Estimating the prevalence of latent tuberculosis in a low-incidence setting: Australia.

Migration is a key driver of tuberculosis (TB) in many low-incidence settings, with the majority of TB cases attributed to reactivation of latent TB (LTBI) acquired overseas. A greater understanding of LTBI risk in heterogeneous migrant populations would aid health planning. We aimed to estimate the LTBI prevalence and distribution among locally born and overseas-born Australians.Annual risks of TB infection estimates were applied to population cohorts (by country of birth, year of arrival and age) in Australian census data in 2006, 2011 and 2016.Both the absolute number and proportion of Australian residents with LTBI increased from 4.6% (interquartile range (IQR) 4.2-5.2%) in 2006 to 5.1% (IQR 4.7-5.5%) in 2016, due to the increasing proportion of the population born overseas (23.8% in 2006 to 28.3% in 2016). Of all residents estimated to have LTBI in 2016; 93.2% were overseas born, 21.6% were aged <35 years and 34.4% had migrated to Australia since 2007.The overall prevalence of LTBI in Australia is low. Some residents, particularly migrants from high-incidence settings, may have considerably higher risk of LTBI, and these findings allow for tailored public health interventions to reduce the risk and impact of future TB disease.

Modular programming for tuberculosis control, the "AuTuMN" platform.

BACKGROUND: Tuberculosis (TB) is now the world's leading infectious killer and major programmatic advances will be needed if we are to meet the ambitious new End TB Targets. Although mathematical models are powerful tools for TB control, such models must be flexible enough to capture the complexity and heterogeneity of the global TB epidemic. This includes simulating a disease that affects age groups and other risk groups differently, has varying levels of infectiousness depending upon the organ involved and varying outcomes from treatment depending on the drug resistance pattern of the infecting strain. RESULTS: We adopted sound basic principles of software engineering to develop a modular software platform for simulation of TB control interventions ("AuTuMN"). These included object-oriented programming, logical linkage between modules and consistency of code syntax and variable naming. The underlying transmission dynamic model incorporates optional stratification by age, risk group, strain and organ involvement, while our approach to simulating time-variant programmatic parameters better captures the historical progression of the epidemic. An economic model is overlaid upon this epidemiological model which facilitates comparison between new and existing technologies. A "Model runner" module allows for predictions of future disease burden trajectories under alternative scenario situations, as well as uncertainty, automatic calibration, cost-effectiveness and optimisation. The model has now been used to guide TB control strategies across a range of settings and countries, with our modular approach enabling repeated application of the tool without the need for extensive modification for each application. CONCLUSIONS: The modular construction of the platform minimises errors, enhances readability and collaboration between multiple programmers and enables rapid adaptation to answer questions in a broad range of contexts without the need for extensive re-programming. Such features are particularly important in simulating an epidemic as complex and diverse as TB.

Palliative care and hematological malignancies: increased referrals at a comprehensive cancer centre.

BACKGROUND: Current evidence indicates that patients with hematological malignancies are less likely to receive input from specialist palliative care services compared to those with solid tumors. AIM: We aimed to analyze data for referrals to our palliative care service, in order to assess trends in the number and proportion of referrals received from hematology, and changes in the characteristics of these referrals. DESIGN: Prospective information was collected for all referrals to the Department of Pain and Palliative Care (DPPC) over a four-year period. This included inpatient/outpatient status, diagnosis, symptoms, and goals of the referring clinician; and information linked to hospital inpatient data to obtain date and location of death. SETTINGS/PARTICIPANTS: All hematology referrals were from January 2007 to December 2010. RESULTS: Hematology referrals constituted 11.6% of all referrals received during the study period. Outpatient referrals increased significantly with each year, as did the proportion of patients referred for symptom control. The median time from referral to death was 34 days, with poorest survival seen in acute leukemia and inpatients. Overall, 54% of inpatient hematology deaths had consultation from the DPPC, with these patients less likely to die in the intensive care unit. CONCLUSIONS: Over recent years, collaboration between hematology and palliative care has resulted in increased referral numbers, with potentially positive results for patients.

Differential effects of pandemic (H1N1) 2009 on remote and indigenous groups, Northern Territory, Australia, 2009.

Pandemic (H1N1) 2009 influenza spread through the Northern Territory, Australia, during June-August 2009. We performed 2 cross-sectional serologic surveys on specimens from Northern Territory residents, with 445 specimens obtained prepandemic and 1,689 specimens postpandemic. Antibody titers were determined by hemagglutination inhibition against reference virus A/California/7/2009 on serum samples collected opportunistically from outpatients. All specimens had data for patients' gender, age, and address, with patients' indigenous status determined for 94.1%. Protective immunity (titer >40) was present in 7.6% (95% confidence interval [CI] 5.2%-10.1%) of prepandemic specimens and 19.5% (95% CI 17.6%-21.4%) of postpandemic specimens, giving a population-standardized attack rate of 14.9% (95% CI 11.0%-18.9%). Prepandemic proportion of immune persons was greater with increasing age but did not differ by other demographic characteristics. Postpandemic proportion of immune persons was greater in younger groups and around double in indigenous persons. Postpandemic proportion immune was geographically heterogeneous, particularly among remote-living and indigenous groups.