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The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality.

ABSTRACT: Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003-2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysis techniques were applied to map the spatial data. Disease rates were calculated for every ZIP code region from 2009 to 2011. The highest rates (4-5.5%) were found in Prairie in Monroe County for three consecutive years. Statistically significant hotspots were observed in urban regions of Jackson and Gulf port with steady increase near urban Jackson and the area between Jackson and meridian metropolis. For 2009-2011, spatial signatures of urban risk factors were found in dense population areas, which was confirmed from regression analysis of asthma patients with population data (linear increase of R2 = 0.648, as it reaches a population size of 3,5000 per ZIP code and the relationship decreased to 59% as the population size increased above 3,5000 to a maximum of 4,7000 per ZIP code). The observed correlation coefficient (r) between monthly mean O3 and asthma prevalence was moderately positive during 2009-2011 (r = 0.57). The regression model also indicated that 2011 annual PM2.5 has a statistically significant influence on the aggravation of the asthma cases (adjusted R-squared 0.93) and the 2011 PM2.5 depended on asthma per capita and poverty rate as well. The present study indicates that Jackson urban area and coastal Mississippi are to be observed for disease prevalence in future. The current results and GIS disease maps may be used by federal and state health authorities to identify at-risk populations and health advisory.

SUBMITTER: Kethireddy SR

PROVIDER: S-EPMC6201236 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality.

Kethireddy Swatantra R SR Adegoye Grace A GA Tchounwou Paul B PB Tuluri Francis F Ahmad H Anwar HA Young John H JH Zhang Lei L

AIMS environmental science 20180912 4

Data enabled research with a spatial perspective may help to combat human diseases in an informed and cost-effective manner. Understanding the changing patterns of environmental degradation is essential to help in determining the health outcomes such as asthma of a community. In this research, Mississippi asthma-related prevalence data for 2003-2011 were analyzed using spatial statistical techniques in Geographic Information Systems. Geocoding by ZIP code, choropleth mapping, and hotspot analysi ...[more]

PMID: 30370331

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Project description:Environmental quality is an important determinant of individuals' well-being and one of the main concerns of the governments is the improvement on air quality and the protection of public health. This is especially the case of sensitive demographic groups, such as the old aged people. However, the question this study attempts to answer is how do individuals value the effects on the environment. The study explores the effects of old and early public pension schemes, as well as the impact of air pollution on health status of retired citizens. The empirical analysis relies on detailed micro-level data derived from the Survey of Health, Ageing and Retirement in Europe (SHARE). As proxies for health, we use the general health status and the Eurod mental health indicator. We examine two air pollutants: the sulphur dioxide (SO2) and ground-level ozone (O3). Next, we calculate the marginal willingness-to-pay (MWTP) which shows how much the people are willing to pay for improvement in air quality. We apply various quantitative techniques and approaches, including the fixed effects ordinary least squares (OLS) and the fixed effects instrumental variables (IV) approach. The last approach is applied to reduce the endogeneity problem coming from possible reverse causality between the air pollution, pensions and the health outcomes. For robustness check, we apply also a structural equation modelling (SEM) which is proper when the outcomes are latent variables. Based on our favoured IV estimates and the health status, we find that the MWTP values for one unit decrease in SO2 and O3 are respectively €221 and €88 per year. The respective MWTP values using the Eurod measure are €155 and €68. Overall, improvement of health status implies reduction in health expenditures, and in previous literature, ageing has been traditionally considered the most important determinant. However, this study shows that health lifestyle and socio-economic status, such as education and marital status, are more important, and furthermore, air pollution cannot be ignored in the agenda of policy makers.

Project description:IntroductionAir and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data are a barrier to the formulation and evaluation of policies to reduce air and noise pollution.Methods and analysisWe designed a year-long measurement campaign to characterise air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area (GAMA), Ghana. Our design uses a combination of fixed (year-long, n=10) and rotating (week-long, n =~130) sites, selected to represent a range of land uses and source influences (eg, background, road traffic, commercial, industrial and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across the GAMA and to identify their potential sources. This protocol can serve as a prototype for other SSA cities.Ethics and disseminationThis environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee (ECH 149/18-19). This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations.

Project description:BackgroundLow-cost sensor networks for monitoring air pollution are an effective tool for expanding spatial resolution beyond the capabilities of existing state and federal reference monitoring stations. However, low-cost sensor data commonly exhibit non-linear biases with respect to environmental conditions that cannot be captured by linear models, therefore requiring extensive lab calibration. Further, these calibration models traditionally produce point estimates or uniform variance predictions which limits their downstream in exposure assessment.ObjectiveBuild direct field-calibration models using probabilistic gradient boosted decision trees (GBDT) that eliminate the need for resource-intensive lab calibration and that can be used to conduct probabilistic exposure assessments on the neighborhood level.MethodsUsing data from Plantower A003 particulate matter (PM) sensors deployed in Baltimore, MD from November 2018 through November 2019, a fully probabilistic NGBoost GBDT was trained on raw data from sensors co-located with a federal reference monitoring station and compared against linear regression trained on lab calibrated sensor data. The NGBoost predictions were then used in a Monte Carlo interpolation process to generate high spatial resolution probabilistic exposure gradients across Baltimore.ResultsWe demonstrate that direct field-calibration of the raw PM2.5 sensor data using a probabilistic GBDT has improved point and distribution accuracies compared to the linear model, particularly at reference measurements exceeding 25 μg/m3, and also on monitors not included in the training set.SignificanceWe provide a framework for utilizing the GBDT to conduct probabilistic spatial assessments of human exposure with inverse distance weighting that predicts the probability of a given location exceeding an exposure threshold and provides percentiles of exposure. These probabilistic spatial exposure assessments can be scaled by time and space with minimal modifications. Here, we used the probabilistic exposure assessment methodology to create high quality spatial-temporal PM2.5 maps on the neighborhood-scale in Baltimore, MD.Impact statementWe demonstrate how the use of open-source probabilistic machine learning models for in-place sensor calibration outperforms traditional linear models and does not require an initial laboratory calibration step. Further, these probabilistic models can create uniquely probabilistic spatial exposure assessments following a Monte Carlo interpolation process.

Dataset Information

The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality.

Publications

The status of geo-environmental health in Mississippi: Application of spatiotemporal statistics to improve health and air quality.

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