The 2023 report of the MJA-Lancet Countdown on health and climate change: sustainability needed in Australia's health care sector.

The MJA-Lancet Countdown on health and climate change in Australia was established in 2017 and produced its first national assessment in 2018 and annual updates in 2019, 2020, 2021 and 2022. It examines five broad domains: health hazards, exposures and impacts; adaptation, planning and resilience for health; mitigation actions and health co-benefits; economics and finance; and public and political engagement. In this, the sixth report of the MJA-Lancet Countdown, we track progress on an extensive suite of indicators across these five domains, accessing and presenting the latest data and further refining and developing our analyses. Our results highlight the health and economic costs of inaction on health and climate change. A series of major flood events across the four eastern states of Australia in 2022 was the main contributor to insured losses from climate-related catastrophes of $7.168 billion - the highest amount on record. The floods also directly caused 23 deaths and resulted in the displacement of tens of thousands of people. High red meat and processed meat consumption and insufficient consumption of fruit and vegetables accounted for about half of the 87 166 diet-related deaths in Australia in 2021. Correction of this imbalance would both save lives and reduce the heavy carbon footprint associated with meat production. We find signs of progress on health and climate change. Importantly, the Australian Government released Australia's first National Health and Climate Strategy, and the Government of Western Australia is preparing a Health Sector Adaptation Plan. We also find increasing action on, and engagement with, health and climate change at a community level, with the number of electric vehicle sales almost doubling in 2022 compared with 2021, and with a 65% increase in coverage of health and climate change in the media in 2022 compared with 2021. Overall, the urgency of substantial enhancements in Australia's mitigation and adaptation responses to the enormous health and climate change challenge cannot be overstated. Australia's energy system, and its health care sector, currently emit an unreasonable and unjust proportion of greenhouse gases into the atmosphere. As the Lancet Countdown enters its second and most critical phase in the leadup to 2030, the depth and breadth of our assessment of health and climate change will be augmented to increasingly examine Australia in its regional context, and to better measure and track key issues in Australia such as mental health and Aboriginal and Torres Strait Islander health and wellbeing.

Towards development of functional climate-driven early warning systems for climate-sensitive infectious diseases: Statistical models and recommendations.

Climate, weather and environmental change have significantly influenced patterns of infectious disease transmission, necessitating the development of early warning systems to anticipate potential impacts and respond in a timely and effective way. Statistical modelling plays a pivotal role in understanding the intricate relationships between climatic factors and infectious disease transmission. For example, time series regression modelling and spatial cluster analysis have been employed to identify risk factors and predict spatial and temporal patterns of infectious diseases. Recently advanced spatio-temporal models and machine learning offer an increasingly robust framework for modelling uncertainty, which is essential in climate-driven disease surveillance due to the dynamic and multifaceted nature of the data. Moreover, Artificial Intelligence (AI) techniques, including deep learning and neural networks, excel in capturing intricate patterns and hidden relationships within climate and environmental data sets. Web-based data has emerged as a powerful complement to other datasets encompassing climate variables and disease occurrences. However, given the complexity and non-linearity of climate-disease interactions, advanced techniques are required to integrate and analyse these diverse data to obtain more accurate predictions of impending outbreaks, epidemics or pandemics. This article presents an overview of an approach to creating climate-driven early warning systems with a focus on statistical model suitability and selection, along with recommendations for utilizing spatio-temporal and machine learning techniques. By addressing the limitations and embracing the recommendations for future research, we could enhance preparedness and response strategies, ultimately contributing to the safeguarding of public health in the face of evolving climate challenges.

Impacts of heatwaves on type 2 diabetes mortality in China: a comparative analysis between coastal and inland cities.

The impacts of extreme temperatures on diabetes have been explored in previous studies. However, it is unknown whether the impacts of heatwaves appear variations between inland and coastal regions. This study aims to quantify the associations between heat exposure and type 2 diabetes mellitus (T2DM) deaths in two cities with different climate features in Shandong Province, China. We used a case-crossover design by quasi-Poisson generalized additive regression with a distributed lag model with lag 2 weeks, controlling for relative humidity, the concentration of air pollution particles with a diameter of 2.5 µm or less (PM2.5), and seasonality. The wet- bulb temperature (Tw) was used to measure the heat stress of the heatwaves. A significant association between heatwaves and T2DM deaths was only found in the coastal city (Qingdao) at the lag of 2 weeks at the lowest Tw = 14℃ (relative risk (RR) = 1.49, 95% confidence interval (CI): 1.11-2.02; women: RR = 1.51, 95% CI: 1.02-2.24; elderly: RR = 1.50, 95% CI: 1.08-2.09). The lag-specific effects were significant associated with Tw at lag of 1 week at the lowest Tw = 14℃ (RR = 1.14, 95% CI: 1.03-1.26; women: RR = 1.15, 95% CI: 1.01-1.31; elderly: RR = 1.15, 95% CI: 1.03-1.28). However, no significant association was found in Jian city. The research suggested that Tw was significantly associated with T2DM mortality in the coastal city during heatwaves on T2DM mortality. Future strategies should be implemented with considering socio-environmental contexts in regions.

Air pollution and liver cancer: A systematic review.

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.

The 2022 report of the MJA-Lancet Countdown on health and climate change: Australia unprepared and paying the price.

The MJA-Lancet Countdown on health and climate change in Australia was established in 2017 and produced its first national assessment in 2018 and annual updates in 2019, 2020 and 2021. It examines 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. In this, the fifth year of the MJA-Lancet Countdown, we track progress on an extensive suite of indicators across these five domains, accessing and presenting the latest data and further refining and developing our analyses. Within just two years, Australia has experienced two unprecedented national catastrophes - the 2019-2020 summer heatwaves and bushfires and the 2021-2022 torrential rains and flooding. Such events are costing lives and displacing tens of thousands of people. Further, our analysis shows that there are clear signs that Australia's health emergency management capacity substantially decreased in 2021. We find some signs of progress with respect to health and climate change. The states continue to lead the way in health and climate change adaptation planning, with the Victorian plan being published in early 2022. At the national level, we note progress in health and climate change research funding by the National Health and Medical Research Council. We now also see an acceleration in the uptake of electric vehicles and continued uptake of and employment in renewable energy. However, we also find Australia's transition to renewables and zero carbon remains unacceptably slow, and the Australian Government's continuing failure to produce a national climate change and health adaptation plan places the health and lives of Australians at unnecessary risk today, which does not bode well for the future.

Understanding current and projected emergency department presentations and associated healthcare costs in a changing thermal climate in Adelaide, South Australia.

BACKGROUND: Exposure to extreme temperatures is associated with increased emergency department (ED) presentations. The resulting burden on health service costs and the potential impact of climate change is largely unknown. This study examines the temperature-EDs/cost relationships in Adelaide, South Australia and how this may be impacted by increasing temperatures. METHODS: A time series analysis using a distributed lag nonlinear model was used to explore the exposure-response relationships. The net-attributable, cold-attributable and heat-attributable ED presentations for temperature-related diseases and costs were calculated for the baseline (2014-2017) and future periods (2034-2037 and 2054-2057) under three climate representative concentration pathways (RCPs). RESULTS: The baseline heat-attributable ED presentations were estimated to be 3600 (95% empirical CI (eCI) 700 to 6500) with associated cost of $A4.7 million (95% eCI 1.8 to 7.5). Heat-attributable ED presentations and costs were projected to increase during 2030s and 2050s with no change in the cold-attributable burden. Under RCP8.5 and population growth, the increase in heat-attributable burden would be 1.9% (95% eCI 0.8% to 3.0%) for ED presentations and 2.5% (95% eCI 1.3% to 3.7%) for ED costs during 2030s. Under the same conditions, the heat effect is expected to increase by 3.7% (95% eCI 1.7% to 5.6%) for ED presentations and 5.0% (95% eCI 2.6% to 7.1%) for ED costs during 2050s. CONCLUSIONS: Projected climate change is likely to increase heat-attributable emergency presentations and the associated costs in Adelaide. Planning health service resources to meet these changes will be necessary as part of broader risk mitigation strategies and public health adaptation actions.

The 2021 report of the MJA-Lancet Countdown on health and climate change: Australia increasingly out on a limb.

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.

Climate variability, socio-ecological factors and dengue transmission in tropical Queensland, Australia: A Bayesian spatial analysis.

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 explained much of the heterogeneity of dengue transmission dynamics in the tropical climate zone of Queensland. Results will help to further understand the risk factors of dengue transmission and will provide scientific evidence in designing effective local dengue control programs in the most needed areas.

Extreme weather conditions and dengue outbreak in Guangdong, China: Spatial heterogeneity based on climate variability.

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 space coverage and lower elevation. CONCLUSIONS: Spatially varied effects of weather conditions on dengue outbreaks necessitate area-specific dengue prevention and control measures. Extremes of temperature and rainfall have strong and positive associations with dengue outbreaks.

A regional suitable conditions index to forecast the impact of climate change on dengue vectorial capacity.

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 alongside additional correlates of infection such as human population density and socioeconomic development indicators, the SCI could be used to develop an early warning system for dengue transmission.

Climate change and socioeconomic determinants are structural constraints to agency in diet-related non-communicable disease prevention in Vanuatu: a qualitative study.

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 amplify the risk, burden, and consequences of DR-NCDs. It is recommended that the DR-NCD narrative in Vanuatu be re-framed with an emphasis on the range of structural determinants of DR-NCD risk. This will serve to enhance individual and collective agency to not only make healthy food and other behavioural choices but also to exercise agency to transform the structures in a culturally appropriate way.

A call for better understanding of social media in surveillance and management of noncommunicable diseases.

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.

Using internet-based query and climate data to predict climate-sensitive infectious disease risks: a systematic review of epidemiological evidence.

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.

Extreme weather events and dengue outbreaks in Guangzhou, China: a time-series quasi-binomial distributed lag non-linear model.

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.

The 2020 special report of the MJA-Lancet Countdown on health and climate change: lessons learnt from Australia's "Black Summer".

The MJA-Lancet Countdown on health and climate change was established in 2017, and produced its first Australian national assessment in 2018 and its first annual update in 2019. 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. In the wake of the unprecedented and catastrophic 2019-20 Australian bushfire season, in this special report we present the 2020 update, with a focus on the relationship between health, climate change and bushfires, highlighting indicators that explore these linkages. In an environment of continuing increases in summer maximum temperatures and heatwave intensity, substantial increases in both fire risk and population exposure to bushfires are having an impact on Australia's health and economy. As a result of the "Black Summer" bushfires, the monthly airborne particulate matter less than 2.5 µm in diameter (PM2.5 ) concentrations in New South Wales and the Australian Capital Territory in December 2019 were the highest of any month in any state or territory over the period 2000-2019 at 26.0 µg/m3 and 71.6 µg/m3 respectively, and insured economic losses were $2.2 billion. We also found growing awareness of and engagement with the links between health and climate change, with a 50% increase in scientific publications and a doubling of newspaper articles on the topic in Australia in 2019 compared with 2018. However, despite clear and present need, Australia still lacks a nationwide adaptation plan for health. As Australia recovers from the compounded effects of the bushfires and the coronavirus disease 2019 (COVID-19) pandemic, the health profession has a pivotal role to play. It is uniquely suited to integrate the response to these short term threats with the longer term public health implications of climate change, and to argue for the economic recovery from COVID-19 to align with and strengthen Australia's commitments under the Paris Agreement.

Different responses of dengue to weather variability across climate zones in Queensland, Australia.

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.

Weather factors, PCV intervention and childhood pneumonia in rural Bangladesh.

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.

Using big data to predict pertussis infections in Jinan city, China: a time series analysis.

This study aims to use big data (climate data, internet query data and school calendar patterns (SCP)) to improve pertussis surveillance and prediction, and develop an early warning model for pertussis epidemics. We collected weekly pertussis notifications, SCP, climate and internet search query data (Baidu index (BI)) in Jinan, China between 2013 and 2017. Time series decomposition and temporal risk assessment were used for examining the epidemic features in pertussis infections. A seasonal autoregressive integrated moving average (SARIMA) model and regression tree model were developed to predict pertussis occurrence using identified predictors. Our study demonstrates clear seasonal patterns in pertussis epidemics, and pertussis activity was most significantly associated with BI at 2-week lag (rBI = 0.73, p < 0.05), temperature at 1-week lag (rtemp = 0.19, p < 0.05) and rainfall at 2-week lag (rrainfall = 0.27, p < 0.05). No obvious relationship between pertussis peaks and school attendance was found in the study. Pertussis cases were more likely to be temporally concentrated throughout the epidemics during the study period. SARIMA models with 2-week-lagged BI and 1-week-lagged temperature had better predictive performance (ßsearch query = 0.06, p = 0.02; ßtemp = 0.16, p = 0.03) with large correlation coefficients (r = 0.67, p < 0.01) and low root mean squared error (RMSE) value (r = 3.59). The regression tree model identified threshold values of potential predictors (search query, climate and SCP) for pertussis epidemics. Our results showed that internet query in conjunction with social and climatic data can predict pertussis epidemics, which is a foundation of using such data to develop early warning systems.

Association of sociodemographic factors and internet query data with pertussis infections in Shandong, China.

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.

The 2019 report of the MJA-Lancet Countdown on health and climate change: a turbulent year with mixed progress.

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, continuing elevated suicide rates at higher temperatures. We conclude that Australia remains at significant risk of declines in health due to climate change, and that substantial and sustained national action is urgently required in order to prevent this.