BACKGROUND AND OBJECTIVES

Climate anxiety is an emerging concept defined as increased anxiety about climate change and has been linked to negative mental health outcomes. As a relatively new concept, only a few have characterized it and its relationship with mental health, especially in the Global South populations. This study aimed to investigate the relationship between climate anxiety and depressive symptoms among undergraduate students from a college in a higher education institution in Manila, Philippines. Specifically, it aimed to (1) determine the proportion of undergraduate students who reported high levels of climate anxiety and depressive symptoms; and (2) determine the association between climate anxiety and depressive symptoms.

Using a cross-sectional design and a convenience sampling method, the investigators collected data through an online survey to assess levels of climate anxiety and depressive symptoms using the Climate Change Anxiety Scale (CCAS) and the Patient Health Questionnaire 9 (PHQ-9), respectively. Data was analyzed using multiple logistic regression.

Among the 148 respondents (N=325), 14.86% had high climate anxiety (i.e., CCAS score >= 3) and 62.84% had high depressive symptoms (i.e., PHQ-9 score >= 10). Regression analysis showed that the odds of those having high climate anxiety reporting high depressive symptoms are higher than those with low climate anxiety, though this is not significant (OR = 2.53, p=0.144).

The study verifies the existence of climate anxiety among undergraduate students and reflects an alarming mental health situation in the selected college. It is recommended that wide-scope investigations on the current state of climate anxiety and mental health among the youth be done to verify their impacts, along with inter-sectoral efforts such as increasing awareness through health education interventions to improve the youths’ mental health literacy and resilience to the effects of climate change, and promoting climate change-responsive mental health services to address these as pressing threats to youth health.

BACKGROUND AND OBJECTIVES

Climate anxiety is an emerging concept defined as increased anxiety about climate change and has been linked to negative mental health outcomes. As a relatively new concept, only a few have characterized it and its relationship with mental health, especially in the Global South populations. This study aimed to investigate the relationship between climate anxiety and depressive symptoms among undergraduate students from a college in a higher education institution in Manila, Philippines. Specifically, it aimed to (1) determine the proportion of undergraduate students who reported high levels of climate anxiety and depressive symptoms; and (2) determine the association between climate anxiety and depressive symptoms.

Using a cross-sectional design and a convenience sampling method, the investigators collected data through an online survey to assess levels of climate anxiety and depressive symptoms using the Climate Change Anxiety Scale (CCAS) and the Patient Health Questionnaire 9 (PHQ-9), respectively. Data was analyzed using multiple logistic regression.

Among the 148 respondents (N=325), 14.86% had high climate anxiety (i.e., CCAS score >= 3) and 62.84% had high depressive symptoms (i.e., PHQ-9 score >= 10). Regression analysis showed that the odds of those having high climate anxiety reporting high depressive symptoms are higher than those with low climate anxiety, though this is not significant (OR = 2.53, p=0.144).

The study verifies the existence of climate anxiety among undergraduate students and reflects an alarming mental health situation in the selected college. It is recommended that wide-scope investigations on the current state of climate anxiety and mental health among the youth be done to verify their impacts, along with inter-sectoral efforts such as increasing awareness through health education interventions to improve the youths’ mental health literacy and resilience to the effects of climate change, and promoting climate change-responsive mental health services to address these as pressing threats to youth health.

BACKGROUND

The relationship between pandemics and climate change has emerged as a critical area of study, particularly underscored by the COVID-19 pandemic, which exposed vulnerabilities in global health systems and environmental governance. Although direct evidence linking climate change to the spread of COVID-19 remains limited, rising global temperatures and ecosystem disruptions have intensified human–wildlife interactions, increasing the risk of zoonotic disease emergence.

This study aims to synthesize existing research on the interconnections between climate change and emerging infectious diseases, identify key knowledge gaps, and provide insights to guide integrated health and environmental policy development.

A systematic literature review was conducted using peer-reviewed articles published within the past two decades. Relevant studies were identified through scientific databases, focusing on evidence linking climate variability, ecosystem shifts, and zoonotic transmission dynamics.

Findings indicate that climate-induced changes—such as rising temperatures, altered precipitation patterns, and habitat disruption—affect vector ecology and wildlife migration, facilitating conditions for pathogen spillover. However, existing research remains fragmented, with limited longitudinal analyses and region-specific data to quantify these associations.

The interconnectedness of human health, environmental health, and biodiversity underscores the need for a holistic One Health approach. Strengthening interdisciplinary collaboration and integrating climate resilience into public health strategies are vital to addressing the multifaceted challenges posed by climate change and emerging pandemics.

Climate change is increasingly affecting public health and safety, disproportionately affecting vulnerable populations and aggravating existing health inequities. Recognizing the urgency of this challenge, the Department of Environmental and Occupational Health (EOH), College of Public Health, University of the Philippines Manila, convened the Seventh EOH Forum on November 21–22, 2024, with the theme Resilient Futures: Enhancing Health and Safety in Communities and Workplaces Amid Climate Challenges. This commentary highlights key points raised during the forum, with a focus on community-and workplace-level initiatives that address climate-related health risks. These efforts include the improvement of heat-health early warning systems, integration of climate resilience in occupational health and safety programs, and hospital-based interventions for environmental footprint reduction. The presentations also emphasized the need for multi-stakeholder collaboration, localized mitigation and adaptation strategies, and climate-informed health promotion activities. The forum highlighted that building resilient communities and workplaces requires not only policy alignment and institutional support but also interventions on the ground that are inclusive and equitable.

BACKGROUND: It is crucial to understand the seasonal variation of Metabolic Syndrome (MetS) for the detection and management of MetS. Previous studies have demonstrated the seasonal variations in MetS prevalence and its markers, but their methods are not robust. To clarify the concrete seasonal variations in the MetS prevalence and its markers, we utilized a powerful method called Seasonal Trend Decomposition Procedure based on LOESS (STL) and a big dataset of health checkups. METHODS: A total of 1,819,214 records of health checkups (759,839 records for men and 1,059,375 records for women) between April 2012 and December 2017 were included in this study. We examined the seasonal variations in the MetS prevalence and its markers using 5 years and 9 months health checkup data and STL analysis. MetS markers consisted of waist circumference (WC), systolic blood pressure (SBP), diastolic blood pressure (DBP), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), fasting plasma glucose (FPG). RESULTS: We found that the MetS prevalence was high in winter and somewhat high in August. Among men, MetS prevalence was 2.64 ± 0.42 (mean ± SD) % higher in the highest month (January) than in the lowest month (June). Among women, MetS prevalence was 0.53 ± 0.24% higher in the highest month (January) than in the lowest month (June). Additionally, SBP, DBP, and HDL-C exhibited simple variations, being higher in winter and lower in summer, while WC, TG, and FPG displayed more complex variations. CONCLUSIONS This finding, complex seasonal variations of MetS prevalence, WC, TG, and FPG, could not be derived from previous studies using just the mean values in spring, summer, autumn and winter or the cosinor analysis. More attention should be paid to factors affecting seasonal variations of central obesity, dyslipidemia and insulin resistance.
Male ; Female ; Humans ; Metabolic Syndrome/epidemiology* ; Seasons ; Prevalence ; Climate ; Insulin Resistance ; Triglycerides

Objective: To study the effect of diurnal temperature range on the number of elderly inpatients with ischemic stroke in Hunan Province. Method: Demographic and disease data, meteorological data, air quality data, population, economic and health resource data of elderly inpatients with ischemic stroke were collected in 122 districts/counties of Hunan Province from January to December 2019. The relationships between the diurnal temperature range and the number of elderly inpatients with ischemic stroke were analyzed by using the distributed lag non-linear model, including the cumulative lag effect of the diurnal temperature range in different seasons, extremely high diurnal temperature range and extremely low diurnal temperature range. Results: In 2019, 152 875 person-times were admitted to the hospital for ischemic stroke in the elderly in Hunan Province. There was a non-linear relationship between the diurnal temperature range and the number of elderly patients with ischemic stroke, with different lag periods. In spring and winter, with the decrease in diurnal temperature range, the risk of admission of elderly patients with ischemic stroke increased (P_trend<0.001, P_trend=0.002);in summer, with the increase in diurnal temperature range, the risk of admission of elderly patients with ischemic stroke increased (P_trend=0.024);in autumn, the change in the diurnal temperature range would not cause a change in admission risk (P_trend=0.089). Except that the lag effect of the extremely low diurnal temperature range in autumn was not obvious, the lag effect occurred in other seasons under extremely low and extremely high diurnal temperature ranges. Conclusion: The high diurnal temperature range in summer and the low diurnal temperature range in spring and winter will increase the risk of admission of elderly patients with ischemic stroke, and the risk of admission of elderly patients with ischemic stroke will lag under the extremely low and extremely high diurnal temperature ranges in the above three seasons.
Humans ; Aged ; Temperature ; Ischemic Stroke ; Inpatients ; Cold Temperature ; Hot Temperature ; Seasons ; China/epidemiology*

Objective: To analyze the spatial and temporal distribution characteristics of seasonal A(H3N2) influenza [influenza A(H3N2)] in China and to provide a reference for scientific prevention and control. Methods: The influenza A(H3N2) surveillance data in 2014-2019 was derived from China Influenza Surveillance Information System. A line chart described the epidemic trend analyzed and plotted. Spatial autocorrelation analysis was conducted using ArcGIS 10.7, and spatiotemporal scanning analysis was conducted using SaTScan 10.1. Results: A total of 2 603 209 influenza-like case sample specimens were detected from March 31, 2014, to March 31, 2019, and the influenza A(H3N2) positive rate was 5.96%(155 259/2 603 209). The positive rate of influenza A(H3N2) was statistically significant in the north and southern provinces in each surveillance year (all P<0.05). The high incidence seasons of influenza A (H3N2) were in winter in northern provinces and summer or winter in southern provinces. Influenza A (H3N2) clustered in 31 provinces in 2014-2015 and 2016-2017. High-high clusters were distributed in eight provinces, including Beijing, Tianjin, Hebei, Shandong, Shanxi, Henan, Shaanxi, and Ningxia Hui Autonomous Region in 2014-2015, and high-high clusters were distributed in five provinces including Shanxi, Shandong, Henan, Anhui, and Shanghai in 2016-2017. Spatiotemporal scanning analysis from 2014 to 2019 showed that Shandong and its surrounding twelve provinces clustered from November 2016 to February 2017 (RR=3.59, LLR=9 875.74, P<0.001). Conclusion: Influenza A (H3N2) has high incidence seasons with northern provinces in winter and southern provinces in summer or winter and obvious spatial and temporal clustering characteristics in China from 2014-2019.
Humans ; Influenza, Human/epidemiology* ; China/epidemiology* ; Influenza A Virus, H3N2 Subtype ; Seasons ; Cluster Analysis

The Russian military invasion of Ukraine on February 24, 2022, and Hamas’ terror attack on Israel on October 7, 2023, signaled the beginning of two of the most recent wars to make international headlines. To date, over 110 armed conflicts are taking place: over 45 in the Middle East and North Africa (Cyprus, Egypt, Iraq, Israel, Libya, Morocco, Palestine, Syria, Turkey, Yemen, Western Sahara); over 35 in Africa (Burkina Faso, Cameroon, the Central African Republic, the Democratic Republic of the Congo, Ethiopia, Mali, Mozambique, Nigeria, Senegal, Somalia, South Sudan, Sudan); 21 in Asia (Afghanistan, India, Myanmar, Pakistan, the Philippines); seven in Europe (Russia, Ukraine, Moldova, Georgia, Armenia, Azerbaijan); and six in Latin America (three each in Mexico and Colombia); with two more international armed conflicts (between India and Pakistan, and between India and China) in Asia.1 This list does not even include such problematic situations as those involving China and the South East Asia region. As though these situations of armed violence were not enough, mankind has already passed or is on the verge of passing several climate tipping points – a recent review lists nine Global core tipping elements (and their tipping points) - the Greenland Ice Sheet (collapse); West Antarctic Ice Sheet (collapse); Labrador-Irminger Seas / SPG Convection (collapse); East Antarctic Subglacial Basins (collapse); Amazon Rainforest (dieback); Boreal Permafrost (collapse); Atlantic M.O. Circulation (collapse); Arctic Winter Sea Ice (collapse); and East Antarctic Ice Sheet (collapse); and seven Regional impact tipping elements (and their tipping points) – Low-latitude Coral Reefs (die-off); Boreal Permafrost (abrupt thaw); Barents Sea Ice (abrupt loss); Mountain Glaciers (loss); Sahel and W. African Monsoon (greening); Boreal Forest (southern dieback); and Boreal Forest (northern expansion).2 Closer to home, how can we forget the disaster and devastation wrought by Super Typhoon Haiyan (Yolanda) 10 years ago to date? Whether international or non-international, armed conflicts raise the risk of nuclear war. Russia has already “rehearsed its ability to deliver a ‘massive’ nuclear strike,” conducting “practical launches of ballistic and cruise missiles,” and stationed a first batch of tactical nuclear weapons in Belarus,3 and the possibility of nuclear escalation in Ukraine cannot be overestimated.4 Meanwhile, in a rare public announcement, the U.S. Central Command revealed that an Ohio- class submarine (560 feet long, 18,750 tons submerged and carrying as many as 154 Tomahawk cruise missiles) had arrived in the Middle East on November 5, 2023.5 Indeed, “the danger is great and growing,” as “any use of nuclear weapons would be catastrophic for humanity.”
Armed Conflicts ; Nuclear Energy ; Radiation ; Climate Change ; Global Warming

Over 200 health journals call on the United Nations, political leaders, and health professionals to recognise that climate change and biodiversity loss are one indivisible crisis and must be tackled together to preserve health and avoid catastrophe. This overall environmental crisis is now so severe as to be a global health emergency.
Armed Conflicts ; Nuclear Energy ; Radiation ; Climate Change ; Global Warming

From 2015 to 2019, the annual average incidence rate of scarlet fever was 7.80/100 000 in Yantai City, which showed an increasing trend since 2017 (χ²_trend=233.59, P<0.001). The peak period of this disease was from April to July and November to January of the next year. The ratio of male to female was 1.49∶1, with a higher prevalence among cases aged 3 to 9 years (2 357/2 552, 92.36%). Children in kindergartens, primary and middle school students, and scattered children were the high risk population, with the incidence rate of 159.86/100 000, 25.57/100 000 and 26.77/100 000, respectively. The global spatial auto-correlation analysis showed that the global Moran's I index of the reported incidence rate of scarlet fever in Yantai from 2015 to 2019 was 0.28, 0.29, 0.44, 0.48, and 0.22, respectively (all P values<0.05), suggesting that the incidence rate of scarlet fever in Yantai from 2015 to 2019 was spatial clustering. The local spatial auto-correlation analysis showed that the "high-high" clustering areas were mainly located in Laizhou City, Zhifu District, Haiyang City, Fushan District and Kaifa District, while the "low-high" clustering areas were mainly located in Haiyang City and Fushan District.
Child ; Humans ; Male ; Female ; Scarlet Fever/epidemiology* ; Spatial Analysis ; Cities/epidemiology* ; Seasons ; Risk Factors ; Incidence ; Cluster Analysis ; China/epidemiology*