Umeå University's logo

This study explores the correlation between heat exposure and perinatal depression in four low- and middle-income countries using a spatial, time-stratified case-crossover study. Cluster-level mental health data from the Demographic and Health Surveys (DHS) of Bangladesh, Lesotho, Mozambique, and Nepal was utilized. Availability of complete data on Patient Health Questionnaire 9 (PHQ-9) was an inclusion criterion. Heat exposure data was provided by the National Aeronautics and Space Administration (NASA). Spatial alignment between DHS clusters and meteorological points was achieved using bilinear interpolation. Heat exposure was defined as the daily maximum temperature exceeding the country-specific 50th percentile.

This study included 1836 perinatal women with depression. The pooled prevalence of perinatal depression was 27% (range: 19%–31%). Using distributed lag non-linear model (DLNM), in Bangladesh, lower maximum ambient temperatures (25th-centile) had 5.34 (4.28, 6.66) times higher cumulative odds for perinatal depression compared to the median temperature. In Lesotho, Mozambique, and Nepal, exposure to higher maximum ambient temperature (75th centile) had cumulative higher odds of 1.19 (0.98, 1.43), 2.51 (1.96, 3.20), and 9.41 (4.88, 18.1), respectively, in comparison to the median temperatures.The results suggest that heat exposure is correlated with perinatal depression, undermining the need for intersectoral responses that address environmental and healthcare system factors.

Background: Tick-borne encephalitis (TBE) is a serious vector-borne neurological disease in Europe, with a complex transmission cycle involving ticks of genus Ixodes, animal hosts, environmental and climatic determinants.

Methods: We modelled annual Geocoded Swedish TBE case data for the period 2005–2023 as a log-Gaussian Cox process in relation to population, environmental and climate data, and wildlife citizen science reports at high spatial resolution. We used the computationally efficient Integrated Nested Laplace Approximation (INLA) and projected the future TBE incidence using fifteen regional climate models.

Results: The covariates significantly associated with TBE incidence in Sweden, ranked based on predictive capacity, were mean temperature, population density, habitat richness, forest cover, precipitation, relative humidity and roe deer density. Specifically, mean temperature above 12° C degrees in the third quarter of the previous year, habitat richness, precipitation in the third quarter, and higher roe deer density were associated with increased TBE risk. The model performed well on testing data, excluded from model building, demonstrating high predictive accuracy in TBE-endemic areas compared to observed data. Our projections indicate TBE cases will increase by 69% under low emissions (RCP2.6) and 121% under high emissions (RCP8.5) by the 2090s, relative to 2014–2023.

Conclusion: The TBE incidence is projected to rise substantially, even under lower emission scenarios. Our findings highlight the growing influence of climate change on TBE transmission in Sweden and provide actionable evidence to inform surveillance, vaccination strategies, and long-term public health planning. Citizen science initiatives and risk maps can help focus resources on areas most vulnerable to transmission. More broadly, the integration of climate models with high-resolution epidemiological data, offers a template for anticipating climate-sensitive vector-borne diseases. Proactive, evidence-based interventions are essential to mitigate the growing health burden posed by TBE in Sweden and beyond.

Background: Structural passive cooling interventions such as cool roofs are used to reduce indoor ambient temperature. However, it is unknown how acceptable and desirable cool roof technology is in rural low-income settings in sub-Saharan Africa, where home occupants are exposed to rising indoor temperatures.

Methods: We engaged 48 participants in four focus group discussions to explore the factors influencing the acceptability of “cool roofs” in Nouna, Burkina Faso. We analysed the data using reflexive thematic analysis. We structured our findings using the acceptability framework developed by Sekhon, Cartwright and Francis (2017), which comprises seven components: affective attitude, burden, perceived effectiveness, ethicality, intervention coherence, opportunity costs, and self-efficacy.

Results: Our participants described an environment of extreme heat and the need for adaptation strategies to reduce the temperature within their homes. The cool roofs would be deemed acceptable if they were affordable, effective in reducing heat, and aligned with values around self-efficacy, particularly in relation to local production and ownership.

Conclusion: Providing communities with technical information regarding how the cool roof functions and can be maintained may support uptake via acceptability. Desirability of the roof may be achieved via a combination of highlighting the indoor cooling of the roof as reported by users, sharing of results with the community so that they have an insight into the effects of the roof, and feedback regarding the products useability and durability.

Background: The rapid spread of the Asian tiger mosquito (Aedes albopictus) poses a notable public health threat in Europe due to its ability to transmit tropical diseases such as dengue and chikungunya. We aimed to quantify the underlying drivers facilitating and accelerating Europe's transition from sporadic arbovirus outbreaks to Aedes-borne disease endemicity, focusing on dengue and chikungunya outbreaks.

Methods: We conducted a time-to-event analysis to investigate the period between establishment of Ae albopictus and autochthonous dengue and chikungunya outbreaks across Nomenclature of Territorial Units for Statistics (NUTS) 3 regions in the EU. We incorporated data from the European Centre for Disease Prevention and Control, WHO, technical and surveillance reports, and other entomological data sources on regional Ae albopictus establishment and subsequent dengue and chikungunya outbreaks from 1990 (when Ae albopictus was first introduced to an EU country) to 2024. The main outcome was survival time (ie, the time from Ae albopictus establishment to an outbreak of dengue or chikungunya), accounting for land-use types, demographic and socioeconomic factors, imported cases, and climatic variables via univariable and multivariable regression. To address recurrent outbreaks, we applied the Andersen–Gill extension of the Cox proportional hazards model to analyse all events. We further stratified regions into warm and cool groups on the basis of mean summer temperatures above or below 20°C and conducted a stratified analysis with Kaplan–Meier curves and the log-rank test to evaluate differences between these groups. We also estimated projected outbreak hazards from the 2030s to the 2060s at a decadal scale under three distinct shared socioeconomic pathways (SSPs; SSP1–2·6, SSP3–7·0, and SSP5–8·5) to assess the future impact of climate change on outbreak hazard estimates.

Findings: Between 1990 and 2024, the interval from the first NUTS 3 regional establishment of Ae albopictus to the first outbreak of dengue or chikungunya decreased from 25 years to less than 5 years. Similarly, the interval from the first outbreak to the second outbreak decreased from 12 years in 1990 to less than 1 year in 2024. Our regression analyses indicate that increasingly favourable climatic conditions play a significant role in this trend. A 1°C rise in mean summer temperature was associated with a hazard ratio of 1·55 (95% CI 1·30–1·85; p<0·0001) after controlling for health-care expenditure and imported cases and land-use type. First outbreak events might have occurred more frequently and earlier in warmer regions than cooler ones (log-rank p=0·088), reflecting a lower probability of remaining outbreak-free over time. This trend is expected to intensify under extreme climate change scenarios, with projections under the SSP5–8·5 scenario suggesting an almost five-fold increase in dengue or chikungunya outbreaks by the 2060s, relative to the 1990–2024 baseline period.

Interpretation: The findings in this study underscore the pressing need for robust control measures, enhanced surveillance, and early warning systems in the EU to mitigate the impending risk of Aedes-borne disease endemicity in the region.

Accurate malaria predictions are essential for implementing timely interventions, particularly in Mozambique, where climate factors strongly influence transmission. This study aims to develop and evaluate a spatial–temporal prediction model for malaria incidence in Mozambique for potential use in a malaria early warning system (MEWS). We used monthly data on malaria cases from 2001 to 2018 in Mozambique, the model incorporated lagged climate variables selected through Deviance Information Criterion (DIC), including mean temperature and precipitation (1–2 months), relative humidity (5–6 months), and Normalized Different Vegetation Index (NDVI) (3–4 months). Predictive distributions from monthly cross-validations were employed to calculate threshold exceedance probabilities, with district-specific thresholds set at the 75th percentile of historical monthly malaria incidence. The model’s ability to predict high and low malaria seasons was evaluated using receiver operating characteristic (ROC) analysis. Results indicated that malaria incidence in Mozambique peaks from November to April, offering a predictive lead time of up to 4 months. The model demonstrated high predictive power with an area under the curve (AUC) of 0.897 (0.893–0.901), sensitivity of 0.835 (0.827–0.843), and specificity of 0.793 (0.787–0.798), underscoring its suitability for integration into a MEWS. Thus, incorporating climate information within a multisectoral approach is essential for enhancing malaria prevention interventions effectiveness.

Introduction: Dengue is currently the fastest-spreading mosquito-borne viral illness in the world, with over half of the world's population living in areas at risk of dengue. As dengue continues to spread and become more of a health burden, it is essential to have tools that can predict when and where outbreaks might occur to better prepare vector control operations and communities' responses. One such predictive tool, the Early Warning and Response System for climate-sensitive diseases (EWARS-csd), primarily uses climatic data to alert health systems of outbreaks weeks before they occur. EWARS-csd uses the robust Distribution Lag Non-linear Model in combination with the INLA Bayesian regression framework to predict outbreaks, utilizing historical data. This study seeks to validate the tool's performance in two states of Colombia, evaluating how well the tool performed in 11 municipalities of varying dengue endemicity levels.

Methods: The validation study used retrospective data with alarm indicators (mean temperature and rain sum) and an outbreak indicator (weekly hospitalizations) from 11 municipalities spanning two states in Colombia from 2015 to 2020. Calibrations of different variables were performed to find the optimal sensitivity and positive predictive value for each municipality.

Results: The study demonstrated that the tool produced overall reliable early outbreak alarms. The median of the most optimal calibration for each municipality was very high: sensitivity (97%), specificity (94%), positive predictive value (75%), and negative predictive value (99%; 95% CI).

Discussion: The tool worked well across all population sizes and all endemicity levels but had slightly poorer results in the highly endemic municipality at predicting non-outbreak weeks. Migration and/or socioeconomic status are factors that might impact predictive performance and should be further evaluated. Overall EWARS-csd performed very well, providing evidence that it should continue to be implemented in Colombia and other countries for outbreak prediction.

OBJECTIVES: This study aims to assess both socioeconomic and climatic factors of cholera morbidity in Mozambique considering both spatial and temporal dimensions.

DESIGN: An ecological longitudinal retrospective study using monthly provincial cholera cases from Mozambican Ministry of Health between 2000 and 2018. The cholera cases were linked to socioeconomic data from Mozambique Demographic and Health Surveys conducted in the period 2000-2018 and climatic data; relative humidity (RH), mean temperature, precipitation and Normalised Difference Vegetation Index (NDVI). A negative binomial regression model in a Bayesian framework was used to model cholera incidence while adjusting for the spatiotemporal covariance, lagged effect of environmental factors and the socioeconomic indicators.

SETTING: Eleven provinces in Mozambique.

RESULTS: Over the 19-year period, a total of 153 941 cholera cases were notified to the surveillance system in Mozambique. Risk of cholera increased with higher monthly mean temperatures above 24°C in comparison to the reference mean temperature of 23°C. At mean temperature of 19°C, cholera risk was higher at a lag of 5-6 months. At a shorter lag of 1 month, precipitation of 223.3 mm resulted in an 57% increase in cholera risk (relative risk, RR 1.57 (95% CI 1.06 to 2.31)). Cholera risk was greatest at 3 lag months with monthly NDVI of 0.137 (RR 1.220 (95% CI 1.042 to 1.430)), compared with the reference value of 0.2. At an RH of 54%, cholera RR was increased by 62% (RR 1.620 (95% CI 1.124 to 2.342)) at a lag of 4 months. We found that ownership of radio RR 0.29, (95% CI 0.109 to 0.776) and mobile phones RR 0.262 (95% CI 0.097 to 0.711) were significantly associated with low cholera risk.

CONCLUSION: The derived lagged patterns can provide appropriate lead times in a climate-driven cholera early warning system that could contribute to the prevention and management of outbreaks.

Record-breaking temperatures were recorded across the globe in 2023. Without climate action, adverse climate-related health impacts are expected to worsen worldwide, affecting billions of people. Temperatures in Europe are warming at twice the rate of the global average, threatening the health of populations across the continent and leading to unnecessary loss of life. The Lancet Countdown in Europe was established in 2021, to assess the health profile of climate change aiming to stimulate European social and political will to implement rapid health-responsive climate mitigation and adaptation actions. In 2022, the collaboration published its indicator report, tracking progress on health and climate change via 33 indicators and across five domains.

This new report tracks 42 indicators highlighting the negative impacts of climate change on human health, the delayed climate action of European countries, and the missed opportunities to protect or improve health with health-responsive climate action. The methods behind indicators presented in the 2022 report have been improved, and nine new indicators have been added, covering leishmaniasis, ticks, food security, health-care emissions, production and consumption-based emissions, clean energy investment, and scientific, political, and media engagement with climate and health. Considering that negative climate-related health impacts and the responsibility for climate change are not equal at the regional and global levels, this report also endeavours to reflect on aspects of inequality and justice by highlighting at-risk groups within Europe and Europe's responsibility for the climate crisis.

Despite significant advances in improving the predictive models for vector-borne diseases, only a few countries have integrated an early warning system (EWS) with predictive and response capabilities into their disease surveillance systems. The limited understanding of forecast performance and uncertainties by decision-makers is one of the primary factors that precludes its operationalization in preparedness and response planning. Further, predictive models exhibit a decrease in forecast skill with longer lead times, a trade-off between forecast accuracy and timeliness and effectiveness of action. This study presents a methodological framework to evaluate the economic value of EWS-triggered responses from the health system perspective. Assuming an operational EWS in place, the framework makes explicit the trade-offs between forecast accuracy, timeliness of action, effectiveness of response, and costs, and uses the net benefit analysis, which measures the benefits of taking action minus the associated costs. Uncertainty in disease forecasts and other parameters is accounted for through probabilistic sensitivity analysis. The output is the probability distribution of the net benefit estimates at given forecast lead times. A non-negative net benefit and the probability of yielding such are considered a general signal that the EWS-triggered response at a given lead time is economically viable. In summary, the proposed framework translates uncertainties associated with disease forecasts and other parameters into decision uncertainty by quantifying the economic risk associated with operational response to vector-borne disease events of potential importance predicted by an EWS. The goal is to facilitate a more informed and transparent public health decision-making under uncertainty.