On epidemiology and geographic information systems: a review and discussion of future directions

Geospatial Modeling Methods in Epidemiological Kidney Research: An Overview and Practical Example

Geospatial modeling methods in population-level kidney research have not been used to full potential because few studies have completed associative spatial analyses between risk factors and exposures and kidney conditions and outcomes. Spatial modeling has several advantages over traditional modeling, including improved estimation of statistical variation and more accurate and unbiased estimation of coefficient effect direction or magnitudes by accounting for spatial data structure. Because most population-level kidney research data are geographically referenced, there is a need for better understanding of geospatial modeling for evaluating associations of individual geolocation with processes of care and clinical outcomes. In this review, we describe common spatial models, provide details to execute these analyses, and perform a case-study to display how results differ when integrating geographic structure. In our case-study, we used U.S. nationwide 2019 chronic kidney disease (CKD) data from Centers for Disease Control and Prevention's Kidney Disease Surveillance System and 2006 to 2010 U.S. Environmental Protection Agency environmental quality index (EQI) data and fit a nonspatial count model along with global spatial models (spatially lagged model [SLM]/pseudo-spatial error model [PSEM]) and a local spatial model (geographically weighted quasi-Poisson regression [GWQPR]). We found the SLM, PSEM, and GWQPR improved model fit in comparison to the nonspatial regression, and the PSEM model decreased the positive relationship between EQI and CKD prevalence. The GWQPR also revealed spatial heterogeneity in the EQI-CKD relationship. To summarize, spatial modeling has promise as a clinical and public health translational tool, and our case-study example is an exhibition of how these analyses may be performed to improve the accuracy and utility of findings.

Characterizing opioid overdose hotspots for place-based overdose prevention and treatment interventions: A geo-spatial analysis of Rhode Island, USA

OBJECTIVE

Examine differences in neighborhood characteristics and services between overdose hotspot and non-hotspot neighborhoods and identify neighborhood-level population factors associated with increased overdose incidence.

METHODS

We conducted a population-based retrospective analysis of Rhode Island, USA residents who had a fatal or non-fatal overdose from 2016 to 2020 using an environmental scan and data from Rhode Island emergency medical services, State Unintentional Drug Overdose Reporting System, and the American Community Survey. We conducted a spatial scan via SaTScan to identify non-fatal and fatal overdose hotspots and compared the characteristics of hotspot and non-hotspot neighborhoods. We identified associations between census block group-level characteristics using a Besag-York-Mollié model specification with a conditional autoregressive spatial random effect.

RESULTS

We identified 7 non-fatal and 3 fatal overdose hotspots in Rhode Island during the study period. Hotspot neighborhoods had higher proportions of Black and Latino/a residents, renter-occupied housing, vacant housing, unemployment, and cost-burdened households. A higher proportion of hotspot neighborhoods had a religious organization, a health center, or a police station. Non-fatal overdose risk increased in a dose responsive manner with increasing proportions of residents living in poverty. There was increased relative risk of non-fatal and fatal overdoses in neighborhoods with crowded housing above the mean (RR 1.19 [95 % CI 1.05, 1.34]; RR 1.21 [95 % CI 1.18, 1.38], respectively).

CONCLUSION

Neighborhoods with increased prevalence of housing instability and poverty are at highest risk of overdose. The high availability of social services in overdose hotspots presents an opportunity to work with established organizations to prevent overdose deaths.

A web-based geographic information system monitoring wildlife diseases in Abruzzo and Molise regions, Southern Italy

BACKGROUND

Nowadays there is a worldwide consensus on the importance of conducting wildlife disease surveillance. Indeed, 60% of emerging infectious diseases are zoonotic in nature, and the majority of these (71.8%) originate in wildlife. Surveillance of wildlife diseases is crucial to prevent negative effects on human and animal health. Data digitization and sharing are among the main goals for the present and coming years. Geographic Information Systems (GIS) are increasingly used to analyze the geographical distribution of diseases and the relationships between pathogenic factors and their geographic environments.

METHODS

Wild animal's samples collected in the Abruzzo and Molise regions and delivered to our laboratory are entered in our Laboratory Information System and processed to be displayed through the Web-GIS mash-up presented in this paper. We built it using both open source and proprietary solutions, to produce data driven interactive maps, charts and tables to help to understand the epidemiology of wild animal diseases, their spread and trend.

RESULTS

Since 2013, 9.606 samples collected from wild animals have been analyzed in the laboratories of the IZS-Teramo and have been recorded in the system, facilitating the reporting to the judicial authorities and the identification of highly risky areas to set up control and repression measures. Moreover, thanks to the monitoring health protocol, a canine distemper epidemic in wolves has been detected and monitored in its temporal and spatial evolution, as well as cases of bovine tuberculosis in wild boars.

CONCLUSIONS

While it is more evident that the starting point is to choose the right sampling method, it is for sure less obvious that the information system in which data is stored is equally important. In fact, it should give the possibility to consult it in an easy and instructive way. GIS allows immediately grasping the spatial relationships between the data itself and those between the data and the territory; it is an important tool to support veterinary services in managing epidemic and non-epidemic emergencies and performing epidemiological investigations, but also to examine control plans and identify new gaps and challenges.

Geospatial Distribution of Obsessive-Compulsive Disorder Specialists: Understanding Access as a Function of Distance, Insurance Status, and Neighborhood Socioeconomic Status

Obsessive-compulsive disorder is an impairing psychiatric condition affecting 1-2% of adults and youth. Cognitive-behavioral therapy with exposure and response prevention (CBT) is an efficacious intervention but requires specialty training and access is often limited. While certain factors are associated with treatment access, one key barrier that has not been explored is the geographic availability of OCD treatment providers. Using integrated geographically-referenced data, we examined the geographic distribution of OCD CBT specialty providers across the state of Texas, with particular attention to the relationship to neighborhood socioeconomic disadvantage, insurance status, and rural versus urban status. We found that specialist providers are almost exclusively located inside the highly urbanized parts of the state, primarily in more affluent areas, and often only accept self-pay. The characteristics of the areas located the furthest away from specialty OCD care include a high proportion of persons identifying as Hispanic; a high proportion of non-English speakers, households with income below poverty; households with no vehicles; and persons with no health insurance. Average household income decreased as distances from specialist providers increased. Broadly, findings confirm that OCD CBT specialty providers are clustered in large socially advantaged areas and that economic disadvantage remains a significant barrier to care. As inadequate or inappropriate treatment of OCD is likely to result in sustained and impairing symptoms, this is of great concern.

Area deprivation index and segregation on the risk of HIV: a U.S. Veteran case-control study

Background

Preventing HIV infection remains a critically important tool in the continuing fight against HIV/AIDS. The primary aim is to evaluate the effect and interactions between a composite area-level social determinants of health measure and an area-level measure of residential segregation on the risk of HIV/AIDS in U.S. Veterans.

Methods

Using the individual-level patient data from the U.S. Department of Veterans Affairs, we constructed a case-control study of veterans living with HIV/AIDS (VLWH) and age-, sex assigned at birth- and index date-matched controls. We geocoded patient's residential address to ascertain their neighborhood and linked their information to two measures of neighborhood-level disadvantage: area deprivation index (ADI) and isolation index (ISOL). We used logistic regression to estimate the odds ratio (OR) and 95% confidence interval (CI) for comparing VLWH with matched controls. We performed analyses for the entire U.S. and separately for each U.S. Census division.

Findings

Overall, living in minority-segregated neighborhoods was associated with a higher risk of HIV (OR: 1.88 (95% CI: 1.79-1.97) while living in higher ADI neighborhoods was associated with a lower risk of HIV (OR: 0.88; 95% CI: 0.84-0.92). The association between living in a higher ADI neighborhood and HIV was inconsistent across divisions, while living in minority-segregated neighborhoods was consistently associated with increased risk across all divisions. In the interaction model, individuals from low ADI and high ISOL neighborhoods had a higher risk of HIV in three divisions: East South Central; West South Central, and Pacific.

Interpretation

Our results suggest that residential segregation may prevent people in disadvantaged neighborhoods from protecting themselves from HIV independent from access to health care. There is the need to advance knowledge about the neighborhood-level social-structural factors that influence HIV vulnerability toward developing interventions needed to achieve the goal of ending the HIV epidemic.

Funding

US National Cancer Institute.

Geographic information science and the United States opioid overdose crisis: A scoping review of methods, scales, and application areas

BACKGROUND

The Opioid Overdose Crisis (OOC) continues to generate morbidity and mortality in the United States, outpacing other prominent accident-related reasons. Multiple disciplines have applied geographic information science (GIScience) to understand geographical patterns in opioid-related health measures. However, there are limited reviews that assess how GIScience has been used.

OBJECTIVES

This scoping review investigates how GIScience has been used to conduct research on the OOC. Specific sub-objectives involve identifying bibliometric trends, the location and scale of studies, the frequency of use of various GIScience methodologies, and what direction future research can take to address existing gaps.

METHODS

The review was pre-registered with the Open Science Framework ((https://osf.io/h3mfx/) and followed the PRISMA-ScR guidelines. Scholarly research was gathered from the Web of Science Core Collection, PubMed, IEEE Xplore, ACM Digital Library. Inclusion criteria was defined as having a publication date between January 1999 and August 2021, using GIScience as a central part of the research, and investigating an opioid-related health measure.

RESULTS

231 studies met the inclusion criteria. Most studies were published from 2017 onward. While many (41.6%) of studies were conducted using nationwide data, the majority (58.4%) occurred at the sub-national level. California, New York, Ohio, and Appalachia were most frequently studied, while the Midwest, north Rocky Mountains, Alaska, and Hawaii lacked studies. The most common GIScience methodology used was descriptive mapping, and county-level data was the most common unit of analysis across methodologies.

CONCLUSIONS

Future research of GIScience on the OOC can address gaps by developing use cases for machine learning, conducting analyses at the sub-county level, and applying GIScience to questions involving illicit fentanyl. Research using GIScience is expected to continue to increase, and multidisciplinary research efforts amongst GIScientists, epidemiologists, and other medical professionals can improve the rigor of research.

Use of Cross-sectional and Perspective Mapping to Spatially and Statistically Represent Inpatient Glucose Control

BACKGROUND

The use of inpatient location for the depiction of glycemic control is an alternative approach to the traditional analysis of hospital-derived glucometric data. Our aim was to develop a method of spatial representation and to test for corresponding statistical variation in inpatient glucose control data.

METHODS

Point-of-care blood glucose data from inpatients with diabetes mellitus were extracted. Calculations included patient-day weighted means (PDWMs) and percentage of patient hospital days with hypoglycemia. Results were overlaid onto hospital floor plans, and room numbers were used as geolocators to generate cross-sectional (2-dimensional) and perspective (3-dimensional) views of the data. Linear mixed and mixed-effects logistic regression models were used to compare the location effect and to assess statistical variation in the data after adjusting for age, sex, and severity of illness.

RESULTS

Visual inspection of cross-sectional and perspective maps demonstrated variation in glucometric outcomes across areas within the hospital. Statistical analysis confirmed significant variation between some hospital wings and floors.

CONCLUSIONS

Spatial depiction of glucometric data within the hospital could yield insights into hot spots of poor glycemic control. Future studies on how to operationalize this approach, and whether this method of analysis can drive changes in glycemic management practices, need to be conducted.

The Spatiality of COVID-19 in Kermanshah Metropolis, Iran

The COVID-19 pandemic is a severe ongoing health crisisworldwide. Studying the socio-economic impacts of COVID-19 can help policymakers develop successful pandemic management plans. This paper focuses on the spatial epidemiology of COVID-19 among different social classes in the Kermanshah metropolis, Iran. This cross-sectional study uses the data of people infected with COVID-19 in the Kermanshah metropolis in 2020, acquired from the official COVID-19 Registry of Kermanshah. The results show that 2013 people were infected with COVID-19 (male = 1164 and female = 849). The mean age of the patients was 45 ± 18.69. The Moran’s I show that COVID-19 in different social classes was clustered across the neighbourhoods in the Kermanshah metropolis. The mean ages of men and women were 44.51 ± 18.62 and 45.69 ± 18.76, respectively. Importantly, COVID-19 was highly prevalent in the middle-class groups. Age group comparisons indicate that older people were the most infected in poorer neighbourhoods. In the middle-classtheage group of 0–14 years and in the rich neighbourhoods the age group of 15–64 years were the most exposed to the disease. The findings of this study suggest that older people and lower socioeconomic classes should be prioritised while developing and implementing preventative programs for COVID-19 and similar pandemics.

Incidence and geographical distribution of tuberculosis disease in Erbil city, Iraq

OBJECTIVES

This study aimed to identify the geographical distribution of TB in Erbil city, Kurdistan Region, Iraq, determine potential risk factors associated with TB distribution, and provide recommendations to improve TB control programs in the region.

METHODS

The records of TB patients registered at the Chest and Respiratory Disease Center in Erbil, Iraqi Kurdistan Region, from January 2012 to December 2016 were reviewed and analyzed. The number of cases by the quarter of residence within Erbil city were analyzed spatially and presented in an appropriate map.

RESULTS

The estimated annual incidence of TB in Erbil city constantly increased from 16 per 100,000 inhabitants in the year 2012 to 21.7 per 100,000 inhabitants in the year 2016. Most of the TB cases were from the middle zone of Erbil city (44%), followed by the outer zone (43.8%) and the central zone (12.2%). The largest number of the cases were from Brayaty quarter (40), followed by Badawa (35) Nawroz (35) and Kurdistan (31) quarters. Clusters with a large number of cases were mainly located in the southern part of the city.

CONCLUSION

The estimated TB incidence constantly increased in Erbil city from 2012 to 2016. Mapping the distribution of TB cases in Erbil city provided useful information about the epidemiological situation of TB that can be used to direct future TB control strategies.

Spatiotemporal characteristics analysis and potential distribution prediction of peste des petits ruminants (PPR) in China from 2007-2018

Peste des petits ruminants (PPR) is a highly infectious disease that mainly infects small ruminants. To date, PPR has been confirmed in more than 70 countries. In China, PPR has occurred in more than 20 provinces and cities. In this study, based on geographic information system (GIS), spatial analysis was used to examine the occurrence of PPR in China from 2007 to 2018. The results showed that PPR first occurred in Tibet and gradually spread to other provinces. The outbreaks of PPR were concentrated in 2014, 2015 and 2018. Combining climate factors with the maximum entropy (MaxEnt), the results also suggested that the potential risk areas of PPR outbreaks in China were mainly Jiangsu, Yunnan and Anhui in Southeast China. Finally, a phylogenetic tree was used to analyse the evolutionary relationship between the peste des petits ruminants virus (PPRV) in China and the global ones, and it was found that the one in China had a close genetic relationship with the one in Mongolia, India and Bangladesh. Understanding and forecasting the distribution of PPR in China will help policymakers develop targeted monitoring plans. Likewise, analysing the global PPRV epidemic trends will play an important role in the elimination and prevention of PPR.

Motor vehicle crashes on tribal reservations: mapping and statistics

Background

Motor vehicle crashes (MVC’s) in the American Indian/Alaska Native (AI/AN) communities account for 43% of unintentional injury deaths. This article introduces MVC data and geographic information system (GIS) mapping for tribal reservations.

Methods

Utilizing a sample of Montana Department of Transportation (DOT) data for the Flathead reservations to calculate frequencies and proportions of crash types (i.e., property damage or no-injury, injury, fatality or unknown), while also mapping these data to provide a cross-sectional snapshot of MVC’s.

Results

Overall, 515 MVC’s occurred for years 2016 through 2018, with no-injury, injury, and fatality accounting for 72.2%, 24.9% and 1.8% of all crashes, respectively, with the number of MVC’s ranging up to 30 per square mile.

Conclusion

Examining DOT data and utilizing it for visual representation of MVC’s can be used as an additional source in uncovering patterns and trends on Tribal reservations and supporting MVC prevention efforts.

Geo-Mapping of Early Childhood Caries Risk: A Community Oriented Preventive Oral Health Promotional Approach

Context:

Given the uneven distribution of dental caries, there is an exigent need for a database of dental caries and its spatial distribution for better planning and efficient delivery of health promotional and preventive programs. Geo-mapping is a helpful tool for policy makers/administrators for efficient allocation of limited resources.

Aims:

To geo-map spatial distribution of caries risk in preschoolers of Lucknow and to identify associated predisposing factors.

Settings and design:

A cross-sectional study was done among 1000 preschool children (3-5 years of age) attending pediatrics, outpatient department at a medical college in Lucknow, Uttar Pradesh.

Methods and Material:

Children were enrolled using the systematic random sampling. Each child was geo-coded with respect to his/her residence, clinically examined for dental caries and given a Decayed Missing Filled Tooth (DMFT) index score. A pre-tested questionnaire was used to collect socio-demographic data. Caries prevalence was geo-mapped using color codes.

Statistical Analysis Used:

Median DMFT scores were compared using Mann-Whitney and Kruskal-Wallis test. QQ plot/Shapiro-Wilk's test was used to check the normality of data.

Results:

Prevalence of caries was found to be 76%. 10% children had DMFT score of 4 and more. A significant difference in distribution of DMFT score was observed for gender, income levels and between wards. Wards closer to the center of Lucknow district had a higher prevalence of caries.

Conclusions:

Geo-mapping of caries prevalence gives a quick visual glance of specific areas vulnerable to caries and help deliver specific tailor-made services.

Particulate matter exposure predicts residence in high-risk areas for community acquired pneumonia among hospitalized children

Particulate matter exposure is a risk factor for lower respiratory tract infection in children. Here, we investigated the geospatial patterns of community-acquired pneumonia and the impact of PM_2.5 (particulate matter with an aerodynamic diameter ≤2.5 µm) on geospatial variability of pneumonia in children. We performed a retrospective analysis of prospectively collected population-based surveillance study data of community-acquired pneumonia hospitalizations among children <18 years residing in the Memphis metropolitan area, who were enrolled in the Centers for Disease Control and Prevention sponsored Etiology of Pneumonia in the Community (EPIC) study from January 2010 to June 2012. The outcome measure, residence in high- and low-risk areas for community-acquired pneumonia, was determined by calculating pneumonia incidence rates and performing cluster analysis to identify areas with higher/lower than expected rates of community-acquired pneumonia for the population at risk. High PM_2.5 was defined as exposure to PM_2.5 concentrations greater than the mean value (>10.75 μg/m³), and low PM_2.5 is defined as exposure to PM_2.5 concentrations less than or equal to the mean value (≤10.75 μg/m³). We also assessed the effects of age, sex, race/ethnicity, history of wheezing, insurance type, tobacco smoke exposure, bacterial etiology, and viral etiology of infection. Of 810 (96.1%) subjects with radiographic community-acquired pneumonia, who resided in the Memphis metropolitan area and had addresses which were successfully geocoded (Supplementary Figure F2), 220 (27.2%) patients were identified to be from high- (n = 126) or low-risk (n = 94) community-acquired pneumonia areas. Community-acquired pneumonia in Memphis metropolitan area had a non-homogenous geospatial pattern. PM_2.5 was associated with residence in high-risk areas for community-acquired pneumonia. In addition, children with private insurance and bacterial, as opposed to viral, etiology of infection had a decreased risk of residence in a high-risk area for community-acquired pneumonia. The results from this paper suggest that environmental exposures as well as social risk factors are associated with childhood pneumonia.

Exploring Vector-Borne Disease Surveillance and Response Systems in Beijing, China: A Qualitative Study from the Health System Perspective

Background: Climate change may contribute to higher incidence and wider geographic spread of vector borne diseases (VBDs). Effective monitoring and surveillance of VBDs is of paramount importance for the prevention of and timely response to outbreaks. Although international regulations exist to support this, barriers and operational challenges within countries hamper efficient monitoring. As a first step to optimise VBD surveillance and monitoring, it is important to gain a deeper understanding of system characteristics and experiences in to date non-endemic regions at risk of becoming endemic in the future. Therefore, this study qualitatively analyses the nature and flexibility of VBD surveillance and response in Beijing. Methods: In this qualitative study, eleven experts working in Beijing’s vector-borne diseases surveillance and response system were interviewed about vector-borne disease surveillance, early warning, response, and strengths and weaknesses of the current approach. Results: Vector-borne disease surveillance occurs using passive syndromic surveillance and separate vector surveillance. Public health authorities use internet reporting networks to determine vector-borne disease risk across Beijing. Response toward a vector-borne disease outbreak is uncommon in this setting due to the currently low occurrence of outbreaks. Conclusions: A robust network of centralised institutions provides the continuity and flexibility needed to adapt and manage possible vector-borne disease threats. Opportunities exist for population-based health promotion and the integration of environment and climate monitoring in vector-borne disease surveillance.

Oncomelania hupensis quadrasi: Snail intermediate host of Schistosoma japonicum in the Philippines

Oncomelania hupensis quadrasi is the snail intermediate host of Schistosoma japonicum in the Philippines. It was discovered by Dr. Marcos Tubangui in 1932 more than two decades after the discovery of the disease in the country in 1906. This review, the first for O. h. quadrasi, presents past and present works on the taxonomy, biology, ecology, control, possible paleogeographic origin of the snail intermediate host and future in research, control and surveillance of the snail. Extensive references are made of other subspecies of O. hupensis such as the subspecies in China for which majority of the advances has been accomplished. Contrasting views on whether the snail is to be considered an independent species of Oncomelania or as one of several subspecies of Oncomelania hupensis are presented. Snail control methods such as chemical methods using synthetic and botanical molluscicides, environmental manipulation and biological control are reviewed. Use of technologies such as Remote Sensing, Geographical Information System and landscape genetics is stressed for snail surveillance. Control and prevention efforts in the Philippines have consistently focused on mass drug administration which has proved inadequate in elimination of the disease. An integrated approach that includes snail control, environmental sanitation and health education has been proposed. Population movement such as migration for employment and economic opportunities and ecotourism and global climate change resulting in heavy rains and flooding challenge the gains of control and elimination efforts. Concern for possible migration of snails to non-endemic areas is expressed given the various changes both natural and mostly man-made favoring habitat expansion.

Detecting Local Clusters of Under-5 Malnutrition in the Province of Marinduque, Philippines Using Spatial Scan Statistic

Underweight and overweight among under-5 children continue to persist in the island Province of Marinduque, Philippines. Local spatial cluster detection provides a spatial perspective in understanding this phenomenon, specifically in which areas the double burden of malnutrition occurs. Using data from a province-wide census conducted in 2014-2016, we aimed to identify spatial clusters of different forms of malnutrition in the province and determine its relative risk. Weight-for-age z score was used to categorize the malnourished children into severely underweight, moderately underweight, and overweight. We used the multinomial model of Kulldorff’s elliptical spatial scan statistic, adjusting for age and socioeconomic status. Four significant clusters across municipalities of Boac, Buenavista, Gasan, and Torrijos were found to have high risk of overweight and underweight simultaneously, indicating existence of double burden of malnutrition within these communities. These clusters should be targeted with tailored plans to respond to malnutrition, at the same time maximizing the resources and benefits.

Epidemic investigations within an arm's reach - role of google maps during an epidemic outbreak

Epidemics such as novel Coronavirus 2019 (COVID-19) can be contained and the rate of infection reduced by public health measures such as epidemiologic inquiries and social distancing. Epidemiologic inquiry requires resources and time which may not be available or reduced when the outbreak is excessive. We evaluated the use of Google Maps Timeline (GMTL) for creating spatial epidemiologic timelines. The study compares locations, routes, and means of transport between GMTL and user recall for 17 suitable users who were recruited during March 2020. They were interviewed about their timeline using the Timeline Follow-Back (TLFB) method which was then compared to their GMTL and discrepancies between both methods were addressed. Interviewer conclusions were divided into categories: (1) participant recalled, (2) no recall (until shown). Categories were subdivided by GMTL accuracy: [a] GMTL accurate, [b] GMTL inaccurate, [c] GMTL data missing. A total of 362 locations were compared. Participants recalled 322 (88.95% SD = 8.55) locations compared with 40 (11.05%, SD = 2.05) locations not recalled. There were 304 locations found accurate on GMTL (83.98%, SD = 9.49), 29 (8.01%, SD = 1.11) inaccurate locations, and 29 (8.01%, SD = 0.54) missing locations. The total discrepancy between GMTL and TLFB recall was 95 cases (26.24%, SD = 3.25). Despite variations between users, Google Maps with GMTL technology may be useful in identifying potentially exposed individuals in a pandemic. It is especially useful when resources are limited. Further research is required with a larger number of users who are undergoing a real epidemiologic investigation to corroborate findings and establish further recommendations.

Issues of Healthcare Planning and GIS: A Review

Introduction: For the past 2400 years, the spatial relationship between health and location has been a concern for researchers. Studies have been conducted for decades to understand such a relationship, which has led to the identification of a number of healthcare planning issues. Geographic Information Systems (GIS) technology has contributed to addressing such issues by applying analytical approaches at the level of epidemiological surveillance and evaluating the spatial inequality of access to healthcare. Consequently, the importance of reviewing healthcare planning issues and recognition of the role of GIS are integral to relevant studies. Such research will contribute to increasing the understanding of how to apply analytical approaches for dealing with healthcare planning issues using GIS. Methods: This paper aims to provide an examination of healthcare planning issues and focuses on reviewing the potential of GIS in dealing with such issues by applying analytical approaches. The method of a typical literature review was used through collecting data from various studies selected based on temporal and descriptive considerations. Results: Researchers have focused on developing and applying analytical approaches using GIS to support two important aspects of healthcare planning: first, epidemic surveillance and modeling, despite a lack of health information and its management, and, second, evaluating the spatial inequality of access to healthcare in order to determine the optimum distribution of health resources. Conclusion: GIS is an effective tool to support spatial decision-making in public health through applying the evolving analytical approaches to dealing with healthcare planning issues. This requires a literature review before preparing relevant studies, particularly because of the continuous development of GIS technologies.

Review of Geospatial Technology for Infectious Disease Surveillance: Use Case on COVID-19

This paper discusses on the increasing relevancy of geospatial technologies such as geographic information system (GIS) in the public health domain, particularly for the infectious disease surveillance and modelling strategies. Traditionally, the disease mapping tasks have faced many challenges—(1) authors rarely documented the evidence that were used to create map, (2) before evolution of GIS, many errors aroused in mapping tasks which were expanded extremely at global scales, and (3) there were no fidelity assessment of maps which resulted in inaccurate precision. This study on infectious diseases geo-surveillance is divided into four broad sections with emphasis on handling geographical and temporal issues to help in public health decision-making and planning policies: (1) geospatial mapping of diseases using its spatial and temporal information to understand their behaviour across geography; (2) the citizen’s involvement as volunteers in giving health and disease data to assess the critical situation for disease’s spread and prevention in neighbourhood effect; (3) scientific analysis of health-related behaviour using mathematical epidemiological and geo-statistical approaches with (4) capacity building program. To illustrate each theme, recent case studies are cited and case studies are performed on COVID-19 to demonstrate selected models.

Predicting Risk of Imported Disease with Demographics: Geospatial Analysis of Imported Malaria in Minnesota, 2010-2014

Although immigrants who visit friends and relatives (VFRs) account for most of the travel-acquired malaria cases in the United States, there is limited evidence on community-level risk factors and best practices for prevention appropriate for various VFR groups. Using 2010–2014 malaria case reports, sociodemographic census data, and health services data, we explored and mapped community-level characteristics to understand who is at risk and where imported malaria infections occur in Minnesota. We examined associations with malaria incidence using Poisson and negative binomial regression. Overall, mean incidence was 0.4 cases per 1,000 sub-Saharan African (SSA)–born in communities reporting malaria, with cases concentrated in two areas of Minneapolis–St. Paul. We found moderate and positive associations between imported malaria and counts of SSA- and Asian-born populations, respectively. Our findings may inform future studies to understand the knowledge, attitudes, and practices of VFR travelers and facilitate and focus intervention strategies to reduce imported malaria in the United States.

Using a GIS to support the spatial reorganization of outpatient care services delivery in Italy

BACKGROUND

Studying and measuring accessibility to care services has become a major concern for health care management, particularly since the global financial collapse. This study focuses on Tuscany, an Italian region, which is re-organizing its inpatient and outpatient systems in line with new government regulations. The principal aim of the paper is to illustrate the application of GIS methods with real-world scenarios to provide support to evidence-based planning and resource allocation in healthcare.

METHODS

Spatial statistics and geographical analyses were used to provide health care policy makers with a real scenario of accessibility to outpatient clinics. Measures for a geographical potential spatial accessibility index using the two-step floating catchment area method for outpatient services in 2015 were calculated and used to simulate the rationalization and reorganization of outpatient services. Parameters including the distance to outpatient clinics and volumes of activity were taken into account.

RESULTS

The spatial accessibility index and the simulation of reorganization in outpatient care delivery are presented through three cases, which highlight three different managerial strategies. The results revealed the municipalities where health policy makers could consider a new spatial location, a shutdown or combining selected outpatient clinics while ensuring equitable access to services.

CONCLUSIONS

A GIS-based approach was designed to provide support to healthcare management and policy makers in defining evidence-based actions to guide the reorganization of a regional health care delivery system. The analysis provides an example of how GIS methods can be applied to an integrated framework of administrative health care and geographical data as a valuable instrument to improve the efficiency of healthcare service delivery, in relation to the population's needs.

Spatial Distribution of Necrophagous Flies of Infraorder Muscomorpha in Iran Using Geographical Information System

Medically important flies that majority of them are classified to synanthropic flies can be mechanical vectors for some of contagious diseases to human and many animals. Also some species of their larvae are cause of myiasis in human and livestock animals. The aim of this study was to determine the geographical distribution of medically important flies in Iran using Geographical Information System (GIS). All published English and Persian documents on medically important flies of Iran from reliable medical sciences resources were gathered. A database was then designed in Excel format, including all available information regarding flies. The valid data were transferred to ArcGIS 10.3 to prepare the first spatial database of medically important flies and human myiasis foci of Iran. The Iran Medical Important Flies base includes 71 papers and three PhD/MSc theses, reporting studies conducted during 1977-2017. This database contains different available data covering their faunistic and all myiasis data reports related to flies of 102 species in nine families of medical important flies in Iran. Twenty-three out of the 31 provinces of Iran, had some reports of medically important flies. However, these flies have high species diversity, wide geographical distribution and with various ecological niches in different provinces in Iran. Besides that, there are many high-risk foci of human myiasis in a number of provinces of Iran.

Geospatial Assessment of Cholera in a Rapidly Urbanizing Environment

This study mapped out and investigated the spatial relationship between cholera incidences and environmental risk factors in the study area. The study area was stratified into eight zones. Water samples from each zone were collected and analyzed to determine the colony forming units. GIS layers including housing density, digitized roads, rivers, buildings, and cholera incidence data from hospital archives were also collected and analyzed using ArcGIS 10.1. It was observed that there was an association between the ERFs (p < 0.001). Similarly, 18 out of the 44 waste dump sites, seven out of 18 markets, and two out of 36 abattoirs were found near the historical cholera cases. Similarly, 4 (21.1%) locations were traced to be predominantly close to rivers and waste dump site. All the historical cholera cases were found adjoining to roads and buildings. Highest CFU count was found in the wells and streams of areas with a cluster of all the environmental risk factors and high housing density. This study revealed that waste dump sites and market had the highest predisposing attribute while the least was abattoir. The uniqueness of the study lies in the combination of mapping and microbial analyses to identify and assess the pattern of cholera risk and also to provide clear information for development of strategies for environmental supervision.

Spatiotemporal Distribution of Gastrointestinal Tract Cancer through GIS over 2007-2012 in Kermanshah-Iran

BACKGROUND

Cancer is one of the common causes of disability and mortality in the world. The present study aimed to define the spatiotemporal distribution of gastrointestinal tract cancers using a geographic information system (GIS) over the time period of 2007-2012 in Kermanshah-Iran.

MATERIALS AND METHODS

The method of studying was descriptive-analytical as well as comparative with gastrointestinal tract cancer patients based in the City of Kermanshah over the time period covered. For data analysis, the GIS and SPSS 16.0 were applied.

RESULTS

According to the pathological reports within the space of 5 years, 283 cases of gastrointestinal tract cancer (157 in males, 156 in females) were reported. The performed tests in terms of spatial distribution in the environment of GIS indicated that the disease demonstrated a clustered pattern in the City of Kermanshah. More to the point, some loci of this disease have emerged in the City of Kermanshah that in the first level, 6 neighborhoods with 29-59 cases of this disease per square kilometer and in the second level, 15-29 cases.

CONCLUSIONS

Gastrointestinal tract cancer demonstrated an ascending trend within the space of 5 years of research and the spatiotemporal distribution of cancer featured a concentrated and clustered pattern in the City of Kermanshah.

Improving Surveillance of Sexually Transmitted Diseases through Geocoded Morbidity Assignment

OBJECTIVES

We assessed the added value of using a geocoder to improve sexually transmitted disease (STD) surveillance data and decision support through redistribution of inaccurately assigned morbidity in Richmond, Virginia.

METHODS

Virginia initiated geocoding of STD data as a data quality tool in 2002. Geocoded output files were assessed and discordant proportions of reported gonorrhea and chlamydia morbidity were reassigned appropriately for the city of Richmond, Chesterfield County, and Henrico County (2002 to 2006). We used Chi-square analysis to compare assignment proportions and calculated crude odds ratios for 2006 data to estimate increased case reassignment likelihood.

RESULTS

From 2002 to 2006, 149,229 cases of gonorrhea and chlamydia were reported within the Commonwealth of Virginia. Of the reported morbidity, 81% of cases (n=120,875) were successfully geocoded; 7% (n=8,461) of geocoded addresses were reassigned. Approximately 76% (n=6,412) of all reassigned cases occurred within Richmond and Chesterfield and Henrico counties. In 2006, 84% (n=654) of reassigned cases in this tri-city/county area were initially reported as Richmond morbidity. Data quality improvements reduced Richmond's artificially inflated morbidity by 18% and increased Chesterfield and Henrico morbidity by 17% and 55%, respectively. Richmond morbidity was three times more likely to be reassigned than Chesterfield cases (odds ratio [OR] = 2.93, 95% confidence interval [CI] 2.21, 3.90), and two times more likely than Henrico cases (OR=2.12, 95% CI 1.63, 2.76). Richmond's number one national rank for STD rates was reduced beginning in 2002.

CONCLUSIONS

Declining rates of STDs were statistically associated with geocoded morbidity reassignments. Implementation of this data quality business process has improved epidemiologic analyses, prevention planning, and assessment of resource allocations. The reduction in Richmond's national STD rankings is indicative of the effect geocoding can have on surveillance data.

Assessment of Geographic Information Systems and Data Confidentiality Guidelines in STD Programs

OBJECTIVE

Advancements in technology, such as geographic information systems (GIS), expand sexually transmitted disease (STD) program capacity for data analysis and visualization, and introduce additional confidentiality considerations. We developed a survey to examine GIS use among STD programs and to better understand existing data confidentiality practices.

METHODS

A Web-based survey of eight to 22 questions, depending on program-specific GIS capacity, was e-mailed to all STD program directors through the National Coalition of STD Directors in November 2004. Survey responses were accepted until April 15, 2005.

RESULTS

Eighty-five percent of the 65 currently funded STD programs responded to the survey. Of those, 58% used GIS and 54% used geocoding. STD programs that did not use GIS (42%) identified lack of training and insufficient staff as primary barriers. Mapping, spatial analyses, and targeting program interventions were the main reasons for geocoding data. Nineteen of the 25 programs that responded to questions related to statistical disclosure rules employed a numerator rule, and 56% of those used a variation of the "Rule of 5." Of the 28 programs that responded to questions pertaining to confidentiality guidelines, 82% addressed confidentiality of GIS data informally.

CONCLUSIONS

Survey findings showed the increasing use of GIS and highlighted the struggles STD programs face in employing GIS and protecting confidentiality. Guidance related to data confidentiality and additional access to GIS software and training could assist programs in optimizing use of spatial data.

Application of GIS technology in public health: successes and challenges

The uptake and acceptance of Geographic Information Systems (GIS) technology has increased since the early 1990s and public health applications are rapidly expanding. In this paper, we summarize the common uses of GIS technology in the public health sector, emphasizing applications related to mapping and understanding of parasitic diseases. We also present some of the success stories, and discuss the challenges that still prevent a full scope application of GIS technology in the public health context. Geographical analysis has allowed researchers to interlink health, population and environmental data, thus enabling them to evaluate and quantify relationships between health-related variables and environmental risk factors at different geographical scales. The ability to access, share and utilize satellite and remote-sensing data has made possible even wider understanding of disease processes and of their links to the environment, an important consideration in the study of parasitic diseases. For example, disease prevention and control strategies resulting from investigations conducted in a GIS environment have been applied in many areas, particularly in Africa. However, there remain several challenges to a more widespread use of GIS technology, such as: limited access to GIS infrastructure, inadequate technical and analytical skills, and uneven data availability. Opportunities exist for international collaboration to address these limitations through knowledge sharing and governance.

A Rapid Monitoring and Evaluation Method of Schistosomiasis Based on Spatial Information Technology

Thanks to Spatial Information Technologies (SITs) such as Remote Sensing (RS) and Geographical Information System (GIS) that are being quickly developed and updated, SITs are being used more widely in the public health field. The use of SITs to study the characteristics of the temporal and spatial distribution of Schistosoma japonicum and to assess the risk of infection provides methods for the control and prevention of schistosomiasis japonica has gradually become a hot topic in the field. The purpose of the present paper was to use RS and GIS technology to develop an efficient method of prediction and assessment of the risk of schistosomiasis japonica. We choose the Yueyang region, close to the east DongTing Lake (Hunan Province, China), as the study area, where a recent serious outbreak of schistosomiasis japonica took place. We monitored and evaluated the transmission risk of schistosomiasis japonica in the region using SITs. Water distribution data were extracted from RS images. The ground temperature, ground humidity and vegetation index were calculated based on RS images. Additionally, the density of oncomelania snails, which are the Schistosoma japonicum intermediate host, was calculated on the base of RS data and field measurements. The spatial distribution of oncomelania snails was explored using SITs in order to estimate the area surrounding the residents with transmission risk of schistosomiasis japonica. Our research result demonstrated: (1) the risk factors for the transmission of schistosomiasis japonica were closely related to the living environment of oncomelania snails. Key factors such as water distribution, ground temperature, ground humidity and vegetation index can be quickly obtained and calculated from RS images; (2) using GIS technology and a RS deduction technique along with statistical regression models, the density distribution model of oncomelania snails could be quickly built; (3) using SITs and analysis with overlaying population distribution data, the range of transmission risk of schistosomiasis japonica of the study area can be quickly monitored and evaluated. This method will help support the decision making for the control and prevention of schistosomiasis and form a valuable application using SITs for the schistosomiasis research.

Integrative Biological Chemistry Program Includes The Use Of Informatics Tools, GIS And SAS Software Applications

Wesley College is a private, primarily undergraduate minority-serving institution located in the historic district of Dover, Delaware (DE). The College recently revised its baccalaureate biological chemistry program requirements to include a one-semester Physical Chemistry for the Life Sciences course and project-based experiential learning courses using instrumentation, data-collection, data-storage, statistical-modeling analysis, visualization, and computational techniques. In this revised curriculum, students begin with a traditional set of biology, chemistry, physics, and mathematics major core-requirements, a geographic information systems (GIS) course, a choice of an instrumental analysis course or a statistical analysis systems (SAS) programming course, and then, students can add major-electives that further add depth and value to their future post-graduate specialty areas. Open-sourced georeferenced census, health and health disparity data were coupled with GIS and SAS tools, in a public health surveillance system project, based on US county zip-codes, to develop use-cases for chronic adult obesity where income, poverty status, health insurance coverage, education, and age were categorical variables. Across the 48 contiguous states, obesity rates are found to be directly proportional to high poverty and inversely proportional to median income and educational achievement. For the State of Delaware, age and educational attainment were found to be limiting obesity risk-factors in its adult population. Furthermore, the 2004-2010 obesity trends showed that for two of the less densely populated Delaware counties; Sussex and Kent, the rates of adult obesity were found to be progressing at much higher proportions when compared to the national average.

Mapping malaria in municipalities of the Coffee Triangle region of Colombia using Geographic Information Systems (GIS)

Geographical Information Systems (GIS) have been used extensively for the development of epidemiological maps of malaria but not in the Coffee Triangle region of Colombia, endemic for P. vivax, P. falciparum and P. malariae. Surveillance case data (2007-2011) were used to estimate annual incidence rates per Plasmodium spp. (cases/100,000 pop) to develop the first malaria maps in the 53 municipalities of this region (departments Caldas, Quindío, Risaralda). The GIS software used was Kosmo Desktop 3.0RC1(®). Thirty thematic maps were developed according to the municipalities, years, parasite etiology, and uncomplicated and complicated cases. A total of 6582 cases were reported (6478 uncomplicated and 104 complicated, 77.8% Risaralda), for a cumulated rate of 269.46 cases/100,000 pop. Among uncomplicated cases, 5722 corresponded to P. vivax (234.25 cases/100,000 pop), 475 to P. falciparum (19.45 cases/100,000 pop), 8 to P. malariae (0.33 cases/100,000 pop) and 273 mixed (P. falciparum/P. vivax) (11.18 cases/100,000 pop). The highest rate reported was in the more undeveloped and rural municipality of Risaralda (Pueblo Rico, 57.7 cases/1000 pop, 2009). The burden of disease was concentrated in one department (>75% of the region). The use of GIS-based epidemiological maps helps to guide decision-making for the prevention and control of this public health problem that still represents a significant issue in the region and the country, particularly in children.

Spatial clustering of measles cases during endemic (1998-2002) and epidemic (2010) periods in Lusaka, Zambia

BACKGROUND

Measles cases may cluster in densely populated urban centers in sub-Saharan Africa as susceptible individuals share spatially dependent risk factors and may cluster among human immunodeficiency virus (HIV)-infected children despite high vaccination coverage.

METHODS

Children hospitalized with measles at the University Teaching Hospital (UTH) in Lusaka, Zambia were enrolled in the study. The township of residence was recorded on the questionnaire and mapped; SaTScan software was used for cluster detection. A spatial-temporal scan statistic was used to investigate clustering of measles in children hospitalized during an endemic period (1998 to 2002) and during the 2010 measles outbreak in Lusaka, Zambia.

RESULTS

Three sequential and spatially contiguous clusters of measles cases were identified during the 2010 outbreak but no clustering among HIV-infected children was identified. In contrast, a space-time cluster among HIV-infected children was identified during the endemic period. This cluster occurred prior to the introduction of intensive measles control efforts and during a period between seasonal peaks in measles incidence.

CONCLUSIONS

Prediction and early identification of spatial clusters of measles will be critical to achieving measles elimination. HIV infection may contribute to spatial clustering of measles cases in some epidemiological settings.

Using geographical information systems and cartograms as a health service quality improvement tool

INTRODUCTION

Disease prevalence can be spatially analysed to provide support for service implementation and health care planning, these analyses often display geographic variation. A key challenge is to communicate these results to decision makers, with variable levels of Geographic Information Systems (GIS) knowledge, in a way that represents the data and allows for comprehension. The present research describes the combination of established GIS methods and software tools to produce a novel technique of visualising disease admissions and to help prevent misinterpretation of data and less optimal decision making. The aim of this paper is to provide a tool that supports the ability of decision makers and service teams within health care settings to develop services more efficiently and better cater to the population; this tool has the advantage of information on the position of populations, the size of populations and the severity of disease.

METHODS

A standard choropleth of the study region, London, is used to visualise total emergency admission values for Chronic Obstructive Pulmonary Disease and bronchiectasis using ESRI's ArcGIS software. Population estimates of the Lower Super Output Areas (LSOAs) are then used with the ScapeToad cartogram software tool, with the aim of visualising geography at uniform population density. An interpolation surface, in this case ArcGIS' spline tool, allows the creation of a smooth surface over the LSOA centroids for admission values on both standard and cartogram geographies. The final product of this research is the novel Cartogram Interpolation Surface (CartIS).

RESULTS

The method provides a series of outputs culminating in the CartIS, applying an interpolation surface to a uniform population density. The cartogram effectively equalises the population density to remove visual bias from areas with a smaller population, while maintaining contiguous borders. CartIS decreases the number of extreme positive values not present in the underlying data as can be found in interpolation surfaces.

DISCUSSION

This methodology provides a technique for combining simple GIS tools to create a novel output, CartIS, in a health service context with the key aim of improving visualisation communication techniques which highlight variation in small scale geographies across large regions. CartIS more faithfully represents the data than interpolation, and visually highlights areas of extreme value more than cartograms, when either is used in isolation.

Farm management, environment, and weather factors jointly affect the probability of spinach contamination by generic Escherichia coli at the preharvest stage

The National Resources Information (NRI) databases provide underutilized information on the local farm conditions that may predict microbial contamination of leafy greens at preharvest. Our objective was to identify NRI weather and landscape factors affecting spinach contamination with generic Escherichia coli individually and jointly with farm management and environmental factors. For each of the 955 georeferenced spinach samples (including 63 positive samples) collected between 2010 and 2012 on 12 farms in Colorado and Texas, we extracted variables describing the local weather (ambient temperature, precipitation, and wind speed) and landscape (soil characteristics and proximity to roads and water bodies) from NRI databases. Variables describing farm management and environment were obtained from a survey of the enrolled farms. The variables were evaluated using a mixed-effect logistic regression model with random effects for farm and date. The model identified precipitation as a single NRI predictor of spinach contamination with generic E. coli, indicating that the contamination probability increases with an increasing mean amount of rain (mm) in the past 29 days (odds ratio [OR] = 3.5). The model also identified the farm's hygiene practices as a protective factor (OR = 0.06) and manure application (OR = 52.2) and state (OR = 108.1) as risk factors. In cross-validation, the model showed a solid predictive performance, with an area under the receiver operating characteristic (ROC) curve of 81%. Overall, the findings highlighted the utility of NRI precipitation data in predicting contamination and demonstrated that farm management, environment, and weather factors should be considered jointly in development of good agricultural practices and measures to reduce produce contamination.

Spatial analysis of the home addresses of hospital patients with hepatitis B infection or hepatoma in Shenzhen, China from 2010 to 2012

Background: Hepatoma associated with hepatitis B infection is a major public health problem in Shenzhen (China) and worldwide. China has the largest number of people infected with the hepatitis B virus (HBV), and many studies have demonstrated that HBV infection is associated with hepatoma development. Shenzhen officials have been attempting to monitor and control these diseases for many years. The methodology and the results of this study may be useful in developing a system to monitor, prevent and control these diseases. Methods: The aim of the study was to analyze HBV infection and hepatoma distribution characteristics and patterns in Shenzhen by combining geographic information system (GIS) technology and spatial analysis. The study used data from patients at the district level from the 2010–2012 population censuses. Results: Only one-third of the patients were female, and 70.7% of all cases were 20–50 years of age. There was no global spatial correlation of the distribution of hepatitis B infections and hepatomas; however, there was a local spatial correlation, and certain sub-districts of the Nanshan district had significant agglomeration effects. Based on incidence density and rate maps, we can conclude that the Shenzhen special zone had a higher incidence density and rate of hepatitis B infections and hepatomas compared with the area outside of the Shenzhen special zone during 2010–2012. Conclusions: This study demonstrated substantial geographic variation in the incidence of hepatitis B infection and hepatoma in Shenzhen. The prediction and control of hepatitis B infections and hepatoma development and interventions for these diseases should focus on disadvantaged areas to reduce disparities. GIS and spatial analysis play an important role in public health risk-reduction programs and may become integral components in the epidemiologic description, analysis and risk assessment of hepatitis B and hepatoma.

Use of GIS Mapping as a Public Health Tool-From Cholera to Cancer

The field of medical geographic information systems (Medical GIS) has become extremely useful in understanding the bigger picture of public health. The discipline holds a substantial capacity to understand not only differences, but also similarities in population health all over the world. The main goal of marrying the disciplines of medical geography, public health and informatics is to understand how countless health issues impact populations, and the trends by which these populations are affected. From the 1990s to today, this practical approach has become a valued and progressive system in analyzing medical and epidemiological phenomena ranging from cholera to cancer. The instruments supporting this field include geographic information systems (GIS), disease surveillance, big data, and analytical approaches like the Geographical Analysis Machine (GAM), Dynamic Continuous Area Space Time Analysis (DYCAST), cellular automata, agent-based modeling, spatial statistics and self-organizing maps. The positive effects on disease mapping have proven to be tremendous as these instruments continue to have a great impact on the mission to improve worldwide health care. While traditional uses of GIS in public health are static and lacking real-time components, implementing a space-time animation in these instruments will be monumental as technology and data continue to grow.

Geographic information systems and logistic regression for high-resolution malaria risk mapping in a rural settlement of the southern Brazilian Amazon

BACKGROUND

In Brazil, 99% of the cases of malaria are concentrated in the Amazon region, with high level of transmission. The objectives of the study were to use geographic information systems (GIS) analysis and logistic regression as a tool to identify and analyse the relative likelihood and its socio-environmental determinants of malaria infection in the Vale do Amanhecer rural settlement, Brazil.

METHODS

A GIS database of georeferenced malaria cases, recorded in 2005, and multiple explanatory data layers was built, based on a multispectral Landsat 5 TM image, digital map of the settlement blocks and a SRTM digital elevation model. Satellite imagery was used to map the spatial patterns of land use and cover (LUC) and to derive spectral indices of vegetation density (NDVI) and soil/vegetation humidity (VSHI). An Euclidian distance operator was applied to measure proximity of domiciles to potential mosquito breeding habitats and gold mining areas. The malaria risk model was generated by multiple logistic regression, in which environmental factors were considered as independent variables and the number of cases, binarized by a threshold value was the dependent variable.

RESULTS

Out of a total of 336 cases of malaria, 133 positive slides were from inhabitants at Road 08, which corresponds to 37.60% of the notifications. The southern region of the settlement presented 276 cases and a greater number of domiciles in which more than ten cases/home were notified. From these, 102 (30.36%) cases were caused by Plasmodium falciparum and 174 (51.79%) cases by Plasmodium vivax. Malaria risk is the highest in the south of the settlement, associated with proximity to gold mining sites, intense land use, high levels of soil/vegetation humidity and low vegetation density.

CONCLUSIONS

Mid-resolution, remote sensing data and GIS-derived distance measures can be successfully combined with digital maps of the housing location of (non-) infected inhabitants to predict relative likelihood of disease infection through the analysis by logistic regression. Obtained findings on the relation between malaria cases and environmental factors should be applied in the future for land use planning in rural settlements in the Southern Amazon to minimize risks of disease transmission.

A systematic review of the application of spatial analysis in pharmacoepidemiologic research

PURPOSE

Although current reviews of the use of spatial analysis in general epidemiologic research illustrate an important and well-established role in exploring and predicting health, its application has not been reviewed in the subspecialty field of pharmacoepidemiology.

METHODS

We systematically reviewed the scientific literature to assess to what extent spatial analysis has been applied in pharmacoepidemiologic research and explored its potential added value.

RESULTS

A systematic search in PubMed and Embase/MEDLINE yielded 823 potentially relevant articles; 45 articles met our criteria for review. The studies were reviewed on study objective, applied spatial methods and units of analysis, and author-reported added value of the geographic approach used. Of the 45 included studies, 34 (76%) reported a geographic research objective. Comparative spatial methods were most often used (n = 25; 56%). Eleven studies used spatial statistics (32%); cluster analysis (n = 5) and aggregate data analysis (n = 4) being most common. Mapping was done in 15 studies (33%). The most common added value reported was to aid the planning of health policies and interventions (n = 24; 53%). A minority of pharmacoepidemiologic studies used a geographic approach and the applied methods were less advanced compared with the broader field of epidemiology.

CONCLUSIONS

Further advancements are needed to incorporate currently available spatial techniques to impact health care planning.

Hyaluronan initiates chondrogenesis mainly via CD44 in human adipose-derived stem cells

Cell-matrix adhesion is one of the important interactions that regulates stem cell survival, self-renewal, and differentiation. Our previous report (Wu SC, Chang JK, Wang CK, Wang GJ, Ho ML. Biomaterials 31: 631–640, 2010) indicated that a microenvironment enriched with hyaluronan (HA) initiated and enhanced chondrogenesis in human adipose-derived stem cells (hADSCs). We further hypothesize that HA-induced chondrogenesis in hADSCs is mainly due to the interaction of HA and CD44 (HA-CD44), a cell surface receptor of HA. The HA-CD44 interaction was tested by examining the mRNA expression of hyaluronidase-1 (Hyal-1) and chondrogenic marker genes (SOX-9, collagen type II, and aggrecan) in hADSCs cultured on HA-coated wells. Cartilaginous matrix formation, sulfated glycosaminoglycan, and collagen productions by hADSCs affected by HA-CD44 interaction were tested in a three-dimensional fibrin hydrogel. About 99.9% of hADSCs possess CD44. The mRNA expressions of Hyal-1 and chondrogenic marker genes were upregulated by HA in hADSCs on HA-coated wells. Blocking HA-CD44 interaction by anti-CD44 antibody completely inhibited Hyal-1 expression and reduced chondrogenic marker gene expression, which indicates that HA-induced chondrogenesis in hADSCs mainly acts through HA-CD44 interaction. A 2-h preincubation and coculture of cells with HA in hydrogel (HA/fibrin hydrogel) not only assisted in hADSC survival, but also enhanced expression of Hyal-1 and chondrogenic marker genes. Higher levels of sulfated glycosaminoglycan and total collagen were also found in HA/fibrin hydrogel group. Immunocytochemistry showed more collagen type II, but less collagen type X, in HA/fibrin than in fibrin hydrogels. Our results indicate that signaling triggered by HA-CD44 interaction significantly contributes to HA-induced chondrogenesis and may be applied to adipose-derived stem cell-based cartilage regeneration.

Spatial variation in the risk of hospitalization with childhood pneumonia and empyema in the North of England

The aim of this study was to investigate spatial variation in risk of hospitalization in childhood pneumonia and empyema in the North of England and associated risk factors. Data on childhood (0-14 years) hospital admissions with a diagnosis pneumonia or empyema were linked to postcode districts. Bayesian conditional autoregressive models were used to evaluate spatial variation and the relevance of specific spatial covariates in an area-based study using postcode as the areal unit. There was a sixfold variation in the risk of hospitalization due to pneumonia across the study region. Variation in risk was associated with material deprivation, Child Well-being Index (CWI) health domain score, number of children requiring local authority support, and distance to hospital. No significant spatial variation in risk for empyema was found.

Spatial patterns of malaria reported deaths in Yunnan Province, China

Malaria has been a heavy social and health burden in the remote and poor areas in southern China. Analyses of malaria epidemic patterns can uncover important features of malaria transmission. This study identified spatial clusters, seasonal patterns, and geographic variations of malaria deaths at a county level in Yunnan, China, during 1991-2010. A discrete Poisson model was used to identify purely spatial clusters of malaria deaths. Logistic regression analysis was performed to detect changes in geographic patterns. The results show that malaria mortality had declined in Yunnan over the study period and the most likely spatial clusters (relative risk [RR] = 23.03-32.06, P < 0.001) of malaria deaths were identified in western Yunnan along the China-Myanmar border. The highest risk of malaria deaths occurred in autumn (RR = 58.91, P < 0.001) and summer (RR = 31.91, P < 0.001). The results suggested that the geographic distribution of malaria deaths was significantly changed with longitude, which indicated there was decreased mortality of malaria in eastern areas over the last two decades, although there was no significant change in latitude during the same period. Public health interventions should target populations in western Yunnan along border areas, especially focusing on floating populations crossing international borders.

Geographical information systems and health: current state and future directions

This paper provides an introduction to Geographical Information Systems (GIS) and how they can be used. It reviews the current state of GIS use in health care before identifying the barriers to more pervasive use of GIS in health. Finally, it makes recommendations for the direction of health GIS research over the next decade and concludes with a call to action to health informatics researchers to stop ignoring a tool and methodology that has such immense potential for improving the health of our communities.

A review of spatial methods in epidemiology, 2000-2010

Understanding the impact of place on health is a key element of epidemiologic investigation, and numerous tools are being employed for analysis of spatial health-related data. This review documents the huge growth in spatial epidemiology, summarizes the tools that have been employed, and provides in-depth discussion of several methods. Relevant research articles for 2000-2010 from seven epidemiology journals were included if the study utilized a spatial analysis method in primary analysis (n = 207). Results summarized frequency of spatial methods and substantive focus; graphs explored trends over time. The most common spatial methods were distance calculations, spatial aggregation, clustering, spatial smoothing and interpolation, and spatial regression. Proximity measures were predominant and were applied primarily to air quality and climate science and resource access studies. The review concludes by noting emerging areas that are likely to be important to future spatial analysis in public health.