Research published this week in PLoS Medicine presents the most accurate assessment to date of the severity of the swine flu (H1N1) pandemic in the US. Scientists need to measure the severity of swine flu (how often infection with the swine flu virus results in symptoms leading to illness, hospitalization or death) so that appropriate pandemic plans can be put into place. Severity of swine flu has been difficult to measure for two main reasons: first, people with severe influenza are more likely than those with mild cases to seek care, making it difficult to estimate how many total cases have occurred, and second, the sheer number of cases means that recording routine case data can be difficult due to overburdening of public health systems. In this study, researchers from from Milwaukee (where all medically attended cases were recorded, whether hospitalized or not) and New York City (where only hospitalizations, intensive care admission and deaths were recorded, and a telephone survey of flu-like illness was conducted), along with earlier results from studies by the US CDC, used a statistical approach called Bayesian evidence synthesis. This enabled accurate estimations of severity to be made. Their analyses reveal that the autumn-winter pandemic wave of swine flu should have a death toll only slightly higher than, or considerably lower than, that caused by seasonal influenza in an average year, provided swine flu continues to behave as it did during the summer. Seasonal influenza mainly kills elderly adults, but the authors reveal that most deaths from swine flu will occur in non-elderly adults, a shift in age distribution that has been seen in previous pandemics.
The Severity of Pandemic H1N1 Influenza in the United States, from April to July 2009: A bayesian Analysis. PLoS Med 6(12): e1000207 doi:10.1371/journal.pmed.1000207
Accurate measures of the severity of pandemic (H1N1) 2009 influenza (pH1N1) are needed to assess the likely impact of an anticipated resurgence in the autumn in the Northern Hemisphere. Severity has been difficult to measure because jurisdictions with large numbers of deaths and other severe outcomes have had too many cases to assess the total number with confidence. Also, detection of severe cases may be more likely, resulting in overestimation of the severity of an average case. We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources. We used complementary data from two US cities: Milwaukee attempted to identify cases of medically attended infection whether or not they required hospitalization, while New York City focused on the identification of hospitalizations, intensive care admission or mechanical ventilation (hereafter, ICU), and deaths. New York data were used to estimate numerators for ICU and death, and two sources of data – medically attended cases in Milwaukee or self-reported influenza-like illness (ILI) in New York – were used to estimate ratios of symptomatic cases to hospitalizations. Combining these data with estimates of the fraction detected for each level of severity, we estimated the proportion of symptomatic patients who died (symptomatic case-fatality ratio, sCFR), required ICU (sCIR), and required hospitalization (sCHR), overall and by age category. Evidence, prior information, and associated uncertainty were analyzed in a Bayesian evidence synthesis framework. Using medically attended cases and estimates of the proportion of symptomatic cases medically attended, we estimated an sCFR of 0.048% (95% credible interval [CI] 0.026%–0.096%), sCIR of 0.239% (0.134%–0.458%), and sCHR of 1.44% (0.83%–2.64%). Using self-reported ILI, we obtained estimates approximately 7–96lower. sCFR and sCIR appear to be highest in persons aged 18 y and older, and lowest in children aged 5–17 y. sCHR appears to be lowest in persons aged 5–17; our data were too sparse to allow us to determine the group in which it was the highest. These estimates suggest that an autumn–winter pandemic wave of pH1N1 with comparable severity per case could lead to a number of deaths in the range from considerably below that associated with seasonal influenza to slightly higher, but with the greatest impact in children aged 0–4 and adults 18–64. These estimates of impact depend on assumptions about total incidence of infection and would be larger if incidence of symptomatic infection were higher or shifted toward adults, if viral virulence increased, or if suboptimal treatment resulted from stress on the health care system; numbers would decrease if the total proportion of the population symptomatically infected were lower than assumed.
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Climate change is incontestably a phenomenon of global causes and impacts. However, as the contribution of different regions and countries to climate change differs, so do the impacts. This paper examines the current and potential impact of climate change on infectious diseases in regions that could not be more different: the Arctic and the tropics. Despite obvious differences in environmental and socio-economic contexts, there are commonalities between these areas, both in the mechanisms through which climate change influences disease transmission and in the adaptation responses health systems can and should mount. The authors consider five main common characteristics and requirements, respectively, regarding climate-sensitive infectious diseases:
