Electronic waste management is a global rising concern that is primarily being handled by informal recycling practices. These release a mix of potentially hazardous chemicals, which is an important public health concern. These chemicals include polybrominated diphenyl ethers (PBDEs), used as flame retardants in electronic parts, which are persistent in nature and show bioaccumulative characteristics. Although PBDEs are suspected endocrine disruptors, particularly targeting thyroid and reproductive hormone functions, the relationship of PBDEs with these health effects are not well established. We used the Navigation Guide methodology to conduct a systematic review of studies in populations exposed to e-waste to better understand the relationships of these persistent flame retardants with hormonal and reproductive health. We assessed nineteen studies that fit our pre-determined inclusion criteria for risk of bias, indirectness, inconsistency, imprecision, and other criteria that helped rate the overall evidence for its quality and strength of evidence. The studies suggest PBDEs may have an adverse effect on thyroid hormones, reproductive hormones, semen quality, and neonatal health. However, more research is required to establish a relationship of these effects in the e-waste-exposed population. We identified the limitations of the data available and made recommendations for future scientific work.

Background: Air pollution from the 2023 Quebec wildfires affected New York state (NY) with daily average PM _2.5 levels that peak on June 7. Increased Covid-19 hospitalizations were recorded weeks after the wildfires. This study analyses the trend of Covid-19 hospitalization in NY counties after the 2023 Quebec wildfires and estimates their association with higher PM _2.5 concentration levels, compared to 2022. Design and methods: A Bayesian spatiotemporal regression model was used to estimate the impact of wildfire smoke on Covid-19 hospitalizations. Four periods of pre/post-wildfire and 7-day post-wildfire daily hospitalization periods were considered to compare the association of daily average PM _2.5 levels, from May 1 to June 7, with daily Covid-19 hospitalization rates in NY counties in 2022 and 2023. The pre/post-wildfire and 7-day post-wildfire periods considered a lag of 2, 4, 6, and 8 weeks and 24, 48, 72, and 96 h, respectively. The model was adjusted for sociodemographic factors. Results: The Covid-19 hospitalization rate followed an increasing trend in the second, third and fourth pre/post-wildfire periods in 2023 in contrast with 2022 when no trends were identified. Each PM _2.5 unit increase was associated with a 2%; 6% and 7% Covid-19 higher hospitalization risk in periods 2, 3, and 4, respectively, in 2023 only. These findings identify a potential impact of wildfire smoke on the severity of Covid-19 morbidity after 2 weeks of the wildfires. Robust spatiotemporal analyses can be used to identify specific at-risk areas and communities to support public health decision-making and health strategies. Conclusions: This study identifies a higher risk of Covid-19 hospitalization in New York State associated with higher air pollution levels from the 2023 Quebec wildfires, in the first week and 2, 3, and 4 weeks after the wildfires. These findings concur with the increasingly investigated association of air pollution with severe Covid-19. The methodological approach of this study shows the utility of spatiotemporal epidemiological analyses and need for future research on wildfire smoke as a potential determinant of severe Covid-19. With more frequent and extreme climate events it is paramount to improve our understanding of many potential health impacts of wildfires to prepare strategies to deal with, and potentially anticipate, environmental health and healthcare responses in wildfire-prone regions.

Cardiovascular diseases (CVDs) remain a leading cause of mortality globally, with environmental risk factors playing a significant role in their prevalence. This review aims to critically evaluate the current methodologies employed in spatiotemporal analyses of CVDs and provides recommendations to enhance the accuracy and practical application of these models. A systematic search of the literature was conducted using Scopus, PubMed, and Embase databases. Studies were selected based on their use of spatiotemporal models to assess the relationship between environmental factors and CVDs. We evaluated the methodological quality of included studies using the Spatial Methodology Appraisal of Research Tool (SMART). Significant challenges were noted, including the need for higher spatial resolution data sets and improved methods for addressing the modifiable areal and temporal unit problems and ecological bias. Additionally, the visualization of spatiotemporal data remains underutilized and underdeveloped, limiting the practical utility of the findings. We also discuss combining parameters to form an indicator that better represents environmental conditions, as well as cases where ground, satellite, or modeled data products are suitable. These recommendations could extend to other acquired chronic diseases and their relationship with environmental risk factors to improve the utility of spatiotemporal models. While spatiotemporal modeling holds considerable promise in understanding and mitigating CVD risks associated with environmental factors, appropriate data selection, addressing methodological pitfalls and reporting spatial and temporal model outcomes are necessary to enhance their reliability and impact.

Morbidity statistics can be reported as grouped data for health services rather than for individual residence area, especially in low-middle income countries. Although such reports can support some evidence-based decisions, these are of limited use if the geographical distribution of morbidity cannot be identified. This study estimates the spatial rate of Acute respiratory infections (ARI) in census districts in Cúcuta -Colombia, using an analysis of the spatial distribution of health services providers. The spatial scope (geographical area of influence) of each health service was established from their spatial distribution and the population covered. Three levels of spatial aggregation were established considering the spatial scope of primary, intermediate and tertiary health services providers. The ARI cases per census district were then calculated and mapped using the distribution of cases per health services provider and the proportion of population per district in each level respectively. Hotspots of risk were identified using the Local Moran’s I statistic. There were 98 health services providers that attended 8994, 18450 and 91025 ARI cases in spatial levels 1, 2 and 3, respectively. Higher spatial rates of ARI were found in districts in central south; northwest and northeast; and southwest Cúcuta with hotspots of risk found in central and central south and west and northwest Cucuta. The method used allowed overcoming the limitations of health data lacking area of residence information to implementing epidemiological analyses to identify at risk communities. This methodology can be used in socioeconomic contexts where geographic identifiers are not attached to health statistics.