Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom

Examining select sociodemographic characteristics of sub-county geographies for public health surveillance

BACKGROUND

Mapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them.

METHODS

Mean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract.

RESULTS

The sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program's aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level.

CONCLUSIONS

The Tracking Program's sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data.

Spatial disparities in access to NHS dentistry: a neighbourhood-level analysis in England

Over the past decade, access to National Health Service (NHS) dentistry in England has been problematic. There are increasing media reports of patients being unable to find treatment at a local NHS dentist. However, the extent of this issue varies by location and by the characteristics of the neighbourhood. The study uses official data sources on NHS dental provision and population. Travel accessibility is measured using car journey times. An advanced form of Floating Catchment Area accessibility is used, which accounts for supply competition, varying catchments, and distance decay. Spatial availability and accessibility indices are calculated. Ways in which the method can be used to explore various types of 'what-if' scenarios are outlined. Both availability and accessibility vary by the level of neighbourhood deprivation and the urban/rural nature of the neighbourhood. A case study, based on a real-world situation, shows the impact on the local neighbourhood of the closure of a dental practice. For all neighbourhoods, NHS dental provision is generally less than would be needed to provide basic dental care. The interpretation of outputs needs to take account of edge-effects near to Scotland and Wales. Possible improvements include the inclusion of other modes of travel and the exclusion of the population that does not want to access NHS care.

Evidences support that dengue virus can impart broad-spectrum immunity against betacoronaviruses in dengue endemic regions

COVID-19 tended to be less aggressive in dengue endemic regions. Conversely, dengue cases plummeted in dengue endemic zones during the active years of the pandemic (2020-2021). We and others have demonstrated serological cross-reactivity between these two viruses of different families. We further demonstrated that COVID-19 serum samples that were cross-reactive in dengue virus (DV) serological tests, "cross-neutralized" all DV serotypes in Huh7 cells. Here we showed by co-immunoprecipitation (Co-IP) and atomic force microscopy (AFM) imaging that severe acute respiratory syndrome (SARS)-coronavirus (CoV)-2 (SARS-CoV-2) spike (S) protein subunit S1 and S2 monoclonal antibodies can indeed, bind to DV particles. Likewise, DV envelope antibodies (DV E Abs) showed high docking frequency with other human pathogenic beta-CoVs and murine hepatitis virus-1 (MHV-1). SARS-CoV-2 Ab didn't show docking or Co-IP with MHV-1 supporting poor cross-protection among CoVs. DV E Abs showed binding to MHV-1 (AFM, Co-IP, and immunofluorescence) and prepandemic dengue patients' serum samples even "cross-neutralized" MHV-1 plaques in cell culture. Furthermore, dengue serum samples showed marked inhibition potential in a surrogate virus-based competitive enzyme-linked immunosorbent assay, used for determining neutralizing Abs against SARS-CoV-2 S protein receptor-binding domain in COVID-19 serum samples. We therefore, provide multiple evidence as to why CoVs are epidemiologically less prevalent in highly dengue endemic regions globally.

Future Possible Changes in Medically Underserved Areas in Japan: A Geographic Information System-Based Simulation Study

BACKGROUND

A decrease in populations could affect healthcare access and systems, particularly in medically underserved areas (MUAs) where depopulation is becoming more prevalent. This study aimed to simulate the future population and land areas of MUAs in Japan.

METHODS

This study covered 380,948 1 km meshes, 87,942 clinics, and 8354 hospitals throughout Japan as of 2020. The areas outside a 4 km radius of medical institutions were considered as MUAs, based on the measure of areas in the current Japanese Medical Care Act. Based on the population estimate for a 1 km mesh, the population of mesh numbers of MUAs was predicted for every 10 years from 2020 to 2050 using geographic information system analysis. If the population within a 4 km radius from a medical institution fell below 1000, the institution was operationally assumed to be closed.

RESULTS

The number of MUAs was predicted to decrease from 964,310 (0.77% of the total Japanese population) in 2020 to 763,410 (0.75%) by 2050. By 2050, 48,105 meshes (13% of the total meshes in Japan) were predicted to be new MUAs, indicating a 31% increase in MUAs from 2020 to 2050. By 2050, 1601 medical institutions were tentatively estimated to be in close proximity.

CONCLUSIONS

In Japan, the population of MUAs will decrease, while the land area of MUAs will increase. Such changes may reform rural healthcare policy and systems.

Geospatial methodology for determining the regional prevalence of hospital-reported childhood intussusception in patients from India

Both developed and developing countries carry a large burden of pediatric intussusception. Sentinel site surveillance-based studies have highlighted the difference in the regional incidence of intussusception. The objectives of this manuscript were to geospatially map the locations of hospital-confirmed childhood intussusception cases reported from sentinel hospitals, identify clustering and dispersion, and reveal the potential causes of the underlying pattern. Geospatial analysis revealed positive clustering patterns, i.e., a Moran's I of 0.071 at a statistically significant (p value < 0.0010) Z score of 16.14 for the intussusception cases across India (cases mapped n = 2221), with 14 hotspots in two states (Kerala = 6 and Tamil Nadu = 8) at the 95% CI. Granular analysis indicated that 67% of the reported cases resided < 50 km from the sentinel hospitals, and the average travel distance to the sentinel hospital from the patient residence was calculated as 47 km (CI 95% min 1 km-max 378 km). Easy access and facility referral preferences were identified as the main causes of the existing clustering pattern of the disease. We recommend designing community-based surveillance studies to improve the understanding of the prevalence and regional epidemiological burden of the disease.

Investigation of spatial pattern of apnea disorder using geographic information system (GIS)-case study of Kermanshah metropolis

Background

Apnea disorder is influenced by social and environmental factors. By assessing its hot spots and geography, the foci of this disorder and its at-risk groups can be identified for health interventions. This study was conducted to investigate the spatial pattern of apnea disorder using GIS in the Kermanshah metropolis.

Methods

In cross-sectional study was conducted in kermanshah, the statistical population were 119 people (73.95% male and 26.05% female) of Kermanshah residents, which referred to the sleep center from 2012 to 2018 due to apnea disorder. Information was collected from the records of patients referred to the Sleep Disorder Center of Farabi Hospital, which is the only service center in the west part of Iran. The statistical tests were the mean center, standard distance, Getis-Ord Gi * index, nearest neighbor index, and kernel density estimation test in GIS software.

Results

The spatial pattern of patients with apnea disorder has cluster formation in the Kermanshah metropolis. The age group of 50-54 had more apnea disorder than other age groups. In this age group, women were more prone to apnea than men. In terms of education, people with high education are more affected by this disorder; So that with the increase in education level, apnea disorder had also increased. Also, the findings showed that the disorder was more common in unemployed, married, overweight people with BMI (25-30), and obese people (30-40).

Conclusion

The spatial pattern of patients with apnea disorder was clustered and does not correspond to the high population density centers located in the marginal and slum areas of the city. These can be used by stakeholders, including governmental organizations and health authorities at the national-regional level.

Inequities in spatial accessibility to COVID-19 testing in 30 large US cities

Testing for SARS-CoV-2 infection has been a key strategy to mitigate and control the COVID-19 pandemic. Wide spatial and racial/ethnic disparities in COVID-19 outcomes have emerged in US cities. Previous research has highlighted the role of unequal access to testing as a potential driver of these disparities. We described inequities in spatial accessibility to COVID-19 testing locations in 30 large US cities. We used location data from Castlight Health Inc corresponding to October 2021. We created an accessibility metric at the level of the census block group (CBG) based on the number of sites per population in a 15-minute walkshed around the centroid of each CBG. We also calculated spatial accessibility using only testing sites without restrictions, i.e., no requirement for an appointment or a physician order prior to testing. We measured the association between the social vulnerability index (SVI) and spatial accessibility using a multilevel negative binomial model with random city intercepts and random SVI slopes. Among the 27,195 CBG analyzed, 53% had at least one testing site within a 15-minute walkshed, and 36% had at least one site without restrictions. On average, a 1-decile increase in the SVI was associated with a 3% (95% Confidence Interval: 2% - 4%) lower accessibility. Spatial inequities were similar across various components of the SVI and for sites with no restrictions. Despite this general pattern, several cities had inverted inequity, i.e., better accessibility in more vulnerable areas, which indicates that some cities may be on the right track when it comes to promoting equity in COVID-19 testing. Testing is a key component of the strategy to mitigate transmission of SARS-CoV-2 and efforts should be made to improve accessibility to testing, particularly as new and more contagious variants become dominant.

An External Patient Healthcare Index (EPHI) for Simulating Spatial Tendencies in Healthcare Seeking Behavior

Background

Healthcare resources are always more limited compared with demand, but better matching supply with demand can improve overall resource efficiency. In countries like China where patients are free to choose healthcare facilities, over-utilization and under-utilization of healthcare resources co-exist because of unreasonable healthcare seeking behavior. However, scholarship regarding the spatial distribution of utilization for healthcare resources, resulting from unreasonable spatial tendencies in healthcare seeking, is rare.

Methods

In this article, we propose a new External Patient Healthcare Index (EPHI) to simulate the spatial distribution of utilization for healthcare resources, based on the Two-Step Floating Catchment Area (2SFCA) method, which is widely used to assess potential spatial accessibility. Instead of using individual-level healthcare utilization data which is difficult to obtain, the EPHI uses institution-level aggregated data, including numbers of inpatient/outpatient visits. By comparing the estimated utilization (based on local healthcare institution services provision) with the expected utilization (based on local population morbidity), guest patients (e.g., patients flowing in for treatment) and bypass patients (patients flowing out) can be identified. To test the applicability of this index, a case study was carried out on China's Hainan Island. The spatial tendencies of patients for inpatient and outpatient services were simulated, then incorporated with spatial access to healthcare resources to evaluate overall resource allocation efficiency, thus guiding future resource allocations and investment for policy makers and healthcare providers.

Results

The EPHI revealed that bypass activities widely exist on Hainan Island in both inpatient and outpatient care, with patients tending to travel from less developed regions with fewer healthcare resources to more highly developed regions with more healthcare resources to receive healthcare. Comparison with spatial accessibility demonstrated how bypass activities on Hainan produced an under-utilization of doctors in less developed regions and over-utilization of doctors in more developed coastal regions.

Conclusions

This case study on Hainan Island demonstrates that this new index can very clearly identify both the sources and sinks of patient spatial tendencies. Combining these results with spatial accessibility of healthcare resources, how efficiently the available supply matches the utilization can be revealed, indicating wide-ranging applicability for local governments and policymakers.

Information Disclosure Contents of the COVID-19 Data Dashboard Websites for South Korea, China, and Japan: A Comparative Study

Official responses to the COVID-19 pandemic have prioritized information disclosure. Timely and comprehensive information released by the authorities is conveyed mainly through dashboards, which can better inform the public and help them prepare for the pandemic. However, there is limited evidence regarding the COVID-19 dashboard data presentation for South Korea, China, and Japan. This study aimed to describe the current COVID-19 situation in the three countries and compare the information disclosure content on their COVID-19 dashboards. Based on the COVID-19 data released and updated by each country's official authorities, two dashboard websites used by many people in each country were selected. We conducted content analysis and developed a checklist (39 items in five categories: cases, testing, vaccines, health information, and additional items) based on the structure of each country's COVID-19 dashboard website to assess COVID-19 information disclosure. Japan experienced the worst outbreak among the three countries. They all provided basic dynamic data displayed on the dashboard, while the performance in key categories varied substantially between the countries (South Korea: 30/39 items; China: 25/39 items; Japan: 30/39 items). Moreover, as part of the publicly accessible information recorded by each nation, there were differences in the key indicators published and important facts disclosed. Improvement in reporting techniques and disclosure methods will help countries communicate more effectively with the public and conduct more efficient public health research.

Using Electronic Medical Records to Identify Potentially Eligible Study Subjects for Lung Cancer Screening with Biomarkers

Lung cancer screening trials using low-dose computed tomography (LDCT) show reduced late-stage diagnosis and mortality rates. These trials have identified high-risk groups that would benefit from screening. However, these sub-populations can be difficult to access and retain in trials. Implementation of national screening programmes further suggests that there is poor uptake in eligible populations. A new approach to participant selection may be more effective. Electronic medical records (EMRs) are a viable alternative to population-based or health registries, as they contain detailed clinical and demographic information. Trials have identified that e-screening using EMRs has improved trial retention and eligible subject identification. As such, this paper argues for greater use of EMRs in trial recruitment and screening programmes. Moreover, this opinion paper explores the current issues in and approaches to lung cancer screening, whether records can be used to identify eligible subjects for screening and the challenges that researchers face when using EMR data.

Spatial analysis and modelling for primary healthcare site selection in Midnapore town, West Bengal

Unprecedented and chaotic growth of cities results in reducing open spaces and water bodies, worsening infrastructure facilities and changes in ecological morphology. This unregulated growth of the urban population led to uneven distribution of urban amenities, facilities and healthcare services. Considering this, the study aimed to draw attention to the existing spatial pattern of healthcare facility centres as well as to find out the possible sites for the provision of healthcare facility centres in the municipal ward (micro-scale) of Midnapore town. This prototype study was conducted using Analytical Hierarchy Process (AHP) and Ordinary Least Square (OLS) evaluation model based on various criteria through Arc GIS environment. The findings indicate that the spatial distribution patterns of existing public healthcare centres were significantly dispersed. Weights based on a set of criteria were calculated by AHP and OLS algorithm and generated suitability evaluation maps classified from 1 (poor suitable) to 4 (most suitable). According to the employed criteria in this study unveil those existing hospitals and primary healthcare centres have not been located in the appropriate locations. The model is found to be valid for the given study area and there is no significant difference between AHP and OLS results. Further, it can be used for preparing the suitability map for the other areas with similar geo-environmental conditions for the proviso of healthcare services as well as will be most effective in preventing disease progression and reducing healthcare inequality on a large scale.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10708-021-10528-w.

Dental specialist workforce and distribution in the United Kingdom: a specialist map

Objectives To illustrate, identify and assess a contemporary model of the geographic distribution of specialist dentists in relation to population age groups and rurality.Methods All UK dental specialists registered with the General Dental Council were extracted and paired with publicly available locations of work. Geographic information system tools were used to map specialist locations against population and rural-urban classifications of England, Wales, Scotland and Northern Ireland. The latest 2019 population estimates and health board areas were superimposed to create a specialist map. All other data were collected at the smallest geographic statistical areas and corresponding population data from the latest census.Results A total of 4,439 specialist titles were held by 3,041 individuals, linked to 3,459 unique locations of work. Specialist locations were mapped against 135 Clinical Commissioning Groups (CCGs) of England, seven Local Health Boards of Wales, 14 Health Boards of Scotland and five Health and Social Care Trusts of Northern Ireland. NHS Central London CCG had the highest specialist dentists per 100,000 people at 118.9; paediatric dentists per 20,000 children at 6.4; orthodontic dentists per 20,000 schoolchildren at 23.2; oral surgery dentists at 4.8 per 20,000 adults; and prosthodontic dentists at 7.2 per 20,000 adults. Orthodontics and oral surgery had the highest specialist-to-population ratios at 1:45,545 and 1:77,510, compared to oral and maxillofacial radiology and oral microbiology with the lowest ratios of 1:2,178,316 and 1:9,024,452, respectively. In England, Wales, Scotland and Northern Ireland, there were 79.5% (n = 42,140,039), 55.6% (n = 1,703,248), 46.9% (n = 2,481,996) and 42.9% (n = 776,295) of the respective populations that lived within 2.5 km of a specialist location. There were significant disparities in rural proximity to specialist locations across all nations. In Scotland, 40.8% of the rural population lived outside 10 km of a specialist location.Conclusions Stark inequalities exist in the geographic distribution of UK specialist dentists and high disparities were found in accessing a specialist, especially for vulnerable populations.

Disparities in the geographic distribution of NHS general dental care services in England

Objectives To assess the geographic distribution of NHS dental practices in England in relation to deprivation and rurality.Methods A total of 7,851 dental practices in England were included in this study against a population of 53,004,517 from 171,349 statistical areas. The practices were mapped using geographic information systems technology and integrated with census population data, the Index of Multiple Deprivation and the rural-urban classification.Results Results showed that 92.1% of children and adolescents, 91.8% of working-age adults and 88.8% of older adults lived within 2.5 km of a practice. In general, dental practices were not socioeconomically distributed; 99.7% of people from the most deprived areas lived close to a practice compared to 92.9% of people from the least deprived areas. However, when age groups were delineated, much higher proportions of deprived children and adolescents lived within 2.5 km distance from a dental service, while the opposite was true for the older age group (high proportions of the least deprived older adults lived within 2.5 km distance from a dental service). In rural areas, 54.8%, 54.3% and 53.5% of children and adolescents, working-age adults and older adults lived outside 2.5 km of a dental practice, respectively.Conclusions Disparities exist in the distribution of NHS dental practices in England. The need to increase spatial availability of dental practices in England is evident.

Use of geographic information systems web mapping application to support active case search to guide public health and social measures in the context of COVID-19 in Zimbabwe: a preliminary report to guide replication of methods in similar resource settings

INTRODUCTION

the new coronavirus (COVID-19) that emerged from Wuhan, Hubei Province of China in December 2019, causing severe acute respiratory syndrome (SARS) has fast spread across the entire globe, with most countries struggling to slow and reduce the spread of the virus through rapid screening, testing, isolation, case management, contact tracing, implementing social distancing and lockdowns. This has been shown to be a major factor in countries that have been successful in containing COVID-19 transmission. Early detection of cases is important, and the use of geospatial technology can support to detect and easily identify potential hotspots that will require timely response. The use of spatial analysis with geographic information systems (GIS) had proved to be effective in providing timely and effective solutions in supporting epidemic response and pandemics over the years. It has developed and evolved rapidly with a complete technological tool for representing data, model construction, visualization and platform construction among others.

METHODS

we conducted a geospatial analysis to develop a web mapping application using ArcMap and ArcGIS online to guide and support active case search of potential COVID-19 cases, within 500m radius of COVID-19 confirmed cases to improve detection and testing of suspected cases.

RESULTS

the web mapping application tool guides the active case search teams in the field, with clear boundaries on the houses to be visited within 500-meter radius of confirmed positive cases, to conduct active case search of all cases of severe acute respiratory illnesses (SARI), acute respiratory illnesses (ARI), pneumonia etc, to detect and test for COVID-19 towards containing the pandemic.

CONCLUSION

the use of GIS and spatial statistical tools have become an important and valuable tool in decision-making and, more importantly, guiding health care professional and other stakeholders in the response being carried out in a more coherent and easy manner. It has proven to be effective in supporting the active case search process to rapidly detect, test and isolate cases during the process, towards containing the COVID-19 pandemic.

Analyzing Socio-Economic and Geographical Factors that Affect the Health of the Elderly

The aim of the current research is mainly to investigate socio-economic indicators that affect the health status of the elderly. Specific objectives are the recording of the current profile of the elderly and the investigation of the perception of the health status by elderly, the proportion of older people suffering from health problems, their most common health problems, the percentage of elderly whose health status affects their life quality, the overall life quality of elderly, and the degree of correlation of socio-economic indicators to the quality of life of elderly. A sample of 897 questionnaires, collected from a number of open elderly care centers in Greece, was the basis of the current survey. The collected data concerning the location of certain health problems and aspects of perceived general health and perceived quality of life of participants were located automatically in geographic maps as geographic information systems (GIS), in order to be shared and analyzed easily. There is almost complete coincidence of our research findings and their statistical analysis with the theoretical approaches of the related literature, with the exception of the relation of marital status with the perception of the quality of life. Direct geographic mapping aims at identifying health problems and socio-economic indicators of the elderly in Greece and transferring information to healthcare professionals in order to impose proper control measures in a very small period of time.

Evaluation of the quality of service delivery in private sector, primary care clinics in Kenya: A descriptive patient survey

BACKGROUND

The quality of service delivery in primary care (PC) is an important determinant of clinical outcomes. The patients' perspective is one significant predictor of this quality. Little is known of the quality of such service delivery in the private sector in Kenya. The aim of the study was to evaluate the quality of service delivery in private sector, PC clinics in Nairobi, Kenya.

METHODS

The study employed a descriptive cross-sectional survey by using the General Practice Assessment Questionnaire in 378 randomly selected patients from 13 PC clinics. Data were analysed using the Statistical Package for Social Sciences.

RESULTS

Overall, 76% were below 45 years, 74% employed and 73% without chronic diseases. Majority (97%) were happy to see the general practitioner (GP) again, 99% were satisfied with their consultation and 83% likely to recommend the GP to others. Participants (97%) found in receptionist helpful and the majority were happy with the opening hours (73%) and waiting times (85%). Although 84% thought appointments were important, only 48% felt this was easy to make, and only 44% were able to access a particular GP on the same day. Overall satisfaction was higher in employed (98%) versus those unemployed (95%), studying (93%) or retired (94%) (p 0.001).

CONCLUSION

Patients reported a high quality of service delivery. Utilisation was skewed towards younger, employed adults, without chronic conditions, suggesting that PC was not fully comprehensive. Services were easily accessible, although with little expectation of relational continuity. Further studies should continue to evaluate the quality of service delivery from other perspectives and tools.

Spatial prediction of COVID-19 epidemic using ARIMA techniques in India

The latest Coronavirus (COVID-19) has become an infectious disease that causes millions of people to infect. Effective short-term prediction models are designed to estimate the number of possible events. The data obtained from 30th January to 26 April, 2020 and from 27th April 2020 to 11th May 2020 as modelling and forecasting samples, respectively. Spatial distribution of disease risk analysis is carried out using weighted overlay analysis in GIS platform. The epidemiologic pattern in the prevalence and incidence of COVID-2019 is forecasted with the Autoregressive Integrated Moving Average (ARIMA). We assessed cumulative confirmation cases COVID-19 in Indian states with a high daily incidence in the task of time-series forecasting. Such efficiency metrics such as an index of increasing results, mean absolute error (MAE), and a root mean square error (RMSE) are the out-of-samples for the prediction precision of model. Results shows west and south of Indian district are highly vulnerable for COVID-2019. The accuracy of ARIMA models in forecasting future epidemic of COVID-2019 proved the effectiveness in epidemiological surveillance. For more in-depth studies, our analysis may serve as a guide for understanding risk attitudes and social media interactions across countries.

Incorporating Geographic Information Science and Technology in Response to the COVID-19 Pandemic

Incorporating geographic information science and technology (GIS&T) into COVID-19 pandemic surveillance, modeling, and response enhances understanding and control of the disease. Applications of GIS&T include 1) developing spatial data infrastructures for surveillance and data sharing, 2) incorporating mobility data in infectious disease forecasting, 3) using geospatial technologies for digital contact tracing, 4) integrating geographic data in COVID-19 modeling, 5) investigating geographic social vulnerabilities and health disparities, and 6) communicating the status of the disease or status of facilities for return-to-normal operations. Locations and availability of personal protective equipment, ventilators, hospital beds, and other items can be optimized with the use of GIS&T. Challenges include protection of individual privacy and civil liberties and closer collaboration among the fields of geography, medicine, public health, and public policy.

Current State of Geospatial Methodologic Approaches in Canadian Population Oncology Research

Geospatial analyses are increasingly used in population oncology. We provide a first review of geospatial analysis in Canadian population oncology research, compare to international peers, and identify future directions. Geospatial-focused peer-reviewed publications from 1992-2020 were compiled using PubMed, MEDLINE, Web of Science, and Google Scholar. Abstracts were screened for data derived from a Canadian cancer registry and use of geographic information systems. Studies were classified by geospatial methodology, geospatial unit, location, cancer site, and study year. Common limitations were documented from article discussion sections. Our search identified 71 publications using data from all provincial and national cancer registries. Thirty-nine percent (N = 28) were published in the most recent 5-year period (2016-2020). Geospatial methodologies included exposure assessment (32.4%), identifying spatial associations (21.1%), proximity analysis (16.9%), cluster detection (15.5%), and descriptive mapping (14.1%). Common limitations included confounding, ecologic fallacy, not accounting for residential mobility, and small case/population sizes. Geospatial analyses are increasingly used in Canadian population oncology; however, efforts are concentrated among a few provinces and common cancer sites, and data are over a decade old. Limitations were similar to those documented internationally, and more work is needed to address them. Organized efforts are needed to identify common challenges, develop leading practices, and identify shared priorities.

Addressing the data guardian and geospatial scientist collaborator dilemma: how to share health records for spatial analysis while maintaining patient confidentiality

BACKGROUND

The utility of being able to spatially analyze health care data in near-real time is a growing need. However, this potential is often limited by the level of in-house geospatial expertise. One solution is to form collaborative partnerships between the health and geoscience sectors. A challenge in achieving this is how to share data outside of a host institution's protection protocols without violating patient confidentiality, and while still maintaining locational geographic integrity. Geomasking techniques have been previously championed as a solution, though these still largely remain an unavailable option to institutions with limited geospatial expertise. This paper elaborates on the design, implementation, and testing of a new geomasking tool Privy, which is designed to be a simple yet efficient mechanism for health practitioners to share health data with geospatial scientists while maintaining an acceptable level of confidentiality. The basic premise of Privy is to move the important coordinates to a different geography, perform the analysis, and then return the resulting hotspot outputs to the original landscape.

RESULTS

We show that by transporting coordinates through a combination of random translations and rotations, Privy is able to preserve location connectivity among spatial point data. Our experiments with typical analytical scenarios including spatial point pattern analysis and density analysis shows that, along with protecting spatial privacy, Privy maintains the spatial integrity of data which reduces information loss created due to data augmentation.

CONCLUSION

The results from this study suggests that along with developing new mathematical techniques to augment geospatial health data for preserving confidentiality, simple yet efficient software solutions can be developed to enable collaborative research among custodians of medical and health data records and GIS experts. We have achieved this by developing Privy, a tool which is already being used in real-world situations to address the spatial confidentiality dilemma.

A One Health perspective to identify environmental factors that affect Rift Valley fever transmission in Gezira state, Central Sudan

BACKGROUND

Rift Valley fever (RVF) is a zoonotic viral vector-borne disease that affects both animals and humans and leads to severe economic consequences. RVF outbreaks are triggered by a favorable environment and flooding, which enable mosquitoes to proliferate and spread the virus further. RVF is endemic to Africa and has spread to Saudi Arabia and Yemen. There is great concern that RVF may spread to previously unaffected geographic regions due to climate change. We aimed to better understand the spatiotemporal pattern of the 2007 RVF outbreak at the human-animal-environment interface and to determine environmental factors that may have effects on RVF occurrence in Gezira state, Sudan.

MATERIALS AND METHODS

We compiled epidemiological, environmental, and spatiotemporal data across time and space using remote sensing and a geographical information system (GIS). The epidemiological data included 430 RVF human cases as well as human and animal population demographic data for each locality. The cases were collected from 41 locations in Gezira state. The environmental data represent classified land cover during 2007, the year of the RVF outbreak, and the average of the Normalized Difference Vegetation Index (NDVI) for 6 months of 2007 is compared with those of 2010 and 2014, when there was no RVF outbreak. To determine the effect of the environmental factors such as NDVI, soil type, and RVF case's location on the Blue Nile riverbank on RVF incidence in Gezira state, a multilevel logistic regression model was carried out.

RESULTS

We found that the outbreak in Gezira state occurred as a result of interaction among animals, humans, and the environment. The multilevel logistic regression model (F = 43,858, df = 3, p = 0.000) explained 23% of the variance in RVF incidence due to the explanatory variables. Notably, soil type (β = 0.613, t = 11.284, p = 0.000) and NDVI (β = - 0.165, t = - 3.254, p = 0.001) were the explanatory environmental factors that had significant effects on RVF incidence in 2007 in Gezira state, Sudan.

CONCLUSIONS

Precise remote sensing and the GIS technique, which rely on environmental indices such as NDVI and soil type that are satellite-derived, can contribute to establishing an early warning system for RVF in Sudan.Future preparedness and strengthening the capacity of regional laboratories are necessary for early notification of outbreaks in animals and humans.

Spatial variations of women's home delivery after antenatal care visits at lay Gayint District, Northwest Ethiopia

BACKGROUND

Home delivery is the most frequent childbirth practice in Ethiopia and brings health risks for many mothers and their babies which in turn affecting the whole families. Characterizing the spatial variations and the associated factors of home deliveries after antenatal care visit is necessary to prioritize risks and facilitate geographically based interventions.

METHOD

A community-based cross-sectional study design was carried out between February and March 2016. A total of 528 women who had just given birth were interviewed face-to-face using a questionnaire. Geo-referenced data were collected using a handheld global positioning system (GPS). The Bernoulli model was applied using the SatScan ™ software to analyze the purely spatial clusters of home deliveries. ArcGIS version 10.1 was used to visualize clusters of home delivery.

RESULTS

The overall proportion of home deliveries was 278(52.7%), and home deliveries had spatial variations. A primary cluster [LLR = 14.54, p < 0.001] was detected in village of Safida Giorgis. Secondary clusters were detected in Checheho [LLR = 9.17, p < 0.05] and ZurAmba [LLR = 8.51, p < 0.05]. Predictors for home delivery included the distance between the health extension worker's and mother's house [AOR = 2.2, 95% CI: 1.1, 4.3], residence [AOR = 3.8, 95% CI: 1.3, 10.9], source of information for ANC [AOR = 0.3, 95% CI: 0.13, 0.7], ANC visits [AOR = 6.1, 95% CI:1.9, 19.3], health education [AOR = 3.4, 95% CI: 1.5, 7.4], decision on place of delivery [AOR = 0.3, 95% CI: 0.1, 0.8], and knowledge on place of delivery [AOR = 0.04, 95% CI: 0.0, 0.1].

CONCLUSION

The proportion of home delivery after ANC visit was decreasing compared to other studies conducted in the region. In addition, spatial variations of home delivery were observed in the study area. Promoting women's education and behavioral change communication at the grass root level, provision of the services both at home and health facilities and improving the quality and capacity of the health providers are some of the recommendations forwarded.

Association between spatial accessibility to fire protection services and unintentional residential fire injuries or deaths: a cross-sectional study in Dallas, Texas

OBJECTIVES

In addition to neighbourhood characteristics encompassing racial composition, socioeconomic status and housing environments, it was unclear if location characteristics relating to the accessibility of fire protection services were risk factors for unintentional residential fire-related injuries in urban areas. This study was aimed to measure spatial accessibility to fire protection services at the census block group level, and examine whether it is associated with unintentional residential fire-related injuries.

DESIGN

A cross-sectional study.

SETTING

Unintentional residential fire incidents between 2012 and 2015 in Dallas City, Texas, USA.

MAIN OUTCOME MEASURES

Using multiple logistic regression, the study analysed association between unintentional residential fire incidents in the city and risk factors, including spatial accessibility to fire protection services, measured by the two-step floating catchment area method.

RESULTS

Compared with incidents without any injuries, fire incidents involving injuries were significantly more likely to include unconfined fire, fire originating in a cooking area, a bedroom for less than five people or a common family area, census block groups with >75% African American residents, >25% elderly single-person households and positive population growth rates <25% or >25%. Incidents involving injuries were significantly less likely to occur in areas with high spatial accessibility, as measured by spatial accessibility score.

CONCLUSIONS

In addition to fire characteristics and neighbourhood demographics, spatial accessibility to fire protection services was significantly associated with unintentional residential fire-related injuries. The findings can be used to help select locations of additional fire stations.

Establishing a Demographic, Development and Environmental Geospatial Surveillance Platform in India: Planning and Implementation

BACKGROUND

Inadequate administrative health data, suboptimal public health infrastructure, rapid and unplanned urbanization, environmental degradation, and poor penetration of information technology make the tracking of health and well-being of populations and their social determinants in the developing countries challenging. Technology-integrated comprehensive surveillance platforms have the potential to overcome these gaps.

OBJECTIVE

This paper provides methodological insights into establishing a geographic information system (GIS)-integrated, comprehensive surveillance platform in rural North India, a resource-constrained setting.

METHODS

The International Clinical Epidemiology Network Trust International established a comprehensive SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in rural Palwal, a district in Haryana, North India. The surveillance platform evolved by adopting four major steps: (1) site preparation, (2) data construction, (3) data quality assurance, and (4) data update and maintenance system. Arc GIS 10.3 and QGIS 2.14 software were employed for geospatial data construction. Surveillance data architecture was built upon the geospatial land parcel datasets. Dedicated software (SOMAARTH-1) was developed for handling high volume of longitudinal datasets. The built infrastructure data pertaining to land use, water bodies, roads, railways, community trails, landmarks, water, sanitation and food environment, weather and air quality, and demographic characteristics were constructed in a relational manner.

RESULTS

The comprehensive surveillance platform encompassed a population of 0.2 million individuals residing in 51 villages over a land mass of 251.7 sq km having 32,662 households and 19,260 nonresidential features (cattle shed, shops, health, education, banking, religious institutions, etc). All land parcels were assigned georeferenced location identification numbers to enable space and time monitoring. Subdivision of villages into sectors helped identify socially homogenous community clusters (418/676, 61.8%, sectors). Water and hygiene parameters of the whole area were mapped on the GIS platform and quantified. Risk of physical exposure to harmful environment (poor water and sanitation indicators) was significantly associated with the caste of individual household (P=.001), and the path was mediated through the socioeconomic status and density of waste spots (liquid and solid) of the sector in which these households were located. Ground-truthing for ascertaining the land parcel level accuracies, community involvement in mapping exercise, and identification of small habitations not recorded in the administrative data were key learnings.

CONCLUSIONS

The SOMAARTH DDESS experience allowed us to document and explore dynamic relationships, associations, and pathways across multiple levels of the system (ie, individual, household, neighborhood, and village) through a geospatial interface. This could be used for characterization and monitoring of a wide range of proximal and distal determinants of health.

Spatial analyzes of HLA data in Rio Grande do Sul, south Brazil: genetic structure and possible correlation with autoimmune diseases

Background

HLA genes are the most polymorphic of the human genome and have distinct allelic frequencies in populations of different geographical regions of the world, serving as genetic markers in ancestry studies. In addition, specific HLA alleles may be associated with various autoimmune and infectious diseases. The bone marrow donor registry in Brazil is the third largest in the world, and it counts with genetic typing of HLA-A, -B, and -DRB1. Since 1991 Brazil has maintained the DATASUS database, a system fed with epidemiological and health data from compulsory registration throughout the country.

Methods

In this work, we perform spatial analysis and georeferencing of HLA genetic data from more than 86,000 bone marrow donors from Rio Grande do Sul (RS) and data of hospitalization for rheumatoid arthritis, multiple sclerosis and Crohn’s disease in RS, comprising the period from 1995 to 2016 obtained through the DATASUS system. The allele frequencies were georeferenced using Empirical Bayesian Kriging; the diseases prevalence were georeferenced using Inverse Distance Weighted and cluster analysis for both allele and disease were performed using Getis-Ord Gi* method. Spearman’s test was used to test the correlation between each allele and disease.

Results

The results indicate a HLA genetic structure compatible with the history of RS colonization, where it is possible to observe differentiation between regions that underwent different colonization processes. Spatial analyzes of autoimmune disease hospitalization data were performed revealing clusters for different regions of the state for each disease analyzed. The correlation test between allelic frequency and the occurrence of autoimmune diseases indicated a significant correlation between the HLA-B*08 allele and rheumatoid arthritis.

Conclusions

Genetic mapping of populations and the spatial analyzes such as those performed in this work have great economic relevance and can be very useful in the formulation of public health campaigns and policies, contributing to the planning and adjustment of clinical actions, as well as informing and educating professionals and the population.

Electronic supplementary material

The online version of this article (10.1186/s12942-018-0154-8) contains supplementary material, which is available to authorized users.

Optimizing Travel Time to Outpatient Interventional Radiology Procedures in a Multi-Site Hospital System Using a Google Maps Application

The purpose of this study is to determine whether a custom Google Maps application can optimize site selection when scheduling outpatient interventional radiology (IR) procedures within a multi-site hospital system. The Google Maps for Business Application Programming Interface (API) was used to develop an internal web application that uses real-time traffic data to determine estimated travel time (ETT; minutes) and estimated travel distance (ETD; miles) from a patient's home to each a nearby IR facility in our hospital system. Hypothetical patient home addresses based on the 33 cities comprising our institution's catchment area were used to determine the optimal IR site for hypothetical patients traveling from each city based on real-time traffic conditions. For 10/33 (30%) cities, there was discordance between the optimal IR site based on ETT and the optimal IR site based on ETD at non-rush hour time or rush hour time. By choosing to travel to an IR site based on ETT rather than ETD, patients from discordant cities were predicted to save an average of 7.29 min during non-rush hour (p = 0.03), and 28.80 min during rush hour (p < 0.001). Using a custom Google Maps application to schedule outpatients for IR procedures can effectively reduce patient travel time when more than one location providing IR procedures is available within the same hospital system.

Obesity and caries in four-to-six year old English children: a cross-sectional study

Background

Obesity and caries are common conditions in childhood and can have significant implications on children’s wellbeing. Evidence into their association remains conflicting. Furthermore, studies examining the ssociation between obesity and caries commonly focus on individual-level determinants. The present study aimed to examine the association between obesity and caries in young English children and to determine the impact of deprivation and area-level characteristics on the distribution of the two conditions.

Methods

This was a cross-sectional study among children in Plymouth city aged four-to-six years. Anthropometric measurements included weight and height (converted to Body Mass Index centiles and z-scores), and waist circumference. Caries was assessed by using the sum of the number of teeth that were decayed, missing or filled. A questionnaire was used to obtain information on children’s demographic characteristics, oral hygiene, and dietary habits. The impact of deprivation on anthropometric variables and caries was determined using Linear and Poisson regression models, respectively. Multiple logistic regression was used to assess the association between different anthropometric measures and caries. Logistic regression models were also used to examine the impact of several demographic characteristics and health behaviours on the presence of obesity and caries.

Results

The total sample included 347 children aged 5.10 ± 0.31 (mean ± SD). Deprivation had a significant impact on caries and BMI z-scores (p < 0.05). Neither BMI- nor waist circumference z-scores were shown to be significantly associated with dental caries. Among the neighbourhood characteristics examined, the percentage of people dependent on benefits was found to have a significant impact on caries rates (p < 0.05). Household’s total annual income was inversely related to caries risk and parental educational level affected children’s tooth brushing frequency.

Conclusions

No associations between any measure of obesity and caries were found. However, deprivation affected both obesity and caries, thus highlighting the need to prioritise disadvantaged children in future prevention programmes.

Utilizing patient geographic information system data to plan telemedicine service locations

Objective

To understand potential utilization of clinical services at a rural integrated health care system by generating optimal groups of telemedicine locations from electronic health record (EHR) data using geographic information systems (GISs).

Methods

This retrospective study extracted nonidentifiable grouped data of patients over a 2-year period from the EHR, including geomasked locations. Spatially optimal groupings were created using available telemedicine sites by calculating patients' average travel distance (ATD) to the closest clinic site.

Results

A total of 4027 visits by 2049 unique patients were analyzed. The best travel distances for site groupings of 3, 4, 5, or 6 site locations were ranked based on increasing ATD. Each one-site increase in the number of available telemedicine sites decreased minimum ATD by about 8%. For a given group size, the best groupings were very similar in minimum travel distance. There were significant differences in predicted patient load imbalance between otherwise similar groupings. A majority of the best site groupings used the same small number of sites, and urban sites were heavily used.

Discussion

With EHR geospatial data at an individual patient level, we can model potential telemedicine sites for specialty access in a rural geographic area. Relatively few sites could serve most of the population. Direct access to patient GIS data from an EHR provides direct knowledge of the client base compared to methods that allocate aggregated data.

Conclusion

Geospatial data and methods can assist health care location planning, generating data about load, load balance, and spatial accessibility.

Estimation and Evaluation of Future Demand and Supply of Healthcare Services Based on a Patient Access Area Model

Accessibility to healthcare service providers, the quantity, and the quality of them are important for national health. In this study, we focused on geographic accessibility to estimate and evaluate future demand and supply of healthcare services. We constructed a simulation model called the patient access area model (PAAM), which simulates patients’ access time to healthcare service institutions using a geographic information system (GIS). Using this model, to evaluate the balance of future healthcare services demand and supply in small areas, we estimated the number of inpatients every five years in each area and compared it with the number of hospital beds within a one-hour drive from each area. In an experiment with the Tokyo metropolitan area as a target area, when we assumed hospital bed availability to be 80%, it was predicted that over 78,000 inpatients would not receive inpatient care in 2030. However, this number would decrease if we lowered the rate of inpatient care by 10% and the average length of the hospital stay. Using this model, recommendations can be made regarding what action should be undertaken and by when to prevent a dramatic increase in healthcare demand. This method can help plan the geographical resource allocation in healthcare services for healthcare policy.

Spatial patterns of childhood diarrhea in Ethiopia: data from Ethiopian demographic and health surveys (2000, 2005, and 2011)

BACKGROUND

Childhood diarrhea is a major public health problem, especially in developing countries, including Ethiopia. Exploring the spatial pattern of childhood diarrhea is important to monitor and design effective intervention programs. Therefore, this study aimed to explore the spatial patterns of childhood diarrhea in Ethiopia over the past one decade.

METHODS

A total of 29,358 under-five children were retrieved from three consecutive Ethiopian demographic and health surveys (2000, 2005, and 2011) and included into the study. Spatial cluster and autocorrelation analysis was done to explore the patterns of childhood diarrhea.

RESULTS

Childhood diarrhea clustered spatially at a national level in all survey periods (Moran's I: 0.3830-1.3296, p < 0.05). Significant spatial clusters were found in different survey periods across the regions. The most likely spatial clusters were found in Southern Nations Nationalities and people, West Oromia, Gambella, Benshangul-Gumuz, and Somali regions. Childhood diarrhea also clustered at the border areas of Southern Nations Nationalities and People and Tigray, Central Somali and Western Oromia, Gambella and Amhara (West Gojam, Awi, Oromia, and Wag Himra) regions. In 2000, the most likely clusters were found in Southern Nations Nationalities and People, West Oromia, and Gambella regions (LLR = 55.37, p < 0.001); in 2005, at Southern Nations Nationalities and People (LLR: 45.69, p < 0.001); and in 2011, at Gambella, West Southern Nations Nationalities and People and Oromia, and Benshangul-Gumuz regions (LLR: 51.09, p < 0.001).

CONCLUSION

In this study, childhood diarrhea remains public health problem and had a spatial variation across the regions. Identifying the risk areas would help in designing effective interventions to reduce childhood diarrhea in these areas.

Access to hyperacute stroke services across Canadian provinces: a geospatial analysis

BACKGROUND

Canada's vast geography creates challenges for ensuring prompt transport to hospital of patients who have had a stroke. We sought to determine the proportion of people across various Canadian provinces for whom hyperacute stroke services are accessible within evidence-based time targets.

METHODS

We calculated, for the 8 provinces with available data, drive-time polygons on a map of Canada that delineated the area around stroke centres and emergency medical services (EMS) base centres to which one can drive in 3.5-6 hours. We calculated the proportional area of each forward sortation area (first 3 digits of the postal code) contained within a drive-time polygon. We applied this ratio to the 2011 Canadian census population of the forward sortation area to estimate the population that can reach a stroke centre in a designated time.

RESULTS

A total of 47.1%-96.4% of Canadians live within a 4.5-hour drive to a stroke centre via road EMS, and 53.3%-96.8% live within a 6-hour drive. Assuming a total travel time of 5 hours by EMS from base centre to patient and patient to hospital, 84.7%-99.8% of the population has access to a current or proposed endovascular thrombectomy site.

INTERPRETATION

Most Canadians live within 6 hours' road access to a stroke centre. Geospatial mapping could be used to inform decisions for additional sites and identify gaps in service accessibility. Coordinated systems of care and ambulance bypass agreements must continue to evolve to ensure maximal access to time-sensitive emergency stroke services.

Verfahren der GIS-Modellierung von Erreichbarkeiten für Schlaganfallversorgungszentren

Der methodisch orientierte Beitrag stellt ein Verfahren zur Modellierung von Versorgungsbereichen und Erreichbarkeiten von sogenannten Schlaganfallversorgungszentren (Stroke Units) in Deutschland mithilfe von Geographischen Informationssystemen (GIS) vor. Auf der Grundlage von OpenStreetMap-Vektordaten werden routingbasierte Einzugsgebiete der Schlaganfallversorgungszentren ermittelt und mit vektorbasierten Daten zur Demographie auf räumlich disaggregierter Ebene verschnitten. Eine Modellierung zeitlicher Erreichbarkeiten mit Rettungsfahrzeugen resultiert in Isochronen-Kartendarstellungen, welche in der Verschneidung mit hoch aufgelösten demographischen Sachdaten verschiedene Analysemöglichkeiten bieten. Diese werden an einem Fallbeispiel aus Unterfranken aufgezeigt, welches die Potenziale des Verfahrens für die raumbezogene Planung von Schlaganfallversorgungszentren skizzieren soll. Eine Anwendung des Verfahrens auf andere medizinische Einrichtungen oder die Verschneidung mit weiteren themenbezogenen Sachdaten ist möglich.

A scoping review of geographic information systems in maternal health

BACKGROUND

Geographic information systems (GIS) are increasingly recognized tools in maternal health.

OBJECTIVES

To evaluate the use of GIS in maternal health and to identify knowledge gaps and opportunities.

SEARCH STRATEGY

Keywords broadly related to maternal health and GIS were used to search for academic articles and gray literature.

SELECTION CRITERIA

Reviewed articles focused on maternal health, with GIS used as part of the methods.

DATA COLLECTION AND ANALYSIS

Peer reviewed articles (n=40) and gray literature sources (n=30) were reviewed.

MAIN RESULTS

Two main themes emerged: modeling access to maternal services and identifying risks associated with maternal outcomes. Knowledge gaps included a need to rethink spatial access to maternal care in low- and middle-income settings, and a need for more explicit use of GIS to account for the geographical variation in the effect of risk factors on adverse maternal outcomes. Limited evidence existed to suggest that use of GIS had influenced maternal health policy. Instead, application of GIS to maternal health was largely influenced by policy priorities in global maternal health.

CONCLUSIONS

Investigation of the role of GIS in contributing to future policy directions is warranted, particularly for elucidating determinants of global maternal health.

Determinants of prompt and adequate care among presumed malaria cases in a community in eastern Rwanda: a cross sectional study

Background

In order to understand factors influencing fever/malaria management practices among community-based individuals, the study evaluated psychosocial, socio-demographic and environmental determinants of prompt and adequate healthcare-seeking behaviours.

Methods

A quantitative household (HH) survey was conducted from December 2014 to February 2015 in Ruhuha sector, Bugesera district in the Eastern province of Rwanda. HHs that reported having had at least one member who experienced a fever and/or malaria episode in the previous 3 months prior to the study were included in the analysis. Healthcare-seeking behaviours associated with the last episode of illness were analysed. Socio-demographic, health facility access, long-lasting insecticidal-treated nets (LLINs), data on malaria knowledge, data and theory of planned behaviour (TPB) related variables (attitudes, subjective norms, perceived behavioural control) with regard to fever/malaria healthcare seeking, were collected. The primary outcome was prompt and adequate care defined as: (1) seeking advice or treatment at a health facility (health centre or hospital) or from a community health worker (CHW); (2) advice or treatment seeking within same/next day of symptoms onset; (3) received a laboratory diagnosis; (4) received advice or treatment; and, (5) reported completing the prescribed dose of medication. Determinants of prompt and adequate care among presumed malaria cases were evaluated using a logistic regression analysis.

Results

Overall, 302 (21 %) of the 1410 interviewed HHs reported at least one member as having experienced a fever or malaria within the 3 months prior to the survey. The number of HHs (where at least one member reported fever/malaria) that reported seeking advice or treatment at a health facility (health centre or hospital) or from a CHW was 249 (82.4 %). Of those who sought advice or treatment, 87.3 % had done so on same/next day of symptoms developing, 82.8 % received a laboratory diagnosis, and more than 90 % who received treatment reported completing the prescribed dosage. Prompt and adequate care was reported from 162 of the 302 HHs (53.6 %) that experienced fever or malaria for one or more HH members. Bivariate analyses showed that head of household (HoH)-related characteristics including reported knowledge of three or more malaria symptoms, having health insurance, being able to pay for medical services, use of LLINs the night before the survey, having a positive attitude, perceiving social support, as well as a high-perceived behavioural control with regard to healthcare seeking, were all significantly associated with prompt and adequate care. In the final logistic regression model, a high-perceived behavioural control (odds ratio (OR) 5.068, p = 0.042), having a health insurance (OR 2.410, p = 0.044) and having knowledge of malaria symptoms (OR 1.654, p = 0.049) significantly predicted prompt and adequate care.

Conclusions

To promote prompt and adequate care seeking for malaria in the area, particular emphasis should be placed on community-focused strategies that promote early malaria symptom recognition, increased health insurance coverage and enhanced perceived behavioural control with regard to healthcare-seeking.

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.

Web-based geo-visualisation of spatial information to support evidence-based health policy: a case study of the development process of HealthTracks

Place is of critical importance to health as it can reveal patterns of disease spread and clustering, associations with risk factors, and areas with greatest need for, or least access to healthcare services and promotion activities. Furthermore, in order to get a good understanding of the health status and needs of a particular area a broad range of data are required which can often be difficult and time consuming to obtain and collate. This process has been expedited by bringing together multiple data sources and making them available in an online geo-visualisation, HealthTracks, which consists of a mapping and reporting component. The overall aim of the HealthTracks project is to make spatial health information more accessible to policymakers, analysts, planners and program managers to inform decision-making across the Department of Health Western Australia. Preliminary mapping and reporting applications that have been utilised to inform service planning, increased awareness of the utility of spatial information and improved efficiency in data access were developed. The future for HealthTracks involves expanding the range of data available and developing new analytical capabilities in order to work towards providing external agencies, researchers and eventually the general public access to rich local area spatial data.

An Analysis of Spatial Clustering of Stroke Types, In-hospital Mortality, and Reported Risk Factors in Alberta, Canada, Using Geographic Information Systems

BACKGROUND

Despite advances in the quality and delivery of stroke care, regional disparities in stroke incidence and outcome persist. Spatial analysis using geographic information systems (GIS) can assist in identifying high-risk populations and regional differences in efficacy of stroke care. The aim of this study was to identify and locate geographic clusters of high or low rates of stroke, risk factors, and in-hospital mortality across a provincial health care network in Alberta, Canada.

METHODS

This study employed a spatial epidemiological approach using population-based hospital administrative data. Getis-Ord Gi* and Spatial Scan statistics were used to identify and locate statistically significant "hot" and "cold" spots of stroke occurrence by type, risk factors, and in-hospital mortality.

RESULTS

Marked regional variations were found. East central Alberta was a significant hot spot for ischemic stroke (relative risk [RR] 1.43, p<0.001), transient ischemic attack (RR 2.25, p<0.05), and in-hospital mortality (RR 1.50, p<0.05). Hot spots of intracerebral hemorrhage (RR 1.80, p<0.05) and subarachnoid hemorrhage (RR 1.64, p<0.05) were identified in a major urban centre. Unexpectedly, stroke risk factor hot spots (RR 2.58, p<0.001) were not spatially associated (did not overlap) with hot spots of ischemic stroke, transient ischemic attack, or in-hospital mortality.

CONCLUSIONS

Integration of health care administrative data sets with geographic information systems contributes valuable information by identifying the existence and location of regional disparities in the spatial distribution of stroke occurrence and outcomes. Findings from this study raise important questions regarding why regional differences exist and how disparities might be mitigated.

Geographical information system (GIS) as a new tool to evaluate epidemiology based on spatial analysis and clinical outcomes in acromegaly

Geographical information systems (GIS) have emerged as a group of innovative software components useful for projects in epidemiology and planning in Health Care System. This is an original study to investigate environmental and geographical influences on epidemiology of acromegaly in Brazil. We aimed to validate a method to link an acromegaly registry with a GIS mapping program, to describe the spatial distribution of patients, to identify disease clusters and to evaluate if the access to Health Care could influence the outcome of the disease. Clinical data from 112 consecutive patients were collected and home addresses were plotted in the GIS software for spatial analysis. The buffer spatial distribution of patients living in Brasilia showed that 38.1% lived from 0.33 to 8.66 km, 17.7% from 8.67 to 18.06 km, 22.2% from 18.07 to 25.67 km and 22% from 25.68 to 36.70 km distant to the Reference Medical Center (RMC), and no unexpected clusters were identified. Migration of 26 patients from 11 others cities in different regions of the country was observed. Most of patients (64%) with adenomas bigger than 25 mm lived more than 20 km away from RMC, but no significant correlation between the distance from patient's home to the RMC and tumor diameter (r = 0.45 p = 0.20) nor for delay in diagnosis (r = 0.43 p = 0.30) was found. The geographical distribution of diagnosed cases did not impact in the latency of diagnosis or tumor size but the recognition of significant migration denotes that improvements in the medical assistance network are needed.

Catchments of general practice in different countries--a literature review

The purpose of this paper is to review the current research on catchment areas of private general practices in different developed countries because healthcare reform, including primary health care, has featured prominently as an important political issue in a number of developed countries. The debates around health reform have had a significant health geographic focus.

Conceptually, GP catchments describe the distribution, composition and profile of patients who access a general practitioner or a general practice (i.e. a site or facility comprising one or more general practitioners). Therefore, GP catchments provide important information into the geographic variation of access rates, utilisation of services and health outcomes by all of the population or different population groups in a defined area or aggregated area.

This review highlights a wide range of diversity in the literature as to how GP catchments can be described, the indicators and measures used to frame the scale of catchments. Patient access to general practice health care services should be considered from a range of locational concepts, and not necessarily constrained by their place of residence. An analysis of catchment patterns of general practitioners should be considered as dynamic and multi-perspective. Geographic information systems provide opportunities to contribute valuable methodologies to study these relationships. However, researchers acknowledge that a conceptual framework for the analysis of GP catchments requires access to real world data. Recent studies have shown promising developments in the use of real world data, especially from studies in the UK.

Understanding the catchment profiles of individual GP surgeries is important if governments are serious about patient choice being a key part of proposed primary health reforms. Future health planning should incorporate models of GP catchments as planning tools, at the micro level as well as the macro level, to assist policies on the allocation of resources so that opportunities for good health outcomes for all groups within society, especially those who have been systematically denied equitable access, are maximised.

A linear programming model for preserving privacy when disclosing patient spatial information for secondary purposes

Background

A linear programming (LP) model was proposed to create de-identified data sets that maximally include spatial detail (e.g., geocodes such as ZIP or postal codes, census blocks, and locations on maps) while complying with the HIPAA Privacy Rule’s Expert Determination method, i.e., ensuring that the risk of re-identification is very small. The LP model determines the transition probability from an original location of a patient to a new randomized location. However, it has a limitation for the cases of areas with a small population (e.g., median of 10 people in a ZIP code).

Methods

We extend the previous LP model to accommodate the cases of a smaller population in some locations, while creating de-identified patient spatial data sets which ensure the risk of re-identification is very small.

Results

Our LP model was applied to a data set of 11,740 postal codes in the City of Ottawa, Canada. On this data set we demonstrated the limitations of the previous LP model, in that it produces improbable results, and showed how our extensions to deal with small areas allows the de-identification of the whole data set.

Conclusions

The LP model described in this study can be used to de-identify geospatial information for areas with small populations with minimal distortion to postal codes. Our LP model can be extended to include other information, such as age and gender.

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.

A Novel Disease Outbreak Prediction Model for Compact Spatial-Temporal Environments

One of the popular research areas in clinical decision supporting system (CDSS) is Spatial and temporal (ST) data mining. The basic concept of ST concerns about two combined dimensions of analyzing: time and space. For prediction of disease outbreak, we attempt to locate any potential uninfected by the predicted virus prevalence. A popular ST-clustering software called “SaTScan” works by predicting the next likely infested areas by considering the history records of infested zones and the radius of the zone. However, it is argued that using radius as a spatial measure suits large and perhaps evenly populated area. In urban city, the population density is relatively high and uneven. In this paper, we present a novel algorithm, by following the concept of SaTScan, but in consideration of spatial information in relation to local populations and full demographic information in proximity (e.g. that of a street or a cluster of buildings). This higher resolution of ST data mining has an advantage of precision and applicability in some very compact urban cities. For proving the concept a computer simulation model is presented that is based on empirical but anonymized and processed data.

Spatial clustering of all-cause and HIV-related mortality in a rural South African population (2000-2006)

BACKGROUND

Sub-Saharan Africa bears a disproportionate burden of HIV infection. Knowledge of the spatial distribution of HIV outcomes is vital so that appropriate public health interventions can be directed at locations most in need. In this regard, spatial clustering analysis of HIV-related mortality events has not been performed in a rural sub-Saharan African setting.

METHODOLOGY AND RESULTS

Kulldorff's spatial scan statistic was used to identify HIV-related and all-cause mortality clusters (p<0.05) in a population-based demographic surveillance survey in rural KwaZulu Natal, South Africa (2000-2006). The analysis was split pre (2000-2003) and post (2004-2006) rollout of antiretroviral therapy, respectively. Between 2000-2006 a total of 86,175 resident individuals ≥15 years of age were under surveillance and 5,875 deaths were recorded (of which 2,938 were HIV-related) over 343,060 person-years of observation (crude all-cause mortality rate 17.1/1000). During both time periods a cluster of high HIV-related (RR = 1.46/1.51, p = 0.001) and high all-cause mortality (RR = 1.35/1.38, p = 0.001) was identified in peri-urban communities near the National Road. A consistent low-risk cluster was detected in the urban township in both time periods (RR = 0.60/0.39, p = 0.003/0.005) and in the first time period (2000-2003) a large cluster of low HIV-related and all-cause mortality in a remote rural area was identified.

CONCLUSIONS

HIV-related and all-cause mortality exhibit strong spatial clustering tendencies in this population. Highest HIV-related mortality and all-cause mortality occurred in the peri-urban communities along the National Road and was lowest in the urban township and remote rural communities. The geography of HIV-related mortality corresponded closely to the geography of HIV prevalence, with the notable exception of the urban township where high HIV-related mortality would have been expected on the basis of the high HIV prevalence. Our results suggest that HIV treatment and care programmes should be strengthened in easy-to-reach high density, peri-urban populations near National Roads where both HIV-related and all-cause mortality are highest.

Putting chronic disease on the map: building GIS capacity in state and local health departments

Techniques based on geographic information systems (GIS) have been widely adopted and applied in the fields of infectious disease and environmental epidemiology; their use in chronic disease programs is relatively new. The Centers for Disease Control and Prevention’s Division for Heart Disease and Stroke Prevention is collaborating with the National Association of Chronic Disease Directors and the University of Michigan to provide health departments with capacity to integrate GIS into daily operations, which support priorities for surveillance and prevention of chronic diseases. So far, 19 state and 7 local health departments participated in this project. On the basis of these participants’ experiences, we describe our training strategy and identify high-impact GIS skills that can be mastered and applied over a short time in support of chronic disease surveillance. We also describe the web-based resources in the Chronic Disease GIS Exchange that were produced on the basis of this training and are available to anyone interested in GIS and chronic disease (www.cdc.gov/DHDSP/maps/GISX). GIS offers diverse sets of tools that promise increased productivity for chronic disease staff of state and local health departments.