An all-island approach to mapping bovine tuberculosis in Ireland

This study used techniques in Geographical Information Systems (GIS) to explore the spatial patterns of bovine tuberculosis (TB) in the whole island of Ireland over an 11-year period. This is the first time that data pertaining to TB from the Republic of Ireland and Northern Ireland have been collated and examined in an all-Ireland context. The analyses were based on 198, 156 point locations representing active farms with cattle in Northern Ireland and the Republic of Ireland between the years 1996 and 2006. The results consist of a series of maps giving a visual representation of cattle populations and associated detected bTB levels on the island of Ireland over this time interval

Evaluation of SOVAT: An OLAP-GIS decision support system for community health assessment data analysis

Background. Data analysis in community health assessment (CHA) involves the collection, integration, and analysis of large numerical and spatial data sets in order to identify health priorities. Geographic Information Systems (GIS) enable for management and analysis using spatial data, but have limitations in performing analysis of numerical data because of its traditional database architecture. On-Line Analytical Processing (OLAP) is a multidimensional datawarehouse designed to facilitate querying of large numerical data. Coupling the spatial capabilities of GIS with the numerical analysis of OLAP, might enhance CHA data analysis. OLAP-GIS systems have been developed by university researchers and corporations, yet their potential for CHA data analysis is not well understood. To evaluate the potential of an OLAP-GIS decision support system for CHA problem solving, we compared OLAP-GIS to the standard information technology (IT) currently used by many public health professionals. Methods. SOVAT, an OLAP-GIS decision support system developed at the University of Pittsburgh, was compared against current IT for data analysis for CHA. For this study, current IT was considered the combined use of SPSS and GIS ("SPSS-GIS"). Graduate students, researchers, and faculty in the health sciences at the University of Pittsburgh were recruited. Each round consisted of: an instructional video of the system being evaluated, two practice tasks, five assessment tasks, and one post-study questionnaire. Objective and subjective measurement included: task completion time, success in answering the tasks, and system satisfaction. Results. Thirteen individuals participated. Inferential statistics were analyzed using linear mixed model analysis. SOVAT was statistically significant (α = .01) from SPSS-GIS for satisfaction and time (p < .002). Descriptive results indicated that participants had greater success in answering the tasks when using SOVAT as compared to SPSS-GIS. Conclusion. Using SOVAT, tasks were completed more efficiently, with a higher rate of success, and with greater satisfaction, than the combined use of SPSS and GIS. The results from this study indicate a potential for OLAP-GIS decision support systems as a valuable tool for CHA data analysis. © 2008 Scotch et al; licensee BioMed Central Ltd

Quantitative measurements of inequality in geographic accessibility to pediatric care in Oita Prefecture, Japan: Standardization with complete spatial randomness

<p>Abstract</p> <p>Background</p> <p>A quantitative measurement of inequality in geographic accessibility to pediatric care as well as that of mean distance or travel time is very important for priority setting to ensure fair access to pediatric facilities. However, conventional techniques for measuring inequality is inappropriate in geographic settings. Since inequality measures of access distance or travel time is strongly influenced by the background geographic distribution patterns, they cannot be directly used for regional comparisons of geographic accessibility. The objective of this study is to resolve this issue by using a standardization approach.</p> <p>Methods</p> <p>Travel times to the nearest pediatric care were calculated for all children in Oita Prefecture, Japan. Relative mean differences were considered as the inequality measure for secondary medical service areas, and were standardized with an expected value estimated from a Monte Carlo simulation based on complete spatial randomness.</p> <p>Results</p> <p>The observed mean travel times in the area considered averaged 4.50 minutes, ranging from 1.83 to 7.02 minutes. The mean of the observed inequality measure was 1.1, ranging from 0.9 to 1.3. The expected values of the inequality measure varied according to the background geographic distribution pattern of children, which ranged from 0.3 to 0.7. After standardizing the observed inequality measure with the expected one, we found that the ranks of the inequality measure were reversed for the observed areas.</p> <p>Conclusions</p> <p>Using the indicator proposed in this paper, it is possible to compare the inequality in geographic accessibility among regions. Such a comparison may facilitate priority setting in health policy and planning.</p

Assessing the context of health care utilization in Ecuador: A spatial and multilevel analysis

<p>Abstract</p> <p>Background</p> <p>There are few studies that have analyzed the context of health care utilization, particularly in Latin America. This study examines the context of utilization of health services in Ecuador; focusing on the relationship between provision of services and use of both preventive and curative services.</p> <p>Methods</p> <p>This study is cross-sectional and analyzes data from the 2004 National Demographic and Maternal & Child Health dataset. Provider variables come from the Ecuadorian System of Social Indicators (SIISE). Global Moran's I statistic is used to assess spatial autocorrelation of the provider variables. Multilevel modeling is used for the simultaneous analysis of provision of services at the province-level with use of services at the individual level.</p> <p>Results</p> <p>Spatial analysis indicates no significant differences in the density of health care providers among Ecuadorian provinces. After adjusting for various predisposing, enabling, need factors and interaction terms, density of public practice health personnel was positively associated with use of preventive care, particularly among rural households. On the other hand, density of private practice physicians was positively associated with use of curative care, particularly among urban households.</p> <p>Conclusions</p> <p>There are significant public/private, urban/rural gaps in provision of services in Ecuador; which in turn affect people's use of services. It is necessary to strengthen the public health care delivery system (which includes addressing distribution of health workers) and national health information systems. These efforts could improve access to health care, and inform the civil society and policymakers on the advances of health care reform.</p

Geographical distribution of American cutaneous leishmaniasis and its phlebotomine vectors (Diptera: Psychodidae) in the state of São Paulo, Brazil

<p>Abstract</p> <p>Background</p> <p>American cutaneous leishmaniasis (ACL) is a re-emerging disease in the state of São Paulo, Brazil. It is important to understand both the vector and disease distribution to help design control strategies. As an initial step in applying geographic information systems (GIS) and remote sensing (RS) tools to map disease-risk, the objectives of the present work were to: (i) produce a single database of species distributions of the sand fly vectors in the state of São Paulo, (ii) create combined distributional maps of both the incidence of ACL and its sand fly vectors, and (iii) thereby provide individual municipalities with a source of reference material for work carried out in their area.</p> <p>Results</p> <p>A database containing 910 individual records of sand fly occurrence in the state of São Paulo, from 37 different sources, was compiled. These records date from between 1943 to 2009, and describe the presence of at least one of the six incriminated or suspected sand fly vector species in 183/645 (28.4%) municipalities. For the remaining 462 (71.6%) municipalities, we were unable to locate records of any of the six incriminated or suspected sand fly vector species (<it>Nyssomyia intermedia</it>, <it>N. neivai</it>, <it>N. whitmani</it>, <it>Pintomyia fischeri</it>, <it>P. pessoai </it>and <it>Migonemyia migonei</it>). The distribution of each of the six incriminated or suspected vector species of ACL in the state of São Paulo were individually mapped and overlaid on the incidence of ACL for the period 1993 to 1995 and 1998 to 2007. Overall, the maps reveal that the six sand fly vector species analyzed have unique and heterogeneous, although often overlapping, distributions. Several sand fly species - <it>Nyssomyia intermedia </it>and <it>N. neivai </it>- are highly localized, while the other sand fly species - <it>N. whitmani, M. migonei, P. fischeri </it>and <it>P. pessoai </it>- are much more broadly distributed. ACL has been reported in 160/183 (87.4%) of the municipalities with records for at least one of the six incriminated or suspected sand fly vector species, while there are no records of any of these sand fly species in 318/478 (66.5%) municipalities with ACL.</p> <p>Conclusions</p> <p>The maps produced in this work provide basic data on the distribution of the six incriminated or suspected sand fly vectors of ACL in the state of São Paulo, and highlight the complex and geographically heterogeneous pattern of ACL transmission in the region. Further studies are required to clarify the role of each of the six suspected sand fly vector species in different regions of the state of São Paulo, especially in the majority of municipalities where ACL is present but sand fly vectors have not yet been identified.</p

An overview of geospatial methods used in unintentional injury epidemiology

BACKGROUND: Injuries are a leading cause of death and disability around the world. Injury incidence is often associated with socio-economic and physical environmental factors. The application of geospatial methods has been recognised as important to gain greater understanding of the complex nature of injury and the associated diverse range of geographically-diverse risk factors. Therefore, the aim of this paper is to provide an overview of geospatial methods applied in unintentional injury epidemiological studies. METHODS: Nine electronic databases were searched for papers published in 2000-2015, inclusive. Included were papers reporting unintentional injuries using geospatial methods for one or more categories of spatial epidemiological methods (mapping; clustering/cluster detection; and ecological analysis). Results describe the included injury cause categories, types of data and details relating to the applied geospatial methods. RESULTS: From over 6,000 articles, 67 studies met all inclusion criteria. The major categories of injury data reported with geospatial methods were road traffic (n = 36), falls (n = 11), burns (n = 9), drowning (n = 4), and others (n = 7). Grouped by categories, mapping was the most frequently used method, with 62 (93%) studies applying this approach independently or in conjunction with other geospatial methods. Clustering/cluster detection methods were less common, applied in 27 (40%) studies. Three studies (4%) applied spatial regression methods (one study using a conditional autoregressive model and two studies using geographically weighted regression) to examine the relationship between injury incidence (drowning, road deaths) with aggregated data in relation to explanatory factors (socio-economic and environmental). CONCLUSION: The number of studies using geospatial methods to investigate unintentional injuries has increased over recent years. While the majority of studies have focused on road traffic injuries, other injury cause categories, particularly falls and burns, have also demonstrated the application of these methods. Geospatial investigations of injury have largely been limited to mapping of data to visualise spatial structures. Use of more sophisticated approaches will help to understand a broader range of spatial risk factors, which remain under-explored when using traditional epidemiological approaches