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Geographically weighted regression (GWR) takes a prominent role in spatial regression analysis, providing a nuanced perspective on the intricate interplay of variables within geographical landscapes (Brunsdon et al., 1998). However, it is essential to have a strong rationale for employing GWR, either as an addition to, or a complementary analysis alongside, non-spatial (global) regression models (Kiani, Mamiya et al., 2023). Moreover, the proper selection of bandwidth, weighting function or kernel types, and variable choices constitute the most critical configurations in GWR analysis (Wheeler, 2021). [...].

This study integrates geographical information systems (GIS) with a mathematical optimization technique to enhance emergency medical services (EMS) coverage in a county in the northeast of Iran. EMS demand locations were determined through one-year EMS call data analysis. We formulated a maximal covering location problem (MCLP) as a mixed-integer linear programming model with a capacity threshold for vehicles using the CPLEX optimizer, an optimization software package from IBM. To ensure applicability to the EMS setting, we incorporated a constraint that maintains an acceptable level of service for all EMS calls. Specifically, we implemented two scenarios: a relocation model for existing ambulances and an allocation model for new ambulances, both using a list of candidate locations. The relocation model increased the proportion of calls within the 5-minute coverage standard from 69% to 75%. With the allocation model, we found that the coverage proportion could rise to 84% of total calls by adding ten vehicles and eight new stations. The incorporation of GIS techniques into optimization modelling holds promise for the efficient management of scarce healthcare resources, particularly in situations where time is of the essence.

Appropriate accessibility to coronavirus disease 2019 (COVID-19) services is essential in the efficient management of the pandemic. Different geospatial methods and approaches have been used to measure accessibility to COVID-19 health-related services. This scoping review aimed to summarize and synthesize the geospatial studies conducted to measure accessibility to COVID-19 healthcare services. Web of Science, Scopus, and PubMed were searched to find relevant studies. From 1113 retrieved unique citations, 26 articles were selected to be reviewed. Most of the studies were conducted in the USA and floating catchment area methods were mostly used to measure the spatial accessibility to COVID-19 services including vaccination centres, Intensive Care Unit beds, hospitals and test sites. More attention is needed to measure the accessibility of COVID-19 services to different types of users especially with combining different non-spatial factors which could lead to better allocation of resources especially in populations with limited resources.

Pre-hospital care is provided by emergency medical services (EMS) staff, the initial health care providers at the scene of disaster. This study aimed to describe the characteristics of EMS callers and space-time distribution of emergency requests in a large urban area. Descriptive thematic maps of EMS requests were created using an empirical Bayesian smoothing approach. Spatial, temporal and spatio-temporal clustering techniques were applied to EMS data based on Kulldorff scan statistics technique. Almost 225,000 calls were registered in the EMS dispatch centre during the study period. Approximately two-thirds of these calls were associated with an altered level of patient consciousness, and the median response time for rural and urban EMS dispatches was 12.2 and 10.1 minutes, respectively. Spatio-temporal clusters of EMS requests were mostly located in central parts of the city, particularly near the downtown area. However, high-response time clustered areas had a low overlap with these general, spatial clusters. This low convergence shows that some unknown factors, other than EMS requests, influence the high-response times. The findings of this study can help policymakers to better allocate EMS resources and implement tailored interventions to enhance EMS system in urban areas.

The vision of health for all by Dr. Halfdan Mahler, Director General of the World Health Organization (WHO) 1973 to 1988, guided public health approaches towards improving life for all those mired in poverty and disease. Research on the Neglected Tropical Diseases (NTDs) of the world's poor was advancing strongly when the coronavirus disease 2019 (COVID-19) struck. Although work on the NTDs did not grind to a halt, the situation is reminiscent of the author Stefan Zweig's passionate account of culture destruction in his book The World of Yesterday from 1941, which gives an insight as to how the war ended traditional life. His thoughts parallel the present situation; however, this time societies are not torn apart by war but instead isolated by a pandemic. It comes upon today's scientists to move fast to make COVID-19 less devastating than the Spanish flu of 1918-1920 that killed more than 3% of the world population...

Colorectal cancer (CRC) is the second most common cancer among females and the third most common malignancy in males in the world. No single risk factor has been identified, but there are many interrelated factors that together cause the disease. This retrospective, cross-sectional study aimed to identify potential spatial factors contributing to its geographical distribution. Data concerning 1,089 individuals with CRC from the Khorasan-Razavi Province in Iran, located in the North-East of the country, were obtained from the national CRC registry. Local Moran's I statistic was performed to identify clustered areas of CRC occurrence and ordinary least squared regression was calculated to predict frequency of the disease based on a set of variables, such as diet, body mass index (BMI) and the proportion of the population ≥50 years of age. Some dissimilarities related to the geography in the province and also some neighbourhood-related similarities and dissimilarities of CRC occurrence in the city of Mashhad were found. A significant regression equation was found (F (4,137)=38.304, P<.000) with an adjusted R2 of 0.6141. The predicted CRC frequency was -58.3581 with the coefficients for average BMI=+1.594878; fibre intake=-0.610335; consumption of red meat +0.078970; and ≥50-year age group =+0.000744. All associations were statistically significant, except the consumption of red meat one. The study results illuminate the potential underlying risk factors in areas where the CRC risk is comparatively high and how the CRC risk factors may play a role in CRC geographic disparity. Further research is required to explain the patterns observed. We conclude that people should include more fibre in their daily diet and decline their BMI to decrease risk of CRC.

End-stage renal disease patients regularly need haemodialysis three times a week. Their poor access to haemodialysis facilities is significantly associated with a high mortality rate. The present cross-sectional study aimed to measure the potential spatial access to dialysis services at a small area level (census tract level) in North Khorasan Province, Iran. The patients were interviewed to obtain their travel information. The two-step floating catchment area (2SFCA) method was used to measure the spatial accessibility of patients to the dialysis centres. The capacity of the dialysis centre was defined as the number of active dialysis facilities in each centre and the haemodialysis patients in each area were considered as the users of dialysis services. The travel cost from each patient's residence to the haemodialysis facilities was visualized by the Kriging interpolation algorithm in the study area. Spatial accessibility to the dialysis centre was poor in the northern part of the study area. Fortunately, there were not many haemodialysis patients in that area. Patients' travel costs were high in the northern areas compared to the rest of study area. We observed a statistically significant reverse correlation between the self-reported travel time and computed spatial accessibility (-0.570, P value <0.01, two-tailed spearman test). This study supports the notion that the 2SFCA method could be associated with revealed access time to dialysis facilities, especially in low traffic and in flat areas such as northern Khorasan. The mapping of patients' distribution and interpolated travel cost to the haemodialysis facilities could help policymakers to allocate health resources to the areas where the need is greater.

Equitable access to healthcare services constitutes one of the leading priorities of healthcare provision and access to dialysis services (ADS) has an essential impact on patients depending on renal dialysis. The many existing GIS-based ADS evaluations include various spatial and non-spatial factors affecting ADS. We systematically mapped and reviewed the available literature with reference to this area identifying gaps in current GIS-based ADS measurements and developing recommendations for future studies. A threestep, systematic mapping review of the available GIS-related evidence in PubMed, Embase, Web of science, Scopus, Science Direct and IEEE Xplore was performed in May 2016 and the information collected updated October 2017 by two independent selection processes. The quality of the studies was assessed using an informal, mixed-approach scoring system. Out of 1119 literature references identified, 36 were identified and used for final review after removal of duplicates, study screenings and applying inclusion/exclusion criteria. Given the contents of the selected studies, three study groups were identified and 41 factors with potential effects on ADS determined. These studies mainly addressed the potential and/or spatial aspects of ADS. Our systematic mapping review of the evidence revealed that current GIS-based measures of ADS tend to calculate potential ADS instead of a realized one. It was also noted that listed factors affecting ADS were mainly nonspatial bringing forth the hypothesis that designing an integrated ADS index could possibly produce better ADS score than those currently advocated. Some primary and secondary research suggestions are made and a list of recommendations offered.

Poor access to haemodialysis facilities is associated with high mortality and morbidity rates. This study investigated factors affecting revealed access to the haemodialysis facilities considering patients living in rural and urban areas without any haemodialysis facility (Group A) and those living urban areas with haemodialysis facilities (Group B). This study is based on selfreported Actual Access Time (AAT) to referred haemodialysis facilities and other information regarding travel to haemodialysis facilities from patients. All significant variables on univariate analysis were entered into a univariate general linear model in order to identify factors associated with AAT. Both spatial (driving time and distance) and non-spatial factors (sex, income level, caregivers, transportation mode, education level, ethnicity and personal vehicle ownership) influenced the revealed access identified in Group A. The non-spatial factors for Group B patients were the same as for Group A, but no spatial factor was identified in Group B. It was found that accessibility is strongly underestimated when driving time is chosen as accessibility measure to haemodialysis facilities. Analysis of revealed access determinants provides policymakers with an appropriate decision base for making appropriate decisions and finding solutions to decrease the access time for patients under haemodialysis therapy. Driving time alone is not a good proxy for measuring access to haemodialysis facilities as there are many other potential obstacles, such as women's special travel problems, poor other transportation possibilities, ethnicity disparities, low education levels, low caregiver status and low-income.

Chronic kidney disease is growing and the current estimated global prevalence exceeds 13%. As the use of haemodialysis machines for patients with end stage renal disease increases survival considerably, it is critical to plan correctly for the allocation of these machines. This study aimed to develop a geographical information systems (GIS)-based approach to predict the need for this service in the northeastern region of Iran taking into account where patients live and where haemodialysis is the most needed and identifying areas with poor access to haemodialysis centres. Patients were interviewed to obtain self-reported actual travel time and the inverse distance-weighting algorithm was used to determine access in each area. The prediction is based on the domestic growth rate for haemodialysis services and the estimated active hours of machine use for the next five years. We estimate that six new haemodialysis machines are required in northeastern Iran at the present time with 50 machines required over the next five years. Ashkhane City was identified to have the least access to haemodialysis centres in the study area. Our GIS-based model can be used to investigate not only the need for new haemodialysis machines but also to examine geographic disparities in the allocation of haemodialysis centres and to identify areas most in need of this service. It is important that policymakers consider both spatial and non-spatial dimensions of access to enable better allocation of haemodialysis services ensuring they are targeted to reach those in need.