Seasonal activity and abundance of Orosius orientalis (Hemiptera : Cicadellidae) at agricultural sites in Southeastern Australia

Orosius orientalis is a leafhopper vector of several viruses and phytoplasmas affecting a broad range of agricultural crops. Sweep net, yellow pan trap and yellow sticky trap collection techniques were evaluated. Seasonal distribution of O. orientalis was surveyed over two successive growing seasons around the borders of commercially grown tobacco crops. Orosius orientalis seasonal activity as assessed using pan and sticky traps was characterised by a trimodal peak and relative abundance as assessed using sweep nets differed between field sites with peak activity occurring in spring and summer months. Yellow pan traps consistently trapped a higher number of O. orientalis than yellow sticky traps.

Analysing Seasonal Health Data

Seasonal patterns have been found in a remarkable range of health conditions, including birth defects, respiratory infections and cardiovascular disease. Accurately estimating the size and timing of seasonal peaks in disease incidence is an aid to understanding the causes and possibly to developing interventions. With global warming increasing the intensity of seasonal weather patterns around the world, a review of the methods for estimating seasonal effects on health is timely. This is the first book on statistical methods for seasonal data written for a health audience. It describes methods for a range of outcomes (including continuous, count and binomial data) and demonstrates appropriate techniques for summarising and modelling these data. It has a practical focus and uses interesting examples to motivate and illustrate the methods. The statistical procedures and example data sets are available in an R package called ‘season’. Adrian Barnett is a senior research fellow at Queensland University of Technology, Australia. Annette Dobson is a Professor of Biostatistics at The University of Queensland, Australia. Both are experienced medical statisticians with a commitment to statistical education and have previously collaborated in research in the methodological developments and applications of biostatistics, especially to time series data. Among other projects, they worked together on revising the well-known textbook "An Introduction to Generalized Linear Models," third edition, Chapman Hall/CRC, 2008. In their new book they share their knowledge of statistical methods for examining seasonal patterns in health.

Seasonal Variation in Measured Solar Ultraviolet Radiation Exposure of Adults in Subtropical Australia

Generating accurate population-specific public health messages regarding sun protection requires knowledge about seasonal variation in sun exposure in different environments. To address this issue for a subtropical area of Australia, we used polysulphone badges to measure UVR for the township of Nambour (26° latitude) and personal UVR exposure among Nambour residents who were taking part in a skin cancer prevention trial. Badges were worn by participants for two winter and two summer days. The ambient UVR was approximately three times as high in summer as in winter. However, participants received more than twice the proportion of available UVR in winter as in summer (6.5%vs 2.7%, P < 0.05), resulting in an average ratio of summer to winter personal UVR exposure of 1.35. The average absolute difference in daily dose between summer and winter was only one-seventh of a minimal erythemal dose. Extrapolating from our data, we estimate that ca. 42% of the total exposure received in the 6 months of winter (June–August) and summer (December–February) is received during the three winter months. Our data show that in Queensland a substantial proportion of people’s annual UVR dose is obtained in winter, underscoring the need for dissemination of sun protection messages throughout the year in subtropical and tropical climates.

The seasonal phenology of Bactrocera tryoni (Froggatt) (Diptera: Tephritidae) in Queensland

Bactrocera tryoni is a polyphagous fruit fly, originally endemic to tropical and subtropical coastal eastern Australia, but now also widely distributed in temperate eastern Australia. In temperate parts of its range, B. tryoni populations show distinct seasonal peaks driven by changing seasonal climates, especially changing temperature. In contrast to temperate areas, the seasonal phenology of B. tryoni in subtropical and tropical parts of its range is poorly documented and the role of climate unknown. Using a large, historical (1940s and 1950s) fruit fly trapping data set, we present the seasonal phenology of B. tryoni at nine sites across Queensland for multiple (two to seven) years per site. We correlate monthly trap data for each site with monthly weather averages (temperature, rainfall and relative humidity) to investigate climatic influences. We also correlate observed population data with predicted population data generated by an existing B. tryoni population model. Supporting predictions from climate driven models, B. tryoni did show year-round breeding at most Queensland sites. However, contrary to predictions, there was a common pattern of a significant population decline in autumn and winter, followed by a rapid population increase in August and then one or more distinct peaks of abundance in spring and summer. Mean monthly fly abundance was significantly different across sites, but was not correlated with altitudinal, latitudinal or longitudinal gradients. There were very few significant correlations between monthly fly population size and weather variables for eight of the nine sites. For the southern site of Gatton fly population abundance was correlated with temperature. Results suggest that while climate factors may be influencing B. tryoni populations in southern subtropical Queensland, they appear to be having only minor or no influence in northern sub-tropical and tropical Queensland. In the discussion we focus on the role of other factors, particularly larval host plant availability, as likely drivers of B. tryoni abundance in tropical and subtropical parts of its range.

Hot Summers and Heart Failure: Seasonal Variations in Morbidity and Mortality in Australian Heart Failure Patients (1994-2005)

Background There are minimal reports of seasonal variations in chronic heart failure (CHF)-related morbidity and mortality beyond the northern hemisphere. Aims and methods We examined potential seasonal variations with respect to morbidity and all-cause mortality over more than a decade in a cohort of 2961 patients with CHF from a tertiary referral hospital in South Australia subject to mild winters and hot summers. Results Seasonal variation across all event-types was observed. CHF-related morbidity peaked in winter (July) and was lowest in summer (February): 70 (95% CI: 65 to 76) vs. 33 (95% CI: 30 to 37) admissions/1000 at risk (p<0.005). All-cause admissions (113 (95% CI: 107 to 120) vs. 73 (95% CI 68 to 79) admissions/1000 at risk, p<0.001) and concurrent respiratory disease (21% vs. 12%,p<0.001) were consistently higher in winter. 2010 patients died, mortality was highest in August relative to February: 23 (95% CI: 20 to 27) vs. 12 (95% CI: 10 to 15) deaths per 1000 at risk, p<0.001. Those aged 75 years or older were most at risk of seasonal variations in morbidity and mortality. Conclusion Seasonal variations in CHF-related morbidity and mortality occur in the hot climate of South Australia, suggesting that relative (rather than absolute) changes in temperature drive this global phenomenon.

The impact of nonlinear exposure risk relationships on seasonal time series data: modelling danish neonatal birth anthropometric data

Background Birth weight and length have seasonal fluctuations. Previous analyses of birth weight by latitude effects identified seemingly contradictory results, showing both 6 and 12 monthly periodicities in weight. The aims of this paper are twofold: (a) to explore seasonal patterns in a large, Danish Medical Birth Register, and (b) to explore models based on seasonal exposures and a non-linear exposure-risk relationship. Methods Birth weight and birth lengths on over 1.5 million Danish singleton, live births were examined for seasonality. We modelled seasonal patterns based on linear, U- and J-shaped exposure-risk relationships. We then added an extra layer of complexity by modelling weighted population-based exposure patterns. Results The Danish data showed clear seasonal fluctuations for both birth weight and birth length. A bimodal model best fits the data, however the amplitude of the 6 and 12 month peaks changed over time. In the modelling exercises, U- and J-shaped exposure-risk relationships generate time series with both 6 and 12 month periodicities. Changing the weightings of the population exposure risks result in unexpected properties. A J-shaped exposure-risk relationship with a diminishing population exposure over time fitted the observed seasonal pattern in the Danish birth weight data. Conclusion In keeping with many other studies, Danish birth anthropometric data show complex and shifting seasonal patterns. We speculate that annual periodicities with non-linear exposure-risk models may underlie these findings. Understanding the nature of seasonal fluctuations can help generate candidate exposures.

Methodological challenges when estimating the effects of season and seasonal exposures on birth outcomes

Background Many previous studies have found seasonal patterns in birth outcomes, but with little agreement about which season poses the highest risk. Some of the heterogeneity between studies may be explained by a previously unknown bias. The bias occurs in retrospective cohorts which include all births occurring within a fixed start and end date, which means shorter pregnancies are missed at the start of the study, and longer pregnancies are missed at the end. Our objective was to show the potential size of this bias and how to avoid it. Methods To demonstrate the bias we simulated a retrospective birth cohort with no seasonal pattern in gestation and used a range of cohort end dates. As a real example, we used a cohort of 114,063 singleton births in Brisbane between 1 July 2005 and 30 June 2009 and examined the bias when estimating changes in gestation length associated with season (using month of conception) and a seasonal exposure (temperature). We used survival analyses with temperature as a time-dependent variable. Results We found strong artificial seasonal patterns in gestation length by month of conception, which depended on the end date of the study. The bias was avoided when the day and month of the start date was just before the day and month of the end date (regardless of year), so that the longer gestations at the start of the study were balanced by the shorter gestations at the end. After removing the fixed cohort bias there was a noticeable change in the effect of temperature on gestation length. The adjusted hazard ratios were flatter at the extremes of temperature but steeper between 15 and 25°C. Conclusions Studies using retrospective birth cohorts should account for the fixed cohort bias by removing selected births to get unbiased estimates of seasonal health effects.

Seasonal variation in cardiovascular disease risk factors in a subarctic population : the Tromso Study 1979-2008

Background Seasonal changes in cardiovascular disease (CVD) risk factors may be due to exposure to seasonal environmental variables like temperature and acute infections or seasonal behavioural patterns in physical activity and diet. Investigating the seasonal pattern of risk factors should help determine the causes of the seasonal pattern in CVD. Few studies have investigated the seasonal variation in risk factors using repeated measurements from the same individual, which is important as individual and population seasonal patterns may differ. Methods The authors investigated the seasonal pattern in systolic and diastolic blood pressure, heart rate, body weight, total cholesterol, triglycerides, high-density lipoprotein cholesterol, C reactive protein and fibrinogen. Measurements came from 38 037 participants in the population-based cohort, the Tromsø Study, examined up to eight times from 1979 to 2008. Individual and population seasonal patterns were estimated using a cosinor in a mixed model. Results All risk factors had a highly statistically significant seasonal pattern with a peak time in winter, except for triglycerides (peak in autumn), C reactive protein and fibrinogen (peak in spring). The sizes of the seasonal variations were clinically modest. Conclusions Although the authors found highly statistically significant individual seasonal patterns for all risk factors, the sizes of the changes were modest, probably because this subarctic population is well adapted to a harsh climate. Better protection against seasonal risk factors like cold weather could help reduce the winter excess in CVD observed in milder climates.

Analysing seasonal data

Many common diseases, such as the flu and cardiovascular disease, increase markedly in winter and dip in summer. These seasonal patterns have been part of life for millennia and were first noted in ancient Greece by both Hippocrates and Herodotus. Recent interest has focused on climate change, and the concern that seasons will become more extreme with harsher winter and summer weather. We describe a set of R functions designed to model seasonal patterns in disease. We illustrate some simple descriptive and graphical methods, a more complex method that is able to model non-stationary patterns, and the case–crossover for controlling for seasonal confounding.

Role of land use and seasonal factors in water quality degradation

Surface water and groundwater are the most important water sources in the natural environment. Land use and seasonal factors play an important role in influencing the quality of these water sources. An in-depth understanding of the role of these two influential factors can help to implement an effective catchment management strategy for the protection of these water sources. This paper discusses the outcomes of an extensive research study which investigated the role of land use and seasonal factors on surface water and groundwater pollution in a mixed land use coastal catchment. The study confirmed that the influence exerted on the water environment by seasonal factors is secondary to that of land use. Furthermore, the influence of land use and seasonal factors on surface water and groundwater quality varies with the pollutant species. This highlights the need to specifically take into consideration the targeted pollutants and the key influential factors for the effective protection of vulnerable receiving water environments.

Seasonal amplitude of hemorrhagic fever with renal syndrome in China : a call for attention to neglected regions

Hemorrhagic fever with renal syndrome (HFRS), a rodent-borne viral disease characterized by fever, hemorrhagic, kidney damage and hypotension, is caused by different species of hantaviruses [1]. Every year, HFRS affects thousands of people in Asia, and more than 90% of these cases are reported in China [2, 3]. Due to its high fatality, HFRS has attracted considerable research attention, and prior studies have predominantly focused on quantifying HFRS morbidity [4], identifying high risk areas [5] and populations [6], or exploring peak time of HFRS occurrence [3]. To date, no study has assessed the seasonal amplitude of HFRS in China, even though it reveals the seasonal fluctuation and thus may provide pivotal information on the possibility of HFRS outbreaks.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Seasonal factors influencing the failure of buried water reticulation pipes

While the use of environmental factors in the analysis and prediction of failures of buried reticulation pipes in cold environments has been the focus of extensive work, the same cannot be said for failures occurring on pipes in other (non-freezing) environments. A novel analysis of pipe failures in such an environment is the subject of this paper. An exploratory statistical analysis was undertaken, identifying a peak in failure rates during mid to late summer. This peak was found to correspond to a peak in the rate of circumferential failures, whilst the rate of longitudinal failures remained constant. Investigation into the effect of climate on failure rates revealed that the peak in failure rates occurs due to differential soil movement as the result of shrinkage in expansive soils.