Geohealth最新文献

Housing hardship can create a range of health issues. However, little attention has been paid to the relationship between housing hardship and cancer incidence. This study examines the Yangtze River Economic Belt (YREB) in China as a case study to develop a model of factors related to housing hardship that can affect cancer incidence. This study employs spatial regression models to investigate the correlation between housing hardship and cancer incidence and further explores the variation in the correlation between urban areas (UAs) and non-urban areas (NUAs). The research conclusions are as follows: (a) a palpable correlation exists between housing hardship and cancer incidence. The housing price-to-income ratio (HPIR) and the rental household proportion (RHP) are positively correlated to cancer incidence, whereas the per capita living area (PCLA) has a negative correlation with cancer incidence. (b) The differences in the impact of housing hardship on cancer incidence between the UAs and the NUAs are reflected mainly in the differences in the PCLA and the RHP. The PCLA has a strong association with cancer incidence in the UAs, whereas the RHP demonstrates a strong correlation with cancer incidence in the NUAs. (c) Significant spatial heterogeneity is observed in housing hardship in the YREB.

Influenza is an acute respiratory infection that spreads rapidly and widely in densely populated areas with low vaccination coverage. The trends and drivers of the spatial and temporal dynamics of influenza incidence among residents of mainland China have not been systematically studied. This study comprehensively analyses the dynamics and spatial correlation of influenza using long-term scale data and spatial panel data. It then identifies the interactive process of socio-economic and natural elements on the incidence of influenza. The highest prevalence of influenza was found in the 0–4 years age group in mainland China (mean prevalence, 67.56/100,000). In addition, influenza in mainland China shows seasonality, with fall and winter being the periods of high incidence. Between 2014 and 2017, influenza incidence was clustered in Hubei and Anhui provinces, and the spatial clustering was statistically significant (Z value > 1.96, P < 0.05). Moreover, the directionality of influenza onset continued to increase each year. Specifically, the clustering of influenza onset was stronger in the northwest-southeast direction than in the southwest-northeast direction between 2014 and 2018. The significant role of socioeconomic factors as a primary influence on influenza incidence, while their interaction with natural factors, such as air quality (NO_x and PM_2.5) and climatic conditions can exacerbate regional outbreaks. This study provides a novel perspective for better prevention and control of influenza disease among mainland Chinese residents.

Planetary Health (PH) is a relatively new field that studies the sciences behind the interaction between the environment, living organisms, and human health. The number of publications in this field has increased exponentially in the past few years. This study analyzed the trend of using the term “PH” in literature. We searched PubMed for all publications (APP) and those mentioning Planetary Health (APmPH) without limitations on text availability, dates, or species. The publication trend was estimated using the average annual percent change (AAPC). Joinpoint Regression Program 4.9.1.0 identified periods with statistically distinct log-linear trends in publication numbers over time. Bonferroni adjustment determined significant trend shifts. The time frame of the retrieved APP and APmPH results ranged from 2002 to 2022 with a publication boom since 2017. The most common study designs were reviews, followed by comments and editorials. The APP, APmPH, and the proportion of APmPH to APP steadily increased from 2002 to 2022, with an AAPC of 6.0% (95% CI: 4.4%–7.6%, p < 0.001), 35.7 (95% CI: 21.3%–51.9%, p < 0.001), and 28.1% (95% CI: 15.2–42.5, p < 0.001), respectively. The term “PH” is increasingly prominent in academic literature, underscoring the need for interdisciplinary efforts. Its growing usage also highlights the need for recognition in Medical Subject Headings by the National Library of Medicine.

Higher concentrations of ambient air pollutants, including PM_2.5 and NO₂, and other pollutants have been found near active oil and gas wells and may be associated with adverse COVID-19 outcomes. We assessed whether residential exposure to nearby oil and gas production was associated with higher rates of the respiratory infection COVID-19 and related mortality using a population-based ecological study in California. Using gridded population estimates, we estimated area-level exposure to annual average oil and gas production volume from active wells within 1 kilometer (km) of populated areas within census block groups from 2018 to 2020. We geocoded confirmed cases and associated deaths to assess block group case and mortality rates from COVID-19 from February 2020 to January 2021. We fit hierarchical Poisson models with individual and area covariates (e.g., age, sex, socioeconomic disadvantage), and included time and other interactions to assess additional variation (e.g., testing, reporting rates). In the first 4 months of the study period (February–May 2020), block groups in the highest tertile of oil and gas production exposure had 34% higher case rates (IRR: 1.34 95% CI: 1.20, 1.49) and 55% higher mortality rates (MRR: 1.52 95%: CI: 1.14, 2.03) than those with no estimated production, after accounting for area-level covariates. Over the entire study period, we observed moderately higher mortality rates in the highest group (MRR: 1.16 95%: CI: 1.01, 1.33) and null associations for case rates.

There is limited evidence regarding the effects of long-term exposure to PM_2.5 constituents on the risk of arthritis and rheumatoid arthritis, and the interaction between PM_2.5 and green space remains unclear. This study examined the relationship between long-term exposure to PM_2.5 constituents and the risk of arthritis and rheumatoid arthritis, with the exposure period extending from recruitment until self-reported outcomes, death, loss to follow-up, or end of follow-up. Additionally, the study assessed whether there was an interactive effect between PM_2.5 and green space on these risks. We gathered cohort data on 18,649 individuals aged ≥45 years. We applied generalized linear mixed-effects models to estimate the effects of PM_2.5 constituents, NDVI, and their interaction on arthritis and rheumatoid arthritis. The quantile g-computation and weighted quantile sum regression model were applied to estimate the combined effect of PM_2.5 constituents. Our results showed that exposure to single and mixed PM_2.5 constituents adversely affected arthritis and rheumatoid arthritis, and was mainly attributed to the black carbon component. We observed “U” or “J” shaped exposure-response curves for the effects of PM_2.5, OM, NO₃⁻ and NH₄⁺ exposure on the development of arthritis/rheumatoid arthritis. Additionally, the odds ratio of arthritis for per interquartile range (IQR) increase in PM_2.5 was 1.209 (95% CI:1.198, 1.221), per 0.1-unit decrease in NDVI was 1.091 (95% CI:1.033, 1.151), and the interaction term was 1.005 (95% CI:1.002, 1.007). These findings flesh out the existing evidence for PM_2.5 constituents, NDVI and arthritis, rheumatoid arthritis, but the underlying mechanisms still require further exploration.

Identifying sources of air pollution exposure is crucial for addressing their health impacts and associated inequities. Researchers have developed modeling approaches to resolve source-specific exposure for application in exposure assessments, epidemiology, risk assessments, and environmental justice. We explore six source-specific air pollution exposure assessment approaches: Photochemical Grid Models (PGMs), Data-Driven Statistical Models, Dispersion Models, Reduced Complexity chemical transport Models (RCMs), Receptor Models, and Proximity Exposure Estimation Models. These models have been applied to estimate exposure from sources such as on-road vehicles, power plants, industrial sources, and wildfires. We categorize these models based on their approaches for assessing emissions and atmospheric processes (e.g., statistical or first principles), their exposure units (direct physical measures or indirect measures/scaled indices), and their temporal and spatial scales. While most of the studies we discuss are from the United States, the methodologies and models are applicable to other countries and regions. We recommend identifying the key physical processes that determine exposure from a given source and using a model that sufficiently accounts for these processes. For instance, PGMs use first principles parameterizations of atmospheric processes and provide source impacts exposure variability in concentration units, although approaches within PGMs for source attribution introduce uncertainties relative to the base model and are difficult to evaluate. Evaluation is important but difficult—since source-specific exposure is difficult to observe, the most direct evaluation methods involve comparisons with alternative models.

Indonesia accounts for more than one third of the world's tropical peatlands. Much of the peatland in Indonesia has been deforested and drained, meaning it is more susceptible to fires, especially during drought and El Niño events. Fires are most common in Riau (Sumatra) and Central Kalimantan (Borneo) and lead to poor regional air quality. Measurements of air pollutant concentrations are sparse in both regions contributing to large uncertainties in both fire emissions and air quality degradation. We deployed a network of 13 low-cost PM_2.5 sensors across urban and rural locations in Central Kalimantan and measured indoor and outdoor PM_2.5 concentrations during the onset of an El Niño dry season in 2023. During the dry season (September 1st to October 31st), mean outdoor PM_2.5 concentrations were 136 μg m⁻³, with fires contributing 90 μg m⁻³ to concentrations. Median indoor/outdoor (I/O) ratios were 1.01 in rural areas, considerably higher than those reported during wildfires in other regions of the world (e.g., USA), indicating housing stock in the region provides little protection from outdoor PM_2.5. We combined WRF-Chem simulated PM_2.5 concentrations with the median fire-derived I/O ratio and questionnaire results pertaining to participants' time spent I/O to estimate 1.62 million people in Central Kalimantan were exposed to unhealthy, very unhealthy and dangerous air quality (>55.4 μg m⁻³) during the dry season. Our work provides new information on the exposure of people in Central Kalimantan to smoke from fires and highlights the need for action to help reduce peatland fires.

The relationship between exposure to ambient air pollutants and emergency attendance for upper gastrointestinal bleeding (UGIB) remains inconclusive. This study examines the association between short-term exposure to various ambient pollutants and the risk of UGIB emergency attendance. Data on daily UGIB emergency attendance, ambient pollutants, and meteorological conditions in Hong Kong were collected from 2017 to 2022. A time-series study using a distributed lag non-linear model to analyze the data, considering lag days. Stratified analysis was performed based on sex, seasons, and the COVID-19 pandemic period. The burden was quantified using attributable fraction (AF) and number (AN). The study included 31,577 UGIB emergency records. Exposure to high levels of PM_2.5 significantly increased the risk of UGIB emergency attendance from lag day 3 (RR: 1.012) to day 6 (RR: 1.008). High NO₂ exposure also posed a significant risk from lag day 0 (RR: 1.026) to day 2 (RR: 1.014), and from lag day 5 (RR: 1.013) to day 7 (RR: 1.024). However, there was no association between UGIB and high O₃ levels. The attributable burden of high-concentration NO₂ exposure was higher compared to those of PM_2.5. Males and elderly individuals (≥65 years) faced a higher risk of UGIB emergencies, particularly during cold seasons. Our study suggests that both PM_2.5 and NO₂ exposure are associated with an increased risk of emergency attendance for UGIB. Ambient pollutant exposure has a stronger effect on UGIB in males and the elderly, particularly during cold seasons.

The COVID-19 outbreak in 2020 resulted in rapidly rising infection rates with high associated mortality rates. In response, several epidemiological studies aimed to define ways in which the spread and severity of COVID-19 can be curbed. As a result, there is a steady increase in the evidence linking greenspaces and COVID-19 impact. However, the evidence of the benefits of greenspaces or greenness to human wellbeing in the context of COVID-19 is fragmented and sometimes contradictory. This calls for a meta-analysis of existing studies to clarify the matter. Here, we identified 621 studies across the world on the matter, which were then filtered down to 13 relevant studies for meta-analysis, covering Africa, Asia, Europe, and the USA. These studies were meta-analyzed, with the impacts of greenness on COVID-19 infection rate quantified using regression estimates whereas impacts on mortality rates were measured using mortality rate ratios. We found evidence of significant negative correlations between greenness and both COVID-19 infection and mortality rates. We further found that the impacts on COVID-19 infection and related mortality are moderated by year of publication, greenness metrics, sample size, health and political covariates. This clarification has far-reaching implications for policy development toward the establishment and management of green infrastructure for the benefit of human wellbeing.