Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach

Hashtarkhani, Soheil; Kiani, Behzad; Mohammadi, Alireza; MohammadEbrahimi, Shahab; Dehghan-Tezerjani, Mohammad; Samimi, Tahereh; Tara, Mahmood; Matthews, Stephen A.

doi:10.4081/gh.2021.1043

Cited by 7 publications

(6 citation statements)

References 43 publications

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“…Indeed, the burden of influenza fluctuates by location, and its distribution could be driven by diffusion patterns through larger population hubs to surrounding communities [13,14]. Geographical Information System (GIS) is a useful tool to visualize space-time information and can be considered as a decision support system [15]. In Iran, several epidemiological studies on influenza have been conducted [7]; however, little attention has been paid to the spatial correlation of adjacent areas and exploration of hotspot clusters [16,17].…”

Section: Introductionmentioning

confidence: 99%

Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016–2018

et al. 2022

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Introduction Seasonal influenza is a significant public health challenge worldwide. This study aimed to investigate the epidemiological characteristics and spatial patterns of severe hospitalized influenza cases confirmed by polymerase chain reaction (PCR) in Iran. Methods Data were obtained from Iran’s Ministry of Health and Medical Education and included all hospitalized lab-confirmed influenza cases from January 1, 2016, to December 30, 2018 (n = 9146). The Getis-Ord Gi* and Local Moran’s I statistics were used to explore the hotspot areas and spatial cluster/outlier patterns of influenza. We also built a multivariable logistic regression model to identify covariates associated with patients’ mortality. Results Cumulative incidence and mortality rate were estimated at 11.44 and 0.49 (per 100,000), respectively, and case fatality rate was estimated at 4.35%. The patients’ median age was 40 (interquartile range: 22–63), and 55.5% (n = 5073) were female. The hotspot and cluster analyses revealed high-risk areas in northern parts of Iran, especially in cold, humid, and densely populated areas. Moreover, influenza hotspots were more common during the colder months of the year, especially in high-elevated regions. Mortality was significantly associated with older age (adjusted odds ratio [aOR]: 1.01, 95% confidence interval [CI]: 1.01–1.02), infection with virus type-A (aOR: 1.64, 95% CI: 1.27–2.15), male sex (aOR: 1.77, 95% CI: 1.44–2.18), cardiovascular disease (aOR: 1.71, 95% CI: 1.33–2.20), chronic obstructive pulmonary disease (aOR: 1.82, 95% CI: 1.40–2.34), malignancy (aOR: 4.77, 95% CI: 2.87–7.62), and grade-II obesity (aOR: 2.11, 95% CI: 1.09–3.74). Conclusions We characterized the spatial and epidemiological heterogeneities of severe hospitalized influenza cases confirmed by PCR in Iran. Detecting influenza hotspot clusters could inform prioritization and geographic specificity of influenza prevention, testing, and mitigation resource management, including vaccination planning in Iran.

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Section: Introductionmentioning

confidence: 99%

Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016–2018

et al. 2022

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“…A central dispatch center in the city of Mashhad manages all EMS requests of Mashhad County. Spatio-temporal patterns of EMS requests were investigated using spatio-temporal scan statistics approach in Mashhad [ 9 ]. Here, a comprehensive dataset related to this study is offered for further investigation to identify clustering patterns and potential geographic characteristics of EMS requests in a densely populated city.…”

Section: Objectivementioning

confidence: 99%

“…Notably, the location of callers was recorded by latitude and longitude in the EMS system. Altered level of consciousness, trauma injuries and cardiovascular incidents are the main reasons of emergency requests in Mashhad [ 9 ]. In order to protect the privacy of callers, the point location was linked to a larger area, a census tract, using the spatial join tool of ArcGIS v. 10.6.…”

Section: Data Descriptionmentioning

confidence: 99%

One-year spatiotemporal database of Emergency Medical Service (EMS) calls in Mashhad, Iran: data on 224,355 EMS calls

et al. 2022

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Objectives Emergency Medical Services (EMS) is the first point of service for the people who are in critical condition and in need of urgent health care. In Iran, as in other countries, people in need of emergency services often die or are left with a permanent injury due to the poor EMS-related infrastructure. It has been shown that a detailed examination of the response times and the spatiotemporal pattern of EMS calls for service can lead to improvements in time-sensitive patient outcomes. We performed a spatiotemporal study in city of Mashhad, the second-most populous city of Iran, to investigate the pattern of the EMS calls and now wish to release a comprehensive dataset resulting from this study. Data description The data include three data files plus a data dictionary file. Data file 1 contains the characteristics of EMS requests including sex, age group, date of call, different time periods of each EMS missions, the census tracts’ ID of callers, the chief complaint, and the EMS mission result. Two spatial data files include the boundaries of the census tracts in Mashhad and the point location of all EMS stations, respectively. A data dictionary file defines all fields and values across the data files.

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“…These include the availability of necessary services, including appropriate vehicles, socioeconomic factors, access to appropriate materials, well-equipped personnel, and coordination of the response process [ 9 , 10 ]. Accordingly, different studies have investigated the impact of these factors, which can affect EMS in rural and urban communities [ 7 , 11 , 12 , 13 , 14 ]. For instance, previous investigations show that patients living in rural countries have longer waiting times for ambulances [ 12 , 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Prehospital Time Interval for Urban and Rural Emergency Medical Services: A Systematic Literature Review

Alruwaili

Alanazy

2022

Healthcare

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The aim of this study was to discuss the differences in pre-hospital time intervals between rural and urban communities regarding emergency medical services (EMS). A systematic search was conducted through various relevant databases, together with a manual search to find relevant articles that compared rural and urban communities in terms of response time, on-scene time, and transport time. A total of 37 articles were ultimately included in this review. The sample sizes of the included studies was also remarkably variable, ranging between 137 and 239,464,121. Twenty-nine (78.4%) reported a difference in response time between rural and urban areas. Among these studies, the reported response times for patients were remarkably variable. However, most of them (number (n) = 27, 93.1%) indicate that response times are significantly longer in rural areas than in urban areas. Regarding transport time, 14 studies (37.8%) compared this outcome between rural and urban populations. All of these studies indicate the superiority of EMS in urban over rural communities. In another context, 10 studies (27%) reported on-scene time. Most of these studies (n = 8, 80%) reported that the mean on-scene time for their populations is significantly longer in rural areas than in urban areas. On the other hand, two studies (5.4%) reported that on-scene time is similar in urban and rural communities. Finally, only eight studies (21.6%) reported pre-hospital times for rural and urban populations. All studies reported a significantly shorter pre-hospital time in urban communities compared to rural communities. Conclusions: Even with the recently added data, short pre-hospital time intervals are still superior in urban over rural communities.

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Spatio-temporal epidemiology of emergency medical requests in a large urban area. A scan-statistic approach

Cited by 7 publications

References 43 publications

Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016–2018

Geospatial epidemiology of hospitalized patients with a positive influenza assay: A nationwide study in Iran, 2016–2018

One-year spatiotemporal database of Emergency Medical Service (EMS) calls in Mashhad, Iran: data on 224,355 EMS calls

Prehospital Time Interval for Urban and Rural Emergency Medical Services: A Systematic Literature Review

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