A dynamic mathematical model of red blood cell clinical demand to assess the impact of prolonged blood shortages and transfusion restriction policies.

BACKGROUND: Estimating change in clinical demand for red blood cells (RBCs) from a disaster, as well as triaging introduced in response, is essential to plan effectively for a major blood shortage. We aimed to develop a RBC demand model to assess the impact of restriction policies on RBC use and patient outcomes. STUDY DESIGN AND METHODS: A compartmental dynamic model was developed in which patients require RBCs acutely (within 1 hr), urgently (24 hr), semiurgently (1-7 days), or nonurgently; outcomes included death or remaining at or transitioning to more or less urgent categories. A mathematical model was developed with transitions governed by differential equations and calibrated to a baseline scenario of adequate blood supply (using population-based hospital data sets, registries, and RBC issues). Distribution into urgency categories was based on a prospective study of 5132 randomly selected RBC units. Scenarios when the blood supply is limited compared to baseline were investigated. Transition rates between urgency categories under these scenarios were established by clinician survey. RESULTS: In the baseline 21-day scenario, patients requiring the most RBCs were other surgery (2162, 22%), medical anemia (1916, 12%), malignant hematology (1092, 16%), and gastrointestinal hemorrhage (1115, 8%). A policy of withholding RBCs for all nonurgent indications results in an estimated reduction of only 1007 (11.2%) RBC units and, if extended to semiurgent, a reduction of 2567 (28.5%) RBC units. CONCLUSIONS: Based on this model, restrictions that withhold transfusion from nonurgent patients have minimal impact on RBC demand and may not be sufficient to address changed demand and/or decreased supply during a prolonged disaster.

Weather-driven variation in dengue activity in Australia examined using a process-based modeling approach.

The impact of weather variation on dengue transmission in Cairns, Australia, was determined by applying a process-based dengue simulation model (DENSiM) that incorporated local meteorologic, entomologic, and demographic data. Analysis showed that inter-annual weather variation is one of the significant determinants of dengue outbreak receptivity. Cross-correlation analyses showed that DENSiM simulated epidemics of similar relative magnitude and timing to those historically recorded in reported dengue cases in Cairns during 1991-2009, (r = 0.372, P < 0.01). The DENSiM model can now be used to study the potential impacts of future climate change on dengue transmission. Understanding the impact of climate variation on the geographic range, seasonality, and magnitude of dengue transmission will enhance development of adaptation strategies to minimize future disease burden in Australia.

Searching for sharp drops in the incidence of pandemic A/H1N1 influenza by single year of age.

BACKGROUND: During the 2009 H1N1 pandemic (pH1N1), morbidity and mortality sparing was observed among the elderly population; it was hypothesized that this age group benefited from immunity to pH1N1 due to cross-reactive antibodies generated from prior infection with antigenically similar influenza viruses. Evidence from serologic studies and genetic similarities between pH1N1 and historical influenza viruses suggest that the incidence of pH1N1 cases should drop markedly in age cohorts born prior to the disappearance of H1N1 in 1957, namely those at least 52-53 years old in 2009, but the precise range of ages affected has not been delineated. METHODS AND FINDINGS: To test for any age-associated discontinuities in pH1N1 incidence, we aggregated laboratory-confirmed pH1N1 case data from 8 jurisdictions in 7 countries, stratified by single year of age, sex (when available), and hospitalization status. Using single year of age population denominators, we generated smoothed curves of the weighted risk ratio of pH1N1 incidence, and looked for sharp drops at varying age bandwidths, defined as a significantly negative second derivative. Analyses stratified by hospitalization status and sex were used to test alternative explanations for observed discontinuities. We found that the risk of laboratory-confirmed infection with pH1N1 declines with age, but that there was a statistically significant leveling off or increase in risk from about 45 to 50 years of age, after which a sharp drop in risk occurs until the late fifties. This trend was more pronounced in hospitalized cases and in women and was independent of the choice in smoothing parameters. The age range at which the decline in risk accelerates corresponds to the cohort born between 1951-1959 (hospitalized) and 1953-1960 (not hospitalized). CONCLUSIONS: The reduced incidence of pH1N1 disease in older individuals shows a detailed age-specific pattern consistent with protection conferred by exposure to influenza A/H1N1 viruses circulating before 1957.

The association of seasonal influenza vaccination with pandemic influenza H1N1 2009 infection.

In 2010 Skowronski and colleagues reported that seasonal influenza vaccine appeared to increase the risk of pandemic influenza H1N1 2009 (pH1N1) infection during the first pandemic wave in Canada [1]. They suggested a number of possible explanations for their unexpected finding: firstly, that the results were an artefact of selection bias or confounding; secondly, that the results were due to partial mediation through a biological mechanism; and thirdly, that the results were due to a direct immune mechanism, such as antibody dependent enhancement [1]. In a recent paper in Vaccine, Rosella and colleagues have investigated in detail the first of these possibilities, confirming that it is unlikely an unidentified confounder could have explained the findings [2].

Pandemic (H1N1) 2009 influenza community transmission was established in one Australian state when the virus was first identified in North America.

BACKGROUND: In mid-June 2009 the State of Victoria in Australia appeared to have the highest notification rate of pandemic (H1N1) 2009 influenza in the world. We hypothesise that this was because community transmission of pandemic influenza was already well established in Victoria at the time testing for the novel virus commenced. In contrast, this was not true for the pandemic in other parts of Australia, including Western Australia (WA). METHODS: We used data from detailed case follow-up of patients with confirmed infection in Victoria and WA to demonstrate the difference in the pandemic curve in two Australian states on opposite sides of the continent. We modelled the pandemic in both states, using a susceptible-infected-removed model with Bayesian inference accounting for imported cases. RESULTS: Epidemic transmission occurred earlier in Victoria and later in WA. Only 5% of the first 100 Victorian cases were not locally acquired and three of these were brothers in one family. By contrast, 53% of the first 102 cases in WA were associated with importation from Victoria. Using plausible model input data, estimation of the effective reproductive number for the Victorian epidemic required us to invoke an earlier date for commencement of transmission to explain the observed data. This was not required in modelling the epidemic in WA. CONCLUSION: Strong circumstantial evidence, supported by modelling, suggests community transmission of pandemic influenza was well established in Victoria, but not in WA, at the time testing for the novel virus commenced in Australia. The virus is likely to have entered Victoria and already become established around the time it was first identified in the US and Mexico.