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Background Vector surveillance is an essential public health tool to aid in the prediction and prevention of mosquito borne diseases. This study compared spatial and temporal trends of vector surveillance indices for Aedes vectors in the southern Philippines, and assessed potential links between vector indices and climate factors. Methods We analysed routinely collected larval and pupal surveillance data from residential areas of 14 cities and 51 municipalities during 2013–2018 (House, Container, Breteau and Pupal Indices), and used linear regression to explore potential relationships between vector indices and climate variables (minimum temperature, maximum temperature and precipitation). Results We found substantial spatial and temporal variation in monthly Aedes vector indices between cities during the study period, and no seasonal trend apparent. The House (HI), Container (CI) and Breteau (BI) Indices remained at comparable levels across most surveys (mean HI = 15, mean CI = 16, mean BI = 24), while the Pupal Productivity Index (PPI) was relatively lower in most months (usually below 5) except for two main peak periods (mean = 49 overall). A small proportion of locations recorded high values across all entomological indices in multiple surveys. Each of the vector indices were significantly correlated with one or more climate variables when matched to data from the same month or the previous 1 or 2 months, although the effect sizes were small. Significant associations were identified between minimum temperature and HI, CI and BI in the same month (R2 = 0.038, p = 0.007; R2 = 0.029, p = 0.018; and R2 = 0.034, p = 0.011, respectively), maximum temperature and PPI with a 2-month lag (R2 = 0.031, p = 0.032), and precipitation and HI in the same month (R2 = 0.023, p = 0.04). Conclusions Our findings indicated that larval and pupal surveillance indices were highly variable, were regularly above the threshold for triggering vec

Air pollution has previously been linked to several adverse health outcomes, but the potential association between air pollution and liver cancer remains unclear. We searched PubMed, EMBASE, and Web of Science from inception to 10 October 2021, and manually reviewed the references of relevant papers to further identify any related literature investigating possible associations between air pollution and liver cancer. Risk estimates values were represented by statistical associations based on quantitative analyses. A total of 13 cohort studies obtained from 11 articles were included, with 10,961,717 participants. PM2.5 was the most frequently examined pollutant (included in 11 studies), followed by NO2 and NOx (included in 6 studies), and fewer studies focused on other pollutants (PM2.5 absorbance, PM10, PM2.5–10, O3, and BC). In all the 16 associations for liver cancer mortality, 14 associations reported the effect of PM2.5 on liver cancer mortality. Eight associations on PM2.5 were significant, showing a suggestive association between PM2.5 and liver cancer mortality. Among 24 associations shown by risk estimates for liver cancer incidence, most associations were not statistically significant. For other air pollutants, no positive associations were presented in these studies. PM2.5 was the most frequently examined pollutant, followed by NO2 and NOx, and fewer studies focused on other pollutants. PM2.5 was associated with liver cancer mortality, but there was no association for other air pollutants. Future research should use advanced statistical methods to further assess the impact of multiple air pollutants on liver cancer in the changing socio-environmental context.

Since the beginning of the COVID-19 pandemic in early 2020, global efforts to respond to and control COVID-19 have varied widely with some countries, including Australia, successfully containing local transmission, and minimising negative impacts to health and economies. Over this time, global awareness of climate variability due to climate change and the risk factors for emerging infectious diseases transmission has increased alongside an understanding of the inextricable relationship between the health of the environment, humans, and animals. Overall, the global response to the current pandemic suggests there is an urgent need for a One Health approach in controlling and preventing future pandemics, through developing integrated, dynamic, spatiotemporal early warning systems based on a One Health approach for emerging infectious diseases.

What is already known on this topic? Different socioecological factors were associated with childhood pneumonia in Bangladesh. However, previous studies did not assess spatial patterns, and socioecological factors and spatial variation have the potential to improve the accuracy and predictive ability of existing models. What is added by this report? The spatial random effects were present at the district level and were heterogeneous. Average temperature, temperature variation, and population density may influence the spatial pattern of childhood pneumonia in Bangladesh. What are the implications for public health practice? The study results will help policymakers and health managers to identify the vulnerable districts, plan further investigations, help to improve proper resource allocation, and improve health interventions.

Severe COVID-19 cases place immediate pressure on hospital resources. To assess this, we analysed survival duration in the first 39 fatal cases in Wuhan, China. Time from onset and hospitalization to death declined rapidly, from ~40 to 7 days, and ~25 to 4 days, respectively, in the outbreak’s first month.

COVID-19 is causing a significant burden on medical and healthcare resources globally due to high numbers of hospitalisations and deaths recorded as the pandemic continues. This research aims to assess the effects of climate factors (i.e., daily average temperature and average relative humidity) on effective reproductive number of COVID-19 outbreak in Wuhan, China during the early stage of the outbreak. Our research showed that effective reproductive number of COVID-19 will increase by 7.6% (95% Confidence Interval: 5.4% ~ 9.8%) per 1°C drop in mean temperature at prior moving average of 0–8 days lag in Wuhan, China. Our results indicate temperature was negatively associated with COVID-19 transmissibility during early stages of the outbreak in Wuhan, suggesting temperature is likely to effect COVID-19 transmission. These results suggest increased precautions should be taken in the colder seasons to reduce COVID-19 transmission in the future, based on past success in controlling the pandemic in Wuhan, China.

Background: The health impacts of heatwaves are a growing public health concern with the frequency, intensity, and duration of heatwaves increasing with global climate change. However, little is known about the healthcare costs and the attributable morbidity associated with heatwaves Objective This study aims to examine the relationship between heatwaves and costs of emergency department (ED) presentations, and to quantify heat-attributable burden during the warm seasons of 2014–2017, in Adelaide, South Australia. Methods: Daily data on ED presentations and associated costs for the period 2014–2017 were obtained from the South Australian Department of Health and Wellbeing. Heatwave intensity was determined using the excess heat factor (EHF) index, obtained from the Australian Bureau of Meteorology. A distributed lag non-linear model (DLNM) was used to quantify the cumulative risk of heatwave-intensity over a lag of 0–7 days on ED presentations and costs. Effects of heatwaves were estimated relative to no heatwave. The number of ED presentations and costs attributable to heatwaves was calculated separately for two EHF severity categories (low-intensity and severe/extreme heatwaves). Subgroup analyses by disease-diagnosis groups and age categories were performed. Results: For most disease diagnosis and age categories, low-intensity and severe heatwaves were associated with higher rates of ED presentations and costs. We estimated a total of 1161 (95% empirical confidence interval (eCI): 342, 1944) heatwave-attributable all-cause ED presentations and associated healthcare costs (thousands) of AU$1020.3 (95% eCI: 224.9, 1804.7) during the warm seasons of 2014–2017. The heat-related illness was the disease category contributing most to ED presentations and costs. Age groups 0–14 and ≥ 65 years were most susceptible to heat. Conclusions: Heatwaves produced a statistically significant case-load and cost burden to the ED. Developing tailored interventions for the most vul

The use of internet-based query data offers a novel approach to improve disease surveillance and provides timely disease information. This paper systematically reviewed the literature on infectious disease predictions using internet-based query data and climate factors, discussed the current research progress and challenges, and provided some recommendations for future studies. We searched the relevant articles in the PubMed, Scopus, and Web of Science databases between January 2000 and December 2019. We initially included studies that used internet-based query data to predict infectious disease epidemics, then we further filtered and appraised the studies that used both internet-based query data and climate factors. In total, 129 relevant papers were included in the review. The results showed that most studies used a simple descriptive approach (n=80; 62%) to detect epidemics of influenza (including influenza-like illness (ILI)) (n=88; 68%) and dengue (n=9; 7%). Most studies (n=61; 47%) purely used internet search metrics to predict the epidemics of infectious diseases, while only 3 out of the 129 papers included both climate variables and internet-based query data. Our research shows that including internet-based query data and climate variables could better predict climate-sensitive infectious disease epidemics; however, this method has not been widely used to date. Moreover, previous studies did not sufficiently consider the spatiotemporal uncertainty of infectious diseases. Our review suggests that further research should use both internet-based query and climate data to develop predictive models for climate-sensitive infectious diseases based on spatiotemporal models.

Background: Pacific Island countries, many of which are low- and middle-income countries, have some of the highest rates of diet-related non-communicable diseases (DR-NCDs) globally. These countries also face some of the earliest and most significant impacts of climate change. Several pathways between climate change and DR-NCDs have been described in the literature; however, the scope is broad and lacks context specificity. This paper uses a case study of one Pacific Island country, Vanuatu, to investigate links between climate change and DR-NCDs. Methods: An ethnographic qualitative research approach was used to share the lived experiences of community participants and to explore and contrast these with the perspectives of key informants at the national level. Data collection comprised thirty-two semi-structured interviews and community fieldwork in two villages using a mix of methods, including group workshops, informal conversations, and observations. Reflexive thematic analysis was conducted on both data sets. Results: This study found that DR-NCDs are a prominent health concern for ni-Vanuatu people and that structural determinants, including climate change, are the main driving forces for increased DR-NCD risk in the country. However, there was a lack of understanding of the links between climate change and DR-NCDs both at the community and national levels. Structural factors, such as social determinants and climate change, constrained individual and community agency in making optimal food and health choices and promoted the nutrition transition in Vanuatu. Despite the critical role of social determinants and climate change in driving DR-NCD risk, the responsibility for prevention and treatment was considered to rest mainly with the individual. A systems approach is advocated to grasp the complexity and interrelatedness of the causes of DR-NCD risk. Conclusions: The interaction of structural determinants creates food and health environments that am

Food security is identified as one of the main challenges for the 21st century. In the Pacific islands this is especially the case due to natural, topographical, social, environmental, and economic constraints and the influence of globalisation that has forced many populations to resort to imported food. While hunger is rarely experienced by households, poor nutrition leads to a considerable noncommunicable disease burden. The effects of climate change will further exacerbate food security challenges in the region. Kiribati has one of the highest prevalence rates for obesity, diabetes and hypertension anywhere in the world. While past studies have explored regional and state-level food security challenges, there is comparatively little literature on household-level decision making processes related to food. Through focus group discussions and semi-structured interviews, we identified several themes describing challenges to achieving food security in the island nation, including several environmental challenges that hinder food security. We also include some policy recommendations based on these findings. These challenges and policy recommendations may be applicable to other atoll island communities in the Pacific and other remote coastal communities worldwide.

Increasing temperature and its impact on population health is an emerging significant public health issue in the context of climate change in Australia. While previous studies have primarily focused on risk assessment, very few studies have evaluated heat-attributable emergency department (ED) visits and associated healthcare costs, or projected future health and economic burdens. This study used a distributed lag non-linear model to estimate heat attributable ED visits and associated healthcare costs from 13 hospitals in Perth, Western Australia, and to project the future healthcare costs in 2030s and 2050s under three climate change scenarios - Representative Concentration Pathways (RCPs)2.6, RCP4.5 and RCP8.5. There were 3697 ED visits attributable to heat (temperatures above 20.5 C) over the study period 2012-2019, accounting for 4.6% of the total ED visits. This resulted in AU$ 2.9 million in heat-attributable healthcare costs. The number of ED visits projected to occur in the 2030s and 2050s ranges from 5707 to 9421 under different climate change scenarios, which would equate to AU$ 4.6-7.6 million in heat associated healthcare costs. The heat attributable fraction for ED visits and associated healthcare costs would increase from 4.6% and 4.1% in 2010s to 5.0%-6.3% and 4.4%-5.6% in 2030s and 2050s, respectively. Future heat attributable ED visits and associated costs will increase in Perth due to climate change. Excess heat will generate a substantial population health challenge and economic burdens on the healthcare system if there is insufficient heat adaptation. It is vital to reduce greenhouse gas emissions, develop heat-related health interventions and optimize healthcare resources to mitigate the negative impact on the healthcare system and population health in the face of climate change.

Using social media for health purposes has attracted much attention over the past decade. Given the challenges of population ageing and changes in national health profile and disease patterns following the epidemiologic transition, researchers and policy-makers should pay attention to the potential of social media in chronic disease surveillance, management and support. This commentary overviews the evidence base for this inquiry and outlines the key challenges to research laying ahead. The authors provide concrete suggestions and recommendations for developing a research agenda to guide future investigation and action on this topic.

Background: Previous studies have shown associations between local weather factors and dengue incidence in tropical and subtropical regions. However, spatial variability in those associations remains unclear and evidence is scarce regarding the effects of weather extremes. Objectives: We examined spatial variability in the effects of various weather conditions on the unprecedented dengue outbreak in Guangdong province of China in 2014 and explored how city characteristics modify weather-related risk. Methods: A Bayesian spatial conditional autoregressive model was used to examine the overall and city-specific associations of dengue incidence with weather conditions including (1) average temperature, temperature variation, and average rainfall; and (2) weather extremes including numbers of days of extremely high temperature and high rainfall (both used 95th percentile as the cut-off). This model was run for cumulative dengue cases during five months from July to November (accounting for 99.8% of all dengue cases). A further analysis based on spatial variability was used to validate the modification effects by economic, demographic and environmental factors. Results: We found a positive association of dengue incidence with average temperature in seven cities (relative risk (RR) range: 1.032 to 1.153), a positive association with average rainfall in seven cities (RR range: 1.237 to 1.974), and a negative association with temperature variation in four cities (RR range: 0.315 to 0.593). There was an overall positive association of dengue incidence with extremely high temperature (RR:1.054, 95% credible interval (CI): 1.016 to 1.094), without evidence of variation across cities, and an overall positive association of dengue with extremely high rainfall (RR:1.505, 95% CI: 1.096 to 2.080), with seven regions having stronger associations (RR range: 1.237 to 1.418). Greater effects of weather conditions appeared to occur in cities with higher economic level, lower green sp

Background: The mosquitoes Aedes aegypti and Ae. albopictus are the primary vectors of dengue virus, and their geographic distributions are predicted to expand further with economic development, and in response to climate change. We aimed to estimate the impact of future climate change on dengue transmission through the development of a Suitable Conditions Index (SCI), based on climatic variables known to support vectorial capacity. We calculated the SCI based on various climate change scenarios for six countries in the Asia-Pacific region (Australia, China, Indonesia, The Philippines, Thailand and Vietnam). Methods: Monthly raster climate data (temperature and precipitation) were collected for the period January 2005 to December 2018 along with projected climate estimates for the years 2030, 2050 and 2070 using Representative Concentration Pathway (RCP) 4·5, 6·0 and 8·5 emissions scenarios. We defined suitable temperature ranges for dengue transmission of between 17·05–34·61 °C for Ae. aegypti and 15·84–31·51 °C for Ae. albopictus and then developed a historical and predicted SCI based on weather variability to measure the expected geographic limits of dengue vectorial capacity. Historical and projected SCI values were compared through difference maps for the six countries. Findings: Comparing different emission scenarios across all countries, we found that most South East Asian countries showed either a stable pattern of high suitability, or a potential decline in suitability for both vectors from 2030 to 2070, with a declining pattern particularly evident for Ae. albopictus. Temperate areas of both China and Australia showed a less stable pattern, with both moderate increases and decreases in suitability for each vector in different regions between 2030 and 2070. Interpretation: The SCI will be a useful index for forecasting potential dengue risk distributions in response to climate change, and independently of the effects of human activity. When considered al

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006–2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006–2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115–251% around 6 weeks after heatwaves, 173–258% around 6–13 weeks after extremely high rainfall, and 572–587% around 6–13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model’s sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

Background: Dengue is a wide-spread mosquito-borne disease globally with a likelihood of becoming endemic in tropical Queensland, Australia. The aim of this study was to analyse the spatial variation of dengue notifications in relation to climate variability and socio-ecological factors in the tropical climate zone of Queensland, Australia. Methods: Data on the number of dengue cases and climate variables including minimum temperature, maximum temperature and rainfall for the period of January 1st, 2010 to December 31st, 2015 were obtained for each Statistical Local Area (SLA) from Queensland Health and Australian Bureau of Meteorology, respectively. Socio-ecological data including estimated resident population, percentage of Indigenous population, housing structure (specifically terrace house), socio-economic index and land use types for each SLA were obtained from Australian Bureau of Statistics, and Australian Bureau of Agricultural and Resource Economics and Sciences, respectively. To quantify the relationship between dengue, climate and socio-ecological factors, multivariate Poisson regression models in a Bayesian framework were developed with a conditional autoregressive prior structure. Posterior parameters were estimated using Bayesian Markov Chain Monte Carlo simulation with Gibbs sampling. Results: In the tropical climate zone of Queensland, the estimated number of dengue cases was predicted to increase by 85% [95% Credible Interval (CrI): 25%, 186%] and 7% (95% CrI: 0.1%, 14%) for a 1-mm increase in average annual rainfall and 1% increase in the proportion of terrace houses, respectively. The estimated spatial variation (structured random effects) was small compared to the remaining unstructured variation, suggesting that the inclusion of covariates resulted in no residual spatial autocorrelation in dengue data. Conclusions: Climate and socio-ecological factors explai

Vanuatu, a Pacific Small Island Developing State, has high exposure to climate extremes, such as tropical cyclones and interannual rainfall variability, which can have devastating short- and long-term impacts on food and nutrition security (FNS). This paper presents local experiences of the effects of climate extremes on FNS in Vanuatu through a case study of two recent events: Tropical Cyclone Pam (2015) and an El Niño-induced drought (2015-2017). A qualitative research approach, using a range of data collection methods, was used to document people’s lived experiences in two villages in Vanuatu. This study found that climate extremes affected the FNS of people in the two study villages directly, with effects on gardens and food production, and indirectly, by exacerbating the nutrition transition, a shift away from traditional diets energy-dense imported food that is already progressing in Vanuatu. These effects undermine long-term FNS and health. Climate extremes also eroded food-related cultural practices and traditions and constrained local agency to make food choices. The magnitude and extent of these impacts, however, are influenced by structural vulnerabilities and local resiliencies. The adaptive capacity and resilience of communities needs to be strengthened in a way that allows people to exercise agency in their responses to climate extremes and to promote FNS, including cultural acceptability and food preferences, and long-term health.

Small island developing states such as Kiribati in the Equatorial Pacific are among the most climate vulnerable places on Earth. While there is a considerable body of literature on the scale of the problem, there is very little research on the lived experiences of communities, their concerns and priorities, and how they are coping with environmental and climate hazards. As a result, a top-down approach is often adopted in adaptation projects that may not meet the real needs of communities, endangering the long-term sustainability and viability of interventions. Through a series of participatory focus groups carried out in Kiribati in 2019, this study explores what communities prioritise and wish to see implemented to cope and build resilience to present and foreseeable future challenges. The outcomes of this research show environmental risks and hazards faced by communities are strongly associated with climate projections. Communities are currently trying to adapt to challenges, both old and new, with technologies and materials available. While there is a degree of spontaneous adaptation occurring, these problems are insurmountable without solving present challenges in Kiribati, such as access to better technology and markets without becoming over-reliant. Including communities in co-designed interventions can also ensure sustainability in climate adaptation projects. The findings of this research can inform policy and climate change adaptation measures in other similar atoll islands in the Pacific.

Background: A growing number of studies have investigated the effect of increasing temperatures on morbidity and health service use. However, there is a lack of studies investigating the temperature-attributable cost burden. Objectives: This study examines the relationship of daily mean temperature with hospital admissions, length of hospital stay (LoS), and costs; and estimates the baseline temperature-attributable hospital admissions, and costs and in relation to warmer climate scenarios in Adelaide, South Australia. Method: A daily time series analysis using distributed lag non-linear models (DLNM) was used to explore exposure-response relationships and to estimate the aggregated burden of hospital admissions for conditions associated with temperatures (i.e. renal diseases, mental health, diabetes, ischaemic heart diseases and heat-related illnesses) as well as the associated LoS and costs, for the baseline period (2010–2015) and different future climate scenarios in Adelaide, South Australia. Results: During the six-year baseline period, the overall temperature-attributable hospital admissions, LoS, and associated costs were estimated to be 3915 cases (95% empirical confidence interval (eCI): 235, 7295), 99,766 days (95% eCI: 14,484, 168,457), and AU$159 million (95% eCI: 18.8, 269.0), respectively. A climate scenario consistent with RCP8.5 emissions, and including projected demographic change, is estimated to lead to increases in heat-attributable hospital admissions, LoS, and costs of 2.2% (95% eCI: 0.5, 3.9), 8.4% (95% eCI: 1.1, 14.3), and 7.7% (95% eCI: 0.3, 13.3), respectively by mid-century. Conclusions: There is already a substantial temperature-attributable impact on hospital admissions, LoS, and costs which are estimated to increase due to climate change and an increasing aged population. Unless effective climate and public health interventions are put into action, the costs of treating temperature-related admissions will be high.

The association between heat and diseases has been extensively reported. However, its associated healthcare costs and attributable fraction due to heat were scarcely explored. The aim of this study was to estimate hospitalisation costs attributable to heat in Sydney, and to project future costs under climate change scenarios. Using a distributed lag nonlinear model, this study estimated heat-attributable hospitalisation costs in Sydney; and using 2010–2016 data as baseline, future costs for 2030s and 2050s were estimated under three climate change scenarios depending on greenhouse gas emissions - Representative Concentration Pathway (RCP)2.6, RCP4.5, and RCP8.5. Higher temperatures were found to be associated with increased hospitalisation costs. About 8–9% of the total hospitalisation costs were attributable to heat. The total costs attributable to heat over the baseline period 2010–2016 were estimated to be AU$252 million, with mental health hospitalisation making the largest contribution. Hospitalisation costs are estimated to increase substantially to AU$387–399 million in the 2030s, and AU$506–570 million by mid-century under different climate change scenarios. Urgent action is required to reduce heat-attributable illness in our communities, particularly for mental health conditions. Relevant preparations including healthcare workforce capacity building and resource allocation are needed to deal with these challenges in the context of climate change.

Climate change with increasing temperature is making a significant impact on human health, including more heat-related diseases, and increasing the burden on the healthcare system. Although many studies have explored the association between increasing temperatures and negative health outcomes, research on the associated costs of heat-related diseases remains relatively sparse. Furthermore, estimations of future costs associated with heat-attributable hospital healthcare have not been well explored. This study used a distributed lag nonlinear model to estimate heat-attributable hospital healthcare costs in Perth, Western Australia. Using 2006–2012 as the baseline, future costings for 2026–2032 and 2046–2052 were estimated under RCP2.6, RCP4.5, and RCP8.5. Higher temperatures were found to be associated with increased hospital healthcare costs. The total hospital costs attributable to heat over the baseline period 2006–2012 was estimated to be 79.5 million AUD, with costs for mental health hospitalizations being the largest contributor of the heat-related conditions examined. Costs are estimated to increase substantially to 125.8–129.1 million AUD in 2026–2032, and 174.1–190.3 million AUD by midcentury under climate change scenarios. Our findings of a notable burden of heat-attributable healthcare costs now and in the future emphasize the importance of climate change adaptation measures to reduce the adverse health effects of increasing temperatures and heat exposure on the people of Perth.

The MJA–Lancet Countdown on health and climate change in Australia was established in 2017, and produced its first national assessment in 2018, its first annual update in 2019, and its second annual update in 2020. It examines indicators across five broad domains: climate change impacts, exposures and vulnerability; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. Our special report in 2020 focused on the unprecedented and catastrophic 2019–20 Australian bushfire season, highlighting indicators that explore the relationships between health, climate change and bushfires. For 2021, we return to reporting on the full suite of indicators across each of the five domains and have added some new indicators. We find that Australians are increasingly exposed to and vulnerable to excess heat and that this is already limiting our way of life, increasing the risk of heat stress during outdoor sports, and decreasing work productivity across a range of sectors. Other weather extremes are also on the rise, resulting in escalating social, economic and health impacts. Climate change disproportionately threatens Indigenous Australians’ wellbeing in multiple and complex ways. In response to these threats, we find positive action at the individual, local, state and territory levels, with growing uptake of rooftop solar and electric vehicles, and the beginnings of appropriate adaptation planning. However, this is severely undermined by national policies and actions that are contrary and increasingly place Australia out on a limb. Australia has responded well to the COVID-19 public health crisis (while still emerging from the bushfire crisis that preceded it) and it now needs to respond to and prepare for the health crises resulting from climate change.

Pertussis has resurged in many countries over recent years, especially among adolescents and adults. This study assessed the effect of weather variability on resurging pertussis among different age groups in Jinan, China. Data on weekly pertussis notifications by age group and weather factors (mean temperature (MeanT), mean temperature standard deviation within a week (MeanT SD), diurnal temperature range (DTR) and relative humidity (RH)) were collected between 2013 and 2017. Distributed lag non-linear models (DLNMs) and regression tree models were used to examine the non-linear association between weather variability and pertussis infections. The 2-weeks cumulative relative risk (RR) of pertussis infections was 4.46 (95% confidence interval (CI): 2.33–9.51) in 0–4 age group, 6.25 (95% CI: 1.38–22.76) in 5–9 age group and 10.11 (95% CI: 2.83–39.07) in 10+ age group when MeanT was at 30.0 °C. MeanT SD (RR range in the three age groups: 2.82–5.83), DTR (RR range: 6.33–11.56) and RH (RR range: 2.02–7.43) also exert significant influence, with the highest risks at 10+ age group. Regression tree models showed the interactive effects of weather variability. The mean pertussis infections increased by over 1.7-fold in 0–4 years group when MeanT ≥14 °C, RH ≥57% and DTR ≥10 °C; by over 2.3-fold in 5–9 years group when MeanT ≥20 °C and MeanT SD ≥3 °C; by 2.0-fold in 10+ years group when MeanT ≥0.7 °C, DTR ≥8.3 °C and RH ≥74%. The study found significantly different associations between weather variability and pertussis infections by age group, and appeared to be stronger in 10+ years group. Continuing climate change, together with other risk factors such as low antibody levels among adolescents and adults, may facilitate pertussis resurgence. This supports previous suggestions of carefully reconsidering current vaccination programme to effectively curb the resurgence of pertussis.

Background Dengue is a mosquito-borne viral disease and its transmission is closely linked to climate. We aimed to review available information on the projection of dengue in the future under climate change scenarios. Methods Using five databases (PubMed, ProQuest, ScienceDirect, Scopus and Web of Science), a systematic review was conducted to retrieve all articles from database inception to 30th June 2019 which projected the future of dengue under climate change scenarios. In this review, “the future of dengue” refers to disease burden of dengue, epidemic potential of dengue cases, geographical distribution of dengue cases, and population exposed to climatically suitable areas of dengue. Results Sixteen studies fulfilled the inclusion criteria, and five of them projected a global dengue future. Most studies reported an increase in disease burden, a wider spatial distribution of dengue cases or more people exposed to climatically suitable areas of dengue as climate change proceeds. The years 1961–1990 and 2050 were the most commonly used baseline and projection periods, respectively. Multiple climate change scenarios introduced by the Intergovernmental Panel on Climate Change (IPCC), including B1, A1B, and A2, as well as Representative Concentration Pathway 2.6 (RCP2.6), RCP4.5, RCP6.0 and RCP8.5, were most widely employed. Instead of projecting the future number of dengue cases, there is a growing consensus on using “population exposed to climatically suitable areas for dengue” or “epidemic potential of dengue cases” as the outcome variable. Future studies exploring non-climatic drivers which determine the presence/absence of dengue vectors, and identifying the pivotal factors triggering the transmission of dengue in those climatically suitable areas would help yield a more accurate projection for dengue in the future. Conclusions Projecting the future of dengue requires a systematic consider

Pacific Island countries (PICs) have experienced changes to dietary patterns in recent decades, alongside rapid socio-economic and ecological changes. In this study, qualitative research methods were used to explore food and nutrition security (FNS) experiences in a peri-urban, and a remote village, in Vanuatu. The main findings were that there are a range of interrelated socio-economic and environmental influences on food and nutrition insecurity, and continued climate change is a potential contributor to growing food insecurity. Results suggest that FNS in these villages is experienced not as a lack of food availability, but as inadequate and unstable access to healthy, locally produced food leading to impaired diet quality and diversity. FNS was experienced differently in the two village contexts: the peri-urban village demonstrated an advancing nutrition transition with sufficient food availability, but an increasing dependence on imported foods and inadequate diet quality and diversity. The rural village showed adequate diet diversity and quality; however, the food supply was unstable and sensitive to disruptions caused by climate variability and change. The study indicates that the effects of climate change, changes to ways of life, and dietary changes are not only affecting FNS, but also disrupting social relationships, culture, and spirituality, and threatening the social fabric of these villages. There is an imperative to address FNS holistically, considering the contextual socio-economic and environmental influences, and to support the local food system to provide access to an optimal diet that promotes health and cultural preferences in the face of a changing climate.

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.

Available evidence is limited on the association between weather factors and childhood pneumonia, especially in developing countries. This study examined the effects of weather variability on childhood pneumonia after the introduction of pneumococcal conjugate vaccines (PCV) intervention in rural Bangladesh. Data on pneumonia cases and weather variables (temperature and relative humidity) between the 1st January 2012 and the 31st December 2016 were collected from Matlab Hospital, International Centre for Diarrhoeal Disease Research, Bangladesh, and Bangladesh Meteorological Department, respectively. Time series cross-correlation functions were applied to identify the time lags of the effect of each weather factor on pneumonia. Generalized linear regression model with Poisson link was used to quantify the association between weather factors and childhood pneumonia after adjustment of PCV intervention. The annual incidence rate of pneumonia reduced from 5691/100,000 to 2000/100,000 after PCV intervention. Generalized linear regression model suggested that temperature had a negative association with childhood pneumonia (relative risk, 0.985; 95% confidence interval (CI), 0.974–0.997), and PCV intervention was a protective factor with the relative risk estimate of 0.489 (95% CI, 0.435–0.551). However, no substantial association was found with relative humidity. PCV intervention appeared protective against childhood pneumonia, and temperature might be associated with this disease in children. Our findings may help inform public health policy, including the potential of development of early warning systems based on weather factors and PCV for the control and prevention of pneumonia in lower middle-income country like Bangladesh.

BackgroundBangkok plays a central role in the commerce of Thailand. This study aimed to characterize the district-level spatial-temporal patterns of dengue in Thailand and explore if a dengue peak in Bangkok led the peaks of dengue in other Thai provinces.MethodsMonthly dengue data at district level in Thailand from January 2004 to December 2017 were obtained and used to assess the spatial and seasonal patterns of dengue in Thailand. As our seasonal decomposition and cross-correlation analyses showed that dengue in Bangkok peaked in November, which was a few months after the dengue peak in most other provinces, we used a time-series generalized linear model to explore if there was another province in which the dengue case number was most predictive of dengue case numbers in other Thai provinces.ResultsThe highest district-level annual dengue incidence rates (per 10,000) in the three time periods (i.e. 2004–2008, 2009–2013 and 2014–2017) were 58.08 (Samphanthawong), 85.93 (Mueang Krabi), and 66.60 (Mae Sariang), respectively. Dengue incidence rates in the western part of Northern Thailand, southern part of Central Thailand, southern part of Eastern Thailand, and Southern Thailand were higher than in other regions. Dengue in most districts of Thailand peaked in June, July or August, but dengue peaks in all districts of Bangkok occurred in November. The number of dengue cases in Nakhon Ratchasima was most predictive of the number of dengue cases in other provinces in Thailand by a one-month lag.ConclusionsOur results suggest that the dengue peak in Bangkok did not lead the peaks of dengue in other Thai provinces. Future research exploring how changes in socio-ecological factors (e.g. road network and climate factors) in Nakhon Ratchasima have affected the transmission of dengue in Thailand might shed some new light on the prevention and control of dengue.

BACKGROUND: Many studies have shown associations between rising temperatures, El Niño events and dengue incidence, but the effect of sustained periods of extreme high temperatures (i.e., heatwaves) on dengue outbreaks has not yet been investigated. This study aimed to compare the short-term temperature-dengue associations during different dengue outbreak periods, estimate the dengue cases attributable to temperature, and ascertain if there was an association between heatwaves and dengue outbreaks in Hanoi, Vietnam. METHODOLOGY/PRINCIPAL FINDINGS: Dengue outbreaks were assigned to one of three categories (small, medium and large) based on the 50th, 75th, and 90th percentiles of distribution of weekly dengue cases during 2008-2016. Using a generalised linear regression model with a negative binomial link that controlled for temporal trends, temperature variation, rainfall and population size over time, we examined and compared associations between weekly average temperature and weekly dengue incidence for different outbreak categories. The same model using weeks with or without heatwaves as binary variables was applied to examine the potential effects of extreme heatwaves, defined as seven or more days with temperatures above the 95th percentile of daily temperature distribution during the study period. This study included 55,801 dengue cases, with an average of 119 (range: 0 to 1454) cases per week. The exposure-response relationship between temperature and dengue risk was non-linear and differed with dengue category. After considering the delayed effects of temperature (one week lag), we estimated that 4.6%, 11.6%, and 21.9% of incident cases during small, medium, and large outbreaks were attributable to temperature. We found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks (around 14 weeks later) than small and medium outbreaks (4 to 9 weeks later). Compared with non-heatwave years, dengue outbreak

Myocardial infarction (MI) incidence often peaks in winter, but it remains unclear how winter temperature affects MI temporally and spatially. We examined the short-term effects of winter temperature on the risk of MI and explored spatial associations of winter MI hospitalizations with temperature and socioeconomic status (area-based index) in Brisbane, Australia. We used a distributed lag non-linear model to fit the association at the city level between population-weighted daily mean temperature and daily MI hospitalizations during 11 winters of 2005–2015. For each winter, a Bayesian spatial conditional autoregressive model was fitted to examine the associations at postal code level of MI hospitalisations with temperature and socioeconomic status measured as the Index of Relative Socio-Economic Advantage and Disadvantage (IRSAD). Area-specific winter temperature was categorised into three levels: cold (<25th percentile of average winter temperature across postal areas), mild (25th–75th percentile) and warm (>75th percentile). This study included 4978 MI hospitalizations. At the city level, each 1 °C drop in temperature below a threshold of 15.6 °C was associated with a relative risk (RR) of 1.016 (95% confidence interval (CI): 1.008–1.024) for MI hospitalizations on the same day. Low temperature had a much delayed and transient effect on women but an immediate and longer-lasting effect on men. Winter MI incidence rate varied spatially in Brisbane, with a higher incidence rate in warmer areas (RR for mild areas: 1.214, 95%CI: 1.116–1.320; RR for warm areas: 1.251, 95%CI: 1.127–1.389; cold areas as the reference) and in areas with lower socioeconomic levels (RR: 0.900, 95%CI: 0.886–0.914 for each decile increase in IRSAD). This study provides compelling evidence that short-term winter temperature drops were associated with an elevated risk of MI in the subtropical region with a mild winter. Particular attention also needs to be paid to people living in relatively

Background: Dengue in some regions has a bimodal seasonal pattern, with a first big seasonal peak followed by a second small seasonal peak. The factors associated with the second small seasonal peak remain unclear. Methods: Monthly data on dengue cases in the Philippines and its 17 regions from 2008 to 2017 were collected and underwent a time series seasonal decomposition analysis. The associations of monthly average mean temperature, average relative humidity, and total rainfall with dengue in 19 provinces were assessed with a generalized additive model. Logistic regression and a classification and regression tree (CART) model were used to identify the factors associated with the second seasonal peak of dengue. Results: Dengue incidence rate in the Philippines increased substantially in the period 2013–2017, particularly for the regions in south Philippines. Dengue peaks in south Philippines predominantly occurred in August, with the peak in the national capital region (NCR) (i.e., Metropolitan Manila) occurring in September. The association between mean temperature and dengue appeared J-shaped or upside-down-V-shaped, and the association between relative humidity (or rainfall) and dengue was heterogeneous across different provinces (e.g., J shape, reverse J shape, or upside-down V shape, etc). Relative humidity was the only factor associated with the second seasonal peak of dengue (odds ratio: 1.144; 95% confidence interval: 1.023–1.279; threshold: 77%). Conclusions: Dengue control and prevention resources are increasingly required in regions beyond the NCR, and relative humidity can be used as a predictor of the second seasonal peak of dengue in the Philippines.

Climate change, malnutrition, and noncommunicable diseases (NCDs) are three of the most significant health challenges of this century, and they share fundamental underlying drivers. Pacific Island countries (PICs) are at the forefront of the impacts of climate change, which is likely to affect food and nutrition security (FNS) directly and indirectly, and many countries have existing high NCD burdens. This paper surveys the climate change adaptation (CCA) landscape in one PIC, Vanuatu. It explores the extent to which FNS and diet-related NCDs are considered and addressed within CCA initiatives. A comprehensive review of the literature related to CCA, FNS, and NCDs in Vanuatu was combined with 32 semistructured interviews with key experts and stakeholders. This study found that some promising groundwork has been laid for tackling the effects of climate change on FNS in policy and governance, agri-culture, coastal management, and nutrition. However, several opportunities for strengthening CCA were identified: targeting urban populations; complementary integration of disaster risk reduction and CCA; incorporating local knowledge; applying a systems-based framing of NCDs as climate-sensitive health risks; and emphasizing human-centered, community-led CCA. Vanuatu will continue to be affected by accelerating climate change. A strong foundation for CCA presents clear opportunities for further development. As food and nutrition insecurity and diet-related NCD risk factors are increasingly exacerbated by climate change, alongside other socioeconomic drivers, it is crucial to find new and innovative ways to increase transformational resilience and adaptive capacity that also improve nutrition and health outcomes.

Small Island Developing States (SIDS) are heavily affected by climate change, challenging their economic development as a result. Although research on SIDS under climate change is gaining momentum, lack of data and research capacity remains a major problem. Similarly, little is known about the interactions between poverty and responses to climate change. In this chapter, we conduct a case study exploring how coastal communities on Rabi Island, Fiji, are affected by climate change. We identify reactive coping strategies to climate variability and change, and explore the extent to which these responses are conducive to climate change adaptation, adopting the Sustainable Livelihoods Framework and using the CRiSTAL tool. We find that the majority of shocks are of a climatic or weather-related nature. We also find that the communities tend to adopt sustainable coping strategies in response to climatic shocks. However, the increased frequency of such events, and the disproportionate burden on lower-income households, can act as barriers for the effective uptake of adaptation measures. This study can potentially inform local and regional policy as it pinpoints possible areas of intervention where strategic activities could support adaptation strategies to address climate change vulnerabilities.

The MJA–Lancet Countdown on health and climate change was established in 2017 and produced its first Australian national assessment in 2018. It examined 41 indicators across five broad domains: climate change impacts, exposures and vulnerability; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. It found that, overall, Australia is vulnerable to the impacts of climate change on health, and that policy inaction in this regard threatens Australian lives. In this report we present the 2019 update. We track progress on health and climate change in Australia across the same five broad domains and many of the same indicators as in 2018. A number of new indicators are introduced this year, including one focused on wildfire exposure, and another on engagement in health and climate change in the corporate sector. Several of the previously reported indicators are not included this year, either due to their discontinuation by the parent project, the Lancet Countdown, or because insufficient new data were available for us to meaningfully provide an update to the indicator. In a year marked by an Australian federal election in which climate change featured prominently, we find mixed progress on health and climate change in this country. There has been progress in renewable energy generation, including substantial employment increases in this sector. There has also been some progress at state and local government level. However, there continues to be no engagement on health and climate change in the Australian federal Parliament, and Australia performs poorly across many of the indicators in comparison to other developed countries; for example, it is one of the world's largest net exporters of coal and its electricity generation from low carbon sources is low. We also find significantly increasing exposure of Australians to heatwaves and, in most states and territories, continu

Correction to: International Journal of Biometeorology: (2018) 62: 423 https://doi.org/10.1007/s00484-017-1451-9 The authors of the article would like to bring the following correction/corrigendum to attention

This study explored how internet queries vary in facilitating monitoring of pertussis, and the effects of sociodemographic characteristics on such variation by city in Shandong province, China. We collected weekly pertussis notifications, Baidu Index (BI) data and yearly sociodemographic data at the city level between 1 January 2009 and 31 December 2017. Spearman's correlation was performed for temporal risk indices, generalised linear models and regression tree models were developed to identify the hierarchical effects and the threshold between sociodemographic factors and internet query data with pertussis surveillance. The BI was correlated with pertussis notifications, with a strongly spatial variation among cities in temporal risk indices (composite temporal risk metric (CTRM) range: 0.59–1.24). The percentage of urban population (relative risk (RR): 1.05, 95% confidence interval (CI) 1.03–1.07), the proportion of highly educated population (RR: 1.27, 95% CI 1.16–1.39) and the internet access rate (RR: 1.04, 95% CI 1.02–1.05) were correlated with CTRM. Higher RRs in the three identified sociodemographic factors were associated with higher stratified CTRM. The percentage of highly educated population was the most important determinant in the BI with pertussis surveillance. The findings may lead to spatially-specific criteria to inform development of an early warning system of pertussis infections using internet query data.

Background Ethnic variation in the occurrence of type 2 diabetes, complications, mortality, and health behaviours has been reported. The current research examined patterns of health-related outcomes by country of birth in people with diabetes aged 45 years and over in New South Wales, Australia. Methods This study was based on the baseline data of 266,848 participants aged 45 years and over from “The Sax Institute’s 45 and Up Study” (2006–2009), NSW; Australia’s most populous state. Health-related factors including self-rated overall health, Quality of Life (QoL), eyesight, subjective memory complaint, hearing loss, psychological distress and functional limitation were examined according to country of birth among 23,112 people with type 2 diabetes. Logistic regression modelling was used to compare the odds of poor outcomes between Australian-born and overseas-born participants, adjusting for potential confounding and mediating variables. Both age-sex and fully adjusted odds ratios (aORs) are reported. Results Nearly half of the people with diabetes in the sample reported hearing loss and high levels of functional limitations, a third reported poor overall health. Compared to people with diabetes born in Australia, people born in South East Europe, North Africa, the Middle East had significantly greater odds of poor outcomes across the majority of examined health-related factors, with the largest odds observed in the elevated level of psychological distress outcome (aOR = 3.4 in North African and the Middle East group). Higher aORs of poor overall health, QoL, memory problems and poor eyesight, and lower aORs for hearing loss, were also found among those born in the Asian countries. Conclusions The results demonstrated significant ethnic disparity in the prevalence of health-related outcomes. These findings provide important context for the formulation of culturally sensitive secondary prevention strategies.

Objective: Public preparedness for natural hazard events is low. With worsening severe weather events due to climate change, public health policy and practices must evolve to more effectively engage communities. This study's findings identify and suggest new strategic public health policies to shift the practice of all‐hazards preparedness into routine, everyday life. Methods: Semi‐structured interviews, focus groups and Thematic Analysis were used to investigate the interactions between participant groups: emergency responders and animal owners. Results: Three policies designed to improve human safety and well‐being are proposed and discussed. These are (i) a new system of workplace leave, (ii) an innovative regime of financial incentives for fire‐ready properties, and (iii) review of the use of firebreaks on farms and rural blocks. Conclusion: Policies proposed in this research aim to proactively narrow the awareness‐preparedness gap and build adaptive capacity to minimise risk to human health in all‐hazards contexts. Further research could evaluate the efficacy of trialled public policy. Implications for public health: These new policies seek to contribute to establishing and maintaining a culture of preparedness as a routine aspect of everyday life, and thus promote and protect public health in the short, medium and long terms.

Although vaccination is effective in preventing infection, pertussis remains endemic worldwide, including China. To lead better targeted prevention strategies, we examined dynamics of spatial and temporal patterns of pertussis transmission in Shandong, China, from 2009 to 2017. We used space-time cluster analysis, logistic regression analysis, and regression tree model to detect the changes in spatial patterns of pertussis infections in Shandong Province, China, between periods (2009-2011, 2012-2014, and 2015-2017). The yearly pertussis incidence rates dramatically increased by 16.8 times from 2009 to 2017. Shifting patterns of peaks of pertussis infections were observed over both time (from June-July to August-September) and space (from Linyi to Jinan), with increasing RR from 4.1 (95% CI: 2.3-7.4) (2009-2011) to 6.1 (95% CI: 5.6-6.7) (2015-2017) and obvious coincidence of peak time. West Shandong had larger odds of increased infections over the study period (odds ratio: 1.52 [95% CI: 1.05-2.17]), and pertussis had larger odds of spreading to east (odds ratio: 2.32 [95% CI: 1.63-3.31]) and north (odds ratio: 1.69 [95% CI: 1.06-2.99]) over time. Regression tree model indicated that the mean difference in yearly average pertussis incidence between 2009-2011 and 2015-2017 increased by more than 4-fold when the longitudes of counties are < 118.0°E. The geographic expansion of pertussis infection may increase the risk of epidemic peaks, coinciding with increased infections in the future. The findings might offer evidence for targeting preventive measures to the areas most in need to minimize the impact of the disease.

Background: Climate change is recognised as having a ‘multiplier effect’ on food insecurity and adverse health experiences of communities in the Pacific region. Islands are especially at risk due to their limited land availability, population pressures and, in the case of atolls, their low-lying topography making them vulnerable to sea level rise. Aim: This review examines the literature describing the relationship between climate change, food security and health in Kiribati. Method: A narrative review was conducted, looking at both peer-reviewed and non-peer-reviewed literature available online from 1 January 2008 to 14 August 2018, the search date. Sources from three databases of peer-reviewed literature, Google and additional sources from reference lists were included in the review. Results: Thirty-seven items were included in this review. These show climate change is having a noticeable impact on food security and health in Kiribati. Four themes were identified from the literature that provide different perspectives to the problem outlined. Conclusion: Climate change is a pressing concern for the government of Kiribati and communities alike, and yet the problem is worsening, not improving. Further research is required to look at effective policies and cultural perspectives to address this problem.

Ambient temperature is an important determinant of mortality and morbidity, making it necessary to assess temperature-related burden of disease (BD) for the planning of public health policies and adaptive responses. To systematically review existing epidemiological evidence on temperature-related BD, we searched three databases (PubMed, Web of Science, and Scopus) on 1 September 2018. We identified 97 studies from 56 counties for this review, of which 75 reported the fraction or number of health outcomes (include deaths and diseases) attributable to temperature, and 22 reported disability-adjusted life years (include years of life lost and years lost due to disability) related to temperature. Non-optimum temperatures (i.e., heat and cold) were responsible for > 2.5% of mortality in all included high-income countries/regions, and > 3.0% of mortality in all included middle-income countries. Cold was mostly reported to be the primary source of mortality burden from non-optimum temperatures, but the relative role of three different temperature exposures (i.e., heat, cold, and temperature variability) in affecting morbidity and mortality remains unclear so far. Under the warming climate scenario, almost all projections assuming no population adaptation suggested future increase in heat-related but decrease in cold-related BD. However, some studies emphasized the great uncertainty in future pattern of temperature-related BD, largely depending on the scenarios of climate, population adaptation, and demography. We also identified important discrepancies and limitations in current research methodologies employed to measure temperature exposures and model temperature-health relationship, and calculate the past and project future temperature-related BD. Overall, exposure to non-optimum ambient temperatures has become and will continue to be a considerable contributor to the global and national BD, but future research is still needed to develop a stronger methodological framewo

Community biogas facilities are being implemented in many informal urban settlements across Africa, often funded by foreign aid. We measured the public health impacts of a facility in Ethiopia, particularly the effects of training, in the context of extreme poverty. Two waves of panel-data were generated by household surveys (N = 200 per wave), informed by participatory focus groups, and a propensity-score matching technique was applied. After controlling for household distance from the facility, training itself generated positive effects on health including use of improved sanitation facilities and self-assessed health. We conclude that training should be considered a key component in biogas development.

Objectives Evidence is limited on the relative contribution of different temperature exposures (i.e., heat, cold and significant temperature variability) to mortality. This study aims to examine mortality risk and associated mortality burden from heat, cold, and temperature variability in Australia. Methods We collected daily time-series data on all-cause deaths and weather variables for the five most populous Australian cities (Sydney, Melbourne, Brisbane, Adelaide, and Perth), from 2000 to 2009. Temperature variability was calculated from the standard deviation of hourly temperatures between two adjacent days. Three-stage analysis was used. We firstly used quasi-Poisson regression models to model the associations of mortality with heat (mean temperature) during the warm season, with cold (mean temperature) during the cold season, and with temperature variability all year round, while controlling for long-term trend and seasonality, day of week, and population change over time. We then estimated the effects of different non-optimum temperatures using the simplified log-linear regression model. Finally, we computed and compared the fraction (%) of deaths attributable to different non-optimum temperatures. Results The greatest percentage increase in mortality was for cold (2.0%, 95% confidence interval (CI): 1.4%, 2.6%), followed by heat (1.2%, 95% CI: 0.7%, 1.7%), and temperature variability (0.5%, 95% CI: 0.3%, 0.7%). There was no clear temporal pattern in mortality risk associated with any temperature exposure in Australia. Heat, cold and temperature variability together resulted in 42,414 deaths during the study period, accounting for about 6.0% of all deaths. Most of attributable deaths were due to cold (61.4%), and noticeably, contribution from temperature variability (28.0%) was greater than that from heat (10.6%). Conclusions Exposure to either cold or heat or a large variation in temperature was associated with increased mortality

Background Heatwaves affect human health and global heatwave-related disease burden will continue to rise as climate change proceeds, but the effects of heatwaves on cardiovascular and respiratory diseases have not yet been investigated globally and nationally. Objectives This systematic review and meta-analysis aim to quantify heatwave effects on four major health outcomes: cardiovascular and respiratory morbidity and mortality. Methods We searched PubMed, Scopus, Embase, and Web of Science for relevant studies from database inception to November 2018. Categories of morbidity included hospital admissions, emergency department visits, and ambulance attendances/call-outs. A random-effects meta-analysis model was used to pool previous estimates of heatwave effects on mortality and morbidity due to cardiovascular and respiratory diseases. Subgroup analyses by gender, age, and disease cause were conducted. Sensitivity analyses were performed by the categories of morbidity, heatwave definitions, study design, and using a leave-one-out cross validation approach. This study is registered with PROSPERO (number: CRD42018101964). Results We identified 54 studies conducted in 20 countries. In total, there were significant associations between heatwaves and cardiovascular mortality (risk estimates (RE): 1.149, 95% confidence interval (CI): 1.090, 1.210) and respiratory mortality (RE: 1.183, 95%CI: 1.092, 1.282), but the magnitude of these associations varied across countries and studies. Heatwaves appeared to be marginally associated with cardiovascular and respiratory morbidities (RE: 0.999, 95%CI: 0.996, 1.002, p-value = 0.61 for cardiovascular morbidity; RE: 1.043, 95%CI: 0.995, 1.093; p-value = 0.08 for respiratory morbidity). For mortality, significant associations were observed for the elderly, ischemic heart disease, stroke, heart failure, and chronic obstructive pulmonary disease. Sensitivity analyses suggested that these findings were robust. Conclusion Mortality of ca

Dengue is a public health concern in northern Queensland, Australia. This study aimed to explore spatial and temporal characteristics of dengue cases in Queensland, and to identify high-risk areas after a 2009 dengue outbreak at fine spatial scale and thereby help in planning resource allocation for dengue control measures. Notifications of dengue cases for Queensland at Statistical Local Area (SLA) level were obtained from Queensland Health for the period 2010 to 2015. Spatial and temporal analysis was performed, including plotting of seasonal distribution and decomposition of cases, using regression models and creating choropleth maps of cumulative incidence. Both the space-time scan statistic (SaTScan) and Geographical Information System (GIS) were used to identify and visualise the space-time clusters of dengue cases at SLA level. A total of 1,773 dengue cases with 632 (35.65%) autochthonous cases and 1,141 (64.35%) overseas acquired cases were satisfied for the analysis in Queensland during the study period. Both autochthonous and overseas acquired cases occurred more frequently in autumn and showed a geographically expanding trend over the study period. The most likely cluster of autochthonous cases (Relative Risk, RR = 54.52, p<0.001) contained 50 SLAs in the north-east region of the state around Cairns occurred during 2013–2015. A cluster of overseas cases (RR of 60.81, p<0.001) occurred in a suburb of Brisbane during 2012 to 2013. These results show a clear spatiotemporal trend of recent dengue cases in Queensland, providing evidence in directing future investigations on risk factors of this disease and effective interventions in the high-risk areas.

Background Although the association between dengue in Bali, Indonesia, and imported dengue in Australia has been widely asserted, no study has quantified this association so far. Methods Monthly data on dengue and climatic factors over the past decade for Bali and Jakarta as well as monthly data on imported dengue in Australia underwent a three-stage analysis. Stage I: a quasi-Poisson regression with distributed lag non-linear model was used to assess the associations of climatic factors with dengue in Bali. Stage II: a generalized additive model was used to quantify the association of dengue in Bali with imported dengue in Australia with and without including the number of travelers in log scale as an offset. Stage III: the associations of mean temperature and rainfall (two climatic factors identified in stage I) in Bali with imported dengue in Australia were examined using stage I approach. Results The number of dengue cases in Bali increased with increasing mean temperature, and, up to a certain level, it also increased with increasing rainfall but dropped off for high levels of rainfall. Above a monthly incidence of 1.05 cases per 100,000, dengue in Bali was almost linearly associated with imported dengue in Australia at a lag of one month. Mean temperature (relative risk (RR) per 0.5 °C increase: 2.95, 95% confidence interval (CI): 1.87, 4.66) and rainfall (RR per 7.5 mm increase: 3.42, 95% CI: 1.07, 10.92) in Bali were significantly associated with imported dengue in Australia at a lag of four months. Conclusions This study suggests that climatic factors (i.e., mean temperature and rainfall) known to be conducive of dengue transmission in Bali can provide an early warning with 4-month lead time for Australia in order to mitigate future outbreaks of local dengue in Australia. This study also provides a template and framework for future surveillance of travel-related infectious diseases globally.

Background: Vietnam is one of the countries most affected by climate change, but few studies have focused on the population health effects of climate variation. Extreme heatwaves and cold spells might exacerbate underlying chronic conditions and precipitate hospitalization or early death. This study examined the short-term effects of ambient temperature extremes on hospital admissions (HAs) due to acute myocardial infarction (AMI) between different climate zones in the Central Coast region of Vietnam. Methods: Information from medical records of all 3328 cases of AMI HAs (with hospital records cross-checked by clinicians) was collected from three hospitals in the South-Central Coast region (tropical savanna climate) and North-Central Coast region (tropical monsoon climate) for the period 2008–2015. Meteorological data were obtained from the National Hydro-Meteorological and Environment Network Centre. We used distributed lag non-linear models to assess the association between daily average temperature and AMI HAs, accounting for long-term trend and other meteorological variables. Results: We found a negative and significant association between AMI HAs and temperature in the North-Central Coast region while conversely there was a positive and significant association in the South-Central Coast region. In the North-Central Coast region, the risk of AMI HAs increased by 11% (Relative risk (RR): 1.11, 95% CI: 0.91–1.35, p > 0.05) at moderately low temperatures (10th percentile of temperature range – 18.5 °C) and increased by 25% (RR: 1.25, 95% CI: 1.02–1.55, p < 0.05) at extremely low temperatures (5th percentile of temperature range – 16.8 °C). In the South-Central Coast region, the risk of AMI HAs increased by 18% (RR: 1.18, 95% CI: 0.95–1.47, p > 0.05) and 36% (RR: 1.36, 95% CI: 1.06–1.73, p < 0.05) at moderately high temperatures (90th percentile of temperature range −29.5 °C) and extreme high temperatures (95th percentile of temperature range – 29.9 °C

Pneumonia is the leading cause of mortality and morbidity in developing countries, particularly for children and elderly. The main objective of this review paper is to review the epidemiological evidence about the effects of sociodemographic and climatic variability on pneumonia and other lower respiratory tract infections. A detailed literature search was conducted in PubMed and Scopus following PRISMA guidelines. The articles, which considered the effect of only climatic or both climatic and sociodemographic factors on pneumonia and other lower respiratory tract infections, included in this review. A total thirty-four relevant articles were reviewed. Of 34 studies, only 14 articles (41%) examined the joint effects of sociodemographic and climate factors on pneumonia and other lower respiratory infections while most of them (59%) assessed climate factors separately. Among these fourteen, only three articles (8.8%) considered detailed sociodemographic factors. All of the reviewed articles suggested different degrees of positive or negative relationship of temperature with pneumonia or other lower respiratory tract infections. Fifteen (44%) articles suggested an association with relative humidity and 13 (38%) with rainfall. Only 3 articles (8.8%) found a relationship with wind speed. Three articles (8.8%) considered other risk factors such as particulate matter 2.5 (PM2.5) and particulate matter 10 (PM10). One study among the reviewed articles used spatial analysis methods but this study did not examine the joint effects. Among the reviewed articles, 18 (53%) articles used different time series models, one article (3%) used spatiotemporal time series model, 8 (23%) studies used other models and rest 7 (21%) studies used simple descriptive analysis. A total of 18 studies (53%) were conducted in Asia, most of them in China. There were 6 studies (17%) in Europe and 8 studies (23%) in America (South, North and Central). In Africa and Oceania, only one study was found for