Heat waves accelerate the spread of infectious diseases

High wet-bulb temperatures, time allocation, and diurnal patterns of breastfeeding in Bangladeshi fisher-traders

BACKGROUND

Climate change is a growing threat to population health, with dangerous combinations of heat and humidity increasing in frequency, particularly in South Asia. Evidence suggests that high temperatures and heat stress influence breastfeeding behaviour and may lead to suboptimal infant and young child nutrition.

AIM

Few studies have quantified the relationship between ambient heat and breastfeeding. Here we evaluate associations between wet-bulb temperature and daily breastfeeding patterns in a rural community in Bangladesh.

SUBJECTS AND METHODS

We used 23 months of daily time-diary data from 68 maternal-child dyads and regional wet-bulb temperatures to test the hypothesis that increased heat and humidity negatively influence breastfeeding outcomes among Shodagor fisher-traders.

RESULTS

We found that higher wet-bulb temperatures predicted reduced daily breastfeeding time allocation, particularly among fishers, and drove shifts towards increased night-time and decreased mid/late morning feeding. Maternal occupation and the interaction of child age with heat strongly influenced diurnal breastfeeding patterns.

CONCLUSION

These results highlight an important role of maternal work on infants' vulnerability to environmental stress. Dyads' ability to behaviourally compensate for extreme heat may be constrained by extended heatwaves, humidity, and economic circumstances, suggesting that climate change will likely exacerbate heat-related risks to global child health going forward.

Exploring the effect of early-life climate anomalies on child growth in Sub-Saharan African context: Insight from the demographic & health survey

BACKGROUND

Childhood malnutrition remains a critical public health issue in Sub-Saharan Africa (SSA), with about 30 % of children affected by stunting, a marker of chronic malnutrition and micronutrient deficiencies during early development. While previous research suggests a link between climatic conditions and child growth, a deeper understanding is needed, particularly across diverse climatic zones. This study explores the impact of prenatal and postnatal climate anomalies (temperature and precipitation) on stunted growth among children in SSA.

METHODS

We analyzed health and demographic data from 30 SSA countries (2004-2021) within a Big Data framework, incorporating climatic and environmental data. Using three Bayesian hierarchical models, we examined the linear associations between pre- and post-natal climate anomalies and stunted growth, focusing on deviations from reference period means.

FINDINGS

Approximately 34.5 % of children in the study were stunted. The analysis identified significant associations between maximum temperature anomalies and increased stunting, while mean temperature and precipitation anomalies had varying effects. Reduced maximum temperatures were linked to lower stunting rates, whereas increased temperatures consistently correlated with higher stunting probabilities. Additionally, children residing in the Tropical Monsoon (Am) zone had a lower likelihood of stunting, whereas certain temperate zones were associated with increased stunting.

INTERPRETATION

The study highlights a complex relationship between climate anomalies and child growth in SSA. It underscores the need for targeted, context-specific policies that integrate climate adaptation with child health initiatives to address the growing impacts of climate change on child health in the region.

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.

Climate change, its impact on emerging infectious diseases and new technologies to combat the challenge

ABSTRACTImproved sanitation, increased access to health care, and advances in preventive and clinical medicine have reduced the mortality and morbidity rates of several infectious diseases. However, recent outbreaks of several emerging infectious diseases (EIDs) have caused substantial mortality and morbidity, and the frequency of these outbreaks is likely to increase due to pathogen, environmental, and population effects driven by climate change. Extreme or persistent changes in temperature, precipitation, humidity, and air pollution associated with climate change can, for example, expand the size of EID reservoirs, increase host-pathogen and cross-species host contacts to promote transmission or spillover events, and degrade the overall health of susceptible host populations leading to new EID outbreaks. It is therefore vital to establish global strategies to track and model potential responses of candidate EIDs to project their future behaviour and guide research efforts on early detection and diagnosis technologies and vaccine development efforts for these targets. Multi-disciplinary collaborations are demanding to develop effective inter-continental surveillance and modelling platforms that employ artificial intelligence to mitigate climate change effects on EID outbreaks. In this review, we discuss how climate change has increased the risk of EIDs and describe novel approaches to improve surveillance of emerging pathogens that pose the risk for EID outbreaks, new and existing measures that could be used to contain or reduce the risk of future EID outbreaks, and new methods to improve EID tracking during further outbreaks to limit disease transmission.

Confounding amplifies the effect of environmental factors on COVID-19

The global COVID-19 pandemic has severely impacted human health and socioeconomic development, posing an enormous public health challenge. Extensive research has been conducted into the relationship between environmental factors and the transmission of COVID-19. However, numerous factors influence the development of pandemic outbreaks, and the presence of confounding effects on the mechanism of action complicates the assessment of the role of environmental factors in the spread of COVID-19. Direct estimation of the role of environmental factors without removing the confounding effects will be biased. To overcome this critical problem, we developed a Double Machine Learning (DML) causal model to estimate the debiased causal effects of the influencing factors in the COVID-19 outbreaks in Chinese cities. Comparative experiments revealed that the traditional multiple linear regression model overestimated the impact of environmental factors. Environmental factors are not the dominant cause of widespread outbreaks in China in 2022. In addition, by further analyzing the causal effects of environmental factors, it was verified that there is significant heterogeneity in the role of environmental factors. The causal effect of environmental factors on COVID-19 changes with the regional environment. It is therefore recommended that when exploring the mechanisms by which environmental factors influence the spread of epidemics, confounding factors must be handled carefully in order to obtain clean quantitative results. This study offers a more precise representation of the impact of environmental factors on the spread of the COVID-19 pandemic, as well as a framework for more accurately quantifying the factors influencing the outbreak.

Power spectral density and similarity analysis of COVID-19 mortality waves across countries

Background

During the COVID-19 pandemic, the Johns Hopkins University Center for Systems Science and Engineering (CSSE) established a comprehensive database detailing daily mortality rates across countries. This dataset revealed fluctuating global mortality trends attributable to COVID-19; however, the specific differences and similarities in mortality patterns between countries remain insufficiently explored. Consequently, this study employs Fourier and similarity analyses to examine these patterns within the frequency domain, thereby offering novel insights into the dynamics of COVID-19 mortality waves across different nations.

Methods

We employed the Fast Fourier transform to calculate the power spectral density (PSD) of COVID-19 mortality waves in 199 countries from January 22, 2020, to March 9, 2023. Moreover, we performed a cosine similarity analysis of these PSD patterns among all the countries.

Results

We identified two dominant peaks in the grand averaged PSD: one at a frequency of 1.15 waves per year (i.e., one wave every 10.4 months) and another at 2.7 waves per year (i.e., one wave every 4.4 months). We also found a cosine similarity index distribution with a skewness of -0.54 and a global median of cosine similarity index of 0.84, thus revealing a remarkable similarity in the dominant peaks of the COVID-19 mortality waves.

Conclusion

These findings could be helpful for planetary health if a future pandemic of a similar scale occurs so that effective confinement measures or other actions could be planned during these two identified periods.

Disruption of outdoor activities caused by wildfires increases disease circulation

Although climate change poses a well-established risk to human health, present-day health impacts, particularly those resulting from climate-induced behavioral changes, are under-quantified. Analyzing the U.S. West Coast wildfires of September 2020, we found that poor air quality drives people indoors, increasing the circulation of airborne pathogens like COVID-19. Indoor masking rates as low as 10% can mitigate this risk, offering a clear path to enhance public health responses during wildfires.

Assessment of UV radiation effects on airborne mucormycetes and bacterial populations in a hospital environment

Infections, such as mucormycosis, often result from inhaling sporangiospore present in the environment. Surprisingly, the extent of airborne Mucormycetes sporangiospore concentrations remains inadequately explored. This study aimed to assess the influence of UV radiation on microbial populations and Mucormycetes spore levels within a hospital environment in northern Iran. A comprehensive dataset comprising 298 air samples collected from both indoor and outdoor settings was compiled. The culture was conducted using Blood Agar and Dichloran Rose Bengal Chloramphenicol (DRBC) culture media, with Chloramphenicol included for fungal agents and Blood Agar for bacterial. Before UV treatment, the average count of Mucormycetes ranged from 0 to 26.4 ± 25.28 CFU m-3, fungal agents from 2.24 ± 3.22 to 117.24 ± 27.6 CFU m-3, and bacterial agents from 29.03 ± 9.9 to 359.37 ± 68.50 CFU m-3. Following UV irradiation, the averages were as follows: Mucormycetes ranged from 0 to 7.85 ± 6.8 CFU m-3, fungal agents from 16.58 ± 4.79 to 154.98 ± 28.35 CFU m-3, and bacterial agents from 0.38 ± 0.65 to 43.92 ± 6.50 CFU m-3. This study, notably marks the pioneering use of UV light to mitigate Mucormycetes spore counts and bacterial agents in northeastern Iran, contributing to the advancement of environmental health and safety practices in hospital settings.