Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance

INTRODUCTION Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO). METHODS For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient. RESULTS The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations. CONCLUSIONS Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics.The General Practitioner Sentinel Surveillance system is funded by the Victorian Government Department of Health. Ee Laine Tay was supported by a Master of Philosophy in Applied Epidemiology Scholarship funded by the Victorian Infectious Diseases Reference Laboratory and the Victorian Department of Health

The significance of increased influenza notifications during spring and summer of 2010-11 in Australia

Background & objective During the temperate out-of-season months in Australia in late 2010 and early 2011, an unprecedented high number of influenza notifications were recorded. We aimed to assess the significance of these notifications. Methods For Aust

The ancients : Salmonella, Tuberculosis and Influenza

Infections with Tuberculosis (TB), Influenza and Salmonellosis continue to pose significant challenges to public health and result in considerable work for public health agencies. The core aspects of my thesis outline four projects undertaken at the Victorian Government Department of Health (DH) and the Victorian Infectious Diseases Reference Laboratory (VIDRL) to fulfil the core requirements of the Masters of Philosophy in Applied Epidemiology (MAE) program. First, I present a cluster investigation of Salmonella Typhimurium 44 (S. Typhimurium 44) that was ongoing for more than six months and associated with three point source outbreaks, including one where it was possible to perform a cohort study. Investigation findings suggest an association with consumption of eggs, based on epidemiological and microbiological evidence obtained in two outbreaks. The first isolated S. Typhimurium 44 from tartare sauce made from raw eggs and eggs sampled from the source farm, and the second found an association with scrambled eggs in the cohort study. My second project used retrospective analysis of TB surveillance data from 2009 to 2011 to measure health system delay for TB in Victoria (that is, the interval between first health presentation for TB symptoms and treatment initiation), identify the factors associated with delay using logistic regression and explore the reasons behind delay using electronic case notes review. I found the median health system delay to be 31 days for all TB cases, 20 days for pulmonary TB (PTB) and 12 days for sputum smear positive PTB. Multivariable regression analysis found longer delay in females, older adults and extra-PTB sites and shorter delay in positive microscopy or nucleic acid testing. A wide range of reasons were identified, the most common being multiple visits to a General Practitioner. For my third project, I evaluated the TB surveillance system in Victoria using a mixed methods study design incorporating documents review, data analysis and key informant interviews. Overall, I found the TB surveillance system to be a complex but well-functioning system that is sensitive, flexible, widely accepted by stakeholders and produced good quality data. The key recommendations were to improve documentation on the system, improve feedback to stakeholders and increase the use of surveillance data to inform service provision and monitoring and evaluation activities. In addition, I also analysed the surveillance data to examine the epidemiology of TB in Victoria from 1993 to 2012. My fourth project adapted and field tested a new method for deriving influenza thresholds developed by the World Health Organisation (WHO) to calculate thresholds for two routine influenza-like-illness (ILI) datasets, laboratory data and hospital admissions for influenza using data from 2002 to 2011. I found that thresholds were easily derived using the WHO method and the new thresholds were used to revise the current ones used by the Victorian Sentinel General Practice Surveillance System. Finally, my thesis also lists the additional activities undertaken at both placements to capture the breadth of my MAE experience. These activities and projects supported the work of both placements and contributed to evidence base and informing policy and practice

Variable definitions of the influenza season and their impact onvaccine effectiveness estimates

Vaccine effectiveness (VE) studies are often made for a "season" which may refer to different analysis periods in different systems. We examined whether the use of four different definitions of season would materially affect estimates of influenza VE usi

Trends in Tuberculosis Incidence in the Australian-Born in Victoria: Opportunities and Challenges to Elimination

Australia is a low tuberculosis incidence country. In the setting of increasing migration, we aimed to investigate the epidemiology and trends of tuberculosis in the Australian-born population in the state of Victoria between 1992 and 2017. We performed a retrospective descriptive analysis of demographic, clinical and outcome data extracted from a centralized notifiable disease database. The mean incidence of tuberculosis was 1.19 cases per 100,000 population per year with a small but significant reduction of 0.98% per year. The median age of cases decreased from 67.5 years in 1994 to 17 years in 2017. Among 0–14 year-olds, there was an increase from 0.13 cases per 100,000 population in 1996 to 2.15 per 100,000 population in 2017. Data for risk factors were available from 2002 onwards. The most common risk factor in the 0–14 year age group was a household contact with tuberculosis (85.1%), followed by having a parent from a high tuberculosis incidence country (70.2%). We found the rate of tuberculosis in the Australian-born population in Victoria is low. However, there has been an increase in incidence in children, particularly among those with links to countries with high tuberculosis incidence. This could threaten progress towards tuberculosis elimination in Australia

‘Know Your Epidemic’: Are Prisons a Potential Barrier to TB Elimination in an Australian Context?

Globally, rates of tuberculosis (TB) cases in prisons are substantially higher than in the general population. The goal of this study was to review TB notifications in Victorian correctional facilities, and consider whether additional interventions towards TB elimination may be useful in this setting. All patients who were notified with or treated for TB in the Australian state of Victoria from 1 January 2003 to 1 December 2017 were included in this study. Descriptive analysis was performed. Demographic and treatment outcome data for individuals with and without a history of incarceration were reviewed and compared. Of the 5645 TB cases notified during the study period, 26 (0.5%) had a history of being incarcerated in correctional facilities while receiving treatment for TB. There were 73,238 inmates in Victorian correctional facilities over the same study period, meaning that approximately 0.04% of inmates were diagnosed or treated with TB disease in correctional facilities. Incarcerated individuals were more likely to have positive sputum smears and cavitation compared with nonincarcerated people with TB. There was no significant difference in treatment outcomes between the general TB population and those who had a history of incarceration during their treatment. There is a low apparent rate of TB in Victorian prisoners, and prisons do not contribute significantly to TB incidence in Victoria. Overall, TB outcomes do not differ between prisoners and nonprisoners. Ongoing efforts to sustain these lower rates and comparable outcomes in this vulnerable cohort are important for continued progress towards TB elimination

Mortality in the Northern Territory, 1967-2006

This report provides an overview of mortality in the Northern Territory over the forty year period from 1967 to 2006. Information is provided separately for Indigenous and non-Indigenous Territorians with stratification by sex and age groups

Mortality in the Northern Territory, 1967-2006

This report provides an overview of mortality in the Northern Territory over the forty year period from 1967 to 2006. Information is provided separately for Indigenous and non-Indigenous Territorians with stratification by sex and age groups

Exploring a proposed WHO method to determine thresholds for seasonal influenza surveillance.

INTRODUCTION:Health authorities find thresholds useful to gauge the start and severity of influenza seasons. We explored a method for deriving thresholds proposed in an influenza surveillance manual published by the World Health Organization (WHO). METHODS:For 2002-2011, we analysed two routine influenza-like-illness (ILI) datasets, general practice sentinel surveillance and a locum medical service sentinel surveillance, plus laboratory data and hospital admissions for influenza. For each sentinel dataset, we created two composite variables from the product of weekly ILI data and the relevant laboratory data, indicating the proportion of tested specimens that were positive. For all datasets, including the composite datasets, we aligned data on the median week of peak influenza or ILI activity and assigned three threshold levels: seasonal threshold, determined by inspection; and two intensity thresholds termed average and alert thresholds, determined by calculations of means, medians, confidence intervals (CI) and percentiles. From the thresholds, we compared the seasonal onset, end and intensity across all datasets from 2002-2011. Correlation between datasets was assessed using the mean correlation coefficient. RESULTS:The median week of peak activity was week 34 for all datasets, except hospital data (week 35). Means and medians were comparable and the 90% upper CIs were similar to the 95(th) percentiles. Comparison of thresholds revealed variations in defining the start of a season but good agreement in describing the end and intensity of influenza seasons, except in hospital admissions data after the pandemic year of 2009. The composite variables improved the agreements between the ILI and other datasets. Datasets were well correlated, with mean correlation coefficients of >0.75 for a range of combinations. CONCLUSIONS:Thresholds for influenza surveillance are easily derived from historical surveillance and laboratory data using the approach proposed by WHO. Use of composite variables is helpful for describing influenza season characteristics