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Most evidence on seasonal admission patterns for schizophrenia derives from the Northern Hemisphere with results from the Southern Hemisphere less documented. This study examines seasonal patterns in hospital admissions due to schizophrenia in Queensland, Australia, a large area that has a range of different climatic features. Daily hospital admissions data for people with the primary diagnosis of schizophrenia were collected from Queensland Health Department for the period from January 1996 to December 2015. A generalised linear regression model with Quasi-Poisson distribution was used to assess seasonal admission patterns across different climatic regions. The evidence for seasonality was also explored in subgroups that had different socio-demographic characteristics or history of prior hospitalisation for psychiatric disorders. Overall, a significant winter pattern (RR 1.05, 95%CI 1.01-1.13) was found with a peak in August (RR 1.08, 95%CI 1.03-1.17) in temperate Southeast Queensland. However, the hot humid North and Far North Queensland showed a peak in October (RR 1.10, 95%CI 1.02-1.22). Males (RR 1.11, 95%CI 1.07-1.14), people aged 40-59 years old (RR 1.10, 95%CI 1.05-1.15) and those who had never married (RR 1.09, 95%CI 1.06-1.12), were Australian by birth (RR 1.07, 95%CI 1.04-1.10) or were unemployed (RR 1.13, 95%CI 1.09-1.18) had significantly higher risk for hospital admissions, particularly during the winter months. The seasonal admission pattern for schizophrenia did not change significantly according to admission status and history of outpatient or community psychiatric treatment. The study found some evidence for seasonality of hospital admissions for schizophrenia that differed from northern tropical to southern temperate regions of Queensland.

Malaria is a major public health problem in Vishakhapatnam district of Andhra Pradesh, India. To understand malaria prevalence a retrospective surveillance study was conducted in the district from 1995 to 2015. A total of 204,229 malaria cases were reported from 1999 to 2015. Plasmodium falciparum and Plasmodium vivax are the major parasites that accounted for 66.8% and 33.2% of the total cases. Tribal population (67%) affected more than the coastal population (33%). Similarly, males were affected (56%) more than female (44%) populace and the highest prevalence was observed in > 15 years age group (83.74%). The spatial analysis reveals that the distribution of malaria is having high spatial autocorrelation (0.231 to 0.493) and scan statistics declare that the malaria cases were significantly clustered in spatial, temporal and spatiotemporal distribution. The most likely spatiotemporal cluster of malaria (LLR = 26,562.24, RR = 6.62, P < 0.001) occurred in the Northern part of the district covering 11 mandals with the time frame from April 2010 to September 2015. The results confirm that the presence of spatial and space–time clusters concentrated in the North and North-eastern region of the district, which contribute for better understanding of disease spreading dynamics in high-risk areas for future malaria prevention and control.

Background Dengue is a significant public health concern in northern Queensland, Australia. This study compared the epidemic features of dengue transmission among different climate zones and explored the threshold of weather variability for climate zones in relation to dengue in Queensland, Australia. Methods Daily data on dengue cases and weather variables including minimum temperature, maximum temperature and rainfall for the period of January 1, 2010 to December 31, 2015 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Climate zones shape file for Australia was also obtained from Australian Bureau of Meteorology. Kruskal-Wallis test was performed to check whether the distribution of dengue significantly differed between climate zones. Time series regression tree model was used to estimate the threshold effects of the monthly weather variables on dengue in different climate zones. Results During the study period, the highest dengue incidence rate was found in the tropical climate zone (15.09/10,000) with the second highest in the grassland climate zone (3.49/10,000). Dengue responded differently to weather variability in different climate zones. In every climate zone, temperature was the primary predictor of dengue. However, the threshold values, type of temperature (e.g. maximum, minimum, or mean), and lag time for dengue varied between climate zones. Monthly mean temperature above 27°C at a lag of two months and monthly minimum temperature above 22°C at a lag of one month was found to be the most favourable weather condition for dengue in the tropical and subtropical climate zone, respectively. However, in the grassland climate zone, maximum temperature above 38°C at a lag of five months was found to be the ideal condition for dengue. Monthly rainfall with threshold value of 1.7 mm was found to be a significant contributor to dengue only in the tropical climate zone. Conclusions The temperature threshold for dengue was lower in both tropical and subtropical climate zones than in the grassland climate zone. The different temperature threshold between climate zones suggests that an early warning system would need to be developed based on local socio-ecological conditions of the climate zone to manage dengue control and intervention programs effectively.

Schizophrenia is a severe neuropsychiatric disorder with heterogeneous aetiology mostly affecting younger people and causing immense disability. Seasonal patterns may be observed in schizophrenia hospital admissions with possible association with changing climatic parameters and socio-demographic characteristics. This study critically reviewed studies that have assessed seasonal variations of hospital admissions for schizophrenia and/or explored an association with climate parameters and/or other potential factors. Following PRISMA guidelines, a systematic literature search was conducted using electronic databases (e.g. MEDLINE, Science Direct, PsycINFO, Pub Med) from inception to February 29, 2020. Thirty five papers were identified, of which only six (17.1%) examined evidence for a seasonal pattern or monthly excess of hospital admissions and the remaining twenty nine (82.9%) assessed climatic and socio-demographic attributes relating to the seasonal pattern or increased hospitalisation for schizophrenia. While most studies reported a summer peak in hospital admission rates, other studies reported a winter peak. Most of the evidence indicated that higher temperatures (> 28 °C) were positively correlated with schizophrenia admission rates. The individual effects of other climatic parameters (e.g. relative humidity, rainfall, atmospheric pressure, sunlight) were less frequently assessed. Males, people of 21–60 years old, and those married were more vulnerable to climatic variability specifically to higher temperatures. Further studies using large sample sizes, analysis of a wide range of interacting environmental variables and sophisticated statistical approaches are needed to better understand the underlying mechanisms involved. This will also provide more reliable statistical evidence that will help in the prevention and better management of cases.

Dengue, an acute infectious disease, is a public health concern in northern Queensland, Australia characterised by annual outbreaks. This study explored spatio-temporal characteristics of dengue ca...

Checklist of items of the PRISMA protocol addressed. (DOCX 26Â kb)

Search strategies carried on Pubmed, Embase and Scopus. (DOCX 17Â kb)

All measures of risk and covariates reported in the eligible studies. (DOCX 167Â kb)

Data extraction form, Items of data collected from the eligible studies. (DOCX 18Â kb)

Climate change and solar ultraviolet radiation may affect vaccine-preventable infectious diseases (VPID), the human immune response process and the immunization service delivery system. We systematically reviewed the scientific literature and identified 37 relevant publications. Our study shows that climate variability and ultraviolet radiation may potentially affect VPID and the immunization delivery system through modulating vector reproduction and vaccination effectiveness, possibly influencing human immune response systems to the vaccination, and disturbing immunization service delivery. Further research is needed to determine these affects on climate-sensitive VPID and on human immune response to common vaccines. Such research will facilitate the development and delivery of optimal vaccination programs for target populations, to meet the goal of disease control and elimination.

Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992–1993. This study explored spatio-temporal distribution and clustering of locally-acquired dengue cases in Queensland State, Australia and identified target areas for effective interventions. A computerised locally-acquired dengue case dataset was collected from Queensland Health for Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. Dengue hot spots were detected using SatScan method. Descriptive spatial analysis showed that a total of 2,398 locally-acquired dengue cases were recorded in central and northern regions of tropical Queensland. A seasonal pattern was observed with most of the cases occurring in autumn. Spatial and temporal variation of dengue cases was observed in the geographic areas affected by dengue over time. Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in tropical Queensland, Australia. There is a clear evidence for the existence of statistically significant clusters of dengue and these clusters varied over time. These findings enabled us to detect and target dengue clusters suggesting that the use of geospatial information can assist the health authority in planning dengue control activities and it would allow for better design and implementation of dengue management programs.

Background and objectives: Dengue is an emerging and re-emerging infectious disease, transmitted by mosquitoes. It is mostly prevalent in tropical and sub-tropical regions of the world, particularly, in Asia-Pacific region. To understand the epidemiology and spatial distribution of dengue, a retrospective surveillance study was conducted in the state of Andhra Pradesh, India during 2011–2013. Material and methods: District-wise disease endemicity levels were mapped through geographical information system (GIS) tools. Spatial statistical analysis such as Getis-Ord Gi* was performed to identify hot spots and cold spots of dengue disease. Similarly self organizing maps (SOM), a datamining tool was also applied to understand the endemicity patterns in study areas. Results: The analysis shows that districts of Warangal, Karimnagar, Khammam and Vizianagaram are reported as hot spot regions whereas Adilabad and Nizamabad reported as cold spots for dengue. The SOM classify 23 districts in 03 major (07 sub) clusters. These SOM clusters were projected in the geographical space and based on the disease/cases intensity the districts were characterized into low, medium and high endemic areas. Conclusion: This visualization approach, SOM-GIS helps the public health officials to identify the disease endemic zones and take real time decisions for disease management.

Barmah Forest virus (BFV) disease is one of the most widespread mosquito-borne diseases in Australia. The number of outbreaks and the incidence rate of BFV in Australia have attracted growing concerns about the spatio-temporal complexity and underlying risk factors of BFV disease. A large number of notifications has been recorded continuously in Queensland since 1992. Yet, little is known about the spatial and temporal characteristics of the disease. I aim to use notification data to better understand the effects of climatic, demographic, socio-economic and ecological risk factors on the spatial epidemiology of BFV disease transmission, develop predictive risk models and forecast future disease risks under climate change scenarios. Computerised data files of daily notifications of BFV disease and climatic variables in Queensland during 1992-2008 were obtained from Queensland Health and Australian Bureau of Meteorology, respectively. Projections on climate data for years 2025, 2050 and 2100 were obtained from Council of Scientific Industrial Research Organisation. Data on socio-economic, demographic and ecological factors were also obtained from relevant government departments as follows: 1) socio-economic and demographic data from Australian Bureau of Statistics; 2) wetlands data from Department of Environment and Resource Management and 3) tidal readings from Queensland Department of Transport and Main roads. Disease notifications were geocoded and spatial and temporal patterns of disease were investigated using geostatistics. Visualisation of BFV disease incidence rates through mapping reveals the presence of substantial spatio-temporal variation at statistical local areas (SLA) over time. Results reveal high incidence rates of BFV disease along coastal areas compared to the whole area of Queensland. A Mantel-Haenszel Chi-square analysis for trend reveals a statistically significant relationship between BFV disease incidence rates and age groups (ƒO2 = 7587, p

Dengue has been a major public health concern in Australia. This study has explored the spatio-temporal trends of dengue and potential socio- demographic and ecological determinants in Australia. Data on dengue cases, socio-demographic, climatic and land use types for the period January 1999 to December 2010 were collected from Australian National Notifiable Diseases Surveillance System, Australian Bureau of Statistics, Australian Bureau of Meteorology, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. Descriptive and linear regression analyses were performed to observe the spatio-temporal trends of dengue, socio-demographic and ecological factors in Australia. A total of 5,853 dengue cases (both local and overseas acquired) were recorded across Australia between January 1999 and December 2010. Most the cases (53.0%) were reported from Queensland, followed by New South Wales (16.5%). Dengue outbreak was highest (54.2%) during 2008-2010. A highest percentage of overseas arrivals (29.9%), households having rainwater tanks (33.9%), Indigenous population (27.2%), separate houses (26.5%), terrace house types (26.9%) and economically advantage people (42.8%) were also observed during 2008-2010. Regression analyses demonstrate that there was an increasing trend of dengue incidence, potential socio-ecological factors such as overseas arrivals, number of households having rainwater tanks, housing types and land use types (e.g. intensive uses and production from dryland agriculture). Spatial variation of socio-demographic factors was also observed in this study. In near future, significant increase of temperature was also projected across Australia. The projected increased temperature as well as increased socio-ecological trend may pose a future threat to the local transmission of dengue in other parts of Australia if Aedes mosquitoes are being established. Therefore, upgraded mosquito and disease surveillance at different ports should be in place to reduce the chance of mosquitoes and dengue cases being imported into all over Australia.

BACKGROUND: Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. METHODS: Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. RESULTS: 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f. = 1, p

BackgroundMosquito-borne diseases are climate sensitive and there has been increasing concern over the impact of climate change on future disease risk. This paper projected the potential future risk of Barmah Forest virus (BFV) disease under climate change scenarios in Queensland, Australia.Methods/principal findingsWe obtained data on notified BFV cases, climate (maximum and minimum temperature and rainfall), socio-economic and tidal conditions for current period 2000-2008 for coastal regions in Queensland. Grid-data on future climate projections for 2025, 2050 and 2100 were also obtained. Logistic regression models were built to forecast the otential risk of BFV disease distribution under existing climatic, socio-economic and tidal conditions. The model was applied to estimate the potential geographic distribution of BFV outbreaks under climate change scenarios. The predictive model had good model accuracy, sensitivity and specificity. Maps on potential risk of future BFV disease indicated that disease would vary significantly across coastal regions in Queensland by 2100 due to marked differences in future rainfall and temperature projections.Conclusions/significanceWe conclude that the results of this study demonstrate that the future risk of BFV disease would vary across coastal regions in Queensland. These results may be helpful for public health decision making towards developing effective risk management strategies for BFV disease control and prevention programs in Queensland.

Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia, but the linkages of the wetlands and climate zones with BFV transmission remain unclear. We aimed to examine the relationship between the wetlands, climate zones and BFV risk in Queensland, Australia. Data on the wetlands, climate zones, population and BFV cases for the period 1992 to 2008 were obtained from relevant government agencies. BFV risk was grouped as low-, medium- and high-level based on BFV incidence percentiles. The buffer zones around each BFV case were made using 1, 5, 10, 15, 20, 25 and 50 km distances. We performed a discriminant analysis to determine the differences between wetland classes and BFV risk within each climate zone. The discriminant analyses show that saline 1, riverine and saline tidal influence were the most significant contributors to BFV risk in all climate and buffer zones, while lacustrine, palustrine, estuarine and saline 2 and saline 3 wetlands were less important. These models had classification accuracies of 76%, 98% and 100% for BFV risk in subtropical, tropical and temperate climate zones, respectively. This study demonstrates that BFV risk varies with wetland class and climate zone. The discriminant analysis is a useful tool to quantify the links between wetlands, climate zones and BFV risk.

Background Many studies have found associations between climatic conditions and dengue transmission. However, there is a debate about the future impacts of climate change on dengue transmission. This paper reviewed epidemiological evidence on the relationship between climate and dengue with a focus on quantitative methods for assessing the potential impacts of climate change on global dengue transmission. Methods A literature search was conducted in October 2012, using the electronic databases PubMed, Scopus, ScienceDirect, ProQuest, and Web of Science. The search focused on peer-reviewed journal articles published in English from January 1991 through October 2012. Results Sixteen studies met the inclusion criteria and most studies showed that the transmission of dengue is highly sensitive to climatic conditions, especially temperature, rainfall and relative humidity. Studies on the potential impacts of climate change on dengue indicate increased climatic suitability for transmission and an expansion of the geographic regions at risk during this century. A variety of quantitative modelling approaches were used in the studies. Several key methodological issues and current knowledge gaps were identified through this review. Conclusions It is important to assemble spatio-temporal patterns of dengue transmission compatible with long-term data on climate and other socio-ecological changes and this would advance projections of dengue risks associated with climate change.

Barmah Forest virus (BFV) disease is the second most common mosquito-borne disease in Australia but few data are available on the risk factors. We assessed the impact of spatial climatic, socioeconomic and ecological factors on the transmission of BFV disease in Queensland, Australia, using spatial regression. All our analyses indicate that spatial lag models provide a superior fit to the data compared to spatial error and ordinary least square models. The residuals of the spatial lag models were found to be uncorrelated, indicating that the models adequately account for spatial and temporal autocorrelation. Our results revealed that minimum temperature, distance from coast and low tide were negatively and rainfall was positively associated with BFV disease in coastal areas, whereas minimum temperature and high tide were negatively and rainfall was positively associated with BFV disease (all P-value

In this study we examined the impact of weather variability and tides on the transmission of Barmah Forest virus (BFV) disease and developed a weather-based forecasting model for BFV disease in the Gladstone region, Australia. We used seasonal autoregressive integrated moving-average (SARIMA) models to determine the contribution of weather variables to BFV transmission after the time-series data of response and explanatory variables were made stationary through seasonal differencing. We obtained data on the monthly counts of BFV cases, weather variables (e.g., mean minimum and maximum temperature, total rainfall, and mean relative humidity), high and low tides, and the population size in the Gladstone region between January 1992 and December 2001 from the Queensland Department of Health, Australian Bureau of Meteorology, Queensland Department of Transport, and Australian Bureau of Statistics, respectively. The SARIMA model shows that the 5-month moving average of minimum temperature (β = 0.15, p-value < 0.001) was statistically significantly and positively associated with BFV disease, whereas high tide in the current month (β = −1.03, p-value = 0.04) was statistically significantly and inversely associated with it. However, no significant association was found for other variables. These results may be applied to forecast the occurrence of BFV disease and to use public health resources in BFV control and prevention.