presentation journal tf1 midi

• Introduction
• Conclusions
• Article Information

“Did not complete study” indicates did not complete all phases of the trial including the last visit (follow-up).

a Six participants withdrew consent and 1 was not rescreened per protocol.

b Three participants (0.8%) in OASIS 1 were randomized but did not receive treatment intervention and were, therefore, excluded from the safety analysis sets. One participant in the placebo group of each trial incorrectly received elinzanetant initially, and both were assigned to the elinzanetant groups for analyses based on the safety analysis set.

c No information provided by investigator.

d Withdrew because of work.

Score range 0-3 (0 indicates no moderate or severe vasomotor symptoms [at baseline] and no mild, moderate, or severe vasomotor symptoms [post baseline]; 1, mild; 2, moderate; and 3, severe symptoms; higher scores indicate greater vasomotor symptom severity). Data shown are means (95% CIs). Zoomed-in presentation along the y-axis for illustration purposes. Only outliers within the displayed scale are visible. Connecting lines over time join arithmetic means by treatment group. A boxplot presentation of the full distribution is provided in eFigure 1 in Supplement 3 and the descriptive summary and data range are available in eTable 5 in Supplement 3 . Placebo-elinzanetant, 120 mg, refers to the participants receiving placebo who were switched to receive elinzanetant after week 12. Efficacy analyses were performed on the full analysis set. The circles indicate outside values.

A reduction in Patient-Reported Outcomes Measurement Information System Sleep Disturbance Short Form 8b (PROMIS SD SF 8b) total T score and Menopause-Specific Quality of Life (MENQOL) questionnaire total score corresponded to an improvement in symptoms. Efficacy analyses were performed on the full analysis set. Connecting lines over time join arithmetic means by treatment group. The circles indicate outside values.

a For PROMIS SD SF 8b, the score ranges from 28.9 to 76.5 (<55, normal; 55-60, mild; 60-70, moderate; and >70, severe sleep disturbance). Higher scores indicate greater severity of sleep disturbance.

b For MENQOL, the score ranges from 1 to 8; higher scores indicate greater bother; and 0.9-point within-patient change represent a clinically meaningful difference.

Trial Protocol

Statistical Analysis Plan

eTable 1. OASIS 1 Sites Summary

eTable 2. OASIS 2 Sites Summary

eTable 3. Primary and Key Secondary Endpoints

eTable 4. Main Estimand: Intercurrent Events and Strategies to Address Them

eTable 5. Mean Change From Baseline in Average Daily Moderate-to-Severe Vasomotor Symptom Frequency Over Time by Treatment Arm and Study

eFigure 1. Change From Baseline in Average Daily Vasomotor Symptom Frequency in OASIS 1 (Top) and OASIS 2 (Bottom)

eFigure 2. Mean Change From Baseline in Average Daily Moderate-to-Severe Vasomotor Symptom Frequency Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 6. Percentage Change From Baseline in Average Daily Moderate-to-Severe Vasomotor Symptom Frequency Over Time by Treatment Arm and Study

eTable 7. Mean Change From Baseline in Average Daily Vasomotor Symptom Severity Over Time by Treatment Arm and Study

eFigure 3. Mean Change From Baseline in Average Daily Vasomotor Symptom Severity Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 8. Mean Change From Baseline in PROMIS SD SF 8b Total T-Scores Over Time by Treatment Arm and Study

eTable 9. Mean Change From Baseline in PROMIS SD SF 8b Total Raw Scores Over Time by Treatment Arm and Study

eFigure 4. Mean Change From Baseline in PROMIS SD SF 8b Total T-Scores Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 10. Mean Change From Baseline in MENQOL Total Score Over Time by Treatment Arm and Study

eFigure 5. Mean Change From Baseline in MENQOL Total Score Over Time in OASIS 1 (Top) and OASIS 2 (Bottom)

eTable 11. Treatment-Emergent Adverse Events in OASIS 1 by Treatment Arm and During Elinzanetant Exposure

eTable 12. Treatment-Emergent Adverse Events in OASIS 2 by Treatment Arm and During Elinzanetant Exposure

eTable 13. Summary of Treatment-Emergent Adverse Events During Elinzanetant Period (Weeks 13–26) and Treatment-Emergent Adverse Events During Elinzanetant Exposure (Weeks 1–26)

Data Sharing Statement

• A New Era in Menopause Management? JAMA Editorial August 22, 2024 Stephanie S. Faubion, MD, MBA; Chrisandra L. Shufelt, MD, MS

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Pinkerton JV , Simon JA , Joffe H, et al. Elinzanetant for the Treatment of Vasomotor Symptoms Associated With Menopause : OASIS 1 and 2 Randomized Clinical Trials . JAMA. Published online August 22, 2024. doi:10.1001/jama.2024.14618

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Elinzanetant for the Treatment of Vasomotor Symptoms Associated With Menopause : OASIS 1 and 2 Randomized Clinical Trials

• 1 Department of Obstetrics and Gynecology, Division Midlife Health, University of Virginia Health, Charlottesville
• 2 IntimMedicine Specialists, George Washington University, Washington, DC
• 3 Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
• 4 Department of Psychiatry, Psychology, and OB/GYN, University of Illinois at Chicago
• 5 Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, Pavia, Italy
• 6 Research Center for Reproductive Medicine, Gynecological Endocrinology, and Menopause, IRCCS San Matteo Foundation, Pavia, Italy
• 7 Queen Charlotte’s and Chelsea Hospital, Imperial College London, London, United Kingdom
• 8 Department of Psychiatry, Queen’s University School of Medicine, Kingston, Ontario, Canada
• 9 Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
• 10 Bayer CC AG, Basel, Switzerland
• 11 Bayer AG, Berlin, Germany
• 12 Statistics and Data Sciences, Bayer PLC, Reading, United Kingdom
• 13 Bayer AG, Wuppertal, Germany
• 14 Charité–Universitätsmedizin Berlin, Germany
• Editorial A New Era in Menopause Management? Stephanie S. Faubion, MD, MBA; Chrisandra L. Shufelt, MD, MS JAMA

Question   What are the efficacy and safety of elinzanetant, 120 mg, in postmenopausal individuals with moderate to severe vasomotor symptoms (VMS)?

Findings   In 2 pivotal phase 3 clinical trials, elinzanetant demonstrated statistically significant reductions in VMS frequency and severity vs placebo. Elinzanetant also significantly improved sleep disturbances and menopause-related quality of life vs placebo; the safety profile was favorable.

Meaning   Elinzanetant is an efficacious and well-tolerated selective neurokinin-1,3 receptor antagonist for the treatment of moderate to severe VMS associated with menopause. Elinzanetant also improves sleep disturbances and menopause-related quality of life.

Importance   Safe and effective nonhormonal treatments for menopausal vasomotor symptoms (VMS) are needed.

Objective   To evaluate the efficacy and safety of elinzanetant, a selective neurokinin-1,3 receptor antagonist, for the treatment of moderate to severe menopausal vasomotor symptoms.

Design, Setting, and Participants   Two randomized double-blind phase 3 trials (OASIS 1 and 2) included postmenopausal participants aged 40 to 65 years experiencing moderate to severe vasomotor symptoms (OASIS 1: 77 sites in the US, Europe, and Israel from August 27, 2021, to November 27, 2023, and OASIS 2: 77 sites in the US, Canada, and Europe from October 29, 2021, to October 10, 2023).

Intervention   Once daily oral elinzanetant, 120 mg, for 26 weeks or matching placebo for 12 weeks followed by elinzanetant, 120 mg, for 14 weeks.

Main Outcomes and Measures   Primary end points included mean change in frequency and severity of moderate to severe vasomotor symptoms from baseline to weeks 4 and 12, measured by the electronic hot flash daily diary. Secondary end points included Patient-Reported Outcomes Measurement Information System Sleep Disturbance Short Form 8b total T score and Menopause-Specific Quality of Life questionnaire total score from baseline to week 12.

Results   Eligible participants (mean [SD] age, OASIS 1: 54.6 [4.9] years; OASIS 2: 54.6 [4.8] years) were randomized to elinzanetant (OASIS 1: n = 199; OASIS 2: n = 200) or placebo (OASIS 1: n = 197; OASIS 2: n = 200). A total of 309 (78.0%) and 324 (81.0%) completed OASIS 1 and 2, respectively. For the elinzanetant and placebo groups, the baseline mean (SD) VMS per 24 hours were 13.4 (6.6) vs 14.3 (13.9) (OASIS 1) and 14.7 (11.1) v 16.2 (11.2) (OASIS 2). Baseline VMS severity was 2.6 (0.2) vs 2.5 (0.2) (OASIS 1) and 2.5 (0.2) vs 2.5 (0.2) (OASIS 2). Elinzanetant significantly reduced VMS frequency at week 4 (OASIS 1: −3.3 [95% CI, −4.5 to −2.1], P  < .001; OASIS 2: −3.0 [95% CI, −4.4 to −1.7], P  < .001) and at week 12 (OASIS 1: −3.2 [95% CI, −4.8 to −1.6], P  < .001; OASIS 2: −3.2 [95% CI, −4.6 to −1.9], P  < .001). Elinzanetant also improved VMS severity at week 4 (OASIS 1: −0.3 [95% CI, −0.4 to −0.2], P  < .001; OASIS 2: −0.2 [95 CI, −0.3 to −0.1], P  < .001) and week 12 (OASIS 1: −0.4 [95% CI, −0.5 to −0.3], P  < .001; OASIS 2: −0.3 [95% CI, −0.4 to −0.1], P  < .001). Elinzanetant improved sleep disturbances and menopause-related quality of life at week 12, and the safety profile was favorable.

Conclusions and Relevance   Elinzanetant was well tolerated and efficacious for moderate to severe menopausal VMS.

Trial Registration   ClinicalTrials.gov Identifier: OASIS 1: NCT05042362 , OASIS 2: NCT05099159

Women experience a variety of symptoms during their menopausal transition, including vasomotor symptoms (VMS, also known as hot flashes) and sleep disturbances, reported by up to 80% and 60%, respectively. 1 - 3 Menopausal symptoms can negatively impact quality of life, reducing the capacity for daily activities and work productivity, 4 - 6 and may be associated with long-term negative health outcomes such as cardiovascular events, depressive symptoms, cognitive decline, and other adverse brain outcomes. 7 - 13

There are currently different treatment options that address the needs of individuals with menopausal symptoms. Hormone therapy and the selective serotonin reuptake inhibitor paroxetine salt, 7.5 mg, are approved for the treatment of VMS in some countries, while other selective serotonin reuptake inhibitors and serotonin-norepinephrine reuptake inhibitors are used off label. 14 - 17 However, many women have contraindications, have tolerability issues leading to discontinuation, or prefer not to take these treatments. 4 , 17 , 18 Recently, the nonhormonal neurokinin (NK)-3 receptor antagonist fezolinetant was approved by the US Food and Drug Administration (FDA), European Commission, and Switzerland SwissMedic for the treatment of moderate to severe VMS due to menopause. 19 - 21

Hypothalamic kisspeptin/neurokinin/dynorphin (KNDy) neurons play a role in thermoregulation. 22 KNDy neurons express a number of receptor/ligand systems, including NK-1 and NK-3 receptors and their respective ligands, substance P (SP), and neurokinin B (NKB). Declining estrogen levels during and after the menopause transition lead to hypertrophy and hyperactivity of KNDy neurons, accompanied by elevated gene expression of neurotransmitters including NKB and SP. 22 - 25 This hyperactivation has been related to disruption of thermoregulation, which may trigger VMS. 22 Recent data showed the involvement of NK-3 receptors in this disruption; fezolinetant, an NK-3–specific receptor antagonist, demonstrated reductions in VMS in postmenopausal individuals. 26 , 27 SP and NK-1 receptors may have a role in peripheral vasodilatation and primary insomnia. 28 , 29

Elinzanetant is a nonhormonal compound in development for the treatment of VMS associated with menopause that specifically targets both NK-1 and NK-3 receptors. 30 , 31 Based on clinical data, it was hypothesized that the dual inhibition of NK-1 and NK-3 receptors would reduce VMS and may have an effect on sleep disturbances associated with menopause. 30 Indeed, elinzanetant demonstrated significant reductions in VMS frequency and severity compared with placebo, as well as improvements in different aspects of sleep and menopause-related quality of life in the phase 2b SWITCH-1 trial. 30 The aim of the OASIS 1 ( NCT05042362 ) and 2 ( NCT05099159 ) phase 3 randomized, placebo-controlled studies was to assess the efficacy and safety of elinzanetant, 120 mg, in individuals experiencing moderate to severe VMS associated with menopause.

OASIS 1 and 2 were pivotal, multicenter, multinational, double-blind, randomized, placebo-controlled, phase 3, 26-week intervention trials. The trials had similar designs and identical primary and key secondary end points in accordance with regulatory guidance to ensure the results were reliable and reproducible. 32 The trial protocol and statistical analysis plan are in Supplement 1 and Supplement 2 , respectively. The trials were conducted in parallel but involved different study sites mainly located in the US and Europe (eTables 1 and 2 in Supplement 3 ). The trials were conducted in accordance with the Declaration of Helsinki and the Council for International Organizations of Medical Sciences International Ethical Guidelines and reported using the CONSORT reporting guideline. Institutional review board and ethics committee approval were obtained for all study sites. Feedback from menopausal participants was collected to ensure that the patient voice was incorporated in aspects of the study design and assessments. 33 Detailed methodology of the OASIS 1 and 2 trials has been described in a previous publication. 33 Herein, we present the results from both OASIS 1 and 2 trials side by side to demonstrate the reproducibility of findings.

OASIS 1 and 2 included naturally or surgically (bilateral oophorectomy with or without hysterectomy) postmenopausal participants, aged 40 to 65 years and experiencing 50 or more moderate to severe VMS over 7 days during screening ( Table 1 and Figure 1 ). Exclusion criteria included abnormal liver parameters (including alanine aminotransferase or aspartate aminotransferase >2 × upper limit of normal), disordered proliferative endometrium, endometrial hyperplasia or polyps, and current or history of malignancy within the last 5 years (except basal and squamous cell skin tumors). 33 Written informed consent was obtained from all participants prior to study start. Race and ethnicity were determined during the visit by participant and site personnel together and documented in the case report form as part of the demographic characteristics using fixed categories (ethnicity: Hispanic or Latino, not Hispanic or Latino, not reported and race: American Indian or Alaska Native; Asian; Black or African American; Native Hawaiian or Other Pacific Islander; White; not reported).

In both trials, eligible participants were randomized in a 1:1 ratio to receive elinzanetant, 120 mg, or identical-appearing placebo once daily orally for 12 weeks. After 12 weeks, all participants received elinzanetant, 120 mg, for a further 14 weeks, followed by a 4-week posttreatment follow-up. Randomization was performed centrally using an interactive voice/web response system and stratified by North America and the rest of the countries. Investigators, participants, and study personnel remained blinded throughout the trials. The interactive voice/web response system was programmed with blind-breaking instructions in the event of an emergency.

The phase 3 end point strategy was developed in alignment with regulatory recommendations and clinical practice. 32 - 34 Efficacy end points in both trials were assessed using patient-reported outcome instruments collected on an electronic handheld device. Considerable research has been conducted to confirm the fitness for use of the patient-reported outcome instruments in assessing key efficacy end points in phase 3 VMS trials. 35 - 37 Primary and key secondary end points were identical across the OASIS 1 and 2 trials (eTable 3 in Supplement 3 ). The prespecified primary end points included mean change in frequency and severity of moderate to severe VMS from baseline to weeks 4 and 12, as measured by the electronic hot flash daily diary. The prespecified key secondary end points included mean change in moderate to severe VMS frequency from baseline to week 1 also measured by the hot flash daily diary, as well as mean changes in the Patient-Reported Outcomes Measurement Information System Sleep Disturbance Short Form (PROMIS SD SF) 8b total T score and the Menopause-Specific Quality of Life (MENQOL) questionnaire total score from baseline to week 12. The proportion of participants with at least a 50% reduction in VMS frequency at weeks 4 and 12 was an exploratory end point. For additional secondary and exploratory end points, see the statistical analysis plan in Supplement 2 (results not reported here).

The electronic hot flash daily diary was used to record the frequency and severity of VMS twice daily (in the morning when getting up and in the evening when going to bed) on a dedicated handheld device, similar to diaries used in other clinical trials in participants with VMS. 38 Participants were trained on the use of the handheld device for timely and accurate data entry during screening. Participants recorded whether or not they had hot flashes, followed by rating the total number of mild, moderate, and severe hot flashes they experienced. Mild hot flashes were defined as a sensation of heat without sweating, moderate as a sensation of heat with sweating but able to continue activity, and severe as a sensation of heat with sweating that causes cessation of activity. 32 Possible ranges were 0 to 180 for the VMS frequency score and 0 to 3 for the VMS severity score.

The PROMIS SD SF 8b questionnaire is a short form derived from the 27-item PROMIS SD item bank. 39 - 41 It assesses the degree of sleep disturbance over the past 7 days, with the 8 items particularly investigating restless sleep, satisfaction with sleep, refreshing sleep, difficulties falling asleep, staying asleep, getting to sleep, amount of sleep, and sleep quality. Items were scored on a 5-point Likert scale, and the 8 single item scores were summed to yield total raw scores (range, 8-40), which were converted to total T scores for analysis of the key secondary end point (range, 28.9-76.5). Higher scores indicated more disturbed sleep. A T score of 50 (SD, 10) represented the mean sleep disturbance score in a reference population. T scores of 55 or greater, 60 or greater, and 70 or greater represented mild, moderate, and severe levels of sleep disturbances, respectively, in the reference population. 42 , 43 Participants completed the questionnaire at baseline and weeks 1 through 4, 8, 12, 16, 26, and 30.

The 29-item MENQOL questionnaire assesses the presence and degree of bother associated with menopausal symptoms over the past week. 44 Participants indicated whether or not they experienced a particular symptom and rated its level of bother on a 7-point scale (range, 0-6, with higher scores indicating a higher level of bother). The 29 items assess 4 domains of symptoms and functioning: VMS (items 1-3), psychosocial (items 4-10), physical (items 11-26), and sexual (items 27-29) domains. Responses to single items were used to calculate 29 individual item scores. The 4 domain scores were calculated as a mean of converted single-item scores (range, 1-8, with higher scores indicating a higher level of bother), and the mean of the 4 domain scores yielded the MENQOL total score. Participants completed the questionnaire at baseline and weeks 4, 8, 12, 16, 26, and 30. The development and validation of PROMIS SD SF 8b and MENQOL and their use in VMS clinical trials have been described previously. 30 , 33 , 35 - 37 , 44 - 48

Safety was assessed throughout the trials by documenting adverse events (AEs), which were coded using the Medical Dictionary for Regulatory Authorities. Safety assessments also included laboratory assessments, transvaginal ultrasonography, and endometrial biopsies, among others. Consistent with FDA guidance, participants meeting the prespecified criteria for close liver observation, including those with increases in alanine aminotransferase and aspartate aminotransferase levels greater than 3 times the upper limit of normal, were followed up. These cases were assessed in a blinded fashion by an independent external liver safety monitoring board to identify potential drug-induced liver injury. 49 An independent data and safety monitoring board monitored the general safety of the participants in the trials.

A planned sample size of 370 participants per trial was derived based on simulations using the t test and Wilcoxon rank-sum test, considering an assumed drop-out rate of 10% in the first 3 months. Treatment effects and characteristics of the distributions were assumed from phase 2 SWITCH-1 results and a fixed level of correlation of 0.3 was anticipated between the end points. The trials were planned to achieve a power of at least 90% for all primary and key secondary end points considering the multiple testing strategy.

Statistical analysis was performed using SAS (release 9.4; SAS Institute Inc) and ValidR (R version 3.5.2 50 ; Mango Solutions Ltd). Efficacy analyses were performed on the full analysis set where all randomized participants were included. Participants in the full analysis set were analyzed according to the randomized intervention (intention to treat). Safety analyses were performed on the safety analysis set that included all participants who received at least 1 dose of study intervention. Participants in the safety analysis set were analyzed according to the intervention they received.

The primary and key secondary end points were analyzed for each trial using a mixed model with repeated measures on the change from baseline at different weeks. Fixed effects in the model included baseline values of the respective end point, treatment, region, and week, as well as the interaction terms baseline by week and treatment by week. Prior to modeling, missing and collected data that occurred in the presence of intercurrent events were handled in alignment with the predefined estimand, as provided in eTable 4 in Supplement 3 . 51 A multiplicity adjustment strategy for each trial was defined using the graphical approach, 52 controlling the overall type I error rate at a 1-sided α level of .025 for confirmatory statistical superiority testing. All presented P values are from 1-sided statistical testing. Full details of the statistical analysis, including assessed model diagnostics and sensitivity analysis, are provided in the statistical analysis plan in Supplement 2 .

OASIS 1 was conducted between August 27, 2021, and November 27, 2023, across 77 sites in the US, Europe, and Israel. OASIS 2 was conducted between October 29, 2021, and October 10, 2023, across 77 sites in Canada, the US, and Europe (eTables 1 and 2 in Supplement 3 ). The participant disposition is summarized in Figure 1 . Baseline demographics were generally balanced between treatment groups in both trials, with slight imbalances in smoking history in opposite directions in the 2 studies ( Table 1 ).

At baseline, OASIS 1 participants in the elinzanetant and placebo groups experienced a mean (SD) of 13.4 (6.6) and 14.3 (13.9) VMS per 24 hours, respectively; similar numbers of VMS, 14.7 (11.1) and 16.2 (11.2), respectively, were observed in OASIS 2. In OASIS 1, mean (SD) descriptive changes from baseline to week 4 were −7.5 (5.8) and −4.4 (6.7) in the elinzanetant and placebo groups, respectively, corresponding to a mean (SD) percentage change from baseline of −55.9% (34.1%) and −31.4% (33.8%). At week 12, these changes were −8.7 (6.7) and −5.5 (10.2), respectively, with mean (SD) percentage changes of −65.2% (35.3%) and −42.2% (43.3%). In OASIS 2, similar trends were seen, with mean (SD) changes from baseline to week 4 of −8.6 (9.2) and −6.1 (8.9), corresponding to mean (SD) percentage changes of −57.9% (34.7%) and −35.7% (37.4%). Mean (SD) changes to week 12 were −10.0 (10.3) and −7.2 (8.5) and mean (SD) percentage changes were −67.0% (34.9%) and −45.9% (38.1%), respectively ( Figure 2 ; eFigures 1 and 2, eTables 5 and 6 in Supplement 3 ).

At baseline, OASIS 1 participants in the elinzanetant and placebo groups experienced a mean (SD) of 2.6 (0.2) and 2.5 (0.2) in VMS severity, respectively; in OASIS 2, baseline severity was 2.5 (0.2) and 2.5 (0.2), respectively. Reductions were also seen for VMS severity from baseline to weeks 4 and 12 in both studies based on descriptive statistics, with greater mean numerical reductions in the elinzanetant groups than the placebo groups ( Figure 2 ; eFigure 3 and eTable 7 in Supplement 3 ).

In both trials, reductions in VMS frequency and severity from baseline to weeks 4 and 12 were statistically significantly greater for elinzanetant vs placebo. Least square (LS) mean changes in daily VMS frequency vs placebo from baseline to week 4 were −3.3 (95% CI, −4.5 to −2.1; P  < .001) for OASIS 1 and −3.0 (95% CI, −4.4 to −1.7; P  < .001) for OASIS 2. At week 12, LS mean changes from baseline vs placebo were −3.2 (95% CI, −4.8 to −1.6; P  < .001) for OASIS 1 and −3.2 (95% CI, −4.6 to −1.9; P  < .001) for OASIS 2. Reductions in daily VMS frequency vs placebo from baseline to week 1 were statistically significant in both trials (OASIS 1: −2.5 [95% CI, −3.4 to −1.6], P  < .001; OASIS 2: −1.7 [95% CI, −2.7 to −0.6], P  = .001). LS mean changes in daily VMS severity vs placebo from baseline to week 4 were −0.3 (95% CI, −0.4 to −0.2; P  < .001) in OASIS 1 and −0.2 (95% CI, −0.3 to −0.1; P  < .001) in OASIS 2. At week 12, changes from baseline vs placebo were −0.4 (95% CI, −0.5 to −0.3; P  < .001) in OASIS 1 and −0.3 (95% CI, −0.4 to −0.1; P  < .001) in OASIS 2.

At week 4, 62.8% and 62.2% of participants in the elinzanetant group achieved at least a 50% reduction in VMS frequency in OASIS 1 and 2, respectively, compared with 29.2% and 32.3% in the placebo group. At week 12, 71.4% and 74.7% in the elinzanetant group achieved at least a 50% reduction in OASIS 1 and 2, respectively, compared with 42.0% and 48.3% in the placebo group.

Participants reported improvements from baseline in sleep disturbances (assessed by the PROMIS SD SF 8b total T scores) across both treatment groups ( Figure 3 ; eFigure 4 and eTable 8 in Supplement 3 ); differences were statistically significant at week 12 vs placebo in both trials (difference in LS means for OASIS 1: −5.6 [95% CI, −7.2 to −4.0], P  < .001; OASIS 2: −4.3 [95% CI, −5.8 to −2.9], P  < .001). Improvements were also observed with PROMIS total raw scores, based on descriptive analyses (eTable 9 in Supplement 3 ).

Participants reported improvements in menopause-related quality of life (assessed by the MENQOL total score) from baseline ( Figure 3 ; eFigure 5 and eTable 10 in Supplement 3 ). Improvements seen in the elinzanetant group from baseline to week 12 were statistically significant vs placebo (difference in LS means for OASIS 1: −0.4 [95% CI, −0.6 to −0.2], P  < .001; OASIS 2: −0.3 [95% CI, −0.5 to −0.1], P  = .0059).

Based on descriptive analyses, the largest changes from baseline were observed in the VMS domain. At week 12, the mean (SD) changes from baseline in the VMS domain score in the elinzanetant group were −2.86 (2.11) in OASIS 1 and −2.70 (1.99) in OASIS 2. In the placebo group, the mean changes from baseline were −1.50 (1.80) and −1.64 (1.93), respectively, at week 12.

Reductions in the frequency and severity of VMS, PROMIS SD SF 8b total T score, and MENQOL total score in the elinzanetant group were maintained throughout the 26-week treatment period. Further improvements in these measures were also observed in the group that switched from placebo to elinzanetant after week 12 (eTables 5-10 and eFigures 2-5 in Supplement 3 ) based on descriptive analyses.

By week 26, more than 80% of participants had achieved at least a 50% reduction in VMS frequency in the elinzanetant group (81.6% and 81.5% in OASIS 1 and 2, respectively) and in those who switched to elinzanetant after week 12 (84.5% and 86.7% in OASIS 1 and 2, respectively).

In OASIS 1, treatment-emergent adverse events (TEAEs) were reported in 51.3% of participants in the elinzanetant group and 48.5% in the placebo group over the 12-week placebo-controlled treatment period ( Table 2 ; eTables 11 and 12 in Supplement 3 ). In OASIS 2, 44.3% and 38.2% of participants reported TEAEs in the elinzanetant and placebo groups, respectively. In both trials, most TEAEs were of mild or moderate intensity, and there were few serious AEs (eTables 11 and 12 in Supplement 3 ).

Across both trials, headache and fatigue (Medical Dictionary for Regulatory Activities preferred term) occurred more frequently in the elinzanetant groups during the 12-week, placebo-controlled treatment period (7.0%-9.0% vs 2.5%-2.6% for headache and 5.5%-7.0% vs 1.5% for fatigue) ( Table 2 ). Both fatigue and headache were reported less frequently by participants in the placebo group after switching to elinzanetant compared with those initially randomized to elinzanetant (2.2%-3.6% for headache and 0.6%-1.7% for fatigue during weeks 13-26) (eTable 13 in Supplement 3 ). Most events were of mild intensity, and none were severe. Based on a post hoc analysis, for fatigue, the highest reported daily relative frequency (up to 5%) was observed during the first weeks, which was then reduced to less than 3% after week 13. For headache, the highest daily relative frequency (up to 5.5%) was reported around week 7 to 8 and reduced to less than 2% toward the end of the treatment period.

There were no cases of liver enzyme elevations meeting criteria for liver injury as assessed by the liver safety monitoring board. There were no cases of endometrial hyperplasia or malignant neoplasm in either trial as assessed by 3 independent pathologists. There were no clinically relevant changes in vital signs or laboratory parameters throughout the study, and no new safety signals were observed throughout either trial (eTable 13 in Supplement 3 ).

OASIS 1 and 2 are the first phase 3 trials evaluating the efficacy and safety of elinzanetant—a nonhormonal, selective NK-1 and NK-3 receptor antagonist—in postmenopausal individuals with moderate to severe VMS. Results from both studies are consistent with regard to the primary and key secondary end points and in line with previous results of the phase 2 study SWITCH-1, 30 thereby demonstrating the reproducibility of and increasing confidence in these findings. In both OASIS 1 and 2 trials, elinzanetant achieved statistically significant reductions from baseline in VMS frequency and severity vs placebo as well as improvements in sleep disturbances and menopause-related quality of life.

A reduction of at least 2 moderate to severe VMS per day above placebo (14 per week) has been identified by the FDA as a clinically meaningful reduction on a group level. 53 , 54 Considering this, elinzanetant reached a clinically meaningful reduction in daily VMS frequency compared with placebo as early as week 1 in OASIS 1, and at weeks 4 and 12 in both trials. In addition, a reduction from baseline in VMS frequency of at least 50% has been described in the literature as a relevant improvement on an individual level. 55 Based on descriptive analyses, more than 70% of participants in the elinzanetant group achieved a response by week 12, and more than 80% of participants achieved a response by the end of treatment using this threshold. These results have clinically relevant implications because VMS often pose significant impacts on menopausal individual’s overall health, everyday activities, sleep, quality of life, and work productivity. 4 - 6 , 8 - 12

Nighttime VMS can affect sleep quantity and quality, but sleep disturbances experienced during menopause can occur independently of VMS. 56 Sleep disturbances can substantially impair quality of life during menopause 1 , 3 , 4 and additionally have implications for women’s physical health as they age. 13 Individuals experiencing menopausal sleep disturbances often use sleep medications such as benzodiazepines, 57 which can be associated with increased risk of falls as well as dependence or overuse. 58 , 59 Studies have reported mixed results regarding the efficacy of hormone therapy in addressing sleep problems among those with and without VMS. 60 - 62 A small, double-blind, randomized crossover study in healthy male volunteers showed the involvement of SP (NK-1 receptor ligand) in sleep. After intravenous infusion of SP, both polysomnography and subjective sleep ratings indicated a significant decrease in sleep quantity and quality. 63 Improvements in wakefulness after sleep onset in primary insomnia have been shown for an NK-1–specific receptor antagonist. 29 However, a role for SP/NK-1 receptors in sleep disturbance associated with menopause has not previously been established. Elinzanetant demonstrated significant reductions in sleep disturbance and improvements in sleep quality in the SWITCH-1 study. 30 In OASIS 1 and 2, at baseline, participants were on average experiencing moderate sleep disturbances according to the PROMIS SD SF 8b total T score classification established in a reference population (eTable 8 in Supplement 3 ). 42 Following treatment with elinzanetant, mean scores were reduced to the normal range according to the cut points published for the reference population; while scores in the placebo group only fell within the mild sleep disturbance range. 42 These data confirm that elinzanetant, as a selective dual NK-1,3 receptor antagonist, improves sleep disturbances in menopausal women.

A decrease in MENQOL total score of at least 0.9 from baseline has been identified as a clinically relevant response to treatment. 34 On average, both elinzanetant, 120 mg, and placebo demonstrated a clinically relevant improvement in menopause-related quality of life from baseline to week 12 using this threshold. 37 However, elinzanetant demonstrated statistically significant greater improvements in menopause-related quality of life compared with placebo. Further improvements up to 26 weeks were also observed in the participants who switched to elinzanetant after week 12. Consistent with reported findings from SWITCH-1, the MENQOL total score in OASIS 1 and 2 is considered to be predominantly driven by the VMS domain score. This is consistent with the impact of elinzanetant on reducing VMS frequency and severity. The improvements in the VMS domain are suggested to have a beneficial effect on other aspects of quality of life over time.

Elinzanetant maintained a favorable safety profile in line with its phase 2b clinical trial. 30 The most frequently reported adverse events in the elinzanetant group during the 12-week placebo-controlled period were headache and fatigue. In participants who switched from placebo to elinzanetant after week 12, few cases of headache and fatigue were reported. No incidences of endometrial hyperplasia or malignant neoplasm were seen in either trial.

Increases in liver enzymes were closely monitored in the OASIS trials due to previous concerns of elevations in liver transaminases with NK-3 receptor antagonists. 26 , 27 , 64 No incidences of liver toxicity were observed with elinzanetant in the OASIS 1 and 2 trials. Overall, the safety profile of elinzanetant was favorable when compared with placebo in the OASIS 1 and 2 studies over 12 weeks and with extended use up to 26 weeks. Additional safety data will be available from the 52-week, placebo-controlled OASIS 3 study ( NCT05030584 ).

The OASIS 1 and 2 trials had a number of strengths. Both were randomized, double-blind, multinational, and placebo-controlled trials specifically designed to evaluate the efficacy of elinzanetant for the treatment of VMS, while also assessing the effect of elinzanetant on sleep disturbances and menopause-related quality of life. Results demonstrated high reproducibility, with treatment effects consistent across all primary and key secondary end points over time.

The OASIS 1 and 2 trials had some limitations. First, all data for the primary and key secondary end points were collected electronically using diaries or questionnaires (patient-reported outcomes). Such measures are appropriate for measuring end points such as VMS, sleep disturbance, and menopause-related quality of life, which are highly influenced by participant perception. In addition, these can be associated with considerable participant burden. The subjective nature of patient-reported outcomes, together with regression to the mean symptom burden over time and the caring effect experienced during the study, may help explain the placebo response seen in these and other similar clinical trials. 65 , 66 Of note, this is an aspect common to studies investigating VMS treatments, with a recent meta-analysis demonstrating VMS placebo responses typically ranging from 34% to 67%. 67

Second, the OASIS 1 and 2 trials included only individuals with VMS who were naturally or surgically postmenopausal. Similar to other VMS trials, the participants were primarily White, with 12% to 19% Black or African American and less than 10% Hispanic or Latino. Significant unmet needs remain for other populations of individuals experiencing VMS, such as perimenopausal individuals and those experiencing VMS due to endocrine therapy for breast cancer. OASIS 4 ( NCT05587296 ) will assess the efficacy and safety of elinzanetant among individuals with or at high risk of breast cancer receiving tamoxifen or aromatase inhibitors.

Third, the participants in the OASIS 1 and 2 trials were not required to have sleep disturbances to be included. Although significant reductions in sleep disturbances were seen compared with placebo, further characterization is needed to assess the effect of elinzanetant in populations with sleep disturbances associated with menopause.

OASIS 1 and 2 were 2 similar pivotal phase 3 trials performed across different sites and countries that separately demonstrated the efficacy of elinzanetant for the treatment of VMS associated with menopause. Elinzanetant demonstrated a rapid improvement in VMS frequency at 1 week and robust improvements in VMS severity, sleep disturbances, and menopause-related quality of life, and has a favorable safety profile. Elinzanetant has the potential to provide a well-tolerated and efficacious nonhormonal treatment option to address the unmet health needs of many menopausal individuals with moderate to severe VMS.

Accepted for Publication: July 8, 2024.

Published Online: August 22, 2024. doi:10.1001/jama.2024.14618

Corresponding Author: JoAnn V. Pinkerton, MD, MSCP, Northridge Midlife Health, University of Virginia Health, Box 801104, Charlottesville, VA 22908 ( [email protected] ).

Author Contributions: Dr Zuurman and Ms Haseli Mashhadi had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Simon, Joffe, Panay, Caetano, Haberland, Mellinger, Parke, Seitz, Zuurman.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Pinkerton, Maki, Panay, Soares, Caetano, Krahn, Mellinger, Parke, Zuurman.

Critical review of the manuscript for important intellectual content: All authors.

Statistical analysis: Panay, Haseli Mashhadi, Krahn, Mellinger, Parke, Zuurman.

Administrative, technical, or material support: Pinkerton, Caetano, Haberland.

Supervision: Simon, Nappi, Panay, Soares, Caetano, Haberland, Seitz, Zuurman.

Conflict of Interest Disclosures: Dr Pinkerton reported grants from Bayer Pharmaceuticals to University of Virginia during the conduct of the study and consulting fees from Bayer Pharmaceutical to University of Virginia and independent contract work for Merck for chapters on abnormal bleeding and menopause. Dr Simon reported grants from Bayer Healthcare, AbbVie, Daré Bioscience, Mylan, and Myovant/Sumitomo and personal fees from Astellas Pharma, Ascend Therapeutics, California Institute of Integral Studies, Femasys, Khyra, Madorra, Mayne Pharma, Pfizer, Pharmavite, Scynexis Inc, Vella Bioscience, and Bayer; and stock from Sermonix Pharmaceuticals outside the submitted work. Dr Joffe reported personal fees from Bayer, Merck, and Hello Therapeutics and grants from National Institutes of Health and Merck outside the submitted work; Dr Joffe’s spouse is an employee of Arsenal Biosciences and has equity in Merck. Dr Maki reported personal fees from Bayer Consumer Care during the conduct of the study and personal fees from Astellas and Pfizer and equity from Midi Health, Alloy, and Estrigenix outside the submitted work, as well as serving as a trustee of the International Menopause Society. Dr Nappi reported personal fees from Abbott, Astellas, Bayer Healthcare, Besins Healthcare, Exeltis, Fidia, Merck & Co, Novo Nordisk, Organon, Shionogi, Theramex, and Viatris; grants from Gedeon Richter; and nonfinancial support from HRA Pharma outside the submitted work. Dr Panay reported personal fees from Bayer during the conduct of the study and personal fees from Abbott, Astellas, Besins, Gedeon Richter, Lawley, Theramex, and Viatris outside the submitted work. Dr Soares reported grants from Ontario Brain Institute, Eisai, Clairvoyant Therapeutics, and Diamond Therapeutics and personal fees from CAN-BIND Solutions, Otsuka, and Ontario Health outside the submitted work. Dr Thurston reported personal fees from Bayer during the conduct of the study and personal fees from Astellas, Hello Therapeutics, Happify Health (relationship has ended), and Vira Health (relationship has ended) outside the submitted work. No other disclosures were reported.

Funding/Support: The OASIS 1 and 2 trials were sponsored by Bayer.

Role of the Funder/Sponsor: Bayer had a role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Meeting Presentations: Data reported in this article have been previously presented as posters at the American College of Obstetricians and Gynecologists Annual Meeting; May 17-19, 2024; San Francisco, California.

Data Sharing Statement: See Supplement 4 .

Additional Contributions: We acknowledge and thank the individuals who participated in the OASIS 1 and 2 trials and the investigators, including high recruiters in OASIS 1: Ramin Farsad, Diagnamics Inc; Ondrej Mika, GYN-Mika sro; Elliot Shin, Jubilee Clinical Research Inc; Micah Harris, MomDoc Women for Women; Leopold Rotter, Gynekologie MEDA sro; Mona Fakih, Revive Research Institute; Levente Rubliczky, Rub-Int Noi Egeszsegcentrum; Rossella Nappi, IRCCS Policlinico S Matteo; Carrie Swartz, Bosque Women’s Care; and Karl Tamussino, Univ Frauenklinik Graz, and in OASIS 2: Janusz Tomaszewski, Gabinet Gin J Tomaszewski; Adrian Marimon, Sweet Hope Research Specialty; Allan Dinnerstein, Helix Biomedics; Dagmara Makowska-Mainka, Clinical Medical Research; Andrea Heweker, Praxis Fuer Gynaekologie; Robert Smith, Suncoast Clinical Research; Ethel Bellavance, Diex Victoriaville; Sophia Rahman, ClinRx Research LLC; Michael Livingston, Metro Jackson OBGYN/SKYCRNG; Patrik Horvath, GYNARIN sro; Saskia Kerschischnik, emovis GmbH; Michael Moore, Advanced Women’s Health Institute; and Jeffrey Wayne, Clinical Trials Research. They all received compensation. Medical writing assistance was provided by Emma Case, MSci, of Highfield, Oxford, UK with sponsorship from Bayer.

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«Enfin !» : Marie-Sophie Lacarrau annonce son retour au 13h de TF1

Elle avait commencé à occuper le poste en janvier 2021, mais était absente depuis janvier 2022 . La journaliste  Marie-Sophie Lacarrau va pouvoir reprendre son rôle de présentatrice du 13h de TF1 . Elle a annoncé la fin de son absence, qui aura duré près de 5 mois et demi, dans une vidéo jeudi soir sur les réseaux sociaux. "Cela faisait un long moment que je ne vous avais pas donné de nouvelles, et j'en ai des positives. Enfin !", se réjouit la journaliste. "Je sais enfin que je vais vous retrouver. Ce sera le lundi 16 mai, pour le journal de 13h, évidemment, sur TF1. J'ai vraiment, vraiment hâte."

>> Retrouvez les journaux des médias tous les matins à 9h10 sur Europe 1 ainsi qu’en replay et en podcast ici

Deux mois dans le noir et le silence

Marie-Sophie Lacarrau s'est également confiée auprès de nos confrères du journal Le Parisien-Aujourd'hui en France  sur les raisons de sa longue absence : une maladie des yeux, la kératite amibienne. Cette infection de la cornée est causée par des parasites présents dans l'eau du robinet. Cette maladie très rare peut notamment se développer quand on garde ses lentilles sous la douche.

pic.twitter.com/u0raVOPdzu — Marie-Sophie Lacarrau (@MSLacarrau) May 12, 2022

La maladie a valu à Marie-Sophie Lacarrau des semaines et des semaines de traitement douloureux. La présentatrice raconte avoir vécu dans le noir pendant deux mois, rideaux fermés, sans pouvoir lire, regarder un écran, ou même écouter la radio ou de la musique, à cause des maux de tête. 

Elle va désormais mieux. Pour être sûre d'être suffisamment en forme pour revenir, Marie-Sophie Lacarraua a fait des tests toute cette semaine pour voir si elle pouvait tenir des journées de travail entières à écrire le JT sur un ordinateur, lire le prompteur, supporter les lumières du plateau. Des tests réussis. La journaliste sera donc à la présentation du journal de 13h dès lundi.

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Article Contents

Introduction, supplementary data, author contributions, acknowledgements.

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COVID-19 antibody seroprevalence in Santa Clara County, California

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Eran Bendavid, Bianca Mulaney, Neeraj Sood, Soleil Shah, Rebecca Bromley-Dulfano, Cara Lai, Zoe Weissberg, Rodrigo Saavedra-Walker, Jim Tedrow, Andrew Bogan, Thomas Kupiec, Daniel Eichner, Ribhav Gupta, John P A Ioannidis, Jay Bhattacharya, COVID-19 antibody seroprevalence in Santa Clara County, California, International Journal of Epidemiology , Volume 50, Issue 2, April 2021, Pages 410–419, https://doi.org/10.1093/ije/dyab010

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Measuring the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is central to understanding infection risk and fatality rates. We studied Coronavirus Disease 2019 (COVID-19)-antibody seroprevalence in a community sample drawn from Santa Clara County.

On 3 and 4 April 2020, we tested 3328 county residents for immunoglobulin G (IgG) and immunoglobulin M (IgM) antibodies to SARS-CoV-2 using a rapid lateral-flow assay (Premier Biotech). Participants were recruited using advertisements that were targeted to reach county residents that matched the county population by gender, race/ethnicity and zip code of residence. We estimate weights to match our sample to the county by zip, age, sex and race/ethnicity. We report the weighted and unweighted prevalence of antibodies to SARS-CoV-2. We adjust for test-performance characteristics by combining data from 18 independent test-kit assessments: 14 for specificity and 4 for sensitivity.

The raw prevalence of antibodies in our sample was 1.5% [exact binomial 95% confidence interval (CI) 1.1–2.0%]. Test-performance specificity in our data was 99.5% (95% CI 99.2–99.7%) and sensitivity was 82.8% (95% CI 76.0–88.4%). The unweighted prevalence adjusted for test-performance characteristics was 1.2% (95% CI 0.7–1.8%). After weighting for population demographics, the prevalence was 2.8% (95% CI 1.3–4.2%), using bootstrap to estimate confidence bounds. These prevalence point estimates imply that 53 000 [95% CI 26 000 to 82 000 using weighted prevalence; 23 000 (95% CI 14 000–35 000) using unweighted prevalence] people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time.

The estimated prevalence of SARS-CoV-2 antibodies in Santa Clara County implies that COVID-19 was likely more widespread than indicated by the number of cases in late March, 2020. At the time, low-burden contexts such as Santa Clara County were far from herd-immunity thresholds.

Seroprevalence studies of Coronavirus Disease 2019 (COVID-19) provide estimates of the extent of infection that are more representative of true transmission than indicated by case numbers.

In late March 2020, the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in Santa Clara County, California was estimated at 2.8% [95% confidence interval (CI) 1.3–4.2%] after weighting for county demographics and adjusting for test performance (1.5% unadjusted, 95% CI 1.1–2.0%).

These prevalence point estimates imply that 53 000 (95% CI 26 000 to 82 000 using weighted prevalence) people were infected in Santa Clara County by late March—many more than the ∼1200 confirmed cases at the time.

Using the estimated number of infections and the deaths in Santa Clara County at the time, we estimate a local infection fatality rate of 0.17%.

The first two cases of Coronavirus Disease 2019 (COVID-19) in Santa Clara County, California were identified in returning travellers on 31 January and 1 February 2020, and the first COVID-19 death in the county was announced on 9 March. 1 In the following month, nearly 1200 additional cases were identified in Santa Clara County, showing a pattern of rapid case increase that was reflective of community transmission. However, the case definition in Santa Clara and many other locations relies on polymerase chain reaction (PCR)-based tests that check for active infections. 2 In addition, PCR-based tests were initially restricted to those with symptomatic disease. Thus, the true extent of infection remains unknown, as confirmed cases miss those with mild or no symptoms and those who have already recovered from infection.

Measuring the true extent of infection is key for epidemic projections and planning response to the epidemic. For example, early projections suggested that, in the absence of strict measures to reduce transmission, the COVID-19 pandemic could overwhelm existing hospital-bed and intensive-care-unit capacity throughout the USA and lead to >2 million deaths. 3 In the absence of seroprevalence surveys, estimates of the fatality rate in these and other epidemic projections have relied on the number of confirmed cases multiplied by an estimated factor representing unknown or asymptomatic cases to arrive at the number of infections. 4–7 However, the magnitude of that factor is highly uncertain and has been difficult to assess because of three independent processes that introduce measurement error: (i) cases have been diagnosed with PCR-based tests, which do not provide information about resolved infections; (ii) the majority of cases tested early in the course of the epidemic have been acutely ill and highly symptomatic, whereas most asymptomatic or mildly symptomatic individuals have not been tested; and (iii) PCR-based testing rates have been highly variable across contexts and over time, leading to inaccurate relationships between the numbers of cases and infections.

On 3 and 4 April 2020, we conducted a survey of residents of Santa Clara County to measure the seroprevalence of antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) and better approximate the number of infections. To the best of our knowledge, this was the first SARS-CoV-2 seroprevalence survey conducted in the USA. At the time of this study, Santa Clara County had the largest number of confirmed cases of any county in Northern California. The county also had several of the earliest known cases of COVID-19 in the state—including one of the first presumed cases of community-acquired disease—making it an especially appropriate location for testing a population-level sample for the presence of active and past infections.

We conducted serologic testing for SARS-CoV-2 antibodies in 3328 adults and children in Santa Clara County using capillary blood draws and a lateral-flow immunoassay. In this section, we describe our sampling and recruitment approaches, specimen-collection methods, antibody-testing procedure, test-kit validation and statistical methods. Our protocol was informed by a World Health Organization protocol for population-level COVID-19 antibody testing. 8 We conducted our study with the cooperation of the Santa Clara County Department of Public Health.

Study participants and sample recruitment

We recruited participants by placing targeted advertisements on Facebook aimed at residents of Santa Clara County. We used Facebook to quickly reach a large number of county residents and because it allows granular targeting by zip code and socio-demographic characteristics. 9 We posted our advertisements targeting two populations: ads aimed at a representative population of the county by zip code and specially targeted ads to balance our sample for under-represented zip codes. In addition, we capped registrations from overrepresented areas after our registration slots filled up quickly with participants from wealthier zip codes. Individuals who clicked on the advertisement were directed to a survey hosted by the Stanford REDCap platform, which provided information about the study. 10 The survey asked for six data elements: zip code of residence, age, sex, race/ethnicity, underlying co-morbidities and prior clinical symptoms. Over 24 hours, we registered 3285 adults, and each adult was allowed to bring one child from the same household with them (889 children registered). Additional details of the participant-selection process are provided below (and in Supplementary Data , available as Supplementary data at IJE online).

Specimen-collection and testing methods

We established drive-through test sites in three locations spaced across Santa Clara County: two county parks in Los Gatos and San Jose, and a church in Mountain View. Only individuals with a participant identification (participant ID) were allowed into the testing area. Verbal informed consent was obtained to minimize participant and staff exposure. With participants in their vehicles, sample collectors in personal protective equipment drew 50–200µL of capillary blood into an EDTA-coated microtainer. Tubes were barcoded and linked with the participant ID. Samples were couriered from the collection sites to a test-reading facility with steady lighting and climate conditions. Technicians drew whole blood up to a fill line on the manufacturer’s pipette and placed it in the test-kit well, followed by a buffer. Test kits were read 12–20 minutes after the buffer was placed. Technicians barcoded tests to match sample barcodes and documented all test results.

Test kit performance

The manufacturer’s performance characteristics were available prior to the study (using 85 confirmed positive and 371 confirmed negative samples). We conducted additional testing to assess the kit performance and continued collecting information from assessments of test performance to incorporate into the analysis. Broadly, test performance was assessed against gold-standard positive specimens from patients with PCR-confirmed COVID-19 (with or without additional confirmation of antibody presence) for sensitivity and gold-standard negative specimens from pre-COVID-era and early-COVID-era specimens. More details on the data provenance, procedures to assess test-performance characteristics and concordance between gold-standard and kit results are provided below ( Supplementary Data , available as Supplementary data at IJE online).

Statistical analysis

Our estimation of the prevalence of COVID-19 proceeded in three steps. First, we report the raw frequencies of positive tests as a proportion of the final sample size. Second, we report the estimated sample prevalence, adjusted for test performance characteristics. Because SARS-CoV-2 lateral-flow antibody assays are relatively new, we gathered all available information on test performance characteristics (sensitivity and specificity), with a focus on test specificity, which can be of paramount importance when prevalence is not high. We use an estimate of test sensitivity and specificity based on pooling all information available to us. Details of each sample, including test-kit agreement numbers, specimen type and available information on data provenance, are provided in the Supplementary Data , available as Supplementary data at IJE online.

Third, we report the weighted prevalence after weighting for the zip code, sex, age (using four age categories: 0–19, 20–39, 40–69 and 70+) and race/ethnicity (non-Hispanic White, Asian, Hispanic and other) distributions of Santa Clara County (as measured in the 2018 American Community Survey). We use weights obtained through iterative proportional fitting, or raking. 11 Raking generates weights by iteratively adjusting the marginal weights of each population control variable (e.g. sex) until convergence is achieved on all control variables. We match Santa Clara County demographics by zip, sex, age and race.

We use a bootstrap procedure to estimate confidence bounds for the unweighted and weighted prevalence, while accounting for sampling error and propagating the uncertainty in the sensitivity and specificity. We account for clustering of test results within families by drawing household clusters in the bootstrap samples. We use the basic percentile of the bootstrap distribution to construct confidence intervals. 12 This procedure assumes that the community sample, negative control sample and positive control sample are drawn independently. More details on our bootstrap procedures are in the Supplementary Data , available as Supplementary data at IJE online.

Public involvement

Multiple stakeholders in Santa Clara County, including the department of public health, members of the board of supervisors, county parks and multiple community members, were engaged in the design and execution of the study.

The test kit used in this study (Premier Biotech, Minneapolis, MN) was tested prior to field deployment. In all, we collected information on 3404 specimens from 14 sample sets used for assessing the specificity of this particular kit and on 187 specimens from 4 sample sets used for assessing sensitivity. These tests were performed by the test kit manufacturer, US and Chinese regulatory agencies, as well as independent labs. Additional details on the sample sets and the pooling approaches used to combine the estimates are provided in the Supplementary Data , available as Supplementary data at IJE online. The estimated specificity using data from all the samples was 99.53% [95% confidence interval (CI) 99.24–99.73%] and sensitivity was 85.56% (95% CI 79.69–90.26%).

Our study included 3439 individuals who registered for the study and arrived at testing sites. We excluded observations of individuals who could not be tested (e.g. unable to obtain blood or blood clotted, N  = 49), whose test results could not be used (e.g. if an incorrect participant ID was recorded onsite, N  = 32), who did not reside in Santa Clara County ( N  = 29) and who had invalid test results (no control band, N  = 1). This yielded an analytic sample of 3328 individuals with complete records including survey registration, attendance at a test site for specimen collection and lab results ( Figure 1 ). The sample distribution meaningfully deviated from that of the Santa Clara County population along several dimensions: sex (63% in sample was female, 50% in county), race (8% of the sample was Hispanic, 26% in the county; 19% of the sample was Asian, 28% in the county) and zip distribution ( Supplementary Figure 1 , available as Supplementary data at IJE online). Table 1 includes demographic characteristics of our unweighted sample, the weighted sample and Santa Clara County. 13 Supplementary Figure 1 , available as Supplementary data at IJE online, shows the geographical zip-code distribution of study participants in the county (counts and density per 1000 population).

Flow diagram of participants who filled out the survey and registered, visited a site for testing and were associated with a tested specimen. We were able to associate 3328 individuals with complete survey, site and lab-result data. ‘Individuals with completed registrations’ refers to individuals who completed the initial online survey and were able to select a test site and time. ‘No-shows’ refers to participants who filled out the survey and obtained a site registration but for whom we do not have a record of attendance onsite. ‘Unverifiable IDs’ refers to records from the site data with duplicate participant identifications (IDs) for which we cannot verify which individual attended the site (this may be due to participants bringing incorrect IDs and/or technical errors in the REDCap ID assignment process). ‘Samples not collected or not tested’ includes at least 10 individuals who visited the site but did not consent to participate, as well as several children who may have decided not to have their fingers pricked after completing intake onsite. This also includes specimens that were lost before they could be tested in the lab. ‘Unusable or unmatched survey data’ includes individuals with invalid zip codes, participant IDs from the lab results that could not be matched back to the survey responses and participants who withdrew from the study. Unmatched participant IDs may be due to participants stating an incorrect participant ID at the test site or site data collectors incorrectly recording stated participant IDs. ‘Invalid lab result’ refers to one test for which the on-board control failed and the lab result could not be correctly interpreted.

Sample characteristics relative to Santa Clara County population estimates from the 2018 American Community Survey

Table 2 shows that the frequency of positivity in our unweighted sample was similar between men and women, was highest among Hispanic participants and ranged between 1.4% and 1.9% across ages. Positivity among those who reported recent loss of taste in the past 2 weeks ( n  = 59) was ∼22%.

Univariate frequencies of positivity along demographic and clinical features

17 people did not indicate race.

The total number of positive cases by either IgG or IgM in our unadjusted sample was 50—a crude prevalence rate of 1.50% (exact binomial 95% CI 1.11–1.98%; Table 3 ). Accounting for test sensitivity and specificity and sampling error, our point estimate of the unweighted population prevalence was 1.22% (bootstrap 95% CI 0.66–1.79%). After adjusting for test performance and weighting our sample to approximate Santa Clara County demographics by zip, race, age and sex, the prevalence was 2.76% (95% CI 1.32 – 4.22%). The increase in prevalence after weighting is primarily driven by relatively higher prevalence among Hispanic participants residing in under-sampled zip codes of the county. This distribution of higher prevalence in those groups corresponds closely with the distribution of confirmed cases in Santa Clara County in late March: a disproportionate number of cases were experienced by Hispanic populations residing in the eastern portion of Santa Clara County. 14

Prevalence estimation in Santa Clara County

We report the prevalence and uncertainty bounds of estimates from unadjusted frequency counts, estimates adjusted for test-performance characteristics and estimates adjusted for test-performance characteristics and weighted by zip code, sex, age and race/ethnicity. For adjusted prevalences, we estimate the point estimate and uncertainty using the bootstrap as described in the Methods and Supplementary Data , available as Supplementary data at IJE online.

We can use our prevalence estimates to approximate the infection fatality rate from COVID-19 in Santa Clara County. Our prevalence estimate of 2.76% applied to Santa Clara County’s population implies a little over 53 000 infections (95% CI 26 000–82 000). Since the development of antibodies takes ∼7 days from the time of infection, that estimate represents the cumulative incidence of infections in Santa Clara County up to 27 March 2020, 1 week before the first day of testing. We then examine the cumulative COVID-19-associated deaths in Santa Clara County 2, 3 (our preferred) and 4 weeks after 27 March, which allows us to estimate the range of the infection fatality rate given a lag of 2–4 weeks from infection to death. 15 , 16 The number of people who died with COVID-19 in Santa Clara County by 11 April (2 weeks), 18 April (3 weeks) and 25 April (4 weeks) was 65, 90 and 106, respectively ( Supplementary Figure 2 , available as Supplementary data at IJE online, shows the time trends of cases and deaths in Santa Clara County around the time of the study). 17 These estimates of deaths then correspond to an infection fatality rate of 0.12% (65/53 000), 0.17% (90/53 000; preferred) and 0.2% (106/53 000). Finally, we estimated the infection fatality rate within our four age strata (0–19, 20–39, 40–69 and 70+) using the age-specific portion of deaths in Santa Clara County and the implied number of infections from our study and the county demographics. Our data are limited to calculate with accuracy the infection fatality rate in age strata, but they suggest vast differences in fatality risk ( Supplementary Table 4 , available as Supplementary data at IJE online).

After adjusting for test-performance characteristics and weighting for county demographics, we estimate that the seroprevalence of antibodies to SARS-CoV-2 in Santa Clara County in late March was 2.76%, with uncertainty bounds from 1.32% to 4.22%.

The most important implication of these findings is that, early in the pandemic, the number of infections was much greater than the reported number of cases. Using the weighted estimates, our data imply that, by 27 March (7 days prior to our survey), ∼53 000 (95% CI 26 000–82 000) people had been infected in Santa Clara County. The reported number of confirmed positive cases in the county on 27 March was 948 (~1,200 on the days of the study)—56-fold lower than the number of infections predicted by this study. 14 This infection-to-case ratio, also referred to as an under-ascertainment rate, was meaningfully higher than other estimates at the time. 18 , 19 This under-ascertainment rate is a fundamental parameter of many projection and epidemiologic models, and was used as a calibration target for understanding epidemic stage and calculating fatality rates. 20 , 21 The under-ascertainment for COVID-19 is likely due to a combination of reliance on PCR for case identification, which misses convalescent cases; early spread in the absence of systematic testing; and asymptomatic or lightly symptomatic infections that go undetected.

The estimated infection fatality rate of 0.17% is based on the assumption that the prevalence in our study reflects the situation in Santa Clara 7 days prior to the study. If antibodies take longer to appear, or if the average duration from case identification to death is <3 weeks, then the prevalence rate at the time of the survey was higher and the infection fatality rate would be lower. On the other hand, if deaths from COVID-19 are under-reported, then the fatality rate estimates would increase. Our prevalence and fatality rate estimates can be used to update existing models, given the large upwards revision of under-ascertainment.

Whereas our weighted-prevalence estimate of 2.76% is indicative of the situation in Santa Clara County as of late March, other areas are likely to have different seroprevalence estimates based on population demographics, effective contact rates and social-distancing policies. The infection fatality rate in different locations also varies and may be substantially higher in places where the hospitals were overwhelmed (e.g. New York City or Bergamo) or where infections are concentrated among vulnerable individuals (e.g. nursing home residents). 22–24 For example, in many European countries, 42–57% of deaths occurred in nursing homes and preliminary estimates for the USA are approaching the same range. 25 , 26 infection fatality rate estimates may be substantially higher in such settings.

Our prevalence estimate also suggests that, at this time, the large majority of the population in Santa Clara County remains without IgM or IgG antibodies to SARS-CoV-2. However, repeated serologic testing in different geographies, spaced a few weeks apart, is needed to evaluate the extent of infection spread over time.

This study has several limitations. The primary limitation concerns sample selection biases. Our sample may be enriched with COVID-19 participants by selecting for individuals with a belief or curiosity concerning past infection. We discuss further and attempt to quantify the potential impact of this bias in the Supplementary Data , available as Supplementary data at IJE online (Section S4). Notably, we find that, under the most penalizing scenario of selection, the population-prevalence estimate changes from 2.76% to 2.11% ( Supplementary Table 3 , available as Supplementary data at IJE online). Our study may also have selected for groups of people more likely to skew our sample against COVID-19 participants. For example, our sample strategy selected for members of Santa Clara County with ready access to Facebook who viewed our advertisement early after the registration opened. Our sample ended up with an over-representation of White women between the ages of 40 and 70 years, and an under-representation of Hispanic and Asian populations, relative to our community. Those imbalances were partly addressed by weighting our sample by zip code, race, age and sex to match the county. Our survey also selected for members of the population who were able to spare the time to drive to the testing site, which may have skewed our sample against essential workers. Our study was also limited in that it could not ascertain the representativeness of SARS-CoV-2 antibodies in populations with possibly high prevalence, such as homeless populations and those in nursing homes. The overall direction and magnitude of these selection effects are hard to fully bound, and our estimates reflect the prevalence in our sample, weighted to match county demographics.

Another potential limitation is that, with relatively low prevalence estimates, the precision of seroprevalence estimates depends on the performance of the serology tests for SARS-CoV-2 antibodies. Our adjusted results depend on the current estimates of specificity and sensitivity of these tests. We estimated the specificity and sensitivity based on pooled estimates from several independent assessments that use combined IgG and IgM to identify positive-antibody presence. Lower test specificity or greater uncertainty in the test performance could, in principle, meaningfully change the study’s conclusions. The performance of the test kit used in this study has been studied extensively in comparison to other SARS-CoV-2-antibody tests, including for use with capillary blood and in comparison with tests that require venipuncture ( Supplementary Data Section 2, available as Supplementary data at IJE online).

Our infection fatality rate could be biased downward if the number of COVID-19 deaths in the county has been substantially undercounted. This has some plausibility given that some early COVID-19 deaths may have gone undiagnosed due to limited testing. However, many deaths early in the spring of 2020 that were suspected of being due to COVID-19 were retrospectively evaluated, and those in which COVID-19 was implicated in the death were added to the official tally, reducing the effect of undercounting. We note that, even if deaths are 50% higher than the recorded deaths, this would still imply an infection fatality rate of 0.18–0.30%.

Over 100 teams worldwide have tested population samples for SARS-CoV-2 antibodies, with findings consistent with a large under-ascertainment of SARS-CoV-2 infections. Our study was one of the earliest to be done and the large under-ascertainment was partly driven by the limited testing in the early phases of the pandemic. Large under-ascertainment (up to several hundred-fold) was seen in other early surveys, whereas the extent of under-ascertainment decreased as more testing was being performed in most locations. 27 , 28 An early serosurvey in Los Angeles County, California on 10–11 April estimates a seroprevalence of 4.65%. 29 Our data from Santa Clara County suggest that the spread of the infection is similar to other moderately affected areas such as Los Angeles in early spring. Documented infections remained low in Santa Clara County until early summer. Santa Clara was part of a large seroprevalence survey among dialysis patients in the USA. 30 In that study, conducted in the first week of July 2020, the estimated seroprevalence in Santa Clara County was 4.1%, and this corresponded to an infection fatality rate of ∼0.2%, similar to our estimate.

We conclude that, based on seroprevalence sampling of a large regional population, the best estimates for the prevalence of SARS-CoV-2 antibodies in Santa Clara County were 2.76% by late March (95% CI 1.32–4.22%), with large variability by race/ethnicity. This prevalence is far smaller than the theoretical final size of the epidemic 31 and suggests that, in late March, a large majority of the population did not have IgG or IgM antibodies to the virus. Our study offers valuable data for an early period of the pandemic, when PCR testing was limited, and these data can be used as a baseline for comparison against subsequent seroprevalence studies to estimate the evolution of both seroprevalence and infection fatality rates over time. Our prevalence and infection fatality rate estimates do not advocate for or refute the usefulness of any non-pharmaceutical interventions. Instead, these new data reduce uncertainty around the size of the population that has been infected. This allows better monitoring of interventions and reducing uncertainty about the state of the epidemic also may carry intrinsic public benefits. It is important to note that the under-ascertainment of infections in Santa Clara may change over time (e.g. depending on greater availability of testing) and the under-ascertainment rate may be different in other locations. Improved test accuracy and larger sample sizes with random sampling can further reduce uncertainty in estimates. Our work demonstrates the feasibility of seroprevalence surveys of population samples to inform our understanding of this pandemic’s progression, project estimates of community vulnerability and monitor infection fatality rates in different populations over time.

Supplementary data are available at IJE online.

E.B., N.S. and J.B. conceived of the project and were involved in every aspect of the project; B.M., S.S. and J.T. were involved in protocol development and in every aspect of the survey execution; J.P.A.I. conceptualized the data collection and analysis; R.B.D., C.L., Z.W., R.S.W. and A.B. were site directors and led critical components of the data collection and interpretation; T.K. and D.E. were instrumental in assessing, procuring and fielding the test kits; R.G. led recruitment efforts and was involved in data analysis. All authors were critically involved in interpretation of the data, drafting and revising of the manuscript.

This work was supported by gift support from the Stanford COVID-19 Seroprevalence Studies Fund. The funders had no role in the design and conduct of the study, nor in the decision to prepare and submit the manuscript for publication.

The authors acknowledge the support of the Santa Clara County Department of Public Health, the Santa Clara County community, all study participants and the many volunteers and staff without whom this study could not have been accomplished in the midst of the COVID-19 crisis. We would also like to acknowledge the following individuals for critical comments and contributions that improved the data and the paper: David Allison, Manisha Desai, Liran Einav, Julia Gross, Emilia Ling, Tom MaCurdy, Charles McCulloch, Ben Moran, Barry Nalebuff, Richard Olshen, Ken Shotts and Frank Wolak. The Institutional Review Board at Stanford University approved the study prior to recruitment (Protocol # IRB-55702). Sharing of the primary data is restricted under a human-subjects-protection agreement. Sharing of de-identified data may be available upon request and review by the authors.

Conflict of interest

None declared. Of note, test kits were purchased from Premier Biotech for this study, and none of the authors has a relationship to the test manufacturer (beyond purchasing the tests).

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JT 20H : Anne-Claire Coudray quitte l’antenne, Audrey Crespo-Mara menacée sur TF1 ?

Ce dimanche 30 octobre 2022, Audrey Crespo-Mara a assuré la présentation du JT de 20H de TF1 en remplacement d’Anne-Claire Coudray. Sur France 2, Laurent Delahousse a également été remplacé par son joker, Thomas Sotto.

En raison des vacances de la Toussaint, les éditions du JT de 20H du week-end ont été assurées par les jokers. En effet, entre le vendredi 28 et le dimanche 30 octobre 2022, Audrey Crespo-Mara a remplacé Anne-Claire Coudray à la tête du JT de 20H de TF1.

De son côté, Thomas Sotto a pris les rênes du JT de 20H de France 2 durant tout le week-end en lieu et place de Laurent Delahousse . Ainsi, Audrey Crespo-Mara et Thomas Sotto se sont affrontés tout au long du week-end.

Avantage Thomas Sotto face à Audrey Crespo-Mara

Le JT de 20H de TF1 du samedi 29 octobre a réuni 4,87 millions de téléspectateurs, soit 27,1% de l’ensemble du public. Sur une semaine, où Anne-Claire Coudray assurait la présentation, le journal d’Audrey Crespo-Mara est en hausse de 0,1 point.

En frontal, le JT de 20H de France 2 a convaincu 4,86 millions de personnes, soit 27,3% du public présent. Sur une semaine, Thomas Sotto fait mieux que Laurent Delahousse et permet au journal de la chaîne publique de progresser de 2,4 points. Dans le détail, le journaliste permet à France 2 d’être leader des audiences de 20h04 à 20h18 et de 20h21 à 20h26. À noter que les éditions du journal de la semaine ont performé avec Karine Baste .

Audrey Crespo-Mara plus forte qu’Anne-Claire Coudray

Dans l’édition du JT de 20H de TF1 de ce dimanche 30 octobre, Audrey Crespo-Mara est notamment revenue sur le drame à Séoul. Côté audience, le journal de la chaîne a captivé 5,83 millions de Français, soit 27,2% de part de marché sur les quatre ans et plus. Le JT de 20H est en hausse de 0,8 point sur une semaine et permet à TF1 de se positionner leader.

En frontal, le JT de 20H de France 2 avec Thomas Sotto a rassemblé 5,29 millions de fidèles, soit 24,8% de part d’audience. Le journal de la chaîne publique est en baisse de 0,3 point sur une semaine.

Le retour d’Anne-Claire Coudray sur TF1

Ainsi, lors de l’édition du samedi 29 octobre, Audrey Crespo-Mara a été menacée par le JT de 20H de France 2 avec Thomas Sotto puisque la chaîne s’est positionnée sur certaine tranche leader. En revanche, le jour suivant, la journaliste a pris sa revanche en dominant aisément les audiences.

Par ailleurs, après avoir quitté l’antenne, Anne-Claire Coudray fera son retour dès vendredi 4 novembre pour le JT de 20H sur TF1. Laurent Delahousse devrait également revenir aux commandes du JT de France 2.