“…Our results suggested that the annual incidence rates were fluctuated considerably, with the peak incidence rate in 2008. The reasons for spatial clustering of disease rates may put down in the heterogeneous allotment of essential factors such as crowding, social inequality, and access to health services or environmental characteristics
[49-51]. …”
BackgroundAn improved understanding in transmission variation of kala-azar is fundamental to conduct surveillance and implementing disease prevention strategies. This study investigated the spatio-temporal patterns and hotspot detection for reporting kala-azar cases in Vaishali district based on spatial statistical analysis.MethodsEpidemiological data from the study area during 2007–2011 was used to examine the dynamic space-time pattern of kala-azar outbreaks, and all cases were geocoded at a village level. Spatial smoothing was applied to reduce random noise in the data. Inverse distance weighting (IDW) is used to interpolate and predict the pattern of VL cases distribution across the district. Moran’s I Index (Moran’s I) statistics was used to evaluate autocorrelation in kala-azar spatial distribution and test how villages were clustered or dispersed in space. Getis-Ord Gi*(d) was used to identify the hotspot and cold spot areas within the study site.ResultsMapping kala-azar cases or incidences reflects the spatial heterogeneity in the incidence rate of kala-azar affected villages in Vaishali district. Kala-azar incidence rate map showed most of the highest endemic villages were located in southern, eastern and northwestern part of the district; in the middle part of the district generally show the medium occurrence of VL. There was a significant positive spatial autocorrelation of kala-azar incidences for five consecutive years, with Moran’s I statistic ranging from 0.04-0.17 (P <0.01). The results revealed spatially clustered patterns with significant differences by village. The hotspots showed the spatial trend of kala-azar diffusion (P < 0.01).ConclusionsThe results pointed to the usefulness of spatial statistical approach to improve our understanding the spatio-temporal dynamics and control of kala-azar. The study also showed the north-western and southern part of Vaishali district is most likely endemic cluster region. To employ exact and geographically suitable risk-reduction programmes, apply of such spatial analysis tools should suit a vital constituent in epidemiology research and risk evaluation of kala-azar.
“…Our results suggested that the annual incidence rates were fluctuated considerably, with the peak incidence rate in 2008. The reasons for spatial clustering of disease rates may put down in the heterogeneous allotment of essential factors such as crowding, social inequality, and access to health services or environmental characteristics
[49-51]. …”
BackgroundAn improved understanding in transmission variation of kala-azar is fundamental to conduct surveillance and implementing disease prevention strategies. This study investigated the spatio-temporal patterns and hotspot detection for reporting kala-azar cases in Vaishali district based on spatial statistical analysis.MethodsEpidemiological data from the study area during 2007–2011 was used to examine the dynamic space-time pattern of kala-azar outbreaks, and all cases were geocoded at a village level. Spatial smoothing was applied to reduce random noise in the data. Inverse distance weighting (IDW) is used to interpolate and predict the pattern of VL cases distribution across the district. Moran’s I Index (Moran’s I) statistics was used to evaluate autocorrelation in kala-azar spatial distribution and test how villages were clustered or dispersed in space. Getis-Ord Gi*(d) was used to identify the hotspot and cold spot areas within the study site.ResultsMapping kala-azar cases or incidences reflects the spatial heterogeneity in the incidence rate of kala-azar affected villages in Vaishali district. Kala-azar incidence rate map showed most of the highest endemic villages were located in southern, eastern and northwestern part of the district; in the middle part of the district generally show the medium occurrence of VL. There was a significant positive spatial autocorrelation of kala-azar incidences for five consecutive years, with Moran’s I statistic ranging from 0.04-0.17 (P <0.01). The results revealed spatially clustered patterns with significant differences by village. The hotspots showed the spatial trend of kala-azar diffusion (P < 0.01).ConclusionsThe results pointed to the usefulness of spatial statistical approach to improve our understanding the spatio-temporal dynamics and control of kala-azar. The study also showed the north-western and southern part of Vaishali district is most likely endemic cluster region. To employ exact and geographically suitable risk-reduction programmes, apply of such spatial analysis tools should suit a vital constituent in epidemiology research and risk evaluation of kala-azar.
“…The biomedical and public health literature on geographic information systems (GIS) and spatio-temporal analyses features a large number of research papers mentioning or addressing location privacy, e.g., [11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. A must-read paper (not specifically health-related) dating back to 1994 [29] shows how chronic privacy issues are in GIS research.…”
Section: Research Literature: Location Privacy Concerns and Solutionsmentioning
This paper offers a state-of-the-art overview of the intertwined privacy, confidentiality, and security issues that are commonly encountered in health research involving disaggregate geographic data about individuals. Key definitions are provided, along with some examples of actual and potential security and confidentiality breaches and related incidents that captured mainstream media and public interest in recent months and years. The paper then goes on to present a brief survey of the research literature on location privacy/confidentiality concerns and on privacy-preserving solutions in conventional health research and beyond, touching on the emerging privacy issues associated with online consumer geoinformatics and location-based services. The 'missing ring' (in many treatments of the topic) of data security is also discussed. Personal information and privacy legislations in two countries, Canada and the UK, are covered, as well as some examples of recent research projects and events about the subject. Select highlights from a June 2009 URISA (Urban and Regional Information Systems Association) workshop entitled 'Protecting Privacy and Confidentiality of Geographic Data in Health Research' are then presented. The paper concludes by briefly charting the complexity of the domain and the many challenges associated with it, and proposing a novel, 'one stop shop' case-based reasoning framework to streamline the provision of clear and individualised guidance for the design and approval of new research projects (involving geographical identifiers about individuals), including crisp recommendations on which specific privacy-preserving solutions and approaches would be suitable in each case.
“…This information is required by public health authorities making immunization policies, health care practitioners making recommendations about vaccination, and individuals making health decisions for themselves and their families. Maps are one of the most effective ways to rapidly convey information about global issues [20]. However, maps can only display a limited amount of information, and complex decisions must be made about what information to display and how best to display it.…”
BackgroundWorld maps are among the most effective ways to convey public health messages such as recommended vaccinations, but creating a useful and valid map requires careful deliberation. The changing epidemiology of hepatitis A virus (HAV) in many world regions heightens the need for up-to-date risk maps. HAV infection is usually asymptomatic in children, so low-income areas with high incidence rates usually have a low burden of disease. In higher-income areas, many adults remain susceptible to the virus and, if infected, often experience severe disease.ResultsSeveral challenges associated with presenting hepatitis A risk using maps were identified, including the need to decide whether prior infection or continued susceptibility more aptly indicates risk, whether to display incidence or prevalence, how to distinguish between different levels of risk, how to display changes in risk over time, how to present complex information to target audiences, and how to handle missing or obsolete data.ConclusionFor future maps to be comparable across place and time, we propose the use of the age at midpoint of population susceptibility as a standard indicator for the level of hepatitis A endemicity within a world region. We also call for the creation of an accessible active database for population-based age-specific HAV seroprevalence and incidence studies. Health risk maps for other conditions with rapidly changing epidemiology would benefit from similar strategies.
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