Positive association between ambient temperature and salmonellosis notifications in New Zealand, 1965-2006

Objective: To investigate the temporal relationship between the monthly count of salmonellosis notifications and the monthly average temperature in New Zealand during the period 1965–2006.

Methods: A negative binomial regression model was used to analyse monthly average ambient temperature and salmonellosis notifications in New Zealand between 1965 and 2006.

Results: A 1°C increase in monthly average ambient temperature was associated with a 15% increase in salmonellosis notifications within the same month (IRR 1.15; 95% CI 1.07 – 1.24).

Conclusion: The positive association found in this study between temperature and salmonellosis notifications in New Zealand is consistent with the results of studies conducted in other countries. New Zealand is projected to experience an increase in temperature due to climate change. Therefore, all other things being equal, climate change could increase salmonellosis notifications in New Zealand.

Implications: This association between temperature and salmonellosis should be considered when developing public health plans and climate change adaptation policies. Strategically, existing food safety programs to prevent salmonellosis could be intensified during warmer periods. As the association was strongest within the same month, focusing on improving food handling and storage during this time period may assist in climate change adaptation in New Zealand.

Potential microbial and chemical hazards to waterbirds at the Western Treatment Plant

The Western Treatment Plant, a major sewage treatment plant west of Melbourne, Australia, is widely regarded as a significant conservation site for waterbirds. But experiences from various parts of the world suggest that sewage can also be hazardous to waterbirds, and has probably been responsible for mass-kill events. The intent of this contribution is to raise awareness about the potential for adverse impacts of sewage treatment plants on waterbirds, and to stimulate debate on the issue, with the ultimate objective of developing appropriate management strategies to mitigate the risk of mass kills.

Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods

 Background: Efficient and reliable surveillance and notification systems are vital for monitoring public health and disease outbreaks. However, most surveillance and notification systems are affected by a degree of underestimation (UE) and therefore uncertainty surrounds the 'true' incidence of disease affecting morbidity and mortality rates. Surveillance systems fail to capture cases at two distinct levels of the surveillance pyramid: from the community since not all cases seek healthcare (under-ascertainment), and at the healthcare-level, representing a failure to adequately report symptomatic cases that have sought medical advice (underreporting). There are several methods to estimate the extent of under-ascertainment and underreporting. Methods. Within the context of the ECDC-funded Burden of Communicable Diseases in Europe (BCoDE)-project, an extensive literature review was conducted to identify studies that estimate ascertainment or reporting rates for salmonellosis and campylobacteriosis in European Union Member States (MS) plus European Free Trade Area (EFTA) countries Iceland, Norway and Switzerland and four other OECD countries (USA, Canada, Australia and Japan). Multiplication factors (MFs), a measure of the magnitude of underestimation, were taken directly from the literature or derived (where the proportion of underestimated, under-ascertained, or underreported cases was known) and compared for the two pathogens. Results: MFs varied between and within diseases and countries, representing a need to carefully select the most appropriate MFs and methods for calculating them. The most appropriate MFs are often disease-, country-, age-, and sex-specific. Conclusions: When routine data are used to make decisions on resource allocation or to estimate epidemiological parameters in populations, it becomes important to understand when, where and to what extent these data represent the true picture of disease, and in some instances (such as priority setting) it is necessary to adjust for underestimation. MFs can be used to adjust notification and surveillance data to provide more realistic estimates of incidence. © 2014 Gibbons et al.; licensee BioMed Central Ltd.