Climate change and human infectious diseases: A synthesis of research findings from global and spatio-temporal perspectives

Wildfire-Related Air Pollution and Infectious Diseases: Systematic Review and Meta-Analysis

Amid the global rise in wildfire events, the health impacts of wildfire-related air pollution are increasingly scrutinized. While numerous reviews have examined the link between air pollution and infectious diseases, reviews specifically focusing on wildfire-related air pollution and infectious diseases remain scarce. To address this gap, we conducted a comprehensive search in MEDLINE, EMBASE, Scopus and Web of Science databases up to December 31, 2023, using PRISMA (Preferred Reporting Items for Systematic Reviews & Meta-Analyses) guidelines. Search terms included synonyms of wildfire and infectious diseases. Peer-reviewed epidemiological studies that reported any association or trend between wildfire air pollution and infectious diseases were selected against eligibility criteria. Risk of bias and quality of included studies were assessed using modified risk of bias and quality assessment tools. Our review included 30 studies, predominantly from developed countries including the United States (USA), Australia, and Canada. Most focused on respiratory infectious diseases (n = 29), including 9 specifically on the coronavirus disease 2019 (COVID-19). The majority examined short-term wildfire air pollution (n = 27) (exposure of one month or less). Twenty-three studies reported effect estimates for the meta-analysis. We found that a 10 μg/m3 increase in short-term wildfire PM2.5 (particulate matter with a diameter of 2.5 micrometer of less) exposure was associated with a 15% increase in COVID-19 infections (relative risk [RR] = 1.15; 95% confidence interval [CI]: 1.09-1.21; heterogeneity (I 2): 83%), a 3% increase in respiratory diseases (RR = 1.03; 95% CI: 1.01-1.05; I 2: 0%) and a 3% increase in acute upper respiratory infection combined with acute bronchitis (RR = 1.03; 95% CI: 1.02-1.05; I 2: 62%). Medium-term exposure (more than a month but less than a year) to wildfire smoke was associated with 20% rising hospitalization for systemic fungal infections like coccidioidomycosis (95% CI: 5-38%). The current research exclusively examines respiratory infections in developed countries. Future high-quality primary studies should prioritize understanding the impact of wildfire-related air pollution on various infectious diseases.

Perceptions of medical and public health professionals on climate change and emerging health challenges in Pakistan: a multi-scale approach

BACKGROUND AND OBJECTIVES

Climate change is a significant global health threat, disproportionately impacting low- and middle-income countries. Urban centres like Karachi, Pakistan, face rising incidences of vector-borne and waterborne diseases because of changing climate (CC). This study aimed to explore the perceptions of healthcare professionals regarding climate change, its health impacts, and their role in addressing these challenges.

METHODS

A qualitative study was conducted using nine focus group discussions with 46 healthcare professionals, including doctors, nurses, and public health experts from three major hospitals and two postgraduate institutions in Karachi. Participants were recruited through snowball sampling, and data were collected using semi-structured interviews. Thematic analysis was conducted to identify both manifest and latent themes in the data.

RESULTS

The analysis revealed seven major themes: awareness of climate change, health impacts, economic and social consequences, environmental and natural disasters, the role of healthcare professionals, mitigation strategies, and policy challenges. Participants demonstrated varied levels of awareness, influenced by their educational background and professional specialization, with public health professionals exhibiting a broader understanding compared to other healthcare workers. Key concerns included the rise of infectious diseases, food insecurity, and urban resource strain. Participants identified barriers such as inadequate training, limited resources, and weak policy enforcement that hinder their ability to address climate impacts effectively.

CONCLUSIONS

Healthcare professionals in Karachi play a critical role in mitigating climate-related health impacts. Enhanced education, integration of climate change into medical curricula, and interdisciplinary collaboration are essential. Strengthened policies and systemic investments are needed to empower healthcare workers as leaders in climate adaptation and mitigation efforts.

Spatio-temporal variations of heat extremes across the yangtze river delta during 2001-2023 based on remotely sensed seamless air temperature

Heat extremes become increasingly frequent and severe, posing adverse risks to public health and environment. Previous research on extreme heat mostly used meteorological observations or reanalysis data, which cannot well capture detailed spatial patterns. This study developed a seamless air temperature (Ta) dataset from remote sensing data to characterize the spatio-temporal variations of heat extremes in the Yangtze River Delta (YRD) from 2001 to 2023. First, the daily maximum Ta of cloud-free pixels was estimated through machine learning algorithms from MODIS land surface temperature (LST) and other remote sensing data. Then, gaps in the estimated Ta caused by cloud cover were filled using the Temporal Fourier Analysis (TFA) method, generating a seamless daily maximum Ta dataset. The remotely sensed Ta achieved an overall MAE of 1.11 °C. Based on the remotely sensed Ta, six heat indices were calculated to characterize heat extremes, including heat days (HTD), effective accumulated high temperature (EAHT), heatwave frequency (HWF), cumulative heatwave days (HWD), maximum heatwave duration (HWMD) and average heatwave duration (HWAD). Heat extremes occurred frequently in the YRD, with obvious spatial variability. Southern basins experienced intense heat with high frequency and duration, while southern mountains and northern areas experienced weaker heat extremes. Urban areas have substantially more intense heat events than suburbs, attributed to urban heat island effect. 2022 recorded the most severe heat, with notable events also in 2013 and 2003. This study provides valuable insights into heat events in the YRD and serves as a reference for remote sensing research on heat events.

Causal inference concepts can guide research into the effects of climate on infectious diseases

A pressing question resulting from global warming is how climate change will affect infectious diseases. Answering this question requires research into the effects of weather on the population dynamics of transmission and infection; elucidating these effects, however, has proved difficult due to the challenges of assessing causality from the predominantly observational data available in epidemiological research. Here we show how concepts from causal inference-the sub-field of statistics aiming at inferring causality from data-can guide that research. Through a series of case studies, we illustrate how such concepts can help assess study design and strategically choose a study's location, evaluate and reduce the risk of bias, and interpret the multifaceted effects of meteorological variables on transmission. More broadly, we argue that interdisciplinary approaches based on explicit causal frameworks are crucial for reliably estimating the effect of weather and accurately predicting the consequences of climate change.

Targeted prevention strategy: Exploring the interaction effect of environmental and social factors on infectious diseases

Human disease and health issues are globally significant and closely related to environmental and social factors. However, the interaction effects of such factors on diseases are unclear, which has resulted in a lack of targeted prevention strategies. By taking infectious diseases in China as an example, this study uses an interpretable machine learning method to analyze the impact of environmental and social factors on disease, including industrial SO2 emissions, sanitary toilet coverage rate, and sunshine duration. The modeling results confirm the existence of a nonlinear relationship between infectious diseases incidence and each of the potential factors. That is, increased SO2 emissions can increase infectious diseases incidence, whereas broad sanitary toilet coverage can reduce such risk. This study examines the interaction of the driving factors and reveals that variation in the sunshine duration can affect the impact of SO2 emissions on infectious diseases incidence. This study proposes the use of multilevel risk trigger points (RTPs) to develop early warning and targeted regulation measures and classifies the points as primary, secondary, and tertiary. For example, for Henan Province, the RTPs of SO2 emissions are 291,031, 897,579, and 1,381,342 tons, whereas those for Shandong are 362,802, 1,177,650, and 1,658,118 tons. At the tertiary RTP level, SO2 emissions can significantly increase infectious disease incidence, which has prompted policymakers to implement pollution reduction and disease prevention measures. This study clarifies the role and interaction effects of environmental and social factors on infectious diseases to aid in precise disease prevention and environmental health management.

The assessment and detection rate of intrinsic capacity deficits among older adults: a systematic review and meta-analysis

BACKGROUND

Assessing and monitoring intrinsic capacity (IC) is an effective strategy to promote healthy ageing by intervening early in high-risk populations. This review systematically analyzed the global detection rates of IC deficits and explored variations across diverse populations and data collection methods.

METHODS

This study was preregistered with PROSPERO, CRD42023477315. In this systematic review and meta-analysis, we systematically searched ten databases from January 2015 to October 2023, for peer-reviewed, observational studies or baseline survey of trials that assessed IC deficits among older adults aged 50 and above globally following the condition, context and population approach. The main outcome was intrinsic capacity deficits which could be assessed by any tools. Meta-analyses were performed by a random-effect model to pool the detection rates across studies and subgroup analyses were conducted by populations and data collection methods.

RESULTS

Fifty-six studies conducted in 13 countries were included in the review and 44 studies with detection rates of IC were included in the meta-analysis. The pooled detection rate of IC deficits was 72.0% (65.2%-78.8%) and deficits were most detected in sensory (49.3%), followed by locomotion (40.0%), cognition (33.1%), psychology (21.9%), and vitality (20.1%). Variations in detection rates of IC deficits were observed across studies, with higher rates observed in low- and middle-income countries (74.0%) and hyper-aged societies (85.0%). Study population and measurement tools also explained the high heterogeneity across studies.

CONCLUSION

IC deficits are common among older adults, while heterogeneity exists across populations and by measurement. Early monitoring with standardized tools and early intervention on specific subdomains of IC deficits are greatly needed for effective strategies to promote healthy ageing.

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.

Dual stressors of infection and warming can destabilize host microbiomes

Climate change is causing extreme heating events and intensifying infectious disease outbreaks. Animals harbour microbial communities, which are vital for their survival and fitness under stressful conditions. Understanding how microbiome structures change in response to infection and warming may be important for forecasting host performance under global change. Here, we evaluated alterations in the microbiomes of several wild Caenorhabditis elegans isolates spanning a range of latitudes, upon warming temperatures and infection by the parasite Leucobacter musarum. Using 16S rRNA sequencing, we found that microbiome diversity decreased, and dispersion increased over time, with the former being more prominent in uninfected adults and the latter aggravated by infection. Infection reduced dominance of specific microbial taxa, and increased microbiome dispersion, indicating destabilizing effects on host microbial communities. Exposing infected hosts to warming did not have an additive destabilizing effect on their microbiomes. Moreover, warming during pre-adult development alleviated the destabilizing effects of infection on host microbiomes. These results revealed an opposing interaction between biotic and abiotic factors on microbiome structure. Lastly, we showed that increased microbiome dispersion might be associated with decreased variability in microbial species interaction strength. Overall, these findings improve our understanding of animal microbiome dynamics amidst concurrent climate change and epidemics. This article is part of the theme issue 'Sculpting the microbiome: how host factors determine and respond to microbial colonization'.

Bat-associated microbes: Opportunities and perils, an overview

The potential biotechnological uses of bat-associated bacteria are discussed briefly, indicating avenues for biotechnological applications of bat-associated microbes. The uniqueness of bats in terms of their lifestyle, genomes and molecular immunology may predispose bats to act as disease reservoirs. Molecular phylogenetic analysis has shown several instances of bats harbouring the ancestral lineages of bacterial (Bartonella), protozoal (Plasmodium, Trypanosoma cruzi) and viral (SARS-CoV2) pathogens infecting humans. Along with the transmission of viruses from bats, we also discuss the potential roles of bat-associated bacteria, fungi, and protozoan parasites in emerging diseases. Current evidence suggests that environmental changes and interactions between wildlife, livestock, and humans contribute to the spill-over of infectious agents from bats to other hosts. Domestic animals including livestock may act as intermediate amplifying hosts for bat-origin pathogens to transmit to humans. An increasing number of studies investigating bat pathogen diversity and infection dynamics have been published. However, whether or how these infectious agents are transmitted both within bat populations and to other hosts, including humans, often remains unknown. Metagenomic approaches are uncovering the dynamics and distribution of potential pathogens in bat microbiomes, which might improve the understanding of disease emergence and transmission. Here, we summarize the current knowledge on bat zoonoses of public health concern and flag the gaps in the knowledge to enable further research and allocation of resources for tackling future outbreaks.

Effect of temperature on fast transmission of COVID-19 in low per capita GDP Asian countries

An abrupt outbreak of COVID-19 caused enormous global concerns. Although all countries around the world are severely affected, developing Asian countries faced more difficulties due to their low per capita GDP. The temperature was considered a leading variable in spreading viral diseases, including COVID-19. The present study aimed to assess the relationship between temperature and the spread of COVID-19, with a focus on developing Asian countries. In a few Asian countries, COVID-19 spread rapidly in the summer, while in some countries, there is an increase in winter. A linear correlation was developed between COVID-19 cases/deaths and temperature for the selected countries, which were very weak. A coefficient of determination of 0.334 and 0.365 was observed between cases and average monthly max/min temperatures. A correlation of R2 = 0.307 and 0.382 was found between deaths and average max/min monthly temperatures, respectively. There is no scientific reason to assume that COVID-19 is more dominant at low than high temperatures. Therefore, it is believed that the results may be helpful for the health department and decision-makers to understand the fast spread of COVID-19.

Natural and socio-environmental factors contribute to the transmissibility of COVID-19: evidence from an improved SEIR model

COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.

Measuring Impact of Climate Change on Indigenous Health in the Background of Multiple Disadvantages: A Scoping Review for Equitable Public Health Policy Formulation

How climate change is uniquely affecting Indigenous health remains a very less explored area in the existing research literature. The imperative of inclusive climate action to protect indigenous health multiplies manifolds due to their unique vulnerabilities owing to predominant dependence on natural resources and multiple disadvantages faced. The current article attempted to add to the evidence pool regarding climate change impacts on the indigenous population by systematically collecting, processing, and interpreting data as a scoping literature review for effective and inclusive climate policymaking. Twenty-Nine articles of varied study designs were identified employing a systematically organized search strategy using PubMed (Field, MeSH, and advanced search) and Google scholar; relevant data were extracted for further analysis. The Preferred Reporting Items for Systematic Review and Meta-Analysis for Scoping Reviews (PRISMA-ScR) guidelines were followed. Changing climate scenarios had both direct and indirect health-related impacts on indigenous health, and altered the epidemiological triad for various health-related events, causing the emergence and re-emergence of infectious diseases, and increased prevalence of chronic diseases and mental disorders. An expanded framework was developed showcasing the variability of climate change events, multiple disadvantages, and its impacts on indigenous populations. Few studies also reported a wide range of adaptation responses of indigenous peoples towards climate change. It was substantiated that any climate-change mitigation policy must take into account the trials and tribulations of indigenous communities. Also, due to the complexity and large variability of the impacts and differences in mitigation capabilities, policies should be contextualized locally and tailored to meet the climate need of the indigenous community.

Effects of temperature on incidence of bacillary dysentery in a temperate continental arid climate city in northwest China

The effect of ambient temperature on health continues to draw more and more attention with the global warming. Bacillary dysentery (BD) is a major global environmental health issue and affected by temperature and other environmental variables. In the current study, we evaluated the effect of temperature on the incidence of BD from January 1st, 2008 to December 31st, 2011 in Jiayuguan, a temperate continental arid climate city in the Hexi Corridor of northwest China. A distributed lag non-linear model (DLNM) was performed to evaluate the lag effect of temperature on BD up to 30 days. Results showed the risk of BD increased with temperature significantly, especially after 8 °C. The maximum risk of BD was observed at extreme high temperature (29 °C). The effect of temperature on BD risk was significantly divided into short-term effect at lag 5 days and long-term effect at lag 30 days. Age ≤ 15 years were most affected by high temperature. The maximum cumulative risk for lag 30 days (25.8, 95% CIs: 11.8-50.1) was observed at 29 °C. Age ≤ 15 years and females showed short-term effect at lag 5 days and long-term effect at lag 30 days, while age > 15 years and males showed acute short-term effect at lag 0 and light long-term effect at lag 16 days.

Infectious Disease Sensitivity to Climate and Other Driver-Pressure Changes: Research Effort and Gaps for Lyme Disease and Cryptosporidiosis

Climate sensitivity of infectious diseases is discussed in many studies. A quantitative basis for distinguishing and predicting the disease impacts of climate and other environmental and anthropogenic driver-pressure changes, however, is often lacking. To assess research effort and identify possible key gaps that can guide further research, we here apply a scoping review approach to two widespread infectious diseases: Lyme disease (LD) as a vector-borne and cryptosporidiosis as a water-borne disease. Based on the emerging publication data, we further structure and quantitatively assess the driver-pressure foci and interlinkages considered in the published research so far. This shows important research gaps for the roles of rarely investigated water-related and socioeconomic factors for LD, and land-related factors for cryptosporidiosis. For both diseases, the interactions of host and parasite communities with climate and other driver-pressure factors are understudied, as are also important world regions relative to the disease geographies; in particular, Asia and Africa emerge as main geographic gaps for LD and cryptosporidiosis research, respectively. The scoping approach developed and gaps identified in this study should be useful for further assessment and guidance of research on infectious disease sensitivity to climate and other environmental and anthropogenic changes around the world.

Long-term effects of meteorological factors and extreme weather on daily outpatient visits for conjunctivitis from 2013 to 2020: a time-series study in Urumqi, China

Conjunctivitis is a common multifactorial inflammatory ocular surface disease characterized by symptoms such as congestion, edema, and increased secretion of conjunctival tissue, and the potential effects of meteorological factors as well as extreme meteorological factors on conjunctivitis and their lagging effects have not been fully evaluated. We obtained the electronic case information of 59,731 outpatients with conjunctivitis from the Ophthalmology Department of the First Affiliated Hospital of Xinjiang Medical University (Urumqi, Xinjiang, China) for the period from January 1, 2013, to December 31, 2020. Meteorological data for daily mean temperature (°C), daily relative humidity (%), daily average wind speed (m/s), and atmospheric pressure (hPa) were obtained from the China Meteorological Data Sharing Service. The air pollutant data were obtained from 11 standard urban background fixed air quality monitors. A time-series analysis design and a quasi-Poisson generalized linear regression model combined with a distributed lagged nonlinear model (DLNM) were used to fit the effects of exposure to different meteorological factors and extreme weather on conjunctivitis outpatient visits. Subgroup analyses were performed on gender, age and season, and type of conjunctivitis. Univariate and multifactorial model results indicated that each 10-unit increase in mean temperature and relative humidity was associated with an increased risk of conjunctivitis outpatient visits, while each 10-unit increase in atmospheric pressure was associated with a decreased risk. The results of the extreme weather analysis suggested that extremely low levels of atmospheric pressure and relative humidity as well as extreme levels of temperature were associated with an increased risk of outpatient conjunctivitis visits, and extreme wind speeds were associated with a decreased risk. The results of the subgroup analysis suggested gender, age, and seasonal differences. We conducted the first large sample size time-series analysis in the large city furthest from the ocean in the world and confirmed for the first time that elevated mean temperature and extreme low levels of relative humidity in Urumqi were risk factors for local conjunctivitis outpatient visits, while elevated atmospheric pressure and extreme low levels of wind speed were protective factors, and there were lagged effects of temperature and atmospheric pressure. Multicenter studies with larger sample sizes are needed.

Agreement in extreme precipitation exposure assessment is modified by race and social vulnerability

Epidemiologic investigations of extreme precipitation events (EPEs) often rely on observations from the nearest weather station to represent individuals' exposures, and due to structural factors that determine the siting of weather stations, levels of measurement error and misclassification bias may differ by race, class, and other measures of social vulnerability. Gridded climate datasets provide higher spatial resolution that may improve measurement error and misclassification bias. However, similarities in the ability to identify EPEs among these types of datasets have not been explored. In this study, we characterize the overall and temporal patterns of agreement among three commonly used meteorological data sources in their identification of EPEs in all census tracts and counties in the conterminous United States over the 1991-2020 U.S. Climate Normals period and evaluate the association between sociodemographic characteristics with agreement in EPE identification. Daily precipitation measurements from weather stations in the Global Historical Climatology Network (GHCN) and gridded precipitation estimates from the Parameter-elevation Relationships on Independent Slopes Model (PRISM) and the North American Land Data Assimilation System (NLDAS) were compared in their ability to identify EPEs defined as the top 1% of precipitation events or daily precipitation >1 inch. Agreement among these datasets is fair to moderate from 1991 to 2020. There are spatial and temporal differences in the levels of agreement between ground stations and gridded climate datasets in their detection of EPEs in the United States from 1991 to 2020. Spatial variation in agreement is most strongly related to a location's proximity to the nearest ground station, with areas furthest from a ground station demonstrating the lowest levels of agreement. These areas have lower socioeconomic status, a higher proportion of Native American population, and higher social vulnerability index scores. The addition of ground stations in these areas may increase agreement, and future studies intending to use these or similar data sources should be aware of the limitations, biases, and potential for differential misclassification of exposure to EPEs. Most importantly, vulnerable populations should be engaged to determine their priorities for enhanced surveillance of climate-based threats so that community-identified needs are met by any future improvements in data quality.

Arboviruses in Mammals in the Neotropics: A Systematic Review to Strengthen Epidemiological Monitoring Strategies and Conservation Medicine

Arthropod-borne viruses (arboviruses) are a diverse group of ribonucleic acid (RNA) viruses, with the exception of African swine fever virus, that are transmitted by hematophagous arthropods to a vertebrate host. They are the important cause of many diseases due to their ability to spread in different environments and their diversity of vectors. Currently, there is no information on the geographical distribution of the diseases because the routes of transmission and the mammals (wild or domestic) that act as potential hosts are poorly documented or unknown. We conducted a systematic review from 1967 to 2021 to identify the diversity of arboviruses, the areas, and taxonomic groups that have been monitored, the prevalence of positive records, and the associated risk factors. We identified forty-three arboviruses in nine mammalian orders distributed in eleven countries. In Brazil, the order primates harbor the highest number of arbovirus records. The three most recorded arboviruses were Venezuelan equine encephalitis, Saint Louis encephalitis and West Nile virus. Serum is the most used sample to obtain arbovirus records. Deforestation is identified as the main risk factor for arbovirus transmission between different species and environments (an odds ratio of 1.46 with a 95% confidence interval: 1.34-1.59). The results show an increase in the sampling effort over the years in the neotropical region. Despite the importance of arboviruses for public health, little is known about the interaction of arboviruses, their hosts, and vectors, as some countries and mammalian orders have not yet been monitored. Long-term and constant monitoring allows focusing research on the analysis of the interrelationships and characteristics of each component animal, human, and their environment to understand the dynamics of the diseases and guide epidemiological surveillance and vector control programs. The biodiversity of the Neotropics should be considered to support epidemiological monitoring strategies.

Impacts of meteorological factors on the risk of scrub typhus in China, from 2006 to 2020: A multicenter retrospective study

Scrub typhus is emerging as a global public health threat owing to its increased prevalence and remarkable geographic expansion. However, it remains a neglected disease, and possible influences of meteorological factors on its risk are poorly understood. We conducted the largest-scale research to assess the impact of meteorological factors on scrub typhus in China. Weekly data on scrub typhus cases and meteorological factors were collected across 59 prefecture-level administrative regions from 2006 to 2020. First, we divided these regions into 3 regions and analyzed the epidemiological characteristics of scrub typhus. We then applied the distributed lag nonlinear model, combined with multivariate meta-analysis, to examine the associations between meteorological factors and scrub typhus incidence at the total and regional levels. Subsequently, we identified the critical meteorological predictors of scrub typhus incidence and extracted climate risk windows. We observed distinct epidemiological characteristics across regions, featuring obvious clustering in the East and Southwest with more even distribution and longer epidemic duration in the South. The mean temperature and relative humidity had profound effects on scrub typhus with initial-elevated-descendent patterns. Weather conditions of weekly mean temperatures of 25-33°C and weekly relative humidity of 60-95% were risk windows for scrub typhus. Additionally, the heavy rainfall was associated with sharp increase in scrub typhus incidence. We identified specific climatic signals to detect the epidemic of scrub typhus, which were easily monitored to generalize. Regional heterogeneity should be considered for targeted monitoring and disease control strategies.

The lagged effect and attributable risk of apparent temperature on hand, foot, and mouth disease in Changsha, China: a distributed lag non-linear model

Hand, foot, and mouth disease (HFMD) is the leading Category C infectious disease affecting millions of children in China every year. In the context of global climate change, the understanding and quantification of the impact of weather factors on human health are particularly critical to the development and implementation of climate change adaptation and mitigation strategies. The aim of this study was to quantify the attributable burden of a combined bioclimatic indicator (apparent temperature) on HFMD and to identify temperature-specific sensitive populations. A total of 123,622 HFMD cases were included in the study. The non-linear relationship between apparent temperature and the incidence of HFMD was approximately M-shaped, with hot weather being more likely to be attributable than cold conditions, of which moderately hot accounting for the majority of cases (21,441, 17.34%). Taking the median apparent temperature (19.2 °C) as reference, the cold effect showed a short acute effect with the highest risk on the day of lag 0 (RR = 1.086, 95% CI: 1.024 ~ 1.152), whereas the hot effect lasted longer with the greatest risk at a lag of 7 days (RR = 1.081, 95% CI: 1.059 ~ 1.104). Subgroup analysis revealed that males, children under 3 years old, and scattered children tended to be more vulnerable to HFMD in hot weather, while females, those aged 3 ~ 5 years, and nursery children were sensitive to cold conditions. This study suggests that high temperatures have a greater impact on HFMD than low temperatures as well as lasting longer, of particular concern being moderately high temperatures rather than extreme temperatures. Early intervention takes on greater importance during cold days, while the duration of HFMD intervention must be longer during hot days.

Association between high temperature and heatwaves with heat-related illnesses: A systematic review and meta-analysis

BACKGROUND

A large body of scientific evidence has established the impact of increased temperatures on human health. There is a relationship between extreme heat (either incremental temperature increase or heatwaves), and heat-related illnesses. This study aimed to collate the research findings on the effects of extreme heat on heat-related illness in a systematic review and meta-analysis, and to provide robust evidence for needed public health intervention.

METHODS

We conducted a search of peer-reviewed articles in three electronic databases (PubMed, EMBASE, and SCOPUS), from database inception until January 2022. A random-effects meta-analysis model was used to calculate the pooled relative risks (RRs) of the association between high temperature and heat-related illness outcomes. A narrative synthesis was also performed for studies analysing heatwave effects. Assessment of evidence was performed in three parts: individual study risk of bias; quality of evidence across studies; and overall strength of evidence.

RESULTS

A total of 62 studies meeting the eligibility criteria were included in the review, of which 30 were qualified to be included in the meta-analysis. The pooled results showed that for every 1 °C increase in temperature, when measured from study-specific baseline temperatures, direct heat illness morbidity and mortality increased by 18 % (RR 1.18, 95%CI: 1.16-1.19) and 35 % (RR 1.35, 95%CI: 1.29-1.41), respectively. For morbidity, the greatest increase was for direct heat illness (RR 1.45, 95%CI: 1.38-1.53), compared to dehydration (RR 1.02, 95%CI: 1.02-1.03). There was higher risk for people aged >65 years (RR 1.25; 95 % CI: 1.20-1.30), and those living in subtropical climates (RR 1.25; 95 % CI: 1.21-1.29).

CONCLUSION

Increased temperature leads to higher burden of disease from heat-related illness. Preventative efforts should be made to reduce heat-related illness during hot weather, targeting on the most vulnerable populations. This is especially important in the context of climate change.

Predicting the Impact of Climate Change on the Distribution of a Neglected Arboviruses Vector (Armigeres subalbatus) in China

The geographic boundaries of arboviruses continue to expand, posing a major health threat to millions of people around the world. This expansion is related to the availability of effective vectors and suitable habitats. Armigeres subalbatus (Coquillett, 1898), a common and neglected species, is of increasing interest given its potential vector capacity for Zika virus. However, potential distribution patterns and the underlying driving factors of Ar. subalbatus remain unknown. In the current study, detailed maps of their potential distributions were developed under both the current as well as future climate change scenarios (SSP126 and SSP585) based on CMIP6 data, employing the MaxEnt model. The results showed that the distribution of the Ar. subalbatus was mainly affected by temperature. Mean diurnal range was the strongest predictor in shaping the distribution of Ar. subalbatus, with an 85.2% contribution rate. By the 2050s and 2070s, Ar. subalbatus will have a broader potential distribution across China. There are two suitable expansion types under climate change in the 2050s and 2070s. The first type is continuous distribution expansion, and the second type is sporadic distribution expansion. Our comprehensive analysis of Ar. subalbatus’s suitable distribution areas shifts under climate change and provides useful and insightful information for developing management strategies for future arboviruses.

Separation of Different Blogs from Skin Disease Data using Artificial Intelligence

A combination of environmental conditions may cause skin illness everywhere on the earth, and it is one of the most dangerous diseases that can develop as a result. A major goal in the selection of characteristics is to produce predictions about skin disease instances in connection with influencing variables, which is one of the most important tasks. As a consequence of the widespread usage of sensors, the amount of data collected in the health industry is disproportionately large when compared to data collected in other sectors. In the past, researchers have used a variety of machine learning algorithms to determine the relationship between illnesses and other disorders. Forecasting is a procedure that involves many steps, the most important of which are the preprocessing of any scenario and the selection of forecasting features. A major disadvantage of doing business in the health industry is a lack of data availability, which is particularly problematic when data is provided in an unstructured format. Filling in missing numbers and converting between various types of data take somewhat more than 70% of the total time. When dealing with missing data in machine learning applications, the mean, average, and median, as well as the stand mechanism, may all be employed to solve the problem. Previous research has shown that the characteristics chosen for a model's overall performance may have an influence on the overall performance of the model's overall performance. One of the primary goals of this study is to develop an intelligent algorithm for identifying relevant traits in models while simultaneously eliminating nonsignificant attributes that have an impact on model performance. To present a full view of the data, artificial intelligence techniques such as SVM, decision tree, and logistic regression models were used in conjunction with three separate feature combination methodologies, each of which was developed independently. As a consequence of this, their accuracy, F-measure, and precision are all raised by a factor of ten, respectively. We then have a list of the most important features, together with the weights that have been allocated to each of them.

Phylogeographic dynamics of the arthropod vector, the blacklegged tick (Ixodes scapularis)

BACKGROUND

The emergence of vector-borne pathogens in novel geographic areas is regulated by the migration of their arthropod vectors. Blacklegged ticks (Ixodes scapularis) and the pathogens they vector, including the causative agents of Lyme disease, babesiosis and anaplasmosis, continue to grow in their population sizes and to expand in geographic range. Migration of this vector over the previous decades has been implicated as the cause of the re-emergence of the most prevalent infectious diseases in North America.

METHODS

We systematically collected ticks from across New York State (hereafter referred to as New York) from 2004 to 2017 as part of routine tick-borne pathogen surveillance in the state. This time frame corresponds with an increase in range and incidence of tick-borne diseases within New York. We randomly sampled ticks from this collection to explore the evolutionary history and population dynamics of I. scapularis. We sequenced the mitochondrial genomes of each tick to characterize their current and historical spatial genetic structure and population growth using phylogeographic methods.

RESULTS

We sequenced whole mitochondrial genomes from 277 ticks collected across New York between 2004 and 2017. We found evidence of population genetic structure at a broad geographic scale due to differences in the relative abundance, but not the composition, of haplotypes among sampled ticks. Ticks were often most closely related to ticks from the same and nearby collection sites. The data indicate that both short- and long-range migration events shape the population dynamics of blacklegged ticks in New York.

CONCLUSIONS

We detailed the population dynamics of the blacklegged tick (Ixodes scapularis) in New York during a time frame in which tick-borne diseases were increasing in range and incidence. Migration of ticks occurred at both coarse and fine scales in the recent past despite evidence of limits to gene flow. Past and current tick population dynamics have implications for further range expansion as habitat suitability for ticks changes due to global climate change. Analyses of mitochondrial genome sequencing data will expound upon previously identified drivers of tick presence and abundance as well as identify additional drivers. These data provide a foundation on which to generate testable hypotheses on the drivers of tick population dynamics occurring at finer scales.

On the indirect environmental outcomes of COVID-19: short-term revival with futuristic long-term implications

The aim of this study is to identify and highlight the positive and negative indirect environmental impacts of COVID-19, with a particular focus on the most affected economies (USA, China, Spain, and Italy). In this respect, the empirical and theoretical dimensions of the contents of those impacts are analyzed. Research findings reveal a significant relationship between contingency actions and positive indirect impacts such as air quality improvements, clean beaches, and the decline in environmental noise. Besides, negative indirect impacts also exist, such as the rise in waste level and curtailment in recycling, further threatening the physical spaces (land and water), besides air. It is expected that global businesses will revive in the near future (though slowly), but the reduction in greenhouse gas emissions during this short time span is not a sustainable way of environmental mitigation. Thus, long-term mitigation policies should be strengthened to cope with the undesirable deterioration of the environment. Research findings provide an up-to-date glimpse of the pandemic from the perspectives of current and future indirect environmental impacts and the post-pandemic situation. Finally, it is suggested to invent and prepare action plans to induce a sustainable economic and environmental future in the post-pandemic world scenario.

Meta-analysis of the effects of ambient temperature and relative humidity on the risk of mumps

Many studies have shown that the relationship between ambient temperature, relative humidity and mumps has been highlighted. However, these studies showed inconsistent results. Therefore, the goal of our study is to conduct a meta-analysis to clarify this relationship and to quantify the size of these effects as well as the potential factors. Systematic literature researches on PubMed, Embase.com, Web of Science Core Collection, Cochrane library, Chinese BioMedical Literature Database (CBM) and China National Knowledge Infrastructure (CNKI) were performed up to February 7, 2022 for articles analyzing the relationships between ambient temperature, relative humidity and incidence of mumps. Eligibility assessment and data extraction were conducted independently by two researchers, and meta-analysis was performed to synthesize these data. We also assessed sources of heterogeneity by study region, regional climate, study population. Finally, a total of 14 studies were screened out from 1154 records and identified to estimate the relationship between ambient temperature, relative humidity and incidence of mumps. It was found that per 1 °C increase and decrease in the ambient temperature were significantly associated with increased incidence of mumps with RR of 1.0191 (95% CI: 1.0129–1.0252, I² = 92.0%, Egger’s test P = 0.001, N = 13) for per 1 °C increase and 1.0244 (95% CI: 1.0130–1.0359, I² = 86.6%, Egger’s test P = 0.077, N = 9) for per 1 °C decrease. As to relative humidity, only high effect of relative humidity was slightly significant (for per 1 unit increase with RR of 1.0088 (95% CI: 1.0027–1.0150), I² = 72.6%, Egger’s test P = 0.159, N = 9). Subgroup analysis showed that regional climate with temperate areas may have a higher risk of incidence of mumps than areas with subtropical climate in cold effect of ambient temperature and low effect of relative humidity. In addition, meta-regression analysis showed that regional climate may affect the association between incidence of mumps and cold effect of ambient temperature. Our results suggest ambient temperature could affect the incidence of mumps significantly, of which both hot and cold effect of ambient temperature may increase the incidence of mumps. Further studies are still needed to clarify the relationship between the incidence of mumps and ambient temperature outside of east Asia, and many other meteorological factors. These results of ambient temperature are important for establishing preventive measures on mumps, especially in temperate areas. The policy-makers should pay more attention to ambient temperature changes and take protective measures in advance.

A Review on Microorganisms in Constructed Wetlands for Typical Pollutant Removal: Species, Function, and Diversity

Constructed wetlands (CWs) have been proven as a reliable alternative to traditional wastewater treatment technologies. Microorganisms in CWs, as an important component, play a key role in processes such as pollutant degradation and nutrient transformation. Therefore, an in-depth analysis of the community structure and diversity of microorganisms, especially for functional microorganisms, in CWs is important to understand its performance patterns and explore optimized strategies. With advances in molecular biotechnology, it is now possible to analyze and study microbial communities and species composition in complex environments. This review performed bibliometric analysis of microbial studies in CWs to evaluate research trends and identify the most studied pollutants. On this basis, the main functional microorganisms of CWs involved in the removal of these pollutants are summarized, and the effects of these pollutants on microbial diversity are investigated. The result showed that the main phylum involved in functional microorganisms in CWs include Proteobacteria, Bacteroidetes, Actinobacteria and Firmicutes. These functional microorganisms can remove pollutants from CWs by catalyzing chemical reactions, biodegradation, biosorption, and supporting plant growth, etc. Regarding microbial alpha diversity, heavy metals and high concentrations of nitrogen and phosphorus significantly reduce microbial richness and diversity, whereas antibiotics can cause large fluctuations in alpha diversity. Overall, this review can provide new ideas and directions for the research of microorganisms in CWs.

Precipitation variability and risk of infectious disease in children under 5 years for 32 countries: a global analysis using Demographic and Health Survey data

BACKGROUND

Precipitation variability is a potentially important driver of infectious diseases that are leading causes of child morbidity and mortality worldwide. Disentangling the links between precipitation variability and disease risk is crucial in a changing climate. We aimed to investigate the links between precipitation variability and reported symptoms of infectious disease (cough, fever, and diarrhoea) in children younger than 5 years.

METHODS

We used nationally representative survey data collected between 2014 and 2019 from Demographic and Health Survey (DHS) surveys for 32 low-income to middle-income countries in combination with high-resolution precipitation data (via the Climate Hazards Group InfraRed Precipitation with Station dataset). We only included DHS data for which interview dates and GPS coordinates (latitude and longitude) of household clusters were available. We used a regression modelling approach to assess the relationship between different precipitation variability measures and infectious disease symptoms (cough, fever, and diarrhoea), and explored the effect modification of different climate zones and disease susceptibility factors.

FINDINGS

Our global analysis showed that anomalously wet conditions increase the risk of cough, fever, and diarrhoea symptoms in humid, subtropical regions. These health risks also increased in tropical savanna regions as a result of anomalously dry conditions. Our analysis of susceptibility factors suggests that unimproved sanitation and unsafe drinking water sources are exacerbating these effects, particularly for rural populations and in drought-prone areas in tropical savanna.

INTERPRETATION

Weather shifts can affect the survival and transmission of pathogens that are particularly harmful to young children. As our findings show, the health burden of climate-sensitive infectious diseases can be substantial and is likely to fall on populations that are already among the most disadvantaged, including households living in remote rural areas and those lacking access to safe water and sanitation infrastructure.

FUNDING

University of California, San Diego FY19 Center Launch programme.

The exposure-response association between humidex and bacillary dysentery: A two-stage time series analysis of 316 cities in mainland China

BACKGROUND

Many studies have reported the interactive effects between relative humidity and temperature on infectious diseases. However, evidence regarding the combined effects of relative humidity and temperature on bacillary dysentery (BD) is limited, especially for large-scale studies. To address this research need, humidex was utilized as a comprehensive index of relative humidity and temperature. We aimed to estimate the effect of humidex on BD across mainland China, evaluate its heterogeneity, and identify potential effect modifiers.

METHODS

Daily meteorological and BD surveillance data from 2014 to 2016 were obtained for 316 prefecture-level cities in mainland China. Humidex was calculated on the basis of relative humidity and temperature. A multicity, two-stage time series analysis was then performed. In the first stage, a common distributed lag non-linear model (DLNM) was established to obtain city-specific estimates. In the second stage, a multivariate meta-analysis was conducted to pool these estimates, assess the significance of heterogeneity, and explore potential effect modifiers.

RESULTS

The pooled cumulative estimates showed that humidex could promote the transmission of BD. The exposure-response relationship was nearly linear, with a maximum cumulative relative risk (RR) of 1.45 [95% confidence interval (CI): 1.29-1.63] at a humidex value of 40.94. High humidex had an acute adverse effect on BD. The humidex-BD relationship could be modified by latitude, urbanization rate, the natural growth rate of population, and the number of primary school students per thousand persons.

CONCLUSIONS

High humidex could increase the risk of BD incidence. Thus, it is suitable to incorporate humidex as a predictor into the early warning system of BD and to inform the general public in advance to be cautious when humidex is high. This is especially true for regions with higher latitude, higher urbanization rates, lower natural growth rates of population, and lower numbers of primary school students per thousand persons.

Collaborative healthcare education programmes for continuing professional education in low and middle-income countries: A Best Evidence Medical Education (BEME) systematic review. BEME Guide No. 65

BACKGROUND

Large discrepancies exist between standards of healthcare provision in high-income (HICs) and low and middle-income countries (LMICs). The root cause is often financial, resulting in poor infrastructure and under-resourced education and healthcare systems. Continuing professional education (CPE) programmes improve staff knowledge, skills, retention, and practice, but remain costly and rare in low-resource settings. One potential solution involves healthcare education collaborations between institutions in HICs and LMICs to provide culturally appropriate CPE in LMICs. To be effective, educational partnerships must address the challenges arising from differences in cultural norms, language, available technology and organisational structures within collaborating countries.

METHODS

Seven databases and other sources were systematically searched on 7 July 2020 for relevant studies. Citations, abstracts, and studies were screened and consensus was reached on which to include within the review. 54 studies were assessed regarding the type of educational programme involved, the nature of HIC/LMIC collaboration and quality of the study design.

RESULTS

Studies varied greatly regarding the types and numbers of healthcare professionals involved, pedagogical and delivery methods, and the ways in which collaboration was undertaken. Barriers and enablers of collaboration were identified and discussed. The key findings were: 1. The methodological quality of reporting in the studies was generally poor. 2. The way in which HIC/LMIC healthcare education collaboration is undertaken varies according to many factors, including what is to be delivered, the learner group, the context, and the resources available. 3. Western bias was a major barrier. 4. The key to developing successful collaborations was the quality, nature, and duration of the relationships between those involved.

CONCLUSION

This review provides insights into factors that underpin successful HIC/LMIC healthcare CPE collaborations and outlines inequities and quality issues in reporting.

Dietary supplementation with Yucca schidigera extract alleviated heat stress-induced unfolded protein response and oxidative stress in the intestine of Nile tilapia (Oreochromis niloticus)

Heat stress due to global warming exerts deleterious effects on both humans and animals. However, nutritional strategies to reduce heat stress-induced intestinal mucosal barrier dysfunction and the underlying mechanisms remain largely unknown. In the present study, 240 tilapia were distributed into four treatment groups that were fed a basal diet supplemented with or without 0.1% Yucca schidigera extract under normal (28 °C) temperature or heat stress (36 °C) conditions for 2 weeks. Our results showed that tilapia exposed to heat stress resulted in growth arrest, intestinal dysfunction, oxidative damage, endoplasmic reticulum stress, and pro-inflammatory response, which were significantly relieved by yucca supplementation. The alleviative effect of Yucca schidigera extract was related to the down-regulation of mRNA expression of ubiquitin-proteasome system (Polyubiquitin, Proteasome 26S, Proteasome α5, Proteasome β3, and Ubiquitin-like 3) and inflammatory factors (tumor necrosis factor α, interleukin 1β, and interleukin 8), as well as the improved histological structure and activation of Hsp70, nuclear factor erythroid 2-related factor 2 signaling, interleukin 10, lysozyme, complement 3, and acid phosphatase in the intestine of tilapia. Collectively, these results indicated that heat stress-induced growth arrest, intestinal dysfunction, and oxidative damage were alleviated by dietary supplementation with Yucca schidigera extract. This offers a nutritional way of improving the growth and intestinal health of tilapia exposed to a hot environment.

Effect of temperature and its interaction with other meteorological factors on bacillary dysentery in Jilin Province, China

Bacterial dysentery (BD) brings a major disease burden to developing countries. Exploring the influence of temperature and its interaction with other meteorological factors on BD is significant for the prevention and early warning of BD in the context of climate change. Daily BD cases and meteorological data from 2008 to 2018 were collected in all nine prefecture-level cities in Jilin Province. A one-stage province-level model and a two-stage city-specific multivariate meta-pooled level distributed lag non-linear model were established to explore the correlation between temperature and BD, then the weather-stratified generalised additive model was used to test the interaction. During the study period, a total of 26 971 cases of BD were developed. The one-stage and two-stage cumulative dose-response ‘J’ curves overlapped, and results showed a positive correlation between temperature and BD with a 1–6 days lag effect. Age group ⩾5 years was found to be more sensitive to the effects. Moreover, there was a significant interaction between temperature, humidity and precipitation (P = 0.004, 0.002, respectively) on BD under high temperature (>0 °C), reminding residents and policymakers to pay attention to the prevention of BD in situations with both high temperature and humidity, high temperature and precipitation during the temperate monsoon climate.

Rift Valley Fever and West Nile virus vectors in Morocco: Current situation and future anticipated scenarios

Rift Valley Fever (RVF) and West Nile virus (WNV) are two important emerging Arboviruses transmitted by Aedes and Culex mosquitoes, typically Ae. caspius, Ae. detritus and Cx. pipiens in temperate regions. In Morocco, several outbreaks of WNV (1996, 2003 and 2010), affecting horses mostly, have been reported in north-western regions resulting in the death of 55 horses and one person cumulatively. Serological evidence of WNV local circulation, performed one year after the latest outbreak, revealed WNV neutralizing bodies in 59 out of 499 tested participants (El Rhaffouli et al., 2012). The country also shares common borders with northern Mauritania, where RVF is often documented. Human movement, livestock trade, climate changes and the availability of susceptible mosquito vectors are expected to increase the spread of these diseases in the country. Thus, in this study, we gathered a data set summarizing occurrences of Ae. caspius, Ae. detritus and Cx. pipiens in the country, and generated model prediction for their potential distribution under both current and future (2050) climate conditions, as a proxy to identify regions at-risk of RVF and WNV probable expansion. We found that the north-western regions (where the population is most concentrated), specifically along the Atlantic coastline, are highly suitable for Ae. caspius, Ae. detritus and Cx. pipiens, under present-day conditions. Future model scenarios anticipated possible range changes for the three mosquitoes under all climatic assumptions. All of the studied species are prospected to gain new areas that are currently not suitable, even under the most optimist scenario, thus placing additional human populations at risk. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes. Public health officials, entomological surveillance and control delegation must augment efforts and continuously monitor these areas to reduce and minimize human infection risk.

Effect of ambient temperatures on category C notifiable infectious diarrhea in China: An analysis of national surveillance data

BACKGROUND

Many studies have explored the association between meteorological factors and infectious diarrhea (ID) transmission but with inconsistent results, in particular the roles from temperatures. We aimed to explore the effects of temperatures on the transmission of category C ID, to identify its potential heterogeneity in different climate zones of China, and to provide scientific evidence to health authorities and local communities for necessary public health actions.

METHODS

Daily category C ID counts and meteorological variables were collected from 270 cities in China over the period of 2014-16. Distributed lag non-linear models (DLNMs) were applied in each city to obtain the city-specific temperature-disease associations, then a multivariate meta-analysis was implemented to pool the city-specific effects. Multivariate meta-regression was conducted to explore the potential effect modifiers. Attributable fraction was calculated for both low and high temperatures, defined as temperatures below the 5th percentile of temperature or above the 95th percentile of temperature.

RESULTS

A total of 2,715,544 category C ID cases were reported during the study period. Overall, a M-shaped curve relationship was observed between temperature and category C ID, with a peak at the 81st percentile of temperatures (RR = 1.723, 95% CI: 1.579-1.881) compared to 50th percentile of temperatures. The pooled associations were generally stronger at high temperatures compared to low ambient temperatures, and the attributable fraction due to heat was higher than cold. Latitude was identified as a possible effect modifier.

CONCLUSIONS

The overall positive pooled associations between temperature and category C ID in China suggest the increasing temperature could bring about more category C infectious diarrhea cases, which warrants further public health measurements.

Positive Externalities of Climate Change Mitigation and Adaptation for Human Health: A Review and Conceptual Framework for Public Health Research

Anthropogenic climate change is adversely impacting people and contributing to suffering and increased costs from climate-related diseases and injuries. In responding to this urgent and growing public health crisis, mitigation strategies are in place to reduce future greenhouse gas emissions (GHGE) while adaptation strategies exist to reduce and/or alleviate the adverse effects of climate change by increasing systems' resilience to future impacts. While these strategies have numerous positive benefits on climate change itself, they also often have other positive externalities or health co-benefits. This knowledge can be harnessed to promote and improve global public health, particularly for the most vulnerable populations. Previous conceptual models in mitigation and adaptation studies such as the shared socioeconomic pathways (SSPs) considered health in the thinking, but health outcomes were not their primary intention. Additionally, existing guidance documents such as the World Health Organization (WHO) Guidance for Climate Resilient and Environmentally Sustainable Health Care Facilities is designed primarily for public health professionals or healthcare managers in hospital settings with a primary focus on resilience. However, a detailed cross sectoral and multidisciplinary conceptual framework, which links mitigation and adaptation strategies with health outcomes as a primary end point, has not yet been developed to guide research in this area. In this paper, we briefly summarize the burden of climate change on global public health, describe important mitigation and adaptation strategies, and present key health benefits by giving context specific examples from high, middle, and low-income settings. We then provide a conceptual framework to inform future global public health research and preparedness across sectors and disciplines and outline key stakeholders recommendations in promoting climate resilient systems and advancing health equity.

Systematic review on the biology, ecology, genetic diversity and parasite transmission potential of Panstrongylus geniculatus (Latreille 1811) in Latin America

Panstrongylus geniculatus (Latreille, 1811) is the triatomine with the largest geographic distribution in Latin America. It has been reported in 18 countries from southern Mexico to northern Argentina, including the Caribbean islands. Although most reports indicate that P. geniculatus has wild habitats, this species has intrusive habits regarding human dwellings mainly located in intermediate deforested areas. It is attracted by artificial light from urban and rural buildings, raising the risk of transmission of Trypanosoma cruzi. Despite the wide body of published information on P. geniculatus, many knowledge gaps exist about its biology and epidemiological potential. For this reason, we analysed the literature for P. geniculatus in Scopus, PubMed, Scielo, Google Scholar and the BibTriv3.0 databases to update existing knowledge and provide better information on its geographic distribution, life cycle, genetic diversity, evidence of intrusion and domiciliation, vector-related circulating discrete taxonomic units, possible role in oral T. cruzi transmission, and the effect of climate change on its biology and epidemiology.

Divergent impacts of warming weather on wildlife disease risk across climates

Disease outbreaks among wildlife have surged in recent decades alongside climate change, although it remains unclear how climate change alters disease dynamics across different geographic regions. We amassed a global, spatiotemporal dataset describing parasite prevalence across 7346 wildlife populations and 2021 host-parasite combinations, compiling local weather and climate records at each location. We found that hosts from cool and warm climates experienced increased disease risk at abnormally warm and cool temperatures, respectively, as predicted by the thermal mismatch hypothesis. This effect was greatest in ectothermic hosts and similar in terrestrial and freshwater systems. Projections based on climate change models indicate that ectothermic wildlife hosts from temperate and tropical zones may experience sharp increases and moderate reductions in disease risk, respectively, though the magnitude of these changes depends on parasite identity.

Effects of ambient temperature and precipitation on the risk of dengue fever: A systematic review and updated meta-analysis

OBJECTIVES

We systematically reviewed the published studies on the relationship between dengue fever and meteorological factors and applied a meta-analysis to explore the effects of ambient temperature and precipitation on dengue fever.

METHODS

We completed the literature search by the end of September 1st, 2019 using databases including Science Direct, PubMed, Web of Science, and Google Scholar. We extracted relative risks (RRs) in selected studies and converted all effect estimates to the RRs per 1 °C increase in temperature and 10 mm increase in precipitation, and combined all standardized RRs together using random-effect meta-analysis.

RESULTS

Our results show that dengue fever was significantly associated with both temperature and precipitation. Our subgroup analyses suggested that the effect of temperature on dengue fever was most pronounced in high-income subtropical areas. The pooled RR of dengue fever associated with the maximum temperature was much lower than the overall effect.

CONCLUSIONS

Temperature and precipitation are important risk factors for dengue fever. Future studies should focus on factors that can distort the effects of temperature and precipitation.

The potential distribution and dynamics of important vectors Culex pipiens pallens and Culex pipiens quinquefasciatus in China under climate change scenarios: an ecological niche modelling approach

BACKGROUND

Intense studies have been carried out on the effects of climate change on vector-borne diseases and vectors. Culex pipiens pallens and Culex pipiens quinquefasciatus are two medically concerned mosquito species in temperate and tropical areas, which serve as important disease-transmitting pests of a variety of diseases. The ongoing geographical expansion of these mosquitoes has brought an increasing threat to public health.

RESULTS

Based on mosquito occurrence records and high-resolution environmental layers, an ecological niche model was established to model their current and future potential distribution in China. Our model showed that the current suitable area for Cx. p. pallens is distributed in the central, eastern and northern parts of China, while Cx. p. quinquefasciatus is distributed in vast areas in southern China. Under future climate change scenarios, both species are predicted to expand their range to varying degrees and RCP 8.5 provides the largest expansion. Northward core shifts will occur in ranges of both species. Environmental variables which have significant impact on the distribution of mosquitoes were also revealed by our model.

CONCLUSION

Severe habitat expansion of vectors is likely to occur in the future 21st century. Our models mapped the high-risk areas and risk factors which needs to be paid attention. The results of our study can be referenced in further ecological surveys and will guide the development of strategies for the prevention and control of vector-borne diseases. © 2020 Society of Chemical Industry.

Summer, sun and sepsis-The influence of outside temperature on nosocomial bloodstream infections: A cohort study and review of the literature

BACKGROUND

The incidence of many infections is seasonal e.g. surgical site infections, urinary tract infection and bloodstream infections. We questioned whether there is seasonal variation even in climate-controlled hospitalized patients, and analyzed the influence of climate parameters on nosocomial bloodstream infections.

METHODS AND FINDINGS

The retrospective cohort study is based on two databases: The German national surveillance system for nosocomial infections in intensive care units (ICU-KISS) from 2001 to 2015 and aggregated monthly climate data. Primary bloodstream infection (PBSI) is defined as a positive blood culture with one (or more) pathogen(s) which are not related to an infection on another site and which were not present at admission. Monthly infection data were matched with postal code, calendar month and corresponding monthly climate and weather data. All analyses were exploratory in nature. 1,196 ICUs reported data on PBSI to KISS. The ICUs were located in 779 hospitals and in 728 different postal codes in Germany. The majority of the 19,194 PBSI were caused by gram-positive bacteria. In total, the incidence density of BSI was 17% (IRR 1.168, 95%CI 1.076-1.268) higher in months with high temperatures (≥20°C) compared to months with low temperatures (<5°C). The effect was most prominent for gram-negatives; more than one third (38%) higher followed by gram-positives with 13%. Fungi reached their highest IRR at moderately warm temperatures between 15-20°C. At such temperatures fungi showed an increase of 33% compared to temperatures below 5°C. PBSI spiked in summer with a peak in July and August. PBSI differed by pathogen: The majority of bacteria increased with rising temperatures. Enterococci showed no seasonality. S. pneumoniae reached a peak in winter time. The association of the occurrence of PBSI and temperatures ≥20°C was stronger when the mean monthly temperature in the month prior to the occurrence of BSI was considered instead of the temperature in the month of the occurrence of BSI. High average temperatures ≥20°C increased the risk of the development of a PBSI by 16% compared with low temperatures <5°C.

CONCLUSIONS

Most nosocomial infections are endogenous in nature; the microbiome plays a crucial role in host health. If gut and skin microbiome varies with season, environmental parameters will contribute to the observed incidence patterns. Similarly, the impact of global warming on both local weather patterns and extreme weather events may influence the acquisition of pathogens. A better understanding of the etiology of these infections is needed to provide guidance for future infection control strategies.

Compounding Effects of Climate Warming and Antibiotic Resistance

Bacteria have evolved diverse mechanisms to survive environments with antibiotics. Temperature is both a key factor that affects the survival of bacteria in the presence of antibiotics and an environmental trait that is drastically increasing due to climate change. Therefore, it is timely and important to understand links between temperature changes and selection of antibiotic resistance. This review examines these links by synthesizing results from laboratories, hospitals, and environmental studies. First, we describe the transient physiological responses to temperature that alter cellular behavior and lead to antibiotic tolerance and persistence. Second, we focus on the link between thermal stress and the evolution and maintenance of antibiotic resistance mutations. Finally, we explore how local and global changes in temperature are associated with increases in antibiotic resistance and its spread. We suggest that a multidisciplinary, multiscale approach is critical to fully understand how temperature changes are contributing to the antibiotic crisis.

Climate change effects on Black Sigatoka disease of banana

Climate change has significantly altered species distributions in the wild and has the potential to affect the interactions between pests and diseases and their human, animal and plant hosts. While several studies have projected changes in disease distributions in the future, responses to historical climate change are poorly understood. Such analyses are required to dissect the relative contributions of climate change, host availability and dispersal to the emergence of pests and diseases. Here, we model the influence of climate change on the most damaging disease of a major tropical food plant, Black Sigatoka disease of banana. Black Sigatoka emerged from Asia in the late twentieth Century and has recently completed its invasion of Latin American and Caribbean banana-growing areas. We parametrize an infection model with published experimental data and drive the model with hourly microclimate data from a global climate reanalysis dataset. We define infection risk as the sum of the number of modelled hourly spore cohorts that infect a leaf over a time interval. The model shows that infection risk has increased by a median of 44.2% across banana-growing areas of Latin America and the Caribbean since the 1960s, due to increasing canopy wetness and improving temperature conditions for the pathogen. Thus, while increasing banana production and global trade have probably facilitated Black Sigatoka establishment and spread, climate change has made the region increasingly conducive for plant infection.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’. This issue is linked with the subsequent theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control’.

Lyme Endocarditis as an Emerging Infectious Disease: A Review of the Literature

Lyme endocarditis is extremely rare manifestation of Lyme disease. The clinical manifestations of Lyme endocarditis are non-specific and can be very challenging diagnosis to make when it is the only manifestation of the disease. Until now, only a few cases where reported. Physicians should keep in mind the possibility of borrelial etiology of endocarditis in endemic areas. Appropriate valve tissue sample should be sent for histopathology, culture, and PCR especially in case of endocarditis of unknown origin PCR on heart valve samples is recommended. With more frequent PCR, Borrelia spp. may be increasingly found as a cause of infective endocarditis. Prompt diagnosis and treatment of Lyme carditis may prevent surgical treatment and pacemaker implantations. Due to climate change and global warming Lyme disease is a growing problem. Rising number of Lyme disease cases we can expect and rising number of Lyme endocarditis.

Culture, eating behavior, and infectious disease control and prevention

Humans need to obtain nutrients from foods for survival and health. Culture and belief play important roles in food selection and intake. Throughout human history, dietary factor has been one of the important factors inducing and causing outbreaks of infectious diseases. If unhealthy eating behavior, like eating raw/undercooked food or meat and products from wild animals, are not abandoned, foodborne infectious diseases will remain an important risk factor of outbreaks and epidemics. The misconception of dietary culture is one of the important factors that triggers unhealthy eating behavior. Therefore, it is vital to change people’s conceptions and knowledge about what is healthy to eat, in order to completely eliminate unhealthy eating behavior and prevent the recurrence of foodborne infectious diseases. Meanwhile, many factors such as family, society, region, and religion should be involved in.

Potential effects of heat waves on the population dynamics of the dengue mosquito Aedes albopictus

Extreme weather events affect the development and survival of disease pathogens and vectors. Our aim was to investigate the potential effects of heat waves on the population dynamics of Asian tiger mosquito (Aedes albopictus), which is a major vector of dengue and Zika viruses. We modeled the population abundance of blood-fed mosquito adults based on a mechanistic population model of Ae. albopictus with the consideration of diapause. Using simulated heat wave events derived from a 35-year historical dataset, we assessed how the mosquito population responded to different heat wave characteristics, including the onset day, duration, and the average temperature. Two important observations are made: (1) a heat wave event facilitates the population growth in the early development phase but tends to have an overall inhibitive effect; and (2) two primary factors affecting the development are the unusual onset time of a heat wave and a relatively high temperature over an extended period. We also performed a sensitivity analysis using different heat wave definitions, justifying the robustness of the findings. The study suggests that particular attention should be paid to future heat wave events with an abnormal onset time or a lasting high temperature in order to develop effective strategies to prevent and control Ae. albopictus-borne diseases.

Understanding the population dynamics of Asian Tiger mosquito (Ae. albopictus)–the most prevalent vector of global epidemics including West Nile virus, dengue fever, Zika–could shed lights on improving the understanding of vector transmission as well as developing effective disease control strategies. It is widely acknowledged that the life cycle of Ae. albopictus is firmly regulated by meteorological factors in a non-linear way and is sensitive to climate change. Our study extends the understanding about how extreme heat events manipulate the mosquito population abundance. We adopted an existing mechanistic population model of Ae. albopictus, combined with a rich set of simulated heat wave events derived from a 35-year historical dataset, to quantify the mosquito’s responses to different heat wave characteristics. We found that an abnormal onset time and a lasting high temperature play the most important role in affecting the mosquito population dynamics. We also performed a sensitive analysis by changing the definition of the heat wave, justifying the rigor of the conclusion. This research provides implications for developing public health intervention strategies: to control dengue fever, Zika, as well as other far-reaching mosquito-borne epidemics, priority should be given to heat wave events with an abnormal onset time or a lasting high temperature.