Predictors of in-Hospital Mortality Among Suspected Stroke Patients, Mashhad, Iran: An Application of Autologistic Regression Model

Background: Stroke is the second leading cause of death in adults worldwide. There are remarkable geographical variations in the accessibility to emergency medical services (EMS), and transport delays have been documented worldwide to affect stroke outcomes significantly. Therefore, this study examines whether there are spatial variations in in-hospital mortality among suspected stroke patients transferred by EMS and attempts to determine its related factors using the auto logistic regression model.Methods: In this historical cohort study, suspected stroke patients transferred to Ghaem Hospital of Mashhad by the EMS from March 2018 and March 2019 were included. Using emergency mission IDs, the baseline EMS data were integrated with the follow-up hospital records. The autologistic regression model was applied to examine the possible geographical variations in in-hospital mortality and its related factors. All analysis was carried out by SPSS version 16 and R 4.0.0 at the significant level of 0.05. Results: 1,222 suspected stroke patients were included in this study, and the in-hospital mortality rate was 14.2%. Overall in-hospital stroke mortality was related to age, accessibility rate of an ambulance, screening time, and length of stay (p<0.05). After stratifying by sex, we observed that mortality in men was related to age and length of stay, whereas, in women, variables of age, length of stay, accessibility rate of an ambulance, and screening time had a significant effect on in-hospital mortality among suspected stroke patients (p<0.05).Conclusion: Our results showed considerable geographical variations in in-hospital stroke mortality in Mashhad neighborhoods. Also, age- and sex-adjusted results from this study highlight the direct association between accessibility rate of an ambulance, screening time and length of stay, and in-hospital stroke mortality. The prognosis of in-hospital stroke mortality could be improved by reducing delay time and increasing the EMS access rate.

Spatial Analysis of the 2017 Outbreak of Hemorrhagic Disease and Physiographic Region in the Eastern United States

Hemorrhagic disease (HD) is considered one of the most significant infectious diseases of white-tailed deer in North America. Investigations into environmental conditions associated with outbreaks suggest drought conditions are strongly correlated with outbreaks in some regions of the United States. However, during 2017, an HD outbreak occurred in the Eastern United States which appeared to be associated with a specific physiographic region, the Appalachian Plateau, and not drought conditions. The objective of this study was to determine if reported HD in white-tailed deer in 2017 was correlated with physiographic region. There were 456 reports of HD from 1605 counties across 26 states and 12 physiographic regions. Of the 93 HD reports confirmed by virus isolation, 76.3% (71/93) were identified as EHDV-2 and 66.2% (47/71) were from the Appalachian Plateau. A report of HD was 4.4 times more likely to occur in the Appalachian Plateau than not in 2017. Autologistic regression models suggested a statistically significant spatial dependence. The underlying factors explaining this correlation are unknown, but may be related to a variety of host, vector, or environmental factors. This unique outbreak and its implications for HD epidemiology highlight the importance for increased surveillance and reporting efforts in the future.

Predictors of In-Hospital Mortality Among Suspected Stroke Patients, Mashhad, Iran: An Application of Autologistic Regression Model

Background Stroke is the second leading cause of death in adults worldwide. There are remarkable geographical variations in the accessibility to emergency medical services (EMS), and transport delays have been documented worldwide to affect stroke outcomes significantly. Therefore, this study examines whether there are spatial variations in in-hospital mortality among suspected stroke patients transferred by EMS and attempts to determine its related factors using the auto logistic regression model. Methods In this historical cohort study, suspected stroke patients transferred to Ghaem Hospital of Mashhad by the EMS from April 2018 to March 2019 were included. Using emergency mission IDs, the baseline EMS data were integrated with the follow-up hospital records. The autologistic regression model was applied to examine the possible geographical variations in in-hospital mortality and its related factors. All analysis was carried out by SPSS version 16 and R 4.0.0 at the significant level of 0.05. Results 1,222 suspected stroke patients were included in this study, and the in-hospital mortality rate was 14.2%. Overall in-hospital stroke mortality was related to age, accessibility rate of an ambulance, screening time, and length of stay (p < 0.05). After stratifying by sex, we observed that mortality in men was related to age and length of stay, whereas, in women, variables of age, length of stay, accessibility rate of an ambulance, and screening time had a significant effect on in-hospital mortality among suspected stroke patients (p < 0.05). Conclusion Our results showed considerable geographical variations in in-hospital stroke mortality in Mashhad neighborhoods. Also, age- and sex-adjusted results from this study highlight the direct association between accessibility rate of an ambulance, screening time and length of stay, and in-hospital stroke mortality. The prognosis of in-hospital stroke mortality could be improved by reducing delay time and increasing the EMS access rate.

Incorporating spatial autocorrelation and settlement type segregation to improve the performance of an urban growth model

As rapid urbanization and population growth have become global issues, urban growth modeling has become an essential tool for decision-makers to understand how urban growth works in overall dense environments and to assess the sustainability of current urban forms. However, in urban growth models (particularly when incorporating quantitative approaches to include driving factors of urban growth), spatial autocorrelation may influence the overall model performance. In this paper, an empirical study was conducted in the region of Munich, and an integrated urban growth model was tested to explain current urban growth. The modeling contributes to advances in the state of the art by combining a range of driving factors using autologistic regression with a transition probability matrix from the Markov chain method in a cellular automata model simulation. The autologistic regression employed here addresses the impact of spatial autocorrelation compared to ordinary logistic regression. Furthermore, this study compared modeling of overall settlement growth with modeling high- and low-density settlement types separately. Incorporating spatial dependency into the model through application of autologistic regression showed improvements when compared to the ordinary logistic regression model. The Kappa indexes were higher when separately modeling the two types of settlement density compared to modeling overall settlement growth since the driving factors of settlement growth of different densities might be different. From an urban planning perspective, this novel autologistic regression-Markov chain-based cellular automata model is a powerful tool that offers an opportunity for planners and government authorities to gain a more precise understanding of the different urban growth processes that might occur in an urban region similar to the one tested here. It should allow for a better assessment of the potential costs, benefits, and risks of corresponding planning strategies.