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AimTo estimate the length of stay distributions of hospitalised COVID-19 cases during a mixed Omicron-Delta epidemic in New South Wales, Australia (16 Dec 2021 - 7 Feb 2022), and compare these to estimates produced over a Delta-only epidemic in the same population (1 Jul 2021 - 15 Dec 2022). BackgroundThe distribution of the duration that clinical cases of COVID-19 occupy hospital beds (the length of stay) is a key factor in determining how incident caseloads translate into health system burden as measured through ward and ICU occupancy. ResultsUsing data on the hospital stays of 19,574 individuals, we performed a competing-risk survival analysis of COVID-19 clinical progression. During the mixed Omicron-Delta epidemic, we found that the mean length of stay for individuals who were discharged directly from ward without an ICU stay was, for age groups 0-39, 40-69 and 70+ respectively, 2.16 (95% CI: 2.12-2.21), 3.93 (95% CI: 3.78-4.07) and 7.61 days (95% CI: 7.31-8.01), compared to 3.60 (95% CI: 3.48-3.81), 5.78 (95% CI: 5.59-5.99) and 12.31 days (95% CI: 11.75-12.95) across the preceding Delta epidemic (15 Jul 2021 - 15 Dec 2021). We also considered data on the stays of individuals within the Hunter New England Local Health District, where it was reported that Omicron was the only circulating variant, and found mean ward-to-discharge length of stays of 2.05 (95% CI: 1.80-2.30), 2.92 (95% CI: 2.50-3.67) and 6.02 days (95% CI: 4.91-7.01) for the same age groups. ConclusionsHospital length of stay was substantially reduced across all clinical pathways during a mixed Omicron-Delta epidemic compared to a prior Delta epidemic. These changes in length of stay have contributed to lessened health system burden despite greatly increased infection burden and should be considered in future planning of response to the COVID-19 pandemic in Australia and internationally.
RESUMO
ObjectiveCOVID-19 appears to have caused less severe outbreaks in many low- and middle-income countries (LMIC) compared with high-income countries, possibly because of differing demographics, socio-economics, surveillance, and policy responses. Here, we investigate the role of multiple factors on COVID-19 dynamics in the Philippines, a LMIC that has had a relatively severe COVID-19 outbreak. MethodsWe applied an age-structured compartmental model that incorporated time-varying mobility, testing, and personal protective behaviors (through a "Minimum Health Standards" policy, MHS) to represent the Philippines COVID-19 epidemic nationally and for three highly affected regions (Calabarzon, Central Visayas, and the National Capital Region). We estimated effects of control measures, key epidemiological parameters, and interventions. FindingsPopulation age structure, contact rates, mobility, testing, and MHS were sufficient to explain the Philippines epidemic based on the good fit between modelled and reported cases, hospitalisations, and deaths. Several of the fitted epidemiological parameters were consistent with those reported in high-income settings. The model indicated that MHS reduced the probability of transmission per contact by 15-26%. The February 2021 case detection rate was estimated at [~]9%, population recovered at [~]12%, and scenario projections indicated high sensitivity to MHS adherence. ConclusionsCOVID-19 dynamics in the Philippines are driven by age, contact structure, mobility, and MHS adherence, and the epidemic can be understood within a similar framework as for high-income settings. Continued compliance with low-cost MHS should allow the Philippines to maintain epidemic control until vaccines are widely distributed, but disease resurgence could occur due to low population immunity and detection rates.
