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Background: Respiratory syncytial virus (RSV) is a major cause of respiratory illness, with younger infants at greatest risk of hospitalisation. With the recent approval of a maternal RSV vaccine in Australia, it is timely to evaluate its potential costs and health benefits in Australia., Methods: We applied an integrated dynamic and economic evaluation model to estimate specific outcomes of RSV disease and the cost-effectiveness of a year-round maternal RSV vaccination program in Australia. Cost-effectiveness was estimated using the incremental cost-effectiveness ratio (ICER) per quality-adjusted life year (QALY) gained., Results: From a healthcare system perspective, the maternal vaccination program was estimated to be cost-effective at a vaccine price less than approximately 120 Australian dollars ($AU), assuming a willingness-to-pay (WTP) threshold of $AU 50,000/QALY gained. Most of the estimated cost-savings were from preventing RSV hospitalisations in infants aged <6 months. However, while 82% of the cost-savings were from preventing RSV hospitalisations in infants aged <6 months, only about 25% of the QALY gains were in this age group. The majority of the other QALY gains came via herd effects from prevention of death in older adults and to a lesser extent, prevention of nonmedically-attended illness in older teens and adults. When predicted cost-savings and QALY gains in those ≥6 months of age were excluded, the vaccine price required to meet the assumed WTP threshold fell to $AU 63., Conclusions: A maternal RSV vaccination program in Australia could provide value for money by reducing hospitalisations and associated costs among infants aged <6 months, depending on the vaccine price. We have provided evidence that herd effects beyond the target population may be an important consideration in assessing cost-effectiveness of maternal RSV vaccination., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Allen Nazareno reports financial support was provided by University of New South Wales School of Population Health. James Wood reports a relationship with Australian Technical Advisory Group that includes: board membership. Alexandra Hogan reports a relationship with World Health Organization Immunization and vaccines related implementation research advisory committee (IVIR-AC) that includes: board membership. Alexandra Hogan reports a relationship with National Health and Medical Research Council that includes: funding grants. David Muscatello reports a relationship with National Health and Medical Research Council that includes: funding grants. Anthony Newall reports a relationship with Center for Innovation and Value Research that includes: funding grants. Nusrat Homaira reports a relationship with Sanofi, Pfizer and Merck Sharp & Dohme Australia that includes: consulting or advisory. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: Mortality surveillance was established in the state of Victoria just before the COVID-19 pandemic. Here, we describe the establishment of this surveillance system, justify the modelling approach selected, and provide examples of how the interpretation of changes in mortality rates during the pandemic was influenced by the model chosen., Methods: Registered deaths occurring in Victoria from 1 January 2015 to 31 December 2020 were sourced from the Victoria Death Index. Observed mortality rates were compared to a raw historical 5-year mean and to predicted means estimated from a seasonal robust regression. Differences between the observed mortality rate and the historical mean (∆MR) and excess mortality rate from the observed and predicted rates were assessed., Results: There were 20 375 COVID-19 cases notified in Victoria as of 31 December 2020, of whom 748 (3.7%) died. Victorians aged ≥ 85 years experienced the highest case fatality ratio (34%). Mean observed mortality rates in 2020 (MR: 11.6; 95% confidence interval [CI]: 11.4, 11.9) were slightly reduced when compared with the annual rate expected using the historical mean method (mean MR: 12.2; 95% CI: 12.1-12.3; ∆MR: -0.57; 95% CI: -0.77, -0.38), but not from the rate expected using the robust regression (estimated MR: 11.7; 95% prediction interval [PI]: 11.5, 11.9; EMR: -0.05; 95% CI: -0.26, 0.16). The two methods yielded opposing interpretations for some causes, including cardiovascular and cancer mortality., Discussion: Interpretation of how pandemic restrictions impacted mortality in Victoria in 2020 is influenced by the method of estimation. Time-series approaches are preferential because they account for population trends in mortality over time., Competing Interests: At the time this work was undertaken, LS, SGS, DH and SLR were employed at the Victorian Department of Health. SGS has consulted for CSL Seqirus, Pfizer, Moderna and EvoHealth. The other authors have no conflicts of interest to declare., ((c) 2025 The authors; licensee World Health Organization.)

Background: Respiratory syncytial virus (RSV) is a leading cause of respiratory illness among infants. A maternal RSV vaccine that protects young infants has recently been approved for registration in Australia. We estimated the population benefits of a future year-round maternal RSV vaccination program in terms of prevented RSV infections and hospitalisations in Australia., Methods: We described RSV transmission using an age-structured compartmental model calibrated to Australian aggregated monthly RSV-coded hospitalisations in children aged <5 years. We accounted for mother and infant interactions in the model to capture herd effects more realistically. Using the model, we estimated the annual age-specific RSV infections and hospitalisations prevented for a range of assumptions for vaccine efficacy, coverage, and durability to estimate the future impact of year-round maternal RSV vaccination on infants and the wider population., Results: Assuming base case vaccine efficacy, 6 months duration of protection and 70% coverage, RSV hospitalisations were predicted to fall by 60% (from 3.0 to 1.2 per 100 persons) in infants aged <3 months and 40% (from 1.9 to 1.1 per 100 persons) in 3-5-month-olds. These benefits were primarily due to direct protection to infants of vaccinated mothers. This vaccine program was predicted to reduce the population-level RSV infection by about 4%. Coverage and duration assumptions were influential, with higher coverage leading to larger declines in infants <6 months, and increased duration of protection leading to additional declines in infection and hospitalisation risk in older infants aged 6-8 months., Conclusions: With vaccine uptake similar to that achieved for other maternal vaccines in Australia, a year-round RSV maternal vaccination program is predicted to approximately halve the number of RSV hospitalisations in infants younger than 6 months. There was a small herd effect predicted in the base case but potential for larger benefits if vaccine coverage or the duration of protection exceeds base case assumptions., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Allen Nazareno reports financial support was provided by University of New South Wales School of Population Health. James Wood reports a relationship with Australian Technical Advisory Group on Immunisation that includes: board membership. Alexandra Hogan reports a relationship with World Health Organization Immunization and vaccines related implementation research advisory committee (IVIR-AC) that includes: board membership. Alexandra Hogan reports a relationship with National Health and Medical Research Council that includes: funding grants. David Muscatello reports a relationship with National Institute of Health and Medical Research Great West Regional Delegation that includes: funding grants. Anthony Newall reports a relationship with Center for Innovation and Value Research that includes: funding grants. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: COVID-19 and seasonal influenza are endemic causes of morbidity and mortality. This study aimed to compare the epidemiology of severe illness and risk of death among patients following emergency department (ED) presentation with either infection., Methods: De-identified, population-based, emergency department records in New South Wales, Australia, were probabilistically linked to population-level health outcome databases for the period 1 January 2015 to 28 February 2023. Included were patients allocated an ED diagnosis consistent with an acute respiratory infection. Logistic regression was used to examine the association of infecting virus with risk of a severe outcome (intensive care unit admission or death)., Results: Influenza infection was notified in 2335 and COVID-19 in 5053 patients with a severe outcome. The age distribution was similar for both viruses, except in <15-year-olds, where severe influenza was nearly three times more frequent. Overall, the odds of death among patients with COVID-19 was 1.65 (95% CI 1.43, 1.89) times higher than among those with influenza. This declined to 1.49 (95% CI 1.08, 2.06) times during the COVID-19 Omicron variant period., Conclusions: The Omicron variant arrived when background population COVID-19 vaccination coverage was >90%. Despite that, death was more frequent for COVID-19 than influenza., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Estimates of excess deaths provide critical intelligence on the impact of population health threats including seasonal respiratory infections, pandemics and environmental hazards. Timely estimates of excess deaths can inform the response to COVID-19. However, access to timely mortality data is challenging due to the time interval between the death occurring and the date the death is registered and available for analysis ('registration interval')., Development: Using data from the New South Wales, Australia, Births Deaths and Marriages Registry, we developed a Poisson regression model that estimated near-complete weekly counts, for a given week of death, from partially-complete death registration counts. A 10-weeks lag was considered, and a 2-year baseline of historical registration intervals was used to correct lag weeks., Application: Validation of estimated counts found that the root-mean-square error (as a percentage of mean observed near-complete registrations) was less than 7% for lag week 3, and <5% for lag weeks 4-9. We incorporated this method utilizing an existing rapid weekly mortality surveillance system. Counts corrected for registration interval replaced observed values for the most recent weeks. Excess death estimates, based on corrected counts, were within 1.2% of near-complete counts available 9 weeks from the end of the analysis period., Conclusions: This study demonstrates a method for estimating recent death counts to correct for registration intervals. Estimates obtained at a 3-week lag were acceptable, while those at greater than 3 weeks were optimal., (© The Author(s) 2024. Published by Oxford University Press on behalf of the International Epidemiological Association.)

The effect of deprivation on total bone health status has not been well defined. We examined the relationship between socioeconomic deprivation and poor bone health and falls and we found a significant association. The finding could be beneficial for current public health strategies to minimise disparities in bone health., Purpose: Socioeconomic deprivation is associated with many illnesses including increased fracture incidence in older people. However, the effect of deprivation on total bone health status has not been well defined. To examine the relationship between socioeconomic deprivation and poor bone health and falls, we conducted a cross-sectional study using baseline measures from the United Kingdom (UK) Biobank cohort comprising 502,682 participants aged 40-69 years at recruitment during 2006-2010., Method: We examined four outcomes: 1) low bone mineral density/osteopenia, 2) fall in last year, 3) fracture in the last five years, and 4) fracture from a simple fall in the last five years. To measure socioeconomic deprivation, we used the Townsend index of the participant's residential postcode., Results: At baseline, 29% of participants had low bone density (T-score of heel < -1 standard deviation), 20% reported a fall in the previous year, and 10% reported a fracture in the previous five years. Among participants experiencing a fracture, 60% reported the cause as a simple fall. In the multivariable logistic regression model after controlling for other covariates, the odds of a fall, fracture in the last five years, fractures from simple fall, and osteopenia were respectively 1.46 times (95% confidence interval [CI] 1.42-1.49), 1.26 times (95% CI 1.22-1.30), 1.31 times (95% CI 1.26-1.36) and 1.16 times (95% CI 1.13-1.19) higher for the most deprived compared with the least deprived quantile., Conclusion: Socioeconomic deprivation was significantly associated with poor bone health and falls. This research could be beneficial to minimise social disparities in bone health., (© 2024. The Author(s).)

Background: The interpretation of relative vaccine effectiveness (rVE) of improved influenza vaccines is complex. Estimation of burden averted is useful to contextualise their potential impact across different seasons. For the population aged under 65 years in Australia, this study estimated the additional morbidity and mortality that could be averted using improved influenza vaccines., Methods: We used observed, season-specific (2015-2019) influenza notification and influenza-coded hospitalisation frequencies and published modelled estimates of influenza-associated hospitalisations and deaths that occurred under the prevailing influenza vaccination coverage scenario. After back-calculating to the estimated burden in the population without vaccination, we applied published standard influenza vaccine effectiveness and coverage estimates to calculate the burden potentially averted by standard and improved influenza vaccines. A plausible range of rVE values were used, assuming 50% coverage., Results: The percentage point difference in absolute vaccine effectiveness (VE) of an improved vaccine compared to a standard vaccine is directly proportional to its rVE and inversely proportional to the effectiveness of the standard vaccine. The incremental burden averted by an improved vaccine is a function of both its difference in absolute VE and the severity of the influenza season. Assuming an rVE of 15% with 50% coverage, the improved vaccine was estimated to additionally avert 1517 to 12,641 influenza notifications, 287 to 1311 influenza-coded hospitalisations and 9 to 33 modelled all-cause influenza deaths per year compared to the standard vaccine., Conclusions: Improved vaccines can have substantial clinical and population impact, particularly when the effectiveness of standard vaccines is low, and burden is high., (© 2024 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Background: The influenza mortality burden has remained substantial in the United States (US) despite relatively high levels of influenza vaccine uptake. This has led to questions regarding the effectiveness of the program against this outcome, particularly in the elderly. The aim of this evaluation was to develop and explore a new approach to estimating the population-level effect of influenza vaccination uptake on pneumonia and influenza (P&I) associated deaths., Methods: Using publicly available data we examined the association between state-level influenza vaccination and all-age P&I associated deaths in the US from the 2013-2014 influenza season to the 2018-2019 season. In the main model, we evaluated influenza vaccine uptake in all those age 6 months and older. We used a mixed-effects regression analysis with generalised least squares estimation to account for within state correlation in P&I mortality., Results: From 2013-2014 through 2018-2019, the total number of all-age P&I related deaths during the influenza seasons was 480,111. The mean overall cumulative influenza vaccine uptake (age 6 months and older) across the states and years considered was 46.7%, with higher uptake (64.8%) observed in those aged ≥ 65 years. We found that overall influenza vaccine uptake (6 months and older) had a statistically significant protective association with the P&I death rate. This translated to a 0.33 (95% CI: 0.20, 0.47) per 100,000 population reduction in P&I deaths in the influenza season per 1% increase in overall influenza vaccine uptake., Discussion: These results using a population-level statistical approach provide additional support for the overall effectiveness of the US influenza vaccination program. This reassurance is critical given the importance of ensuring confidence in this life saving program. Future research is needed to expand on our approach using more refined data., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: Compare two approaches to analyzing time series data-interrupted time series with segmented regression (ITS-SR) and Bayesian structural time series using the CausalImpact R package (BSTS-CI)-highlighting advantages, disadvantages, and implementation considerations., Study Design and Setting: We analyzed electronic health records using each approach to estimate the antibiotic prescribing reduction associated with an educational program delivered to Australian primary care physicians between 2012 and 2017. Two outcomes were considered: antibiotics for upper respiratory tract infections (URTIs) and antibiotics of specified formulations., Results: For URTI indication prescribing, average monthly prescriptions changes were estimated at -4,550; (95% confidence interval, -5,486 to -3,614) and -4,270; (95% credible interval, -5,934 to -2,626) for ITS-SR and BSTS-CI, respectively. Similarly for specified formulation prescribing, monthly average changes were estimated at -7,923; (95% confidence interval, -15,887 to 40) for ITS-SR and -20,269; (95% credible interval, -25,011 to -15,635) for BSTS-CI., Conclusion: Differing results between ITS-SR and BSTS-CI appear driven by divergent explanatory and outcome series trends. The BSTS-CI may be a suitable alternative to ITS-SR only if the explanatory series represent the secular trend of the outcome series before the intervention and are equally affected by exogenous or confounding factors. When appropriately applied, BSTS-CI provides an alternative to ITS with more readily interpretable Bayesian effect estimates., Competing Interests: Declaration of competing interest None of the authors have any conflicts to disclose., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Evidence on the effectiveness of influenza vaccine in preventing antibiotic prescriptions for influenza-like illness (ILI) in adults is limited., Methods: A primary care-based case-control study was conducted to estimate influenza vaccine effectiveness (VE) against influenza-like illness (ILI) and antibiotic prescribing for ILI in adults aged ≥40 years. Cases were patients diagnosed with ILI from 1 st June to 30 th September in each year, 2015-2018; a subset of those with ILI prescribed antibiotics was also defined. Controls were patients attending a practice who did not receive an ILI diagnosis. Generalised estimating equations were used to calculate adjusted VE overall, by age (<65 versus ≥65 years) and comorbidity status., Results: The number of ILI cases varied from 558 in 2018 to 2901 in 2017 and controls from 86618 in 2015 to 136763 in 2017. Over 4 years the pooled estimate of VE was 24% (95%CI, 11% to 34%) against ILI and 15% (95%CI, -3% to 29%) against antibiotic prescription for ILI. Influenza vaccine was effective in reducing ILI with an associated antibiotic prescriptions in patients aged <65 years (VE=23%, 95%CI, 3% to 38%) and if no comorbidities were recorded (VE=22%, 95%CI, 1% to 39%) but not in other subgroups., Conclusions: Influenza vaccine reduced the likelihood of antibiotic prescriptions for ILI in low-risk adults (40-64 years and those without comorbidities)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The British Infection Association. Published by Elsevier Ltd. All rights reserved.)

Background: Respiratory syncytial virus (RSV) and influenza are important causes of disease in children and adults. In Australia, information on the burden of RSV in adults is particularly limited., Methods: We used time series analysis to estimate respiratory, acute respiratory infection, pneumonia and influenza, and bronchiolitis hospitalisations attributable to RSV and influenza in Australia during 2009 through 2017. RSV and influenza-coded hospitalisations in &lt;5-year-olds were used as proxies for relative weekly viral activity., Results: From 2009 to 2017, the estimated all-age average annual rates of respiratory hospitalisations attributable to RSV and seasonal influenza (excluding 2009) were 54.8 (95% confidence interval [CI]: 20.1, 88.8) and 87.8 (95% CI: 74.5, 97.7) per 100,000, respectively. The highest estimated average annual RSV-attributable respiratory hospitalisation rate per 100,000 was 464.2 (95% CI: 285.9, 641.2) in &lt;5-year-olds. For seasonal influenza, it was 521.6 (95% CI: 420.9, 600.0) in persons aged ≥75 years. In ≥75-year-olds, modelled estimates were approximately eight and two times the coded estimates for RSV and seasonal influenza, respectively., Conclusions: RSV and influenza are major causes of hospitalisation in young children and older adults in Australia, with morbidity underestimated by hospital diagnosis codes., (© 2022 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Background: Little is known about global variation in early epidemic growth rates and effective reproduction numbers (R e ) of seasonal influenza. We aimed to estimate global variation in R e of influenza type A and B during a single period., Methods: Country influenza detection time series from September 2017 through January 2019 were obtained from an international database. Type A and B epidemics by country were selected on the basis of R e estimates for a five-week moving window, advanced by week. Associations of R e with absolute latitude, human development index, percent of the population aged <15 years, and percent of those living in rural areas in each country were assessed., Results: Time series were included for 119 of 169 available countries. There were 100 countries with influenza A and 79 with B epidemics. Median R e for both influenza A and B epidemics was 1.23 (ranges: A 1.10, 1.60; B 1.06, 1.58). R e of influenza B but not A was independently associated with absolute latitude, increasing by 0.022 (95% confidence interval 0.002, 0.043) per 10 degrees., Conclusion: The R e of influenza A and B were similar. Only the R e of influenza B was associated with country characteristics, which was increasing with distance from the equator. The approach may be suitable for continuous R e surveillance., Competing Interests: Conflict of interest The authors have no competing interest to declare., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)