GeoHealth tools differ from other health-IT platforms, requiring analytical transformation tools, location-based discovery, and responsive design frameworks. While knowledge exists surrounding related platforms, there is a literature gap specific to GeoHealth tool end-user needs. This qualitative focus group study identified design needs for GeoHealth tools. Seven focus groups included non-experts (three sessions, n = 15) and experts (healthcare professionals, four sessions, n = 16) from October 2024 to February 2025. Researchers conducted inductive thematic analysis, identifying emerging themes. Thirty-one participants completed seven sessions: 20 [65%] female, 16 [52%] White, and 16 [52%] with graduate degrees. Both groups identified similar facilitators and barriers: simple interfaces, contrasting colors, cross-device functionality. Both valued filtering, customizing regions, downloading data, and chatbot integration. Non-experts reported frustrations with mobile use and content density, while experts emphasized integrating GeoHealth tools into clinical workflows for decision-making. End-user preferences are critical as GeoHealth tools expand. Key recommendations include: customizable features (filters, personalized regions, data layering, and export options), accessible design with high-contrast color schemes and intuitive navigation, and mobile optimization (tap-triggered overlays, optimized touch targets). Chatbots were valued with transparent data sourcing. Healthcare professionals highlighted integrating tools into Health-IT systems for clinical decision-making. These findings can improve usability and acceptance, making health information more accessible and potentially improving health outcomes. Future work should validate findings through iterative usability testing with diverse samples and investigate technical pathways for Health-IT integrations and trustworthy chatbot development.
Fine particulate matter (PM2.5) poses a public health risk, disproportionately impacting low- and middle-income countries (LMICs). In Peru, where ambient concentrations in urban areas significantly exceed the World Health Organization's annual guideline of 5 µg/m3, lack of air pollution monitoring hinders exposure assessment, health effect research, and policy development. Here, we review efforts to create a national database of estimated ambient PM2.5 in other LMICs, and then discuss our efforts in Peru. We highlight the Peru-based NIH-funded GeoHealth Hub's efforts to establish a nationwide low-cost sensor (LCS) network of 176 PurpleAir monitors. We then describe a hybrid approach for modeling ambient PM2.5 exposure across Peru, leveraging data from LCS, satellite remote sensing, chemical transport models, and advanced machine learning methods. The ground-monitoring network includes sensors in both urban (62.5%) and rural (37.5%) areas, in the 24 Regions of the country, set up in collaboration with national environmental agencies. Initial application of our hybrid approach in Lima demonstrated good prediction for the years 2010-2023, with an R² of 0.88 with existing regulatory ground monitors. We are working to extend the model across Peru at a daily level and at a 5-km2 resolution for 2024-2026. The sustainability of these efforts will depend on building local capacity, securing long-term funding, and integrating the LCS network within the current regulatory environmental monitoring network. The hybrid approach offers a scalable solution to address data scarcity and enable high-resolution exposure modeling in Peru and other LMICs.
Informal e-waste recycling releases complex mixtures of hazardous substances, including heavy metals that bioaccumulate in exposed populations-especially among e-waste workers. Emerging evidence links these metals to telomere shortening, a key marker of cellular aging and DNA damage which can lead to noncommunicable diseases (NCDs). This study therefore examined the effects of metal exposure on telomere length among e-waste workers in Agbogbloshie compared to non-e-waste workers in Madina. A total of 78 samples (53 e-waste workers and 25 controls), each with three repeated measurements, were selected from the GEOHealth II study and analyzed for telomere length using quantitative polymerase chain reaction (qPCR) technique. Restricted cubic spline (RCS) modeling was employed to assess the association between metal exposure and telomere length. This study observed consistently shorter relative telomere length among the e-waste workers, particularly those involved in burning activities. Lead (Pb) and chromium (Cr) levels were negatively associated with relative telomere length and zinc (Zn) showed a positive association while magnesium (Mg) exhibited a nonlinear relationship with telomere length. The consistently shorter relative telomere length among e-waste workers coinciding with higher concentrations of Cr and Pb implicates the role of metals in telomere shortening. Larger, long-term studies are recommended for future studies.
The adjoint of the U.S. EPA's Community Multiscale Air Quality (CMAQ) model is extended for hemispheric scale applications and is used to estimate location-specific health impacts from primary PM2.5, and PM2.5 precursor emissions (NH3, NOX and SO2). We estimate the monetized health burden due to mortality caused by chronic PM2.5 exposure among adults living in the northern hemisphere, using a generalized concentration-response function. The health impact sensitivities show large spatial variability over the northern hemisphere and exhibit a great deal of seasonal variability, especially for inorganic precursor emissions. The largest marginal impacts are seen for NH3 and primary PM2.5. The estimated health impacts for a 10% reduction in emissions reveal a hemispheric burden of 513,700 avoided mortality and monetized health benefits at above 1.2 trillion USD2016. The largest regional contribution to hemispheric mortality is found to be in East and South Asia, particularly China and India (183,760 and 123,440 for a 10% reduction in emissions, respectively). Monetized health burdens are estimated to be highest in China and Europe (∼365 and ∼252 million USD for a 10% reduction in emissions) while it is relatively similar in India (∼175 million USD) as in Canada and the United States (∼177 million USD). Sectoral source contribution analysis demonstrates that the agriculture (19%) and residential (15%) sectors are the largest contributors to the northern hemispheric scale health burden, however, regional differences exist in the results. Examining location- and sector-specific health impacts can inform more effective regulatory measures. Chronic exposure to outdoor PM2.5 is one of the highest mortality risk factors. We used an air quality model to understand how reducing air pollution could improve health across the Northern Hemisphere. We estimated how many deaths could be avoided and the economic value of these health benefits if emissions were reduced. We looked at primary PM2.5 emissions and gases like ammonia, nitrogen oxides, and sulfur dioxide, which can form PM2.5 in the air. Using a sophisticated sensitivity analysis, we linked emissions to health outcomes and economic impacts in specific locations. Our results show that health impacts vary by season and region, and that ammonia and primary PM2.5 emissions cause the greatest harm. Reducing emissions by 10% could prevent more than 500,000 premature deaths each year and save over $1.2 trillion (USD 2016). East and South Asia had the highest number of deaths, while China and Europe faced the greatest economic burden. Emissions from agriculture and residential sources were the biggest contributors to health impacts. Identifying where pollution reductions would bring the most health benefits can support the development of more targeted and effective air quality policies.
Influenza remains a global public health and socio-economic threat, and air pollution is believed to increase its risk. However, relatively few studies have focused specifically on laboratory-confirmed influenza (LCI), which limits the precision and reliability of existing evidence on this association. We sought to explore the relationship between air pollution and LCI cases in Anhui province, China. We employed a two-stage time series analysis to evaluate the associations between PM2.5, PM10, CO, SO2, O3, NO2 and daily LCI cases between January 2015 and March 2023. In the first stage, we applied distributed lag nonlinear model to characterize the lagged effects of each air pollutant in individual cities, followed by a meta-analysis to pool city-specific estimates in the second-stage. We performed sub-group analysis by age and gender, and explored modification effect of population density, median pollution levels, longitude, and latitude, using a meta-regression. A total of 43, 872 LCI cases were reported in Anhui over study period. There was a positive associations between influenza and a 10 μg/m3 increase in PM2.5: RR = 1.0045 (95%CI: 1.0035-1.0054), SO2: RR = 1.0035 (95%CI: 1.0001-1.0069), NO2: RR = 1.0122 (95%CI: 1.0881-1.0162), PM10: RR = 1.0025 (95%CI: 1.0017-1.0033) and CO: RR = 1.2641 (95%CI: 1.1294-1.4150) in a single-day lag model. In the cumulative-day lag model, a 10 μg/m3 increase in PM2.5; RR = 1.0043 (95%CI: 1.0018-1.0068), NO2; RR = 1.0076 (95%CI: 1.0006-1.0145), PM10; RR = 1.0024 (95%CI: 1.0001-1.0046), and CO; RR = 1.3026 (95%CI: 0.8139-2.0849) positively associated with LCI cases. The associations were more pronounced in males and ages 5-24 years. Population density modified the associations. Our findings suggest the need to integrate air quality management in intervention strategies for influenza. Influenza is a major public health issue worldwide, and air pollution may increase the risk of infection. However, few studies have examined laboratory‐confirmed influenza (LCI), which provides more reliable evidence. This study analyzed the relationship between air pollution and LCI cases in Anhui Province, China, from January 2015 to March 2023. We used a two‐stage time series model to assess the effects of six air pollutants (PM2.5, PM10, CO, SO2, O3, and NO2) on daily influenza cases. First, we applied a statistical model to individual cities, and then combined the results using meta‐analysis. We also explored differences by age, gender, and environmental factors. A total of 43,872 LCI cases were reported during the study period. Higher levels of PM2.5, PM10, CO, SO2, and NO2 were linked to an increased risk of influenza, with stronger associations in males and individuals aged 5–24 years. Population density influenced these associations. These findings suggest that air pollution plays a role in influenza risks, highlighting the need to consider pollution levels and population density in disease prevention strategies. Policymakers should integrate air quality improvements into public health interventions to reduce influenza risks.
Growing global demand for natural gas has driven the expansion of liquefied natural gas (LNG) export terminals, which emit pollutants that can pose health risks to nearby communities. This study presents a novel modeling framework using the AMS/EPA Regulatory Model (AERMOD) to assess near-source nitrogen dioxide (NO2) exposure, health impacts, and equity implications at the block-group level. We apply this methodology to four LNG export terminals in the United States, simulating NO2 concentrations within a 50 km radius. Results show that LNG terminals substantially contribute to near-source air pollution, with simulated 1-hr maximum NO2 concentrations reaching up to 16% of the EPA's National Ambient Air Quality Standard (100 ppb). Site-specific maximum concentrations were 15.7 ppb (Site A), 1.6 ppb (B), 10.7 ppb (C), and 0.3 ppb (D). Comparing NO2 concentrations with demographic patterns, Sites A and D showed higher concentrations, higher proportions of People of Color and low-income populations, and greater health burdens in communities closer to the LNG facilities, indicating potential disproportionate impacts. The other sites showed weak or no spatial inequity patterns. Estimated annual NO2-attributable all-cause mortality rates per 100,000 people were 8.2 (A), 0.6 (B), 2.2 (C), and 0.1 (D); annual NO2-attributable pediatric asthma rates per 100,000 children were 75.5 (A), 6.2 (B), 21.8 (C), and 1.1 (D). This study demonstrates how regulatory dispersion models like AERMOD can be adapted to evaluate near-source health and equity impacts of industrial emissions and offers a transferable methodology for similar analyses across other high-emitting facilities. Liquefied natural gas (LNG) export terminals emit nitrogen dioxide (NO2), a pollutant linked to health risks. This study developed a methodology to evaluate near‐source health and equity impacts with the regulatory dispersion model, AMS/EPA Regulatory Model, and applied it to four U.S. LNG export terminals. We modeled NO2 levels near the terminals. We further conducted equity analysis comparing NO2 exposure with the proportion of people of color and low‐income populations at the block group level. We finally estimated potential health impacts from facility emissions, measured as NO2‐attributable all‐cause mortality and pediatric asthma.
Monitoring cyanobacteria is crucial for assessing water quality, safeguarding public health, and understanding ecosystem dynamics impacted by harmful algal blooms. This study explores the potential of satellite remote sensing (SRS) to assess risks of cyanotoxin exposure in California's recreational waters from 2002 to 2011 and 2016 to 2023. Utilizing SRS data, we compared cyanobacteria abundance across five lakes with cyanotoxin data and advisories from the California Department of Water Resources (DWR). SRS-based advisories were aligned with DWR/in situ based advisories 54%-100% of the time. Lake-specific assessments of agreement showed Lake Oroville with the highest overall accuracy (100%) and Pyramid Lake with the lowest (54%). SRS generally overpredicted DWR-based alerts in about 30% of instances and under-detected DWR-based alerts at a rate of 42%, likely due to differences in the way satellites sample across continuous spatial domains but at coarse resolutions versus in situ sampling at discrete locations. We extended our SRS monitoring capability to an additional 71 lakes to conduct a statewide assessment of toxin alerts over time. There were 10 lakes that experienced cyanobacteria alerts 12%-88% of the time across our study. When comparing 2002 to 2011 and 2016 to 2023, we observed higher rates of toxin alert frequency, duration, and a shift toward earlier starts of the year for high-risk blooms across all regions of California, with the greatest in southern California. Despite limitations in spatial resolution, SRS provides consistent, near-real-time data essential for timely cyanotoxin risk assessments and public health alerts, complementing traditional in situ sampling. This study investigates the potential of satellite remote sensing (SRS) to assess risks of cyanotoxin exposure in California's recreational waters from 2002 to 2011 and 2016 to 2023. We validated the SRS‐derived cyanobacteria biomass estimates against concurrent field measurements from the California Department of Water Resources (DWR) across five key California lakes, demonstrating strong statistical agreement. Subsequently, we utilized the validated SRS data to evaluate high‐risk bloom frequency and duration against established public health thresholds for potential cyanotoxin exposure. Our analysis revealed satellite‐based warnings agreed with DWR's alerts between 54% and 100% of the time, with Lake Oroville showing the best agreement and Pyramid Lake the lowest. While satellites sometimes predicted alerts when DWR did not (∼30%) or missed them when DWR issued one (∼42%), this is likely due to the different ways they collect data. We expanded to 71 other lakes, where 10 had high‐risk cyanobacteria alerts 12%–88% of the time. When comparing both periods, we observed higher rates of toxin alert frequency, duration, and an earlier onset of high‐risk blooms across all regions, with the greatest seen in southern California. Despite some differences, satellite data offers a powerful tool for quickly and consistently assessing cyanotoxin risks.
Addressing impacts on human health from climate change will require engaged communities capable of co-creating actionable science. This is particularly the case in Jordan, one of the most vulnerable countries to climate change with a hot, dry climate and rapidly growing population. A key demographic for building capacity to address climate-health challenges is youth. To engage Jordanian youth in developing knowledge and skills related to climate-health science, the Global Center on Climate Change and Water Energy Food Health Systems (GC3WEFH) implemented The DataJam, an annual project-based data science learning program and competition developed in the United States. The GC3WEFH enrolled 87 students from 21 schools in The DataJam Jordan. Fifty-four students in teams of three completed 18 projects over a 2-year period while 33 students started The DataJam but did not complete a project. The aim of the intervention was to build data science capacity to address issues at the intersection of climate and health. To explore the outcomes of this intervention, we used the Consolidated Framework for Implementation Research to identify the primary determinants. This analysis revealed that the complexity of The DataJam and the work infrastructure of the implementation impacted communication across the intervention, which shaped the topics students researched and their use of data science. Importantly, the DataJam increased both the confidence and interest of students in engaging in climate change related challenges facing their communities. Therefore, The DataJam is a positive example of engaging youth through the international translation of a STEM learning program. Building young people's capacity to engage in science at the intersection of climate change and human health can contribute to knowledge and interventions that are more likely to be adopted and sustained. The Global Center on Climate Change and Water Energy Food Health Systems engaged Jordanian high school students using The DataJam, a project‐based data science learning program and competition developed in the United States. This study analyzed the effectiveness of The DataJam in Jordan for engaging youth in climate‐health science using data science. After showing projects tended not to focus on the intersection of climate change and health and some projects did not include data science, we employed the Consolidated Framework for Implementation Science to identify the aspects of the implementation that impacted the outcomes. We found the complexity of The DataJam and the organization of the intervention led to communication challenges throughout the process. Despite these challenges, students reported a positive experience that resulted in greater interest in climate‐health science and confidence in their ability to be engaged in their communities.
Marine vibrios, a group of marine bacteria that are opportunist human pathogens, proliferates faster in coastal environments at warmer temperature. Recently, there have been significant concerns of human infection risks during marine beach recreations due to the elevation of seawater temperature in the United States Eastern Seaboard. This study carried out a quantitative microbial risk assessment (QMRA) to estimate health risks associated with V. vulnificus and V. parahaemolyticus infections from water ingestion during recreational activities under varying climate scenarios that predict sea surface temperatures (SSTs) in the next 75 years. Monte Carlo simulations that incorporate Vibrio concentrations at specific SSTs, ingestion dose during recreational activities, and dose-response relationships were applied to predict the probability of infection under varying exposure scenarios. The risk of Vibrio infections along the U.S. Eastern Seaboard was first estimate for the year 2020 based on measured SSTs. The same approach was applied to predict the risk in the year 2100 based on projected SSTs using two IPCC Representative Concentration Pathways (RCP) scenarios: RCP 2.6 and RCP 8.5. The results indicate that the median risks of V. vulnificus and V. parahaemolyticus from recreational activities along the Eastern Seaboard increase significantly from 2020 to 2100, with rises of up to 1,000-fold under RCP 8.5 and up to 100-fold under RCP 2.6. Furthermore, the estimated risks represent a conservative lower-bound of vibrio-related health impacts because wound infection from water contact was not included. This study highlights the growing public health concern and the need for adaptive management strategies. Marine vibrios are a group of marine bacteria that can cause human diseases. These bacteria grow faster at warmer temperature. There have been more reports of Vibrio diseases in the United States Eastern Seaboard in the recent years due to warmer climate. This study estimates health risks from two types of Vibrio due to accidental ingestion of seawater during water recreation at beaches under future warming scenarios over the next 75 years. Analyses showed that the risks increase significantly from 2020 to 2100. Under the worst‐case climate scenarios, the risk rises to 1,000‐fold. Future climate has a greater impact on infection risks in northern coastlines compared to southern coastlines. This study highlights the growing public health concern and the need for adaptive management strategies.
Ambient PM2.5 exposure poses the greatest environmental risk to public health in India. While several studies have quantified the changing patterns of exposure, the extent of inequality in exposure among population subgroups at the sub-national scale remains unknown. In this study, we examined the disparity in ambient PM2.5 exposure across various population subgroups in urban and rural India and analyzed its changes in recent years by integrating satellite-derived PM2.5 concentrations (1-km × 1-km) with sociodemographic information from the 4th (2015-2016) and 5th (2019-2021) rounds of the National Family Health Survey. We found a larger absolute disparity (60-90 µgm-3) in high socio-demographic index (SDI) states compared to middle and lower SDI states. Moreover, we discovered that ambient PM2.5 exposure was higher (indicated by relative disparities in terms of Z score) among the top and bottom quantiles of wealth index and the other backward caste subgroup (Z score > ±0.02, p < 0.1) than among their demographic counterparts in middle and high SDI states. From 2015-2016 to 2019-2021, the disparity in ambient PM2.5 exposure across subgroups increased in urban areas, while it either remained static or decreased in rural areas. India's urban-centric approach to addressing air pollution may further exacerbate disparities among diverse demographics. Therefore, we recommend the formulation of targeted policies aimed at reducing ambient PM2.5 exposure and alleviating disparities by prioritizing actions for the vulnerable subgroups. Long‐term exposure to ambient fine particulate matter (PM2.5) poses a significant health burden for the Indian population. Studies from the United States and China have reported an unequal distribution of ambient PM2.5 among population subgroups. Whether this applies to a diverse country like India remains uncertain. We integrated satellite‐derived ambient PM2.5 concentrations with socio‐demographic data from a nationwide survey to first estimate the weighted mean PM2.5 exposure at the sub‐national scale for various population subgroups. We then identified vulnerable subgroups classified by gender, caste, and wealth index. Our findings indicate that exposure variation is greater among both wealthier and poorer subgroups, with increasing disparities in urban areas compared to rural areas in recent years. These results underscore the need for tailored mitigation actions for vulnerable subgroups facing higher air pollution levels in regions with considerable inequality.
Aotearoa New Zealand (NZ) was almost unique worldwide in having very limited community transmission of COVID-19 prior to March 2022. Using nationwide data, we examined variation in COVID-19 immunisation coverage in adults in 2022, when NZ transitioned from elimination to mitigation in a largely infection-naïve population. We used the COVID-19 Immunisation Register (CIR) within the Integrated Data Infrastructure (IDI) to calculate immunisation coverage among adults aged 18 years and over between 1st January and 31st December 2022 by age, ethnicity and residential address. Geospatial analyses were undertaken in ArcGIS Pro. Among adults >65 years, between January and March 2022, when widespread community transmission of COVID-19 began, uptake of third doses increased for European ethnicity by 7.9 %, reaching 87.0 % by December 2022 and by 18.5 % in Māori, 21.6 % in Pacific and 24.2 % in Asian people, reaching 80.7 %, 80.2 % and 83.5 % by December. In contrast, the proportion of adults >65 years of any ethnicity who had received zero doses by January 2022 remained stable from 6.3 % (Asian) to 10.8 % (Māori), with less than 0.15 % receiving any doses by December. Third dose uptake was lowest and zero doses highest in adults of all ages living in the most deprived and among Māori and Pacific people. Among Asian people, the proportion zero dose was highest >65 years, whereas in other ethnic groups it was highest in younger adults. There was significant spatial variation by area with a greater proportion of zero-dose populations in more rural areas. Our study is among the first internationally to examine patterns of non-receipt of COVID-19 vaccines and differences in age-related coverage by sociodemographic factors which have implications for tailored communication and community engagement.
The spatial resolution of environmental exposure and sociodemographic population data is often mismatched given limited publicly available population data that complies with privacy requirements for individuals. To address this limitation, we developed a novel matching algorithm to construct a synthetic population at the address-level. To demonstrate how our approach can improve environmental justice (EJ) analyses and health impact assessments (HIAs), we examined sociodemographic patterns of residential proximity to major roadways in Greater Boston (Massachusetts) and HIA results, comparing our method with a random address allocation method. The synthetic population was developed at a census tract-level using US Census microdata and combinatorial optimization methods and then downscaled to address-level parcels by matching building attributes to synthetic households. We designated households within 50 m of a major road "high exposure" and households below state median household income "low income".We found misclassification for individual households (21% of the high exposure/low-income households in the matched data set were identified as such in the random allocation data set). We found modest aggregate differences in matched allocation (3.3% of low-income households had high exposure) compared to random allocation (3.4%). In a HIA, the difference between random and matched allocation would be stronger when there is a strong interactive effect between a sociodemographic effect modifier and exposure on the outcome. Address-level exposure assignment based on synthetic populations can provide more significant and nuanced health impact and EJ analyses. Our novel method can be applied to other regions of the US and expanded to other dimensions of population vulnerability. Communities and decision makers often need to identify if there are disparities in the distribution of hazardous exposures and associated health outcomes. To do so requires understanding of both spatial patterns of exposures and of the attributes of exposed populations. While environmental exposure data are available at increasingly higher spatial resolution, data on high‐resolution population sociodemographic characteristics are limited by privacy requirements in the US. To support the investigation of environmental exposures and health outcomes across sociodemographic characteristics at address‐level resolution, we used publicly available US Census data to simulate an address‐level population with sociodemographic information. In a case study looking at proximity to major roadways in Greater Boston (Massachusetts), we compared exposure patterns between our approach and approaches where household attributes were not used for address assignment. We found large differences in how individual households were identified but modest differences in the percent of households identified as high‐exposure and low‐income. We also showed that differences in estimated health impacts would depend on whether there was a strong interaction between the environmental exposure and sociodemographic variable. The methods used to create the address‐level synthetic population can be replicated in other regions of the US using the same census data resources.
Long-term exposure to particulate matter (PM) pollution may directly increase the risk of developing tuberculosis (TB). Despite the known link, the multi-scale spatiotemporal variations in the burden of TB attributable to long-term PM exposure remain largely unclear in China. In this study, we conducted a nationwide, multi-scale risk assessment of the burden of TB attributable to long-term PM2.5, PM2.5-10, and PM10 exposure from 2013 to 2019, employing the multivariate distributed lag nonlinear model (MVDLNM), Lorenz curve and Gini index. Our health impact assessments indicate that PM exposure has resulted in significant increases in TB burden. Specifically, approximately $1,202 million (95% CI: 801-1,573 million), $486 million (95% CI: 398-572 million), and $944 million (95% CI: 767-1,115 million) of health economic costs could be attributed to long-term exposure to PM2.5, PM2.5-10, and PM10, respectively. Although the overall the burden of TB attributable to PM exposure was significantly reduced from 2013 to 2019, regional inequalities have become more pronounced. The Gini index reveals a clear disparity in the burden of TB related to PM exposure across provincial, city, and county levels. These disparities are most pronounced at the county level (0.4914-0.6801), followed by the city level (0.4135-0.6382), and are least evident at the province level (0.3672-0.6078). Overall, the regional inequalities in the burden of TB are more pronounced at finer spatial scales. Our study highlights the health impacts of long-term exposure to PM on the incidence of TB across different spatiotemporal scales, and the findings provide strong scientific evidence for pollution mitigation and efforts to reduce regional inequality. Ambient particulate matter (PM) pollution is a significant environmental risk factor contributing to the high tuberculosis (TB) burden in China. Although substantial improvements in air quality have been achieved in recent years, the impact of these improvements on TB incidence remains unclear, and regional exposure inequity has seldom been explored. This study systematically evaluates how regional disparities in health economic costs attributable to long‐term exposure to different sizes of PM (PM2.5, PM2.5–10, and PM10) vary over time and across spatial scales, including the macro‐scale (provincial level), meso‐scale (city level), and micro‐scale (county level). It found that long‐term PM exposure caused billions of dollars in TB‐related health costs, with PM2.5 being the largest contributor. While overall PM‐related TB burden decreased nationwide from 2013 to 2019, inequalities between regions grew, especially at finer scales like counties. Disparities in TB burden were highest at the county level, demonstrating that local conditions strongly influence health risks. This study highlights the urgent need for targeted air quality and health policies in high‐risk areas to reduce TB burden and address health inequalities.
Wildfires are a source of air pollution, including PM2.5. Exposure to PM2.5 from wildfire smoke is associated with adverse health effects including premature death and respiratory morbidity. Air quality modeling was performed to quantify seasonal wildfire-PM2.5 exposure across Canada for 2019-2023, and the annual acute and chronic health impacts and economic valuation due to wildfire-PM2.5 exposure were estimated. Exposure to wildfire-PM2.5 varied geospatially and temporally. For 2019-2023, the annual premature deaths attributable to wildfire-PM2.5 ranged from 49 (95% CI: 0-73) to 400 (95% CI: 0-590) due to acute exposure and 660 (95% CI: 340-980) to 5,400 (95% CI: 2,800-7,900) due to chronic exposure, along with numerous non-fatal cardiorespiratory health outcomes. Per year, the economic valuation of the health burden ranged from $550M (95% CI: $19M-$1.2B) to $4.4B (95% CI: $150M-$9.9B) for acute impacts and $6.4B (95% CI: $2.2B-$12.9B) to $52B (95% CI: $18B-$100B) for chronic impacts. Additionally, a long-term average annual exposure for 2013-2023 was estimated using air quality modeling. From this, more than 80% of the population had an average seasonal wildfire-PM2.5 exposure of at least 1.0 μg/m3 and there were 1,900 (95% CI: 980-2,800) attributable premature deaths and a total economic valuation of $18B (95% CI: $6.1B-$36B), per year. Evaluating and understanding the health impacts of wildfire-PM2.5 is important given the sizable contribution of wildfire smoke to air pollution in Canada, as well as the anticipated increases in wildfire activity due to climate change. Wildfires are a source of air pollution that can cause adverse effects on human health. For this study, air quality modeling was used to quantify the exposure levels of particulate matter (PM) from wildfires in Canada. From the exposure levels, the health burden attributable to PM in wildfire smoke was estimated. Hundreds to thousands of premature deaths per year are attributable to wildfire smoke exposure along with many non‐fatal cardiorespiratory health impacts, such as emergency room visits for respiratory issues. The economic burden of the health impacts from wildfire smoke is estimated in the millions to billions of dollars per year. Having an understanding of the health burden of wildfire smoke is important—it is a large source of air pollution that is expected to increase as climate change will increase the frequency and severity of wildfires.
Soil contamination with toxicants such as lead (Pb) is notoriously patchy and requires considerable effort to map. In the present study of three mining-impacted Peruvian towns, nearly 2,000 students collected and tested 1,500 soil samples using a field kit that combines simulated gastric extraction with visual detection of a purple Pb rhodizonate precipitate. An additional 2,000 soil samples were tested by our team's field staff. The combined results were mapped and compared with total soil Pb concentrations measured by X-ray fluorescence (XRF). The degree of soil contamination varied across sites and within each site. Quality control was provided by reanalyzing a subset of 120 soil samples from the three sites in the laboratory. Overall, total Pb concentrations were highest in Cerro de Pasco with 64% and 30% of samples in the 201-2,000 and 2,001-20,000 mg/kg range, respectively. Total Pb concentrations were somewhat lower in La Oroya with 88% of samples in the 201-2,000 mg/kg range and even lower in Callao with 79% below 200 mg/kg. However, extractable Pb proportions followed an inverse pattern, with the sum of the proportions of medium and high visual readings increasing from 46% to 57% and 69% in Cerro de Pasco, La Oroya, and Callao, respectively. This trend relates more closely to blood-Pb concentrations in children measured at the three sites 2 decades ago than total Pb concentrations. The study demonstrates that student-conducted soil screening can effectively identify Pb contamination patterns while providing valuable science education. Nearly 2,000 high school students used a simple visual test to check their soil for dangerous lead contamination in the mining‐impacted towns of Cerro de Pasco, La Oroya, and Callao in Peru. Their results revealed widespread contamination at all three sites, with geographic patterns within the towns and across the towns that were consistent with blood‐lead measurements in children conducted over 2 decades ago. Beyond identifying contamination, the project taught students valuable science skills and helped raise community awareness about the risks of lead exposure. Students presented their findings to parents and local officials, with some even organizing their own awareness campaign.
The associations of polybrominated diphenyl ethers (PBDEs) with biological aging are unclear. This study explores the possible relationship between PBDEs and accelerated aging. Cross-sectional data from 6,091 subjects of the National Health and Nutrition Examination Survey (NHANES) 2005-2010 and 2015-2016 are analyzed. Serum PBDE concentrations are quantified via automated liquid-liquid extraction and subsequent sample purification, with seven PBDEs displaying a capture rate higher than 70%, identified as the exposure. Homeostatic dysregulation (HD), Klemera-Doubal method (KDM), phenoAge (PA), and allostatic load (AL) are utilized to assess biological aging. The associations are assessed with weighted multivariate linear regression models, restricted cubic spline (RCS), weighted quantile sum regression, and Quantile G-computation analysis. Regarding individual exposures, significant positive associations of PBDE47, PBDE99, PBDE100, PBDE154, and PBDE85 with HD, KDM residual, and PA residual, and PBDE100 with AL (β > 0, P < 0.050) are detected. The associations are further validated by RCS. Mixed PBDEs show a positive relationship with HD, KDM residual, PA residual, and AL (β > 0, P < 0.050), with PBDE99, PBDE47, and PBDE85 as the most significant contributing PBDEs. Exposure to the PBDE mixture exhibits a positive association with predicted age metrics, highlighting PBDE99, PBDE47, and PBDE85 as the significant chemicals. Polybrominated diphenyl ethers (PBDEs) raise concerns due to their persistence, accumulation, and detection in wildlife and humans. The associations of PBDEs with biological aging are explored using National Health and Nutrition Examination Survey data from 6,091 subjects. Serum PBDE levels are measured, and various aging metrics are assessed. Positive associations are found between several PBDEs and aging indicators. PBDE99, PBDE47, and PBDE85 are identified as significant contributors to accelerated aging. The study highlights the adverse effects of PBDE exposure on aging processes.
Heavy metal contamination in rivers mostly originates from anthropogenic sources such as industrial activities, mining, agriculture, and urban runoff. These metals accumulate in river sediments and impact living organisms and the surrounding ecosystem. Historically, some rivers in Colombia have faced issues related to heavy metal contamination, in particular from gold mining activities, which can lead to the release of heavy metals into waterways. To assess the current status of heavy metal presence in the Sinú River, one of the most important rivers in northern Colombia, we analyzed sediment samples collected in a section of the river between the towns of Santa Isabel and Montería. ICP-MS analysis found concentrations of Hg, Cd and Ni up to five times larger than the baseline concentrations, while concentrations of Pb, Zn and Cr were consistently below the baseline. The measured concentrations varied with both geographical location and sampling depth. During the dry season heavy metals accumulate near the surface, while rains spread them below the surface and into the groundwater, and across the surrounding land. These abnormal concentrations of potentially toxic elements are probably related to the widespread use of Hg in illegal gold mining activities, which are prevalent in the region, while Cd and Ni contamination probably derives from mining, as well as from agricultural practices and industrial discharges. The results point to the necessity to continuously monitor the state of the river and develop management strategies to reduce heavy metal contamination, protecting the health of the ecosystem and of the neighboring communities. Heavy metal contamination in rivers is a significant threat to the environment, and may affect the surrounding environment and the health of living organisms. The Sinú River is a vital waterway for northern Colombia, supplying water for the population and irrigation for agriculture and enabling the transport of goods. Here, we report our analysis of the presence of heavy metals in a section of the Sinú River between the towns of Santa Isabel and Montería. Soil samples were collected during the rainy and dry seasons of 2021 from the surface and at a depth of 0.5 m. ICP‐MS analysis found concentrations of Hg, Cd and Ni often much larger than the baseline, while those of Pb, Zn and Cr were consistently below these limits. Measured concentrations depend on the location and depth of the samples, and vary seasonally with rainfall due to surface runoff. The abnormal concentrations of these potentially toxic elements are related to anthropogenic activities, such as their widespread use in mining, agricultural practices and industrial discharges. The results point to the necessity of a continuous monitoring of the river and to the implementation of effective management strategies to protect the ecosystem and neighboring communities.
Rising rates of mental health and substance use are significant contributors to illness and disability among adolescents, highlighting a critical area for support and intervention. Existing evidence suggests the physical environment where young people live may impact their mental health. However, research is seldom longitudinal and rarely accounts for the co-location or mixture of potential environmental influences. To assess longitudinal relationships between the physical environment in which young people reside in Aotearoa New Zealand and their mental health outcomes. This study follows a population cohort of 957,381 young people (aged 10-24 years in 2018) over six years (2013-2018), linking their mental health outcomes (emotional, externalising, substance problems, and self-harm) and individual-level characteristics derived from administrative linked microdata with environmental data represented by the Healthy Location Index. Longitudinal Generalised Estimating Equations and quantile g-computing examined longitudinal relationships between the physical environments where young people reside and their mental health. We found evidence of longitudinal associations between the mixture of physical environment and young people's mental health for emotional disorders (aORΨ = 1.09 [1.08, 1.10]), substance use (aORΨ = 1.04 [1.02, 1.05]), and self-harm (aORΨ = 1.14 [1.10, 1.17]) (but not externalising conditions (aORΨ = 1.01 [0.99, 1.02])), present even after adjusting for individual-level and socioeconomic characteristics. Modelling emphasised the importance of the mix of the environments and the combined positive influence of natural spaces (bluespace and greenspace) for mental health outcomes. This study provides longitudinal evidence of meaningful associations between exposure to the combined built and natural environment and mental health in young people. Specifically, living in predominantly health-constraining environments was associated with increased odds of emotional disorders, while greater access to and the mixture of greenspace and bluespace contributed to better mental health outcomes. Our findings are strengthened by a robust longitudinal nationwide study design and comprehensive adjustment, underscoring the significance of the environmental mix. These results extend current evidence and offer novel insights into how physical environments shape young people's mental health over time.
Downscaled climate projections provide valuable information needed to better understand the impacts of climate change on health outcomes and to inform adaptation and mitigation strategies at local to regional scales. Because downscaled climate products vary in their representations of fine-scale spatiotemporal patterns, due to of multiple interacting factors, epidemiologic analyses need to consider how differences across downscaling approaches impact projections of health impacts into the future. We evaluate the projected seasonality of coccidioidomycosis in response to projected temperature and precipitation estimated using global climate models from CMIP6 included in California's Fifth Climate Change Assessment, downscaled using two approaches: (a) dynamical downscaling using the Weather Research and Forecasting model; and (b) hybrid statistical downscaling using the Localized Constructed Analogs approach. Our results indicate that by end of century, coccidioidomycosis transmission is projected to start earlier, end later, and last longer across the California endemic region; however, the magnitude of these changes varies by downscaling method. Specifically, LOCA2-hybrid projected a season onset that is 4.2 weeks earlier and an end that is 4.1 weeks later than historical conditions, while the dynamical approach projected a 4 week earlier onset and a 3.8 week later end compared to the historical period. Overall, the LOCA2-hybrid product estimates that the transmission season will last about 0.3 weeks longer than what is projected using dynamical downscaling by end of century. This analysis highlights the sensitivity of coccidioidomycosis seasonality projections to choice of downscaling product, underscoring the need to account for these differences in mitigation and adaptation planning. Downscaled climate data are essential for projecting future local and regional climate‐related health impacts, but multiple downscaling approaches exist and each capture fine‐scale climate patterns differently. We examined how two common approaches for resolving global climate model data into local projections shape predictions of Valley fever seasonality in California. While both approaches suggest the transmission season will lengthen by the end of the century, they differ in how much change they predict. These differences matter because they can influence the timing of health warnings, resource allocation, and adaptation strategies. Our findings highlight the importance of considering climate data methods when using projections to guide climate and health research and planning.
Campylobacter is the most common bacterial cause of foodborne illness globally. Both symptomatic and asymptomatic infections with Campylobacter species have been associated with growth faltering of children in low-resource settings, while previous prevalence studies primarily focused on diarrheal disease in children. Here, we leverage the data collected from the Campylobacter Genomics and Environmental Enteric Dysfunction (CAGED) project to characterize the spatial patterns of Campylobacter infections among infants with or without diarrhea in rural Eastern Ethiopia. Randomly enrolled infants (n = 106) were followed from birth to around 13 months, with fecal samples collected monthly. Livestock feces, drinking water, and soil samples were collected biannually. Campylobacter was detected and quantified using genus-specific PCR and species-specific PCR for four species. We employed a spatial filtering approach using genus-specific data to generate smoothed prevalence surfaces by month and age group. Temporally, an upward trend of prevalence was observed as the children grew older. Spatially, high-prevalence areas were distributed across the whole study area. To relate disease risk to environmental conditions, we used ecological niche modeling with MaxEnt to estimate habitat suitability of the genus Campylobacter and two dominant species identified by PCR results. Elevation, vegetation index, and slope were the most important contributors, and all distribution models suggested areas in the north were more likely to support the pathogen. These results inform Campylobacter infection patterns and identify target areas with higher risk of Campylobacter in low-resource settings. This further contributes to developing effective intervention strategies in the future. Campylobacters are species of bacteria that cause food‐borne illnesses, leading to clinical signs including diarrhea, fever, and abdominal pain. These infections are particularly concerning in low‐resource settings where they can significantly affect the health and long‐term development of young children. Understanding how these infections spread and identifying high‐risk areas can help in developing effective prevention strategies. Here, we investigated the prevalence of Campylobacter infections in infants in rural Eastern Ethiopia, both with and without diarrhea, to understand their spread and impact on child growth. We followed 106 infants from birth to about 13 months, collecting fecal samples monthly and environmental samples from livestock, water, and soil twice a year. Campylobacter was detected using polymerase chain reaction (PCR) genetic tests, and the infection rates were mapped over time and across different locations. We found that as children aged, the prevalence of Campylobacter increased, and certain areas consistently showed higher infection rates. Using environmental data, we also predicted which areas were most suitable for the bacteria, identifying elevation, vegetation, and slope as key factors. These findings highlight regions with higher infection risks, offering valuable insights for future public health interventions to prevent and control Campylobacter infections in vulnerable populations.