Spatial association between primary Middle East respiratory syndrome coronavirus infection and exposure to dromedary camels in Saudi Arabia.

Middle East respiratory syndrome coronavirus (MERS-CoV) is an emerging zoonotic disease. Exposure to dromedary camels (Camelus dromedaries) has been consistently considered the main source of primary human infection. Although Saudi Arabia reports the highest rate of human MERS-CoV infection and has one of the largest populations of dromedary camels worldwide, their spatial association has not yet been investigated. Thus, this study aimed to examine the correlation between the spatial distribution of primary MERS-CoV cases with or without a history of camel exposure reported between 2012 and 2019 and dromedary camels at the provincial level in Saudi Arabia. In most provinces, a high proportion of older men develop infections after exposure to camels. Primary human infections during spring and winter were highest in provinces characterized by seasonal breeding and calving, increased camel mobilization and camel-human interactions. A strong and significant association was found between the total number of dromedary camels and the numbers of primary camel-exposed and non-exposed MERS-CoV cases. Furthermore, spatial correlations between MERS-CoV cases and camel sex, age and dairy status were significant. Via a cluster analysis, we identified Riyadh, Makkah and Eastern provinces as having the most primary MERS-CoV cases and the highest number of camels. Transmission of MERS-CoV from camels to humans occurs in most primary cases, but there is still a high proportion of primary infections with an ambiguous link to camels. The results from this study include significant correlations between primary MERS-CoV cases and camel populations in all provinces, regardless of camel exposure history. This supports the hypothesis of the role of an asymptomatic human carrier or, less likely, an unknown animal host that has direct contact with both infected camels and humans. In this study, we performed a preliminary risk assessment of prioritization measures to control the transmission of infection from camels to humans.

Spatiotemporal Clustering of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Incidence in Saudi Arabia, 2012-2019.

Middle East respiratory syndrome coronavirus (MERS-CoV) is a great public health concern globally. Although 83% of the globally confirmed cases have emerged in Saudi Arabia, the spatiotemporal clustering of MERS-CoV incidence has not been investigated. This study analysed the spatiotemporal patterns and clusters of laboratory-confirmed MERS-CoV cases reported in Saudi Arabia between June 2012 and March 2019. Temporal, seasonal, spatial and spatiotemporal cluster analyses were performed using Kulldorff's spatial scan statistics to determine the time period and geographical areas with the highest MERS-CoV infection risk. A strongly significant temporal cluster for MERS-CoV infection risk was identified between April 5 and May 24, 2014. Most MERS-CoV infections occurred during the spring season (41.88%), with April and May showing significant seasonal clusters. Wadi Addawasir showed a high-risk spatial cluster for MERS-CoV infection. The most likely high-risk MERS-CoV annual spatiotemporal clusters were identified for a group of cities (n = 10) in Riyadh province between 2014 and 2016. A monthly spatiotemporal cluster included Jeddah, Makkah and Taif cities, with the most likely high-risk MERS-CoV infection cluster occurring between April and May 2014. Significant spatiotemporal clusters of MERS-CoV incidence were identified in Saudi Arabia. The findings are relevant to control the spread of the disease. This study provides preliminary risk assessments for the further investigation of the environmental risk factors associated with MERS-CoV clusters.

Spatial variation in the association between NO2 concentrations and shipping emissions in the Red Sea.

Air pollution from shipping emissions poses significant health and environmental risks, particularly in the coastal regions. For the first time, this region as one of the busiest seas and most important international shipping lane in the world with significant nitrogen dioxide (NO2) emissions has been analyzed comprehensively. This paper aims to characterize and quantify the contribution of maritime transport sector emissions to NO2 concentrations in the Red Sea using local Geographically Weighted Regression (GWR) model in a geographic information system (GIS) environment. Maritime traffic volume was estimated using SaudiSat satellite-based Automatic Identification System (S-AIS) data, and the remotely measured tropospheric NO2 concentrations data was acquired from the ozone monitoring instrument (OMI) satellite. A significant spatial variation in the NO2 values was detected across the Red Sea, with values ranging from 4.03 × 1014 to 41.39 × 1014 molecules/cm2. Most notably, the NO2 concentrations in international waters were more than double those in the western coastal regions, whereas the concentrations close to seaports were 100% higher than those over international waters. The results indicated that the local GWR model performed significantly better than the global ordinary least squares (OLS) regression model. The GWR model had a strong and significant overall coefficient of determination with an r2 of 0.94 (p < 0.005) in comparison to the OLS model with an r2 of 0.45 (p < 0.005). Maritime traffic volume and proximity to seaports weighted by shipping activities explained about 94% of the variations of NO2 concentrations in the Red Sea. The results of this study suggest that the S-AIS data and environmental satellite measurements can be used to assess the impacts of NO2 concentrations from shipping emissions. These findings should stimulate further research into using additional covariates to explain the NO2 concentrations in areas near seaports where the standardized residuals are high.

Spatial autocorrelation of cancer incidence in Saudi Arabia.

Little is known about the geographic distribution of common cancers in Saudi Arabia. We explored the spatial incidence patterns of common cancers in Saudi Arabia using spatial autocorrelation analyses, employing the global Moran's I and Anselin's local Moran's I statistics to detect nonrandom incidence patterns. Global ordinary least squares (OLS) regression and local geographically-weighted regression (GWR) were applied to examine the spatial correlation of cancer incidences at the city level. Population-based records of cancers diagnosed between 1998 and 2004 were used. Male lung cancer and female breast cancer exhibited positive statistically significant global Moran's I index values, indicating a tendency toward clustering. The Anselin's local Moran's I analyses revealed small significant clusters of lung cancer, prostate cancer and Hodgkin's disease among males in the Eastern region and significant clusters of thyroid cancers in females in the Eastern and Riyadh regions. Additionally, both regression methods found significant associations among various cancers. For example, OLS and GWR revealed significant spatial associations among NHL, leukemia and Hodgkin's disease (r² = 0.49-0.67 using OLS and r² = 0.52-0.68 using GWR) and between breast and prostate cancer (r² = 0.53 OLS and 0.57 GWR) in Saudi Arabian cities. These findings may help to generate etiologic hypotheses of cancer causation and identify spatial anomalies in cancer incidence in Saudi Arabia. Our findings should stimulate further research on the possible causes underlying these clusters and associations.

NO(2) and cancer incidence in Saudi Arabia.

Air pollution exposure has been shown to be associated with an increased risk of specific cancers. This study investigated whether the number and incidence of the most common cancers in Saudi Arabia were associated with urban air pollution exposure, specifically NO2. Overall, high model goodness of fit (GOF) was observed in the Eastern, Riyadh and Makkah regions. The significant coefficients of determination (r2) were higher at the regional level (r2 = 0.32-0.71), weaker at the governorate level (r2 = 0.03-0.43), and declined slightly at the city level (r2 = 0.17-0.33), suggesting that an increased aggregated spatial level increased the explained variability and the model GOF. However, the low GOF at the lowest spatial level suggests that additional variation remains unexplained. At different spatial levels, associations between NO2 concentration and the most common cancers were marginally improved in geographically weighted regression (GWR) analysis, which explained both global and local heterogeneity and variations in cancer incidence. High coefficients of determination were observed between NO2 concentration and lung and breast cancer incidences, followed by prostate, bladder, cervical and ovarian cancers, confirming results from other studies. These results could be improved using individual explanatory variables such as environmental, demographic, behavioral, socio-economic, and genetic risk factors.

The standardized live patient and mechanical patient models--their roles in trauma teaching.

BACKGROUND: We have previously demonstrated improved medical student performance using standardized live patient models in the Trauma Evaluation and Management (TEAM) program. The trauma manikin has also been offered as an option for teaching trauma skills in this program. In this study, we compare performance using both models. METHODS: Final year medical students were randomly assigned to three groups: group I (n = 22) with neither model, group II (n = 24) with patient model, and group III (n = 24) with mechanical model using the same clinical scenario. All students completed pre-TEAM and post-TEAM multiple choice question (MCQ) exams and an evaluation questionnaire scoring five items on a scale of 1 to 5 with 5 being the highest. The items were objectives were met, knowledge improved, skills improved, overall satisfaction, and course should be mandatory. Students (groups II and III) then switched models, rating preferences in six categories: more challenging, more interesting, more dynamic, more enjoyable learning, more realistic, and overall better model. Scores were analyzed by ANOVA with p < 0.05 being considered statistically significant. RESULTS: All groups had similar scores (means % +/- SD)in the pretest (group I - 50.8 +/- 7.4, group II - 51.3 +/- 6.4, group III - 51.1 +/- 6.6). All groups improved their post-test scores but groups II and III scored higher than group I with no difference in scores between groups II and III (group I - 77.5 +/- 3.8, group II - 84.8 +/- 3.6, group III - 86.3 +/- 3.2). The percent of students scoring 5 in the questionnaire are as follows: objectives met - 100% for all groups; knowledge improved: group I - 91%, group II - 96%, group III - 92%; skills improved: group I - 9%, group II - 83%, group III - 96%; overall satisfaction: group I - 91%, group II - 92%, group III - 92%; should be mandatory: group I - 32%, group II - 96%, group III - 100%. Student preferences (48 students) are as follows: the mechanical model was more challenging (44 of 48); more interesting (40 of 48); more dynamic (46 of 48); more enjoyable (48 of 48); more realistic (32/48), and better overall model (42 of 48). CONCLUSIONS: Using the TEAM program, we have demonstrated that improvement in knowledge and skills are equally enhanced by using mechanical or patient models in trauma teaching. However, students overwhelmingly preferred the mechanical model.

Bilateral universal nephroblastomatosis in an 8-month-old infant treated with chemotherapy.

We report the case of an 8-month-old female infant presenting with bilateral, diffusely enlarged kidneys. A diagnosis of bilateral, universal nephroblastomatosis was made on tissue biopsies from both kidneys after correlation with the radiological findings. As far as we know, this is the oldest patient reported with this diagnosis in the English literature (they are usually younger than 4 months). The patient was treated with chemotherapy with very good response and almost 1 year later she is showing no signs of recurrence of her disease.