Sickle Cell Illness Awareness among the General Public

Background: Lifelong ickle cell disease (SCD), a group of inherited blood disorders, afflicts millions of individuals. Sickle cell disease (SCD), with a global prevalence of 112 cases per 100,000 individuals, frequently gives rise to this condition. Sickle Cell Disease (SCD) exhibits a high prevalence in various regions, including Sub-Saharan Africa, Saudi Arabia, India, South and Central America, as well as the Mediterranean. We conducted a study in Tabuk, Saudi Arabia to assess the level of public knowledge and awareness of Sickle Cell Disease (SCD). Methods: The present study employed a cross-sectional observational design, encompassing a sample of 386 individuals residing in Tabuk, who were over the age of 18 and represented both genders and various nationalities. Demographic data and sickle cell disease awareness were obtained through the utilization of a structured questionnaire that was developed from previous research. Results: The present study included a total of 386 adults residing in Tabuk, Saudi Arabia, who satisfied the predetermined inclusion criteria. Among the participants, 47.4% fell between the age range of 18 to 25 years. The majority of participants had a satisfactory level of knowledge, with 24.1% of individuals aged 18-25, 10.1% of those aged 26-35, 7.3% and 6.55% of individuals aged 36-45, and a significant proportion of participants aged over 45. Conclusion: The survey participants demonstrated a satisfactory degree of understanding on the prevalence of sickle cell disease (SCD) in the Kingdom of Saudi Arabia (KSA).&nbsp

Assessing the risk for dengue fever based on socioeconomic and environmental variables in a geographical information system environment

An important option in preventing the spread of dengue fever (DF) is to control and monitor its vector (Aedes aegypti) as well as to locate and destroy suitable mosquito breeding environments. The aim of the present study was to use a combination of environmental and socioeconomic variables to model areas at risk of DF. These variables include clinically confirmed DF cases, mosquito counts, population density in inhabited areas, total populations per district, water access, neighbourhood quality and the spatio-temporal risk of DF based on the average, weekly frequency of DF incidence. Out of 111 districts investigated, 17 (15%), covering a total area of 121 km², were identified as of high risk, 25 (22%), covering 133 km², were identified as of medium risk, 18 (16%), covering 180 km², were identified as of low risk and 51 (46%), covering 726 km², were identified as of very low risk. The resultant model shows that most areas at risk of DF were concentrated in the central part of Jeddah county, Saudi Arabia. The methods used can be implemented as routine procedures for control and prevention. A concerted intervention in the medium- and high-risk level districts identified in this study could be highly effective in reducing transmission of DF in the area as a whol

Climate change and the potential global distribution of Aedes aegypti: spatial modelling using GIS and CLIMEX

We examined the potential added risk posed by global climate change on the dengue vector Aedes aegypti abundance using CLIMEX, a powerful tool for exploring the relationship between the fundamental and realised niche of any species. After calibrating the model using data from several knowledge domains, including geographical distribution records, we estimated potential distributions of the mosquito under current and future potential scenarios. The impact of climate change on its potential distribution was assessed with two global climate models, the CSIRO-Mk3.0 and the MIROC-H, run with two potential, future emission scenarios (A1B and A2) published by the Intergovernmental Panel on Climate Change. We compared today’s climate situation with two arbitrarily chosen future time points (2030 and 2070) to see the impact on the worldwide distribution of A. aegypti. The model for the current global climate indicated favourable areas for the mosquito within its known distribution in tropical and subtropical areas. However, even if much of the tropics and subtropics will continue to be suitable, the climatically favourable areas for A. aegypti globally are projected to contract under the future scenarios produced by these models, while currently unfavourable areas, such as inland Australia, the Arabian Peninsula, southern Iran and some parts of North America may become climatically favourable for this mosquito species. The climate models for the Aedes dengue vector presented here should be useful for management purposes as they can be adapted for decision/making regarding allocation of resources for dengue risk toward areas where risk infection remains and away from areas where climatic suitability is likely to decrease in the future

Modeling spatio-temporal risk changes in the incidence of dengue fever in Saudi Arabia: a geographical information system case study

The aim of this study was to use geographical information systems to demonstrate the Dengue fever (DF) risk on a monthly basis in Jeddah, Saudi Arabia with the purpose to provide documentation serving to improve surveillance and monitor the Aedes aegypti mosquito vector. Getis-Ord Gi* statistics and a frequency index covering a five-year period (2006- 2010) were used to map DF and model the risk spatio-temporally. The results show that monthly hotspots were mainly concentrated in central Jeddah districts and that the pattern changes considerably with time. For example, on a yearly basis, for the month of January, the Burman district was identified as a low risk area in 2006, a high-risk area in 2007, medium risk in 2008, very low risk in 2009 and low risk in 2010. The results demonstrate that it would be useful to follow the monthly DF pattern, based on the average weekly frequency, as this can facilitate the allocation of resources for the treatment of the disease, preventing its prevalence and monitoring its vecto

Future malaria spatial pattern based on the potential global warming impact in South and Southeast Asia

We used the Model for Interdisciplinary Research on Climate-H climate model with the A2 Special Report on Emissions Scenarios for the years 2050 and 2100 and CLIMEX software for projections to illustrate the potential impact of climate change on the spatial distributions of malaria in China, India, Indochina, Indonesia, and The Philippines based on climate variables such as temperature, moisture, heat, cold and dryness. The model was calibrated using data from several knowledge domains, including geographical distribution records. The areas in which malaria has currently been detected are consistent with those showing high values of the ecoclimatic index in the CLIMEX model. The match between prediction and reality was found to be high. More than 90% of the observed malaria distribution points were associated with the currently known suitable climate conditions. Climate suitability for malaria is projected to decrease in India, southern Myanmar, southern Thailand, eastern Borneo, and the region bordering Cambodia, Malaysia and the Indonesian islands, while it is expected to increase in southern and south-eastern China and Taiwan. The climatic models for Anopheles mosquitoes presented here should be useful for malaria control, monitoring, and management, particularly considering these future climate scenarios

The importance of appropriate temporal and spatial scales for dengue fever control and management

It is important to have appropriate models for the surveillance and control of mosquito-borne diseases, such as dengue fever (DF). These models need to be based on appropriate temporal and spatial scales. The aim of this study was to illustrate the impact of different temporal and spatial scales on DF control decisions. We applied the Getis-Ord Gi* statistic at different temporal and spatial scales to examine the local level of spatial clusters at these scales in order to identify and visualize areas where numbers of adult female Aedes mosquitoes were extreme and geographically homogenous. The modeled hotspot areas were different, depending on whether they were modeled on weekly, monthly or yearly aggregated data. A similar result was found when using different spatial scales for modeling, with different scales giving different hotspot regions. For 2006, the highest risk areas (18 districts) were mostly identified in the central districts with a high rate of similarity (95%) compared to the highest risk areas (19) identified in the averaged five-year period model. Knowledge of appropriate temporal and spatial scales can provide an opportunity to specify the health burden of DF and its vector within the hotspots, as well as set a platform that can help to pursue further investigations into associated factors responsible for increased disease risk based on different temporal and spatial scales

Using geographic information system and remote sensing to study common mosquito-borne diseases in Saudi Arabia: A review

Mosquito-borne diseases have spatial and temporal patterns, because mosquito density and longevity are based on a number of factors, such as temperature, precipitation and mosquito breeding habitats. Geographic Information System (GIS) and Remote Sensing (RS) and their related tools for mapping and modeling provide new and expanding opportunities for mosquito-borne diseases (MBD) research because they can display and model the temporal and spatial relationships between causes and diseases

Examples of using spatial information technologies for mapping and modelling mosquito-borne diseases based on environmental, climatic and socio-economic factors and different spatial statistics, temporal risk indices and spatial analysis: A review

The best way to avoid the impact of mosquito-borne diseases (MBDs) is to control and monitor their vectors and their environmental conditions. Spatial information technologies (SITs) are required for forecasting, controlling, monitoring and early detection of these environmental conditions and prevention of mosquito-borne diseases. Different SITs have shown promising results in assessing the risk of various MBDs at different spatial scales. SITs such as the geographic information system (GIS) and remote sensing (RS) and their related techniques cannot identify the vectors of MBDs themselves, but they can characterise the environment in which the vectors thrive. As new tools of surveillance, SITs are powerful predictors in the mapping and modelling of the geographical limits, intensity, and dynamics of the risk of MBDs. This literature review concentrates on MBDs that are transmitted by mosquito-borne viruses. Additionally, the main aim of this review is to give overview examples of how mapping and modelling based on SITs (e.g. GIS and its related tools) approaches are used to visualise and analyse mosquito vector and epidemiologic data and to describe the factors that can help in the control of these diseases. Using spatial information technologies and other methods with climatic, socio-economic and environmental factors and mosquito distribution pattern(s), it should be possible to extract the risk-areas at a predetermined spatial scale of investigation. Also, many points that are extracted from this review described the importance of using spatial information technologies and their related spatial statistics, temporal risk indices and analyst methods. This review also highlights the knowledge gaps in this area of research

Identifying and visualizing spatial patterns and hot spots of clinically-confirmed dengue fever cases and female 'Aedes aegypti' mosquitoes in Jeddah, Saudi Arabia

Understanding the distribution of dengue fever in time and space is the foundation for its control and management programmes. Different technologies, especially the Geographic Information System (GIS) and its tools and methods, have been used to illustrate and visualize the prevalence of some mosquito-borne diseases and abundance of their vectors. The aim of this study was to illustrate the spatial distribution and spatial pattern of this disease and female 'Aedes aegypti' mosquitoes in the epidemic-prone area of Jeddah, and also to show the hot spot districts with the highest risk levels. The study was conducted in Jeddah county, Saudi Arabia. The clinically-confirmed cases registries of dengue fever have been continuously and systematically collected since 2006 by the Dengue Fever Operation Room of Jeddah Health Affairs. The computerized databases of these two government departments have recorded weekly notifications of dengue fever cases and its vector (female 'Aedes' mosquito). The female 'Aedes' mosquito counts and identification were provided by the laboratory of mosquito, which belongs to the Jeddah Municipality. Two GIS techniques were used to achieve the aims of this study. The multi-distance spatial cluster (Ripley's K-function) was used to estimate the spatial pattern and distribution while the Getis Ord Gi* statistic was used to model and visualize the hot spots and the risk models. The results showed that the spatial patterns and distribution of dengue fever cases from 2006 to 2009 were clustered at multiple distances with statistically significant clustering. They also showed that most 'Aedes' mosquitoes were clustered while some of them were dispersed at larger distances, especially in 2007, 2008, 2009 and 2010. Also, areas with various risk levels of dengue fever and its vector were identified in different geographical locations (districts) for different epidemic years using the Getis-Ord Gi*. Identifying dengue fever and its vector cluster and hot spots can be greatly enhanced through the use of a variety of analytical techniques that are available in the Geographic Information System. Getis-Ord Gi* and multi-distance spatial cluster (Ripley's K-function) can be implemented as routine procedures along with dengue fever control and prevention programmes