Background While different COVID-19 vaccines have been developed, there has been lack of data on the efficacy comparison between mRNA and inactivated whole virus vaccine among patients with chronic respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD), and bronchiectasis. Methods This was a retrospective case control study on the efficacy of BNT162b2 (mRNA vaccine) and CoronaVac (inactivated whole virus vaccine) against COVID-19 in patients with chronic respiratory diseases. A total of 327 patients were included, with 109 patients infected with COVID-19 matched with 218 patients without COVID-19. The co-primary outcomes were vaccine effectiveness against symptomatic COVID-19, COVID-19-related hospitalization and COVID-19-related respiratory failure. Vaccine effectiveness was calculated using the formula (1-adjusted odds ratio) x 100. Results Patients who received at least 2 doses of CoronaVac had lower risk of being hospitalized for COVID-19 and developing respiratory failure than those who did not have vaccination, with adjusted odds ratio (OR) of 0.189 (95% CI = 0.050–0.714, p = 0.014) and 0.128 (95% CI = 0.026–0.638, p = 0.012) respectively. Patients who received at least 2 doses of BNT162b2 had lower risk of being hospitalized for COVID-19 and developing respiratory failure than those who did not have vaccination with adjusted OR of 0.207 (95% CI = 0.043–0.962, p = 0.050) and 0.093 (95% CI = 0.011–0.827, p = 0.033) respectively. There was no statistically significant difference in the risks of being hospitalized for COVID-19 and developing respiratory failure between patients who received at least 2 doses of CoronaVac or BNT162b2. Conclusion BNT162b2 and CoronaVac vaccines are effective in preventing hospitalization for COVID-19 and respiratory failure complicating COVID-19 among patients with chronic respiratory diseases. Patients with chronic respiratory diseases should be encouraged to have COVID-19 vaccination.

While molnupiravir (MOV) and nirmatrelvir–ritonavir (NMV-r) were developed for treatment of mild to moderate COVID-19 infection, there has been a lack of data on the efficacy among unvaccinated adult patients with chronic respiratory diseases, including asthma, chronic obstructive pulmonary disease (COPD) and bronchiectasis. A territory-wide retrospective cohort study was conducted in Hong Kong to investigate the efficacy of MOV and NMV-r against severe outcomes of COVID-19 in unvaccinated adult patients with chronic respiratory diseases. A total of 3267 patients were included. NMV-r was effective in preventing respiratory failure (66.6%; 95% CI, 25.6–85.0%, p = 0.007), severe respiratory failure (77.0%; 95% CI, 6.9–94.3%, p = 0.039) with statistical significance, and COVID-19 related hospitalization (43.9%; 95% CI, −1.7–69.0%, p = 0.057) and in-hospital mortality (62.7%; 95% CI, −0.6–86.2, p = 0.051) with borderline statistical significance. MOV was effective in preventing COVID-19 related severe respiratory failure (48.2%; 95% CI 0.5–73.0, p = 0.048) and in-hospital mortality (58.3%; 95% CI 22.9–77.4, p = 0.005) but not hospitalization (p = 0.16) and respiratory failure (p = 0.10). In summary, both NMV-r and MOV are effective for reducing severe outcomes in unvaccinated COVID-19 patients with chronic respiratory diseases.

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The paper proposes a sequential monitoring scheme for detecting changes in parameter values for general time series models using pairwise likelihood. Under this scheme, a change-point is declared when the cumulative sum of the first derivatives of pairwise likelihood exceeds a certain boundary function. The scheme is shown to have asymptotically zero Type II error with a prescribed level of Type I error. With the use of pairwise likelihood, the scheme is applicable to many complicated time series models in a computationally efficient manner. For example, the scheme covers time series models involving latent processes, such as stochastic volatility models and Poisson regression models with log link function.