David Muscatello

Emergency department visits, ambulance calls, and mortality associated with an exceptional heat wave in Sydney, Australia, 2011: a time-series analysis

BackgroundFrom January 30-February 6, 2011, New South Wales was affected by an exceptional heat wave, which broke numerous records. Near real-time Emergency Department (ED) and ambulance surveillance allowed rapid detection of an increase in the number of heat-related ED visits and ambulance calls during this period. The purpose of this study was to quantify the excess heat-related and all-cause ED visits and ambulance calls, and excess all-cause mortality, associated with the heat wave.MethodsED and ambulance data were obtained from surveillance and administrative databases, while mortality data were obtained from the state death registry. The observed counts were compared with the average counts from the same period from 2006/07 through 2009/10, and a Poisson regression model was constructed to calculate the number of excess ED visits, ambulance and deaths after adjusting for calendar and lag effects.ResultsDuring the heat wave there were 104 and 236 ED visits for heat effects and dehydration respectively, and 116 ambulance calls for heat exposure. From the regression model, all-cause ED visits increased by 2% (95% CI 1.01-1.03), all-cause ambulance calls increased by 14% (95% CI 1.11-1.16), and all-cause mortality increased by 13% (95% CI 1.06-1.22). Those aged 75 years and older had the highest excess rates of all outcomes.ConclusionsThe 2011 heat wave resulted in an increase in the number of ED visits and ambulance calls, especially in older persons, as well as an increase in all-cause mortality. Rapid surveillance systems provide markers of heat wave impacts that have fatal outcomes.

Urinary symptoms and incontinence in an urban community: prevalence and associated factors in older men and women

Abstract

Probable psittacosis outbreak linked to wild birds.

Residence in the upper Blue Mountains, age of 50–64 years, direct contact with wild birds, and lawn mowing without a grass catcher were associated with psittacosis.

Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments

BackgroundAlthough syndromic surveillance systems are gaining acceptance as useful tools in public health, doubts remain about whether the anticipated early warning benefits exist. Many assessments of this question do not adequately account for the confounding effects of autocorrelation and trend when comparing surveillance time series and few compare the syndromic data stream against a continuous laboratory-based standard. We used time series methods to assess whether monitoring of daily counts of Emergency Department (ED) visits assigned a clinical diagnosis of influenza could offer earlier warning of increased incidence of viral influenza in the population compared with surveillance of daily counts of positive influenza test results from laboratories.MethodsFor the five-year period 2001 to 2005, time series were assembled of ED visits assigned a provisional ED diagnosis of influenza and of laboratory-confirmed influenza cases in New South Wales (NSW), Australia. Poisson regression models were fitted to both time series to minimise the confounding effects of trend and autocorrelation and to control for other calendar influences. To assess the relative timeliness of the two series, cross-correlation analysis was performed on the model residuals. Modelling and cross-correlation analysis were repeated for each individual year.ResultsUsing the full five-year time series, short-term changes in the ED time series were estimated to precede changes in the laboratory series by three days. For individual years, the estimate was between three and 18 days. The time advantage estimated for the individual years 2003–2005 was consistently between three and four days.ConclusionMonitoring time series of ED visits clinically diagnosed with influenza could potentially provide three days early warning compared with surveillance of laboratory-confirmed influenza. When current laboratory processing and reporting delays are taken into account this time advantage is even greater.

Understanding sources of variation in syndromic surveillance for early warning of natural or intentional disease outbreaks

Daily counts of computer records of hospital emergency department arrivals grouped according to diagnosis (called here syndrome groupings) can be monitored by epidemiologists for changes in frequency that could provide early warning of bioterrorism events or naturally occurring disease outbreaks and epidemics. This type of public health surveillance is sometimes called syndromic surveillance. We used transitional Poisson regression models to obtain one-day-ahead arrival forecasts. Regression parameter estimates and forecasts were updated for each day using the latest 365 days of data. The resulting time series of recursive estimates of parameters such as the amplitude and location of the seasonal peaks as well as the one-day-ahead forecasts and forecast errors can be monitored to understand changes in epidemiology of each syndrome grouping. The counts for each syndrome grouping were autocorrelated and non-homogeneous Poisson. As such, the main methodological contribution of the article is the adaptation of Cumulative Sum (CUSUM) and Exponentially Weighted Moving Average (EWMA) plans for monitoring non-homogeneous counts. These plans were valid for small counts where the assumption of normally distributed one-day-ahead forecasts errors, typically used in other papers, breaks down. In addition, these adaptive plans have the advantage that control limits do not have to be trained for different syndrome groupings or aggregations of emergency departments. Conventional methods for signaling increases in syndrome grouping counts, Shewhart, CUSUM, and EWMA control charts of the standardized forecast errors were also examined. Shewhart charts were, at times, insensitive to shifts of interest. CUSUM and EWMA charts were only reasonable for large counts. We illustrate our methods with respiratory, influenza, diarrhea, and abdominal pain syndrome groupings.

All-Cause Mortality during First Wave of Pandemic (H1N1) 2009, New South Wales, Australia, 2009

TOC Summary: Death rates from all causes were lower than in some recent influenza seasons, particularly among older persons.

Immunogenicity and safety of inactivated quadrivalent influenza vaccine in adults: A systematic review and meta-analysis of randomised controlled trials

BACKGROUND A quadrivalent influenza vaccine (QIV) includes two A strains (A/H1N1, A/H3N2) and two B lineages (B/Victoria, B/Yamagata). The presence of both B lineages eliminate potential B lineage mismatch of trivalent influenza vaccine (TIV) with the circulating strain. METHODS Electronic database searches of Medline, Embase, Cochrane Central Register of Controlled Trials (CCRCT), Scopus and Web of Science were conducted for articles published until June 30, 2015 inclusive. Articles were limited to randomised controlled trials (RCTs) in adults using inactivated intramuscular vaccine and published in English language only. Summary estimates of immunogenicity (by seroprotection and seroconversion rates) and adverse events outcomes were compared between QIV and TIV, using a risk ratio (RR). Studies were pooled using inverse variance weights with a random effect model and the I(2) statistic was used to estimate heterogeneity. RESULTS A total of five RCTs were included in the meta-analysis. For immunogenicity outcomes, QIV had similar efficacy for the three common strains; A/H1N1, A/H3N2 and the B lineage included in the TIV. QIV also showed superior efficacy for the B lineage not included in the TIV; pooled seroprotection RR of 1.14 (95%CI: 1.03-1.25, p=0.008) and seroconversion RR of 1.78 (95%CI: 1.24-2.55, p=0.002) for B/Victoria, and pooled seroprotection RR of 1.12 (95%CI: 1.02-1.22, p=0.01) and seroconversion RR of 2.11 (95%CI: 1.51-2.95, p<0.001) for B/Yamagata, respectively. No significant differences were found between QIV and TIV for aggregated local and systemic adverse events within 7days post-vaccination. There were no vaccine-related serious adverse events reported for either QIV or TIV. Compared to TIV, injection-site pain was more common for QIV, with a pooled RR of 1.18 (95%CI: 1.03-1.35, p=0.02). CONCLUSION In adults, inactivated QIV was as immunogenic as seasonal TIV, with equivalent efficacy against the shared three strains included in TIV, and a superior immunogenicity against the non-TIV B lineage.

Mortality attributable to seasonal and pandemic influenza, Australia, 2003 to 2009, using a novel time series smoothing approach.

Background Official statistics under-estimate influenza deaths. Time series methods allow the estimation of influenza-attributable mortality. The methods often model background, non-influenza mortality using a cyclic, harmonic regression model based on the Serfling approach. This approach assumes that the seasonal pattern of non-influenza mortality is the same each year, which may not always be accurate. Aim To estimate Australian seasonal and pandemic influenza-attributable mortality from 2003 to 2009, and to assess a more flexible influenza mortality estimation approach. Methods We used a semi-parametric generalized additive model (GAM) to replace the conventional seasonal harmonic terms with a smoothing spline of time (‘spline model’) to estimate influenza-attributable respiratory, respiratory and circulatory, and all-cause mortality in persons aged <65 and ≥65 years. Influenza A(H1N1)pdm09, seasonal influenza A and B virus laboratory detection time series were used as independent variables. Model fit and estimates were compared with those of a harmonic model. Results Compared with the harmonic model, the spline model improved model fit by up to 20%. In <65 year-olds, the estimated respiratory mortality attributable to pandemic influenza A(H1N1)pdm09 was 0.5 (95% confidence interval (CI), 0.3, 0.7) per 100,000; similar to that of the years with the highest seasonal influenza A mortality, 2003 and 2007 (A/H3N2 years). In ≥65 year-olds, the highest annual seasonal influenza A mortality estimate was 25.8 (95% CI 22.2, 29.5) per 100,000 in 2003, five-fold higher than the non-statistically significant 2009 pandemic influenza estimate in that age group. Seasonal influenza B mortality estimates were negligible. Conclusions The spline model achieved a better model fit. The study provides additional evidence that seasonal influenza, particularly A/H3N2, remains an important cause of mortality in Australia and that the epidemic of pandemic influenza A (H1N1)pdm09 virus in 2009 did not result in mortality greater than seasonal A/H3N2 influenza mortality, even in younger age groups.

Evaluation of alternative respiratory syndromes for specific syndromic surveillance of influenza and respiratory syncytial virus: a time series analysis

BackgroundSyndromic surveillance is increasingly being evaluated for its potential for early warning of increased disease activity in the population. However, interpretation is hampered by the difficulty of attributing a causative pathogen. We described the temporal relationship between laboratory counts of influenza and respiratory syncytial virus (RSV) detection and alternative groupings of Emergency Department (ED) respiratory diagnoses.MethodsED and laboratory data were obtained for the south-eastern area of Sydney, NSW for the period 1 June 2001 - 1 December 2006. Counts of ED visits and laboratory confirmed positive RSV and influenza cases were aggregated by week. Semi-parametric generalized additive models (GAM) were used to determine the association between the incidence of RSV and influenza and the incidence of respiratory syndrome ED presentations while controlling for temporal confounders.ResultsFor every additional RSV laboratory count, ED diagnoses of bronchiolitis increased by 3.1% (95%CI: 2.7%-3.5%) in the same week. For every additional influenza laboratory count, ED diagnoses of influenza-like illness increased by 4.7% (95%CI: 4.2%-5.2%) one week earlier.ConclusionIn this study, large increases in ED diagnoses of bronchiolitis and influenza-like illness were independent and proxy indicators for RSV and influenza activity, respectively.

Early warning CUSUM plans for surveillance of negative binomial daily disease counts

Automated public health surveillance of disease counts for rapid outbreak, epidemic or bioterrorism detection using conventional control chart methods can be hampered by over-dispersion and background (‘in-control’) mean counts that vary over time. An adaptive cumulative sum (CUSUM) plan is developed for signalling unusually high incidence in prospectively monitored time series of over-dispersed daily disease counts with a non-homogeneous mean. Negative binomial transitional regression is used to prospectively model background counts and provide ‘one-step-ahead’ forecasts of the next days count. A CUSUM plan then accumulates departures of observed counts from an offset (reference value) that is dynamically updated using the modelled forecasts. The CUSUM signals whenever the accumulated departures exceed a threshold. The amount of memory of past observations retained by the CUSUM plan is determined by the offset value; a smaller offset retains more memory and is efficient at detecting smaller shifts. Our approach optimises early outbreak detection by dynamically adjusting the offset value. We demonstrate the practical application of the ‘optimal’ CUSUM plans to daily counts of laboratory-notified influenza and Ross River virus diagnoses, with particular emphasis on the steady-state situation (i.e. changes that occur after the CUSUM statistic has run through several in-control counts).