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.

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.

Impact of environmental factors on diabetes mortality: A comparison between inland and coastal areas

BACKGROUND

Diabetes mortality varies between coastal and inland areas in Shandong Province, China. However, evidence about the reasons for this disparity is limited. We assume that distinct environmental conditions may contribute to the disparities in diabetes mortality patterns between coastal and inland areas.

METHOD

Qingdao and Jinan were selected as typical coastal and inland cities in Shandong Province, respectively, with similar socioeconomic but different environmental characteristics. Data on diabetes deaths and environmental factors (i.e., temperature, relative humidity and air pollution particles with a diameter of 2.5 μm or less (PM2.5)) were collected from 2013 to 2020. Spatial kriging methods were used to estimate the aggregated diabetes mortality at the city level. A distributed lag non-linear model (DLNM) was used to quantify the possible cumulative and non-cumulative associations between environmental factors and diabetes mortality by age, sex and location.

RESULTS

In the coastal city (Qingdao), the maximum cumulative relative risks (RRs) of temperature and PM2.5 associated with diabetes deaths were 2.54 (95 % confidence interval (CI): 1.25-5.15), and 1.17 (95 % CI: 1.01-1.37) respectively, at lag 1 week. In the inland city (Jinan), only temperature exhibited significant cumulative associations with diabetes deaths (RR = 1.54, 95 % CI: 1.07-2.23 at 29 °C). Lower relative humidity (22 %-45 %) had a lag-specific association with diabetes deaths in inland areas at lag 3 weeks (RR = 1.33, 95 % CI: 1.03-1.70 at 22 %).

CONCLUSION

Despite the lower PM2.5 concentrations in the coastal location, diabetes mortality exhibited stronger links to environmental variables in the coastal city than in the inland city. These findings suggest that the control of air pollution could decrease the mortality burden of diabetes, even in the region with relatively good air quality. Additionally, the spatial estimation method is recommended to identify associations between environmental factors and diseases in studies with limited data.

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. PM_2.5 was the most frequently examined pollutant (included in 11 studies), followed by NO₂ and NO_x (included in 6 studies), and fewer studies focused on other pollutants (PM_{2.5 absorbance}, PM₁₀, PM_2.5-10, O₃, and BC). In all the 16 associations for liver cancer mortality, 14 associations reported the effect of PM_2.5 on liver cancer mortality. Eight associations on PM_2.5 were significant, showing a suggestive association between PM_2.5 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. PM_2.5 was the most frequently examined pollutant, followed by NO₂ and NO_x, and fewer studies focused on other pollutants. PM_2.5 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.

Does global warming increase the risk of liver cancer in Australia? Perspectives based on spatial variability

Australia has experienced an astonishing increase in liver cancer over the past few decades and the epidemiological reasons behind this are puzzling. The existing recognized risk factors for liver cancer, viral hepatitis, and alcohol consumption, are inconsistent with the trend in liver cancer. Behind the effects of migration and metabolic disease lies a potential contribution of climate change to an increase in liver cancer. This study explored the climate-associated distribution of high-risk areas for liver cancer by comparing liver cancer to lung cancer and finds that the incidence of liver cancer is more pronounced in hot and humid areas. This study showed the risk of liver cancer was higher in the equatorial region and tropical regions. These results will extend the study on the health consequences of climate change and provide more ideas and directions for future researchers.

Age- and Gender-Specific Differences in the Seasonal Distribution of Diabetes Mortality in Shandong, China: A Spatial Analysis

Diabetes mortality in Shandong is higher than the national average in China. This study first explored diabetes mortality variation spatially at the county/district level among adults aged over 30 years in terms of age and gender, specifically by season. Daily diabetes mortality data were collected from 31 mortality surveillance points across Shandong Province in 2014. A geographic information system, spatial kriging interpolation and a spatial clustering method were used to examine the spatial patterns of diabetes mortality at the county/district level by season. Sensitivity analysis was conducted using diabetes mortality data from 10 mortality surveillance points from 2011 to 2020. As a result, the total diabetes mortality in eastern counties/districts was the highest (relative risk (RR) of cluster: 1.58, p = 0.00) across the whole province. For subgroups, women had higher mortality (16.84/100,000) than men (12.15/100,000), people aged over 75 years were the most vulnerable (93.91/100,000) and the highest-risk season was winter. However, the mortality differences between winter and summer were smaller in eastern and coastal regions than in other regions for all gender- and age-specific groups. The findings provide further evidence for early warning and precision preventative strategies for diabetes mortality in different regions of Shandong Province. Future research is required to identify the risk factors for diabetes and understand the differences in the social and environmental contexts.

Species-specific climate Suitable Conditions Index and dengue transmission in Guangdong, China

Background

Optimal climatic conditions for dengue vector mosquito species may play a significant role in dengue transmission. We previously developed a species-specific Suitable Conditions Index (SCI) for Aedes aegypti and Aedes albopictus, respectively. These SCIs rank geographic locations based on their climatic suitability for each of these two dengue vector species and theoretically define parameters for transmission probability. The aim of the study presented here was to use these SCIs together with socio-environmental factors to predict dengue outbreaks in the real world.

Methods

A negative binomial regression model was used to assess the relationship between vector species-specific SCI and autochthonous dengue cases after accounting for potential confounders in Guangdong, China. The potential interactive effect between the SCI for Ae. albopictus and the SCI for Ae. aegypti on dengue transmission was assessed.

Results

The SCI for Ae. aegypti was found to be positively associated with autochthonous dengue transmission (incidence rate ratio: 1.06, 95% confidence interval: 1.03, 1.09). A significant interaction effect between the SCI of Ae. albopictus and the SCI of Ae. aegypti was found, with the SCI of Ae. albopictus significantly reducing the effect of the SCI of Ae. aegypti on autochthonous dengue cases. The difference in SCIs had a positive effect on autochthonous dengue cases.

Conclusions

Our results suggest that dengue fever is more transmittable in regions with warmer weather conditions (high SCI for Ae. aegypti). The SCI of Ae. aegypti would be a useful index to predict dengue transmission in Guangdong, China, even in dengue epidemic regions with Ae. albopictus present. The results also support the benefit of the SCI for evaluating dengue outbreak risk in terms of vector sympatry and interactions in the absence of entomology data in future research.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05453-x.

Hospitalization Costs of Respiratory Diseases Attributable to Temperature in Australia and Projections for Future Costs in the 2030s and 2050s under Climate Change

This study aimed to estimate respiratory disease hospitalization costs attributable to ambient temperatures and to estimate the future hospitalization costs in Australia. The associations between daily hospitalization costs for respiratory diseases and temperatures in Sydney and Perth over the study period of 2010-2016 were analyzed using distributed non-linear lag models. Future hospitalization costs were estimated based on three predicted climate change scenarios-RCP2.6, RCP4.5 and RCP8.5. The estimated respiratory disease hospitalization costs attributable to ambient temperatures increased from 493.2 million Australian dollars (AUD) in the 2010s to more than AUD 700 million in 2050s in Sydney and from AUD 98.0 million to about AUD 150 million in Perth. The current cold attributable fraction in Sydney (23.7%) and Perth (11.2%) is estimated to decline by the middle of this century to (18.1-20.1%) and (5.1-6.6%), respectively, while the heat-attributable fraction for respiratory disease is expected to gradually increase from 2.6% up to 5.5% in Perth. Limitations of this study should be noted, such as lacking information on individual-level exposures, local air pollution levels, and other behavioral risks, which is common in such ecological studies. Nonetheless, this study found both cold and hot temperatures increased the overall hospitalization costs for respiratory diseases, although the attributable fractions varied. The largest contributor was cold temperatures. While respiratory disease hospitalization costs will increase in the future, climate change may result in a decrease in the cold attributable fraction and an increase in the heat attributable fraction, depending on the location.

Climate variability and Aedes vector indices in the southern Philippines: An empirical analysis

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 vector control responses, and that vector indices based on household surveys were weakly yet significantly correlated with city-level climate variables. We suggest that more detailed spatial and temporal analyses of entomological, climate, socio-environmental and Aedes-borne disease incidence data are necessary to ascertain the most effective use of entomological indices in guiding vector control responses, and reduction of human disease risk.

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 association of wildfire air pollution with COVID-19 incidence in New South Wales, Australia

The 2020 COVID-19 outbreak in New South Wales (NSW), Australia, followed an unprecedented wildfire season that exposed large populations to wildfire smoke. Wildfires release particulate matter (PM), toxic gases and organic and non-organic chemicals that may be associated with increased incidence of COVID-19. This study estimated the association of wildfire smoke exposure with the incidence of COVID-19 in NSW. A Bayesian mixed-effect regression was used to estimate the association of either the average PM₁₀ level or the proportion of wildfire burned area as proxies of wildfire smoke exposure with COVID-19 incidence in NSW, adjusting for sociodemographic risk factors. The analysis followed an ecological design using the 129 NSW Local Government Areas (LGA) as the ecological units. A random effects model and a model including the LGA spatial distribution (spatial model) were compared. A higher proportional wildfire burned area was associated with higher COVID-19 incidence in both the random effects and spatial models after adjustment for sociodemographic factors (posterior mean = 1.32 (99% credible interval: 1.05-1.67) and 1.31 (99% credible interval: 1.03-1.65), respectively). No evidence of an association between the average PM₁₀ level and the COVID-19 incidence was found. LGAs in the greater Sydney and Hunter regions had the highest increase in the risk of COVID-19. This study identified wildfire smoke exposures were associated with increased risk of COVID-19 in NSW. Research on individual responses to specific wildfire airborne particles and pollutants needs to be conducted to further identify the causal links between SARS-Cov-2 infection and wildfire smoke. The identification of LGAs with the highest risk of COVID-19 associated with wildfire smoke exposure can be useful for public health prevention and or mitigation strategies.

Current and future threats to human health in the Anthropocene

It has been widely recognised that the threats to human health from global environmental changes (GECs) are increasing in the Anthropocene epoch, and urgent actions are required to tackle these pressing challenges. A scoping review was conducted to provide an overview of the nine planetary boundaries and the threats to population health posed by human activities that are exceeding these boundaries in the Anthropocene. The research progress and key knowledge gaps were identified in this emerging field. Over the past three decades, there has been a great deal of research progress on health risks from climate change, land-use change and urbanisation, biodiversity loss and other GECs. However, several significant challenges remain, including the misperception of the relationship between human and nature; assessment of the compounding risks of GECs; strategies to reduce and prevent the potential health impacts of GECs; and uncertainties in fulfilling the commitments to the Paris Agreement. Confronting these challenges will require rigorous scientific research that is well-coordinated across different disciplines and various sectors. It is imperative for the international community to work together to develop informed policies to avert crises and ensure a safe and sustainable planet for the present and future generations.

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.

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.

Weather Variability, Socioeconomic Factors, and Pneumonia in Children Under Five-Years Old - Bangladesh, 2012-2016

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.

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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-11245-2.

Increasing impacts of temperature on hospital admissions, length of stay, and related healthcare costs in the context of climate change in Adelaide, South Australia

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.

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 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 1^st, 2010 to December 31^st, 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.

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.

Association of weather variability with resurging pertussis infections among different age groups: A non-linear approach

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.

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.

Winter temperature and myocardial infarction in Brisbane, Australia: Spatial and temporal analyses

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 warm and socioeconomically disadvantaged communities in winter.

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.

High relative humidity might trigger the occurrence of the second seasonal peak of dengue in the Philippines

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.

Projecting the future of dengue under climate change scenarios: Progress, uncertainties and research needs

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 consideration of assumptions and uncertainties, which will facilitate the development of tailored climate change adaptation strategies to manage dengue.

Resurgence of Pertussis Infections in Shandong, China: Space-Time Cluster and Trend Analysis

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.

Heatwaves and dengue outbreaks in Hanoi, Vietnam: New evidence on early warning

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 outbreaks (i.e., small, moderate and large outbreaks combined) in heatwave years had higher weekly number of dengue cases (p<0.05). Findings were robust under different sensitivity analyses.

Conclusions

The short-term association between temperature and dengue risk varied by the level of outbreaks and temperature seems more likely affect large outbreaks. Moreover, heatwaves may delay the timing and increase the magnitude of dengue outbreaks.

Dengue fever is one of the most common mosquito-borne viral diseases. Weather extremes such as El Niño event and extreme hot summer can affect dengue incidence rate and dengue outbreaks. More frequent, more intensive and longer lasting heatwaves in the 21st century is anticipated because of global warming, making it necessary to investigate the association between heatwaves and dengue outbreaks. In this study, we estimated 4.6%, 11.6%, and 21.9% of incident dengue cases during small, medium, and large outbreaks attributable to temperature in Hanoi, Vietnam. We also found evidence of an association between heatwaves and dengue outbreaks, with longer delayed effects on large outbreaks than small and medium outbreaks. Compared with non-heatwave years, dengue outbreaks in heatwave years had higher number of dengue cases. Heatwave weather may represent an emerging risk factor or predicator of dengue outbreaks in tropical regions. Future dengue prediction models incorporating heatwaves may help increase the accuracy of predictability.

Does Bangkok have a central role in the dengue dynamics of Thailand?

Background

Bangkok 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.

Methods

Monthly 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.

Results

The 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.

Conclusions

Our 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.

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 (r_BI = 0.73, p < 0.05), temperature at 1-week lag (r_temp = 0.19, p < 0.05) and rainfall at 2-week lag (r_rainfall = 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 (β_{search query} = 0.06, p = 0.02; β_temp = 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.

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.

Cardiorespiratory effects of heatwaves: A systematic review and meta-analysis of global epidemiological evidence

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 cardiovascular and respiratory diseases appeared to be more vulnerable to heatwaves in comparison to morbidity. Considering high heterogeneity detected between studies and limited investigations into subpopulations, more research are required to provide a clearer picture of how heatwaves affect cardiovascular and respiratory diseases in different settings.

Using dengue epidemics and local weather in Bali, Indonesia to predict imported dengue in Australia

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.

Impacts of exposure to ambient temperature on burden of disease: a systematic review of epidemiological evidence

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 framework for assessing and comparing temperature-related BD across different settings.

Short - term effects of temperature on hospital admissions for acute myocardial infarction: A comparison between two neighboring climate zones in Vietnam

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 (90^th percentile of temperature range -29.5 °C) and extreme high temperatures (95th percentile of temperature range - 29.9 °C), respectively.

CONCLUSIONS

Risk of AMI is associated with extremely high and extremely low temperature in Vietnam and the risk varies in relation to the local regional climate. Public health preparedness and multi-level interventions should attempt to reduce people's exposure in periods of disadvantageous temperatures.

The MJA-Lancet Countdown on health and climate change: Australian policy inaction threatens lives

Climate plays an important role in human health and it is well established that climate change can have very significant impacts in this regard. In partnership with The Lancet and the MJA, we present the inaugural Australian Countdown assessment of progress on climate change and health. This comprehensive assessment examines 41 indicators across five broad sections: 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. These indicators and the methods used for each are largely consistent with those of the Lancet Countdown global assessment published in October 2017, but with an Australian focus. Significant developments include the addition of a new indicator on mental health. Overall, we find that Australia is vulnerable to the impacts of climate change on health, and that policy inaction in this regard threatens Australian lives. In a number of respects, Australia has gone backwards and now lags behind other high income countries such as Germany and the United Kingdom. Examples include the persistence of a very high carbon-intensive energy system in Australia, and its slow transition to renewables and low carbon electricity generation. However, we also find some examples of good progress, such as heatwave response planning. Given the overall poor state of progress on climate change and health in Australia, this country now has an enormous opportunity to take action and protect human health and lives. Australia has the technical knowhow and intellect to do this, and our annual updates of this assessment will track Australia's engagement with and progress on this vitally important issue.

Spatial and temporal analysis of dengue infections in Queensland, Australia: Recent trend and perspectives

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.

Climate change, food security and health in Kiribati: a narrative review of the literature

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.

Correction to: Changes in relative fit of human heat stress indices to cardiovascular, respiratory, and renal hospitalizations across five Australian urban populations

The authors of the article would like to bring the following correction/corrigendum to attention: When recently investigating future changes in heat stress indices, we discovered an error in the use of the heatwave indices we compared in Goldie et al. (2017).