ISPRS International Journal of Geo-Information

Editorial

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Open AccessEditorial

Remote Sensing and Geospatial Technologies in Public Health

by Fazlay S. Faruque

ISPRS Int. J. Geo-Inf. 2018, 7(8), 303; https://doi.org/10.3390/ijgi7080303 - 30 Jul 2018

Cited by 3 | Viewed by 3455

Abstract

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(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

Research

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2189 KiB

Open AccessArticle

CALPUFF and CAFOs: Air Pollution Modeling and Environmental Justice Analysis in the North Carolina Hog Industry

by Yelena Ogneva-Himmelberger, Liyao Huang and Hao Xin

ISPRS Int. J. Geo-Inf. 2015, 4(1), 150-171; https://doi.org/10.3390/ijgi4010150 - 26 Jan 2015

Cited by 16 | Viewed by 14022

Abstract

Concentrated animal feeding operations (CAFOs) produce large amounts of animal waste, which potentially pollutes air, soil and water and affects human health if not appropriately managed. This study uses meteorological and CAFO data and applies an air pollution dispersion model (CALPUFF) to estimate [...] Read more.

Concentrated animal feeding operations (CAFOs) produce large amounts of animal waste, which potentially pollutes air, soil and water and affects human health if not appropriately managed. This study uses meteorological and CAFO data and applies an air pollution dispersion model (CALPUFF) to estimate ammonia concentrations at locations downwind of hog CAFOs and to evaluate the disproportionate exposure of children, elderly, whites and minorities to the pollutant. Ammonia is one of the gases emitted by swine CAFOs and could affect human health. Local indicator of spatial autocorrelation (LISA) analysis uses census block demographic data to identify hot spots where both ammonia concentrations and the number of exposed vulnerable population are high. We limit our analysis to one watershed in North Carolina and compare environmental justice issues between 2000 and 2010. Our results show that the average ammonia concentrations in hot spots for 2000 and 2010 were 2.5–3-times higher than the average concentration in the entire watershed. The number of people living in the areas where ammonia concentrations exceeded the minimal risk level was 3647 people in 2000 and 3360 people in 2010. We recommend using air pollution dispersion models in future environmental justice studies to assess the impacts of the CAFOs and to address concerns regarding the health and quality of life of vulnerable populations. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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1687 KiB

Open AccessArticle

Analyzing the Correlation between Deer Habitat and the Component of the Risk for Lyme Disease in Eastern Ontario, Canada: A GIS-Based Approach

by Dongmei Chen, Haydi Wong, Paul Belanger, Kieran Moore, Mary Peterson and John Cunningham

ISPRS Int. J. Geo-Inf. 2015, 4(1), 105-123; https://doi.org/10.3390/ijgi4010105 - 15 Jan 2015

Cited by 14 | Viewed by 10697

Abstract

Lyme borreliosis, caused by the bacterium, Borrelia burgdorferi, is an emerging vector-borne infectious disease in Canada. According to the Public Health Agency of Canada (PHAC), by the year 2020, 80% of Canadians will live in Lyme endemic areas. An understanding of the [...] Read more.

Lyme borreliosis, caused by the bacterium, Borrelia burgdorferi, is an emerging vector-borne infectious disease in Canada. According to the Public Health Agency of Canada (PHAC), by the year 2020, 80% of Canadians will live in Lyme endemic areas. An understanding of the association of Ixodes scapularis, the main vector of Lyme disease, with it hosts is a fundamental component in assessing changes in the spatial distribution of human risk for Lyme disease. Through the application of Geographic Information System (GIS) mapping methods and spatial analysis techniques, this study examines the population dynamics of the black-legged Lyme tick and its primary host, the white-tailed deer, in eastern Ontario, Canada. By developing a habitat suitability model through a GIS-based multi-criteria decision making (MCDM) analysis, the relationship of the deer habitat suitability map was generated and the results were compared with deer harvest data. Tick submission data collected from two public health units between 2006 and 2012 were used to explore the relationship between endemic ticks and deer habitat suitability in eastern Ontario. The positive correlation demonstrated between the deer habitat suitability model and deer harvest data allows us to further analyze the association between deer habitat and black-legged ticks in our study area. Our results revealed that the high tick submission number corresponds with the high suitability. These results are useful for developing management strategies that aim to prevent Lyme from becoming a threat to public health in Canada. Further studies are required to investigate how tick survival, behaviour and seasonal activity may change with projected climate change. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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1211 KiB

Open AccessArticle

Geospatial Technology: A Tool to Aid in the Elimination of Malaria in Bangladesh

by Karen E. Kirk, M. Zahirul Haq, Mohammad Shafiul Alam and Ubydul Haque

ISPRS Int. J. Geo-Inf. 2015, 4(1), 47-58; https://doi.org/10.3390/ijgi4010047 - 31 Dec 2014

Cited by 9 | Viewed by 7666

Abstract

Bangladesh is a malaria endemic country. There are 13 districts in the country bordering India and Myanmar that are at risk of malaria. The majority of malaria morbidity and mortality cases are in the Chittagong Hill Tracts, the mountainous southeastern region of Bangladesh. [...] Read more.

Bangladesh is a malaria endemic country. There are 13 districts in the country bordering India and Myanmar that are at risk of malaria. The majority of malaria morbidity and mortality cases are in the Chittagong Hill Tracts, the mountainous southeastern region of Bangladesh. In recent years, malaria burden has declined in the country. In this study, we reviewed and summarized published data (through 2014) on the use of geospatial technologies on malaria epidemiology in Bangladesh and outlined potential contributions of geospatial technologies for eliminating malaria in the country. We completed a literature review using “malaria, Bangladesh” search terms and found 218 articles published in peer-reviewed journals listed in PubMed. After a detailed review, 201 articles were excluded because they did not meet our inclusion criteria, 17 articles were selected for final evaluation. Published studies indicated geospatial technologies tools (Geographic Information System, Global Positioning System, and Remote Sensing) were used to determine vector-breeding sites, land cover classification, accessibility to health facility, treatment seeking behaviors, and risk mapping at the household, regional, and national levels in Bangladesh. To achieve the goal of malaria elimination in Bangladesh, we concluded that further research using geospatial technologies should be integrated into the country’s ongoing surveillance system to identify and better assess progress towards malaria elimination. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessArticle

Examining Personal Air Pollution Exposure, Intake, and Health Danger Zone Using Time Geography and 3D Geovisualization

by Yongmei Lu and Tianfang Bernie Fang

ISPRS Int. J. Geo-Inf. 2015, 4(1), 32-46; https://doi.org/10.3390/ijgi4010032 - 30 Dec 2014

Cited by 25 | Viewed by 9422

Abstract

Expanding traditional time geography, this study examines personal exposure to air pollution and personal pollutant intake, and defines personal health danger zones by accounting for individual level space-time behavior. A 3D personal air pollution and health risk map is constructed to visualize individual [...] Read more.

Expanding traditional time geography, this study examines personal exposure to air pollution and personal pollutant intake, and defines personal health danger zones by accounting for individual level space-time behavior. A 3D personal air pollution and health risk map is constructed to visualize individual space-time path, personal Air Quality Indexes (AQIs), and personal health danger zones. Personal air pollution exposure level and its variation through space and time is measured by a portable air pollutant sensor coupled with a portable GPS unit. Personal pollutant intake is estimated by accounting for air pollutant concentration in immediate surroundings, individual’s biophysical characteristics, and individual’s space-time activities. Personal air pollution danger zones are defined by comparing personal pollutant intake with air quality standard; these zones are particular space-time-activity segments along an individual’s space-time path. Being able to identify personal air pollution danger zones can help plan for proper actions aiming at controlling health impacts from air pollution. As a case study, this paper reports on an examination and visualization of an individual’s two-day ozone exposure, intake and danger zones in Houston, Texas. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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1444 KiB

Open AccessCommunication

Use of the NASA Giovanni Data System for Geospatial Public Health Research: Example of Weather-Influenza Connection

by James Acker, Radina Soebiyanto, Richard Kiang and Steve Kempler

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1372-1386; https://doi.org/10.3390/ijgi3041372 - 10 Dec 2014

Cited by 20 | Viewed by 10103

Abstract

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, [...] Read more.

The NASA Giovanni data analysis system has been recognized as a useful tool to access and analyze many different types of remote sensing data. The variety of environmental data types has allowed the use of Giovanni for different application areas, such as agriculture, hydrology, and air quality research. The use of Giovanni for researching connections between public health issues and Earth’s environment and climate, potentially exacerbated by anthropogenic influence, has been increasingly demonstrated. In this communication, the pertinence of several different data parameters to public health will be described. This communication also provides a case study of the use of remote sensing data from Giovanni in assessing the associations between seasonal influenza and meteorological parameters. In this study, logistic regression was employed with precipitation, temperature and specific humidity as predictors. Specific humidity was found to be associated (p < 0.05) with influenza activity in both temperate and tropical climate. In the two temperate locations studied, specific humidity was negatively correlated with influenza; conversely, in the three tropical locations, specific humidity was positively correlated with influenza. Influenza prediction using the regression models showed good agreement with the observed data (correlation coefficient of 0.5–0.83). Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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7023 KiB

Open AccessArticle

Mapping Entomological Dengue Risk Levels in Martinique Using High-Resolution Remote-Sensing Environmental Data

by Vanessa Machault, André Yébakima, Manuel Etienne, Cécile Vignolles, Philippe Palany, Yves M. Tourre, Marine Guérécheau and Jean-Pierre Lacaux

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1352-1371; https://doi.org/10.3390/ijgi3041352 - 10 Dec 2014

Cited by 25 | Viewed by 7922

Abstract

Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, [...] Read more.

Controlling dengue virus transmission mainly involves integrated vector management. Risk maps at appropriate scales can provide valuable information for assessing entomological risk levels. Here, results from a spatio-temporal model of dwellings potentially harboring Aedes aegypti larvae from 2009 to 2011 in Tartane (Martinique, French Antilles) using high spatial resolution remote-sensing environmental data and field entomological and meteorological information are presented. This tele-epidemiology methodology allows monitoring the dynamics of diseases closely related to weather/climate and environment variability. A Geoeye-1 image was processed to extract landscape elements that could surrogate societal or biological information related to the life cycle of Aedes vectors. These elements were subsequently included into statistical models with random effect. Various environmental and meteorological conditions have indeed been identified as risk/protective factors for the presence of Aedes aegypti immature stages in dwellings at a given date. These conditions were used to produce dynamic high spatio-temporal resolution maps from the presence of most containers harboring larvae. The produced risk maps are examples of modeled entomological maps at the housing level with daily temporal resolution. This finding is an important contribution to the development of targeted operational control systems for dengue and other vector-borne diseases, such as chikungunya, which is also present in Martinique. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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4355 KiB

Open AccessArticle

Improving Inland Water Quality Monitoring through Remote Sensing Techniques

by Igor Ogashawara and Max J. Moreno-Madriñán

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1234-1255; https://doi.org/10.3390/ijgi3041234 - 14 Nov 2014

Cited by 16 | Viewed by 8697

Abstract

Chlorophyll-a (chl-a) levels in lake water could indicate the presence of cyanobacteria, which can be a concern for public health due to their potential to produce toxins. Monitoring of chl-a has been an important practice in aquatic systems, especially [...] Read more.

Chlorophyll-a (chl-a) levels in lake water could indicate the presence of cyanobacteria, which can be a concern for public health due to their potential to produce toxins. Monitoring of chl-a has been an important practice in aquatic systems, especially in those used for human services, as they imply an increased risk of exposure. Remote sensing technology is being increasingly used to monitor water quality, although its application in cases of small urban lakes is limited by the spatial resolution of the sensors. Lake Thonotosassa, FL, USA, a 3.45-km² suburban lake with several uses for the local population, is being monitored monthly by traditional methods. We developed an empirical bio-optical algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) daily surface reflectance product to monitor daily chl-a. We applied the same algorithm to four different periods of the year using 11 years of water quality data. Normalized root mean squared errors were lower during the first (0.27) and second (0.34) trimester and increased during the third (0.54) and fourth (1.85) trimesters of the year. Overall results showed that Earth-observing technologies and, particularly, MODIS products can also be applied to improve environmental health management through water quality monitoring of small lakes. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessArticle

Impacts of Scale on Geographic Analysis of Health Data: An Example of Obesity Prevalence

by Jay Lee, Mohammad Alnasrallah, David Wong, Heather Beaird and Everett Logue

ISPRS Int. J. Geo-Inf. 2014, 3(4), 1198-1210; https://doi.org/10.3390/ijgi3041198 - 24 Oct 2014

Cited by 12 | Viewed by 8045

Abstract

The prevalence of obesity has increased dramatically in recent decades. It is an important public health issue as it causes many other chronic health conditions, such as hypertension, cardiovascular diseases, and type II diabetics. Obesity affects life expectancy and even the quality of [...] Read more.

The prevalence of obesity has increased dramatically in recent decades. It is an important public health issue as it causes many other chronic health conditions, such as hypertension, cardiovascular diseases, and type II diabetics. Obesity affects life expectancy and even the quality of lives. Eventually, it increases social costs in many ways due to increasing costs of health care and workplace absenteeism. Using the spatial patterns of obesity prevalence as an example; we show how different geographic units can reveal different degrees of detail in results of analysis. We used both census tracts and census block groups as units of geographic analysis. In addition; to reveal how different geographic scales may impact on the analytic results; we applied geographically weighted regression to model the relationships between obesity rates (dependent variable) and three independent variables; including education attainment; unemployment rates; and median family income. Though not including an exhaustive list of explanatory variables; this regression model provides an example for revealing the impacts of geographic scales on analysis of health data. With obesity data based on reported heights and weights on driver’s licenses in Summit County, Ohio, we demonstrated that geographically weighted regression reveals varying spatial trends between dependent and independent variables that conventional regression models such as ordinary least squares regression cannot. Most importantly, analyses carried out with different geographic scales do show very different results. With these findings, we suggest that, while possible, smaller geographic units be used to allow better understanding of the studies phenomena. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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7590 KiB

Open AccessArticle

Geographical Variation of Incidence of Chronic Obstructive Pulmonary Disease in Manitoba, Canada

by Mahmoud Torabi and Katie Galloway

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1039-1057; https://doi.org/10.3390/ijgi3031039 - 29 Jul 2014

Cited by 2 | Viewed by 5841

Abstract

We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial [...] Read more.

We aimed to study the geographic variation in the incidence of COPD. We used health survey data (weighted to the population level) to identify 56,944 cases of COPD in Manitoba, Canada from 2001 to 2010. We used five cluster detection procedures, circular spatial scan statistic (CSS), flexible spatial scan statistic (FSS), Bayesian disease mapping (BYM), maximum likelihood estimation (MLE), and local indicator of spatial association (LISA). Our results showed that there are some regions in southern Manitoba that are potential clusters of COPD cases. The FSS method identified more regions than the CSS and LISA methods and the BYM and MLE methods identified similar regions as potential clusters. Most of the regions identified by the MLE and BYM methods were also identified by the FSS method and most of the regions identified by the CSS method were also identified by most of the other methods. The CSS, FSS and LISA methods identify potential clusters but are not able to control for confounders at the same time. However, the BYM and MLE methods can simultaneously identify potential clusters and control for possible confounders. Overall, we recommend using the BYM and MLE methods for cluster detection in areas with similar population and structure of regions as those in Manitoba. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessCommunication

Holistics 3.0 for Health

by David John Lary, Steven Woolf, Fazlay Faruque and James P. LePage

ISPRS Int. J. Geo-Inf. 2014, 3(3), 1023-1038; https://doi.org/10.3390/ijgi3031023 - 24 Jul 2014

Cited by 11 | Viewed by 6640

Abstract

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting [...] Read more.

Human health is part of an interdependent multifaceted system. More than ever, we have increasingly large amounts of data on the body, both spatial and non-spatial, its systems, disease and our social and physical environment. These data have a geospatial component. An exciting new era is dawning where we are simultaneously collecting multiple datasets to describe many aspects of health, wellness, human activity, environment and disease. Valuable insights from these datasets can be extracted using massively multivariate computational techniques, such as machine learning, coupled with geospatial techniques. These computational tools help us to understand the topology of the data and provide insights for scientific discovery, decision support and policy formulation. This paper outlines a holistic paradigm called Holistics 3.0 for analyzing health data with a set of examples. Holistics 3.0 combines multiple big datasets set in their geospatial context describing as many areas of a problem as possible with machine learning and causality, to both learn from the data and to construct tools for data-driven decisions. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessArticle

Dasymetric Mapping and Spatial Modeling of Mosquito Vector Exposure, Chesapeake, Virginia, USA

by Haley Cleckner and Thomas R. Allen

ISPRS Int. J. Geo-Inf. 2014, 3(3), 891-913; https://doi.org/10.3390/ijgi3030891 - 14 Jul 2014

Cited by 10 | Viewed by 10885

Abstract

Complex biophysical, social, and human behavioral factors influence population vulnerability to vector-borne diseases. Spatially and temporally dynamic environmental and anthropogenic patterns require sophisticated mapping and modeling techniques. While many studies use environmental variables to predict risk, human population vulnerability has been a challenge [...] Read more.

Complex biophysical, social, and human behavioral factors influence population vulnerability to vector-borne diseases. Spatially and temporally dynamic environmental and anthropogenic patterns require sophisticated mapping and modeling techniques. While many studies use environmental variables to predict risk, human population vulnerability has been a challenge to incorporate into spatial risk models. This study demonstrates and applies dasymetric mapping techniques to map spatial patterns of vulnerable human populations and characterize potential exposure to mosquito vectors of West Nile Virus across Chesapeake, Virginia. Mosquito vector abundance is quantified and combined with a population vulnerability index to evaluate exposure of human populations to mosquitoes. Spatial modeling is shown to capture the intersection of environmental factors that produce spatial hotspots in mosquito vector abundance, which in turn poses differential risks over time to humans. Such approaches can help design overall mosquito pest management and identify high-risk areas in advance of extreme weather. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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953 KiB

Open AccessArticle

Modeling Properties of Influenza-Like Illness Peak Events with Crossing Theory

by Ying Wang, Peter R. Waylen and Liang Mao

ISPRS Int. J. Geo-Inf. 2014, 3(2), 764-780; https://doi.org/10.3390/ijgi3020764 - 26 May 2014

Cited by 1 | Viewed by 6502

Abstract

The concept of “peak event” has been used extensively to characterize influenza epidemics. Current definitions, however, could not maximize the amount of pertinent information about the probabilities of peak events that could be extracted from the generally limited available records. This study proposes [...] Read more.

The concept of “peak event” has been used extensively to characterize influenza epidemics. Current definitions, however, could not maximize the amount of pertinent information about the probabilities of peak events that could be extracted from the generally limited available records. This study proposes a new method of defining peak events and statistically characterizing their properties, including: annual event density, their timing, the magnitude over prescribed thresholds and duration. These properties of peak events are analyzed in five counties of Florida using records from the Influenza-Like Illness Surveillance Network (ILINet). Further, the identified properties of peak events are compared between counties to reveal the geographic variability of influenza peak activity. The results of this study illustrate the proposed methodology’s capacity to aid public health professionals in supporting influenza surveillance and implementing timely effective intervention strategies. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessArticle

Correlating Remote Sensing Data with the Abundance of Pupae of the Dengue Virus Mosquito Vector, Aedes aegypti, in Central Mexico

by Max J. Moreno-Madriñán, William L. Crosson, Lars Eisen, Sue M. Estes, Maurice G. Estes Jr., Mary Hayden, Sarah N. Hemmings, Dan E. Irwin, Saul Lozano-Fuentes, Andrew J. Monaghan, Dale Quattrochi, Carlos M. Welsh-Rodriguez and Emily Zielinski-Gutierrez

ISPRS Int. J. Geo-Inf. 2014, 3(2), 732-749; https://doi.org/10.3390/ijgi3020732 - 20 May 2014

Cited by 32 | Viewed by 15029

Abstract

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus [...] Read more.

Using a geographic transect in Central Mexico, with an elevation/climate gradient, but uniformity in socio-economic conditions among study sites, this study evaluates the applicability of three widely-used remote sensing (RS) products to link weather conditions with the local abundance of the dengue virus mosquito vector, Aedes aegypti (Ae. aegypti). Field-derived entomological measures included estimates for the percentage of premises with the presence of Ae. aegypti pupae and the abundance of Ae. aegypti pupae per premises. Data on mosquito abundance from field surveys were matched with RS data and analyzed for correlation. Daily daytime and nighttime land surface temperature (LST) values were obtained from Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua cloud-free images within the four weeks preceding the field survey. Tropical Rainfall Measuring Mission (TRMM)-estimated rainfall accumulation was calculated for the four weeks preceding the field survey. Elevation was estimated through a digital elevation model (DEM). Strong correlations were found between mosquito abundance and RS-derived night LST, elevation and rainfall along the elevation/climate gradient. These findings show that RS data can be used to predict Ae. aegypti abundance, but further studies are needed to define the climatic and socio-economic conditions under which the correlations observed herein can be assumed to apply. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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956 KiB

Open AccessArticle

Canadian Forest Fires and the Effects of Long-Range Transboundary Air Pollution on Hospitalizations among the Elderly

by George E. Le, Patrick N. Breysse, Aidan McDermott, Sorina E. Eftim, Alison Geyh, Jesse D. Berman and Frank C. Curriero

ISPRS Int. J. Geo-Inf. 2014, 3(2), 713-731; https://doi.org/10.3390/ijgi3020713 - 20 May 2014

Cited by 52 | Viewed by 13348

Abstract

In July 2002, lightning strikes ignited over 250 fires in Quebec, Canada, destroying over one million hectares of forest. The smoke plume generated from the fires had a major impact on air quality across the east coast of the U.S. Using data from [...] Read more.

In July 2002, lightning strikes ignited over 250 fires in Quebec, Canada, destroying over one million hectares of forest. The smoke plume generated from the fires had a major impact on air quality across the east coast of the U.S. Using data from the Medicare National Claims History File and the U.S. Environmental Protection Agency (EPA) National air pollution monitoring network, we evaluated the health impact of smoke exposure on 5.9 million elderly people (ages 65+) in the Medicare population in 81 counties in 11 northeastern and Mid-Atlantic States of the US. We estimated differences in the exposure to ambient PM_2.5—airborne particulate matter with aerodynamic diameter of ≤2.5 µm—concentrations and hospitalizations for cardiovascular, pulmonary and injury outcomes, before and during the smoke episode. We found that there was an associated 49.6% (95% confidence interval (CI), 29.8, 72.3) and 64.9% (95% CI, 44.3–88.5) increase rate of hospitalization for respiratory and cardiovascular diagnoses, respectively, when the smoke plume was present compared to before the smoke plume had arrived. Our study suggests that rapid increases in PM_2.5 concentrations resulting from wildfire smoke can impact the health of elderly populations thousands of kilometers removed from the fires. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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Open AccessArticle

Nexus of Health and Development: Modelling Crude Birth Rate and Maternal Mortality Ratio Using Nighttime Satellite Images

by Koel Roychowdhury and Simon Jones

ISPRS Int. J. Geo-Inf. 2014, 3(2), 693-712; https://doi.org/10.3390/ijgi3020693 - 9 May 2014

Cited by 11 | Viewed by 7829

Abstract

Health and development are intricately related. Although India has made significant progress in the last few decades in the health sector and overall growth in GDP, there are still large regional differences in both health and development. The main objective of this paper [...] Read more.

Health and development are intricately related. Although India has made significant progress in the last few decades in the health sector and overall growth in GDP, there are still large regional differences in both health and development. The main objective of this paper is to develop techniques for the prediction of health indicators for all the districts of India and examine the correlations between health and development. The level of electrification and district domestic product (DDP) are considered as two fundamental indicators of development in this research. These data, along with health metrics and the information from two nighttime satellite images, were used to propose the models. These successfully predicted the health indicators with less than a 7%–10% error. The chosen health metrics, such as crude birth rate (CBR) and maternal mortality rate (MMR), were mapped for the whole country at the district level. These metrics showed very strong correlation with development indicators (correlation coefficients ranging from 0.92 to 0.99 at the 99% confidence interval). This is the first attempt to use Visible Infrared Imaging Radiometer Suite (VIIRS) (satellite) imagery in a socio-economic study. This paper endorses the observation that areas with a higher DDP and level of electrification have overall better health conditions. Full article

(This article belongs to the Special Issue Remote Sensing and Geospatial Technologies in Public Health)

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