Mapping distance-decay of cardiorespiratory disease risk related to neighborhood environments

Green and blue space exposure and non-communicable disease related hospitalizations: A systematic review

The global increase in non-communicable diseases (NCDs) presents a critical public health concern. Emerging evidence suggests that exposure to natural environments may reduce the risk of developing NCDs through multiple pathways. The present systematic review aims to synthesize and evaluate the observational evidence regarding associations between exposure to green and blue spaces and hospital admissions related to NCDs. A comprehensive literature search strategy was conducted in Embase (Ovid), PubMed, and Web of Science. The risk of bias and quality of the evidence were assessed using The Navigation Guide methodology, an approach specifically designed for environmental health research. Of 3060 search results, 17 articles were included. Notably, the majority of the studies (n = 14; 82.4%) were published from 2020 onwards. Most studies were conducted in the United States (n = 6; 35.3%) and China (n = 4; 23.5%). Exposure to green spaces was assessed through all studies, while only three included blue spaces. In terms of study design, cohort design was employed in nearly half of the studies (n = 8; 47.1%), followed by case-crossover design (n = 3, 17.6%). Over 75% of the included studies (n = 13) had a high or probably high rating in the risk of bias assessment. The studies encompassed diverse NCD outcome domains; cardiovascular diseases (CVDs) (n = 10), respiratory diseases (RSDs) (n = 2), heat-related diseases (n = 1), metabolic diseases (n = 2), cancer (n = 1), neurodegenerative diseases (NDDs) (n = 2), and mental health disorders (n = 2). The present review suggests that a clear link between blue space exposure and NCD hospital admissions is not evident. However, exposure to green spaces appears to predominantly have a protective effect, although the direction of the association varies across different outcome domains. The heterogeneity among the outcome domains together with the limited number of studies, emphasizes the need for more robust evidence.

Why a New Research Agenda on Green Spaces and Health Is Needed in Latin America: Results of a Systematic Review

(1) Background: Increasing and improving green spaces have been suggested to enhance health and well-being through different mechanisms. Latin America is experiencing fast population and urbanization growth; with rising demand for interventions to improve public health and mitigate climate change. (2) Aim: This study aimed to review the epidemiological evidence on green spaces and health outcomes in Latin America. (3) Methods: A systematic literature review of green spaces and health outcomes was carried out for studies published in Latin America before 28 September 2020. A search strategy was designed to identify studies published in Medline via PubMed and LILACS. The search strategy included terms related to green spaces combined with keywords related to health and geographical location. No time limit for the publication was chosen. The search was limited to English, Spanish, Portuguese, and French published articles and humans’ studies. (4) Findings: This systematic review found 19 epidemiological studies in Latin America related to green spaces and health outcomes. Nine studies were conducted in Brazil, six in Mexico, three in Colombia, and one in Chile. In terms of study design, 14 were cross-sectional studies, 3 ecological, and 2 cohort studies. The population included among the studies ranged from 120 persons to 103 million. The green space definition used among studies was green density or proximity (eight studies), green presence (five studies), green spaces index (four studies), and green space visit (two studies). The health outcomes included were mental health (six studies), overweight and obesity (three studies), quality of life (three studies), mortality (two studies), cardiorespiratory disease (one study), disability (one study), falls (one study), and life expectancy (one study). Eleven studies found a positive association between green spaces and health, and eight studies found no association. (5) Conclusion: This systematic review identified 19 epidemiological studies associating green spaces and health outcomes in Latin America. Most of the evidence suggests a positive association between green spaces and health in the region. However, most of the evidence was supported by cross-sectional studies. Prioritizing longitudinal studies with harmonized exposure and outcome definitions and including vulnerable and susceptible populations is needed in the region.

The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population

BACKGROUND

COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations.

RESULTS

The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion.

CONCLUSIONS

Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries' settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

Heat maps present the spatial distribution of human papillomavirus infection in Zhejiang Province, China

Determining the spatial distribution of human papillomavirus (HPV) and performing accurate public health analyses helps to distinguish areas of healthcare that require further research, and enables therapeutic techniques and approaches in healthcare to be focused more accurately. A total of 4,560 women were enrolled in the present study. Flow-through hybridization and gene chip assays were used to detect the genotypes of HPV infection. Heat maps were then generated to present the spatial distribution of HPV infections in Zhejiang Province according to genotype. Of the exfoliated cervical cell samples from the 4,560 women, HPV was detected in 1,886 samples. HPV-16, -58, -52 and -18 were the most prevalently identified genotypes in the population included in the present study. HPV-16 and -58 infections were mainly distributed in the northern and central regions of Zhejiang Province, such as in Hangzhou and Shaoxing, where the prevalence was higher than that in the southern regions (P<0.05). HPV-18 infection was widespread throughout Zhejiang Province, but had a much lower infection rate in Ningbo and Huzhou (P<0.05). High infection rates of HPV-52 were mainly detected in Hangzhou and the eastern coastal areas of Wenzhou, with a relatively low rate of infection in the center of the province (P<0.05). In conclusion, HPV-16, -58, -52 and -18 were the four most prevalent HPV genotypes observed in Zhejiang Province. Heat maps were created to display the spatial distribution of HPV infection according to genotype, which varied by geographical regions. The results indicate that for individuals in Ningbo or Wenzhou, bivalent or quadrivalent vaccines may be suitable, but for those in Hangzhou and Shaoxing, nonavalent vaccines are strongly recommended.

Spatial dimensions of the influence of urban green-blue spaces on human health: A systematic review

BACKGROUND

There is an increasing volume of literature investigating the links between urban environments and human health, much of which involves spatial conceptualisations and research designs involving various aspects of geographical information science. Despite intensifying research interest, there has been little systematic investigation of pragmatic methodological concerns, such as how studies are realised in terms of the types of data that are gathered and the analytical techniques that are applied, both of which have the potential to impact results. The aim of this systematic review is, therefore, to understand how spatial scale, datasets, methods, and analytics are currently applied in studies investigating the relationship between green and blue spaces and human health in urban areas.

METHOD

We systematically reviewed 93 articles following PRISMA protocol, extracted information regarding different spatial dimensions, and synthesised them in relation to various health indicators.

RESULTS AND DISCUSSION

We found a preponderance of the use of neighbourhood-scale in these studies, and a majority of the studies utilised land-use and vegetation indices gleaned from moderate resolution satellite imagery. We also observed the frequent adoption of fixed spatial units for measuring exposure to green and blue spaces based on physical proximity, typically ranging between 30 and 5000 m. The conceptual frameworks of the studies (e.g., the focus on physical vs. mental health or the definition of exposure to green space) were found to have an influence on the strength of association between exposure and health outcomes. Additionally, the strength and significance of associations also varied by study design, something which has not been considered systematically.

CONCLUSION

On the basis of our findings, we propose a set of recommendations for standardised protocols and methods for the evaluation of the impact of green-blue spaces on health. Our analysis suggests that future studies should consider conducting analyses at finer spatial scales and employing multiple exposure assessment methods to achieve a comprehensive and comparable evaluation of the association between greenspace and health along multiple pathways.

Urban greenspace and the indoor environment: Pathways to health via indoor particulate matter, noise, and road noise annoyance

BACKGROUND/AIM

The exposome includes urban greenspace, which may affect health via a complex set of pathways, including reducing exposure to particulate matter (PM) and noise. We assessed these pathways using indoor exposure monitoring data from the HEALS study in four European urban areas (Edinburgh, UK; Utrecht, Netherlands; Athens and Thessaloniki, Greece).

METHODS

We quantified three metrics of residential greenspace at 50 m and 100 m buffers: Normalised Difference Vegetation Index (NDVI), annual tree cover density, and surrounding green land use. NDVI values were generated for both summer and the season during which the monitoring took place. Indoor PM_2.5 and noise levels were measured by Dylos and Netatmo sensors, respectively, and subjective noise annoyance was collected by questionnaire on an 11-point scale. We used random-effects generalised least squares regression models to assess associations between greenspace and indoor PM_2.5 and noise, and an ordinal logistic regression to model the relationship between greenspace and road noise annoyance.

RESULTS

We identified a significant inverse relationship between summer NDVI and indoor PM_2.5 (-1.27 μg/m³ per 0.1 unit increase [95% CI -2.38 to -0.15]) using a 100 m residential buffer. Reduced (i.e., <1.0) odds ratios (OR) of road noise annoyance were associated with increasing summer (OR = 0.55 [0.31 to 0.98]) and season-specific (OR = 0.55 [0.32 to 0.94]) NDVI levels, and tree cover density (OR = 0.54 [0.31 to 0.93] per 10 percentage point increase), also at a 100 m buffer. In contrast to these findings, we did not identify any significant associations between greenspace and indoor noise in fully adjusted models.

CONCLUSIONS

We identified reduced indoor levels of PM_2.5 and noise annoyance, but not overall noise, with increasing outdoor levels of certain greenspace indicators. To corroborate our findings, future research should examine the effect of enhanced temporal resolution of greenspace metrics during different seasons, characterise the configuration and composition of green areas, and explore mechanisms through mediation modelling.

Distance-Decay Effect in Probabilistic Time Geography for Random Encounter

Probabilistic time geography uses a fixed distance threshold for the definition of the encounter events of moving objects. However, because of the distance-decay effect, different distances within the fixed threshold ensure that the encounter events do not always have the same possibility, and, therefore, the quantitative probabilistic time geography analysis needs to consider the actual distance-decay coefficient (DDC). Thus, this paper introduces the DDC and proposes a new encounter probability measure model that takes into account the distance-decay effect. Given two positions of a pair of moving objects, the traditional encounter probability model is that if the distance between the two positions does not exceed a given threshold, the encounter event may occur, and its probability is equal to the product of the probabilities of the two moving objects in their respective positions. Furthermore, the probability of the encounter at two given positions is multiplied by the DDC in the proposed model, in order to express the influence of the distance-decay effect on the encounter probability. Finally, the validity of the proposed model is verified by an experiment, which uses the tracking data of wild zebras to calculate the encounter probability, and compares it with the former method.

The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data

In recent years, haze pollution has become a serious environmental problem affecting cities in China. Reducing PM_2.5 concentrations through urban planning is a promising method that has been a focus of recent multidisciplinary research. Most existing studies only analyze the relationship between urban planning factors and PM_2.5 concentration, and it is difficult to accurately reflect residents' actual air pollution exposure without considering their space-time behaviors. This study uses satellite remote sensing and location service data to measure PM_2.5 pollution exposure in Wuhan metropolitan area and explores the effects of urban spatial structure, land use, spatial form, transportation, and green space on pollution exposure. The results show that spatial structure, building density, road density, and green space coverage have a significant impact on PM_2.5 pollution exposure. In addition, this study proposes corresponding implications for urban planning to improve public respiratory health.

Urban climate modified short-term association of air pollution with pneumonia mortality in Hong Kong

BACKGROUND

City is becoming warmer, especially in the process of urbanization and climate change. However, it is largely unknown whether this warming urban climate may modify the short-term effects of air pollution.

OBJECTIVES

To test whether warmer urban climates intensify the acute mortality effects of air pollution on pneumonia in Hong Kong.

METHODS

Participants who died of pneumonia from a prospective Chinese elderly cohort between 1998 and 2011 were selected as cases. Urban climatic (UC) classes of cases were determined by an established Urban Climatic Map according to their residential addresses. UC classes were first dichotomized into cool and warm climates and case-crossover analysis was used to estimate the short-term association of pneumonia mortality with air pollution. We further classified UC classes into climate quartiles and used case-only analysis to test the trend of urban climate modification on the short-term association of pneumonia mortality with air pollution.

RESULTS

Among 66,820 elders (≥65 years), 2208 pneumonia deaths (cases) were identified during the 11-14 years of follow-up. The effects of air pollution for cases residing in the warm climate were statistically significant (p < 0.05) higher than those living in the cool climate. There was an increasing linear trend of urban climate modification on the association of pneumonia mortality with NO₂ (nitrogen dioxide) (p for trend = 0.035). Compared to climate Quartile 1 (the lowest), deaths resided in climate Quartile 2, 3, and 4 (the highest) were associated with an additional percent change of 9.07% (0.52%, 17.62%), 12.89% (4.34%, 21.43%), and 8.45% (-0.10%, 17.00%), respectively.

CONCLUSIONS

Warmer urban climate worsened the acute mortality effects of pneumonia associated with air pollutants in Hong Kong. Our findings suggest that warmer urban climate introduced by climate change and urbanization may increase the risks of air pollution-related pneumonia.

Modeling spatial distribution of population for environmental epidemiological studies: Comparing the exposure estimates using choropleth versus dasymetric mapping

Precise population information is critical for identifying more accurate environmental exposures for air pollution impacts analysis. Basically, there are two methods for estimating spatial distribution of population, choropleth and dasymetric mapping. While the choropleth approach accounts for linear distribution of population over area based on census tract units, the dasymetric model accounts for a more heterogeneous population density by quantifying the association between the area-class map data categories and values of the statistical surface as encoded in the census dataset. Environmental epidemiological studies have indicated the dasymetric mapping as a more accurate approach to estimate and characterize population densities in large urban areas. However, investigations that have attempted to compare the exposure estimates from choropleth versus dasymetric mapping in environmental health analysis are still missing. This paper addresses this gap and compares the impact of using choropleth and dasymetric mapping in different exposure metrics. We compare the impact of using choropleth and dasymetric mapping in three case studies, defined here as case study A (relationship between urban structure types and health), case study B (PM_2.5 emissions and human exposure), and case study C (distance-decays of mortality risk related to PM_2.5 emitted by traffic along major highways). These case studies represent previous investigations performed by our research group where spatial distribution of population was an essential input for analysis. Our findings indicate that the method used to estimate spatial distribution of population impacts significantly the exposure estimates. We observed that the choropleth mapping overestimated exposure for the case study A and B, while for the case study C the exposure was underestimated by the choropleth approach. Our findings show that the dasymetric model is a preferred method for creating spatially-explicit information about population distribution for health exposure studies. The results presented here can be useful for the environmental health community to more accurately assess the relationship between environmental factors and health risks.

Role of Geographic Information System in Assessing Determinants of Cardiovascular Disease: An Experience From a Low- and Middle-Income Country

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the world. The determinants of CVD in an urban population using conventional and geographic information system techniques were attempted as a community-based census-type cross-sectional study in Kerala, India, among 1649 individuals residing in 452 households. Sociodemographic details, risk factor exposures, and self-reported disease prevalence were determined. Location of houses, wells from which subjects drew drinking water, and distances of the house from the outer road (proxy for air pollution) were mapped using differential global positioning system and pH of water samples determined. Prevalence of CVD was 5.8%. Significant predictors of CVD were male gender, diabetes mellitus, hypertension, and hypothyroidism. Statistically significant spatial association was found between CVD and groundwater pH. Geographic information system technology is useful in identification of spatial clustering and disease hotspots for designing preventive strategies targeting CVD.

Association of PM2.5 with diabetes, asthma, and high blood pressure incidence in Canada: A spatiotemporal analysis of the impacts of the energy generation and fuel sales

Numerous studies have reported an association between fine particulate matter (PM_2.5) and human health. Often these relationships are influenced by environmental factor that varies spatially and/or temporally. To our knowledge, there are no studies in Canada that have considered energy generation and fuel sales as PM_2.5 effects modifiers. Determining exposure and disease-specific risk factors over space and time is crucial for disease prevention and control. In this study, we evaluated the association of PM_2.5 with diabetes, asthma, and High Blood Pressure (HBP) incidence in Canada. Then we explored the impact of the energy generation and fuel sales on association changes. We fit an age-period-cohort as the study design, and we applied an over-dispersed Poisson regression model to estimate the risk. We conducted a sensitivity analysis to explore the impact of variation in clean energy rates and fuel sales on outcomes changes. The study included 117 health regions in Canada between 2007 and 2014. Our findings showed strong association of PM_2.5 with diabetes, asthma, and HBP incidence. A two-year increase of 10μg/m³ in PM_2.5 was associated with an increased risk of 5.34% (95% CI: 2.28%; 12.53%) in diabetes incidence, 2.24% (95% CI: 0.93%; 5.38%) in asthma incidence, and 8.29% (95% CI: 3.44%; 19.98%) in HBP incidence. Our sensitivity analysis findings suggest higher risks of diabetes, asthma and HBP incidence when there is low clean energy generation. On the other hand, we found lower risk when we considered high rate of clean energy generation. For example, considering only diabetes incidence, we found that the risk in health regions with low rates of clean electricity is approximately 700% higher than the risk in health regions with high rates of clean electricity. Furthermore, our analysis suggested that the risk in regions with low fuel sales is 66% lower than the risk is health regions with low rates of clean electricity. Our study provides support for the creation of effective environmental health public policies that take into account the risk factors present in Canadians health regions.