | ||||
| Some college or less | 20 (95.24%) | 1 (4.76%) | Ref | Ref |
| College graduate or more | 111 (89.52%) | 13 (10.48%) | 2.34 (0.29–18.92) | 3.49 (0.30–40.74) |
| Age | ||||
| 18–29 | 22 (95.65%) | 1 (4.35%) | Ref | Ref |
| 30–49 | 109 (89.34%) | 13 (10.66%) | 2.62 (0.33–21.11) | 4.57 (0.45–46.23) |
| 50–64 | - | - | - | - |
| 65 or older | - | - | - | - |
| CIA | ||||
| Low | 27 (90.00%) | 3 (10.00%) | Ref | Ref |
| Medium | 55 (88.71%) | 7 (11.29%) | 1.15 (0.27–4.78) | 0.82 (0.17–4.02) |
| High | 47 (92.16%) | 4 (7.84%) | 0.77 (0.16–3.68) | 0.33 (0.05–2.22) |
*Any participant over the age of 50 is eligible for nirmatrelvir-ritonavir and therefore not included in this analysis. There were no Black and Other participants who used nirmatrelvir-ritonavir that were not eligible and therefore were omitted from this analysis
DISCUSSION
Nirmatrelvir-ritonavir use was similar among participants who were eligible vs. ineligible for nirmatrelvir-ritonavir, suggesting high rates of “off-label” use. One explanation may be the broad criteria for nirmatrelvir-ritonavir eligibility outlined in the EUA and the absence of out-of-pocket costs, which may have given providers a high degree of autonomy for determining if a patient was “high risk” for severe COVID-19 and allowed patients greater access to the drug irrespective of insurance coverage.
Given the high morbidity and mortality of COVID-19 and disproportionate burden of COVID-19 among underserved populations, it is important to evaluate disparities in use of nirmatrelvir-ritonavir. Among participants that were eligible and not eligible for nirmatrelvir-ritonavir, there were few demographic/socioeconomic differences in those who reported using nirmatrelvir-ritonavir. This suggests there was not a socioeconomic gradient for accessing nirmatrelvir-ritonavir, as is shown with other life-saving medications such as insulin.5 One potential reason for this is because nirmatrelvir-ritonavir was subsidized by the US government, making it free for everyone. Thus, government subsidies for improving access to nirmatrelvir-ritonavir might have both reduced disparities in access and promoted some off-label use. Our findings appear to be discordant with a previous study that found disparities by race/ethnicity in outpatient prescribing of nirmatrelvir-ritonavir.6 However, this study examined data in the early stages following nirmatrelvir-ritonavir authorization (January–July 2022). Following the full FDA authorization of nirmatrelvir-ritonavir in May 2022, there may have been expanded access to the general public, thus decreasing previous disparities in access.
Findings from this study offer preliminary evidence supporting COVID-19 public health emergency orders. The EUA and subsidization of nirmatrelvir-ritonavir have helped address health disparities caused by the COVID-19 pandemic. However, disparities could emerge now that the COVID-19 public health emergency has ended and nirmatrelvir-ritonavir is no longer free. Ongoing efforts are needed to monitor and increase appropriate nirmatrelvir-ritonavir access and use across minoritized and low-income populations and those who lack health insurance to detect disparities.
Study limitations include a limited sample size, especially among those who reported using nirmatrelvir-ritonavir, which resulted in relatively large confidence intervals. It should be noted that our sample reported low overall rates of nirmatrelvir-ritonavir use (9.96%) compared to a larger US study in 2022 (28.4%).3 Other limitations are that eligibility for nirmatrelvir-ritonavir prescriptions was assessed based on self-reported health history and not all risk factors for severe COVID-19 were included in the survey. Additionally, participant medication use was not assessed in the survey; thus, participants using medications that make them ineligible for nirmatrelvir-ritonavir prescription may not have been excluded from the eligible population. The self-reporting of nirmatrelvir-ritonavir use could lead to recall bias which may underestimate use.
Funding
We acknowledge funding from the Conrad N. Hilton Foundation, Office of the President University of Southern California, Los Angeles County Department of Public Health, the US Centers for Disease Control and Prevention, the Keck School of Medicine at USC, the Keck Family Foundation, and several individual donors.
Declarations:
Conflict of Interest:
The authors declare that they do not have a conflict of interest.
Footnotes
Publisher's Note
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References
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