Urban sprawl and delayed ambulance arrival in the U.S

COVID19 in Latin America: informal settlements and the politics of urbicide

Informal settlements in Latin America express pandemic idiosyncrasies, calling global attention to historical problems related to a specific urbanization pattern. This article stresses COVID19 implications in the main urban planning canon: the notion of densification as an urban solution. Traditionally invisible social groups and territories acquire relevance, but now as a source of biological risk. Urban density appears as a contradictory trigger point, outlining new debates about informal settlements and their metrics. Evidence shows that trends in health discourse are striving to legitimize and enhance "urbicides" in this scenario, already underway through State action or inaction.

A New Graph-Based Fractality Index to Characterize Complexity of Urban Form

Examining the complexity of urban form may help to understand human behavior in urban spaces, thereby improving the conditions for sustainable design of future cities. Metrics, such as fractal dimension, ht-index, and cumulative rate of growth (CRG) index have been proposed to measure this complexity. However, as these indicators are statistical rather than spatial, they result in an inability to characterize the spatial complexity of urban forms, such as building footprints. To overcome this problem, this paper proposes a graph-based fractality index (GFI), which is based on a hybrid of fractal theory and deep learning techniques. First, to quantify the spatial complexity, several fractal variants were synthesized to train a deep graph convolutional neural network. Next, building footprints in London were used to test the method, where the results showed that the proposed framework performed better than the traditional indices, i.e., the index is capable of differentiating complex patterns. Another advantage is that it seems to assure that the trained deep learning is objective and not affected by potential biases in empirically selected training datasets Furthermore, the possibility to connect fractal theory and deep learning techniques on complexity issues opens up new possibilities for data-driven GIS science.

Use of prehospital, hospitalization and presence of sequelae and/or disability in road traffic injury victims in Brazil

Objective

To estimate the prevalence and analyze the association between sociodemographic and behavioral variables with the use of prehospital care, hospitalization and sequelae and/or disability in victims of road traffic accidents victims in Brazil.

Methods

Data from the National Health Survey conducted in 2013 in Brazil were used. Data were collected through a direct household survey. The research sample consisted of 1,840 individuals who reported road traffic accidents in the previous 12 months. Poisson regression analysis was used to evaluate the factors associated with the use of prehospital care services, hospitalization, and the presence of sequelae and/or disability.

Results

The prevalence of road traffic accidents victims who received prehospital care was 13.0% (95% Confidence Interval [95% CI]: 10.3–16.3) and the factors associated with this outcome were: residing in the Northeast or North region of Brazil; residing in rural areas; and being a motorcycle occupant at the moment of the road traffic accident. The frequency of hospitalization was 7.7% (95% CI: 6.0–10.0) and the associated factors were: age between 40 and 59 years; being a motorcycle occupant or pedestrian and having received on-site care at the moment of the road traffic accident. The prevalence of sequelae and/or disability was 15.1% (95% CI: 12.5–18.2) and the associated factors were: age range between 30 and 39 years or 40 and 59 years; being a motorcycle occupant, being a pedestrian or belonging to other category of modes of transport and having received on-site care at the moment of the road traffic accident.

Conclusion

The study allowed to evaluate the factors associated with prehospital care, hospitalization and presence of sequelae and/or disability in the victims of road traffic accident and the results can guide the implementation of interventions that prioritize the population exposed to the highest risk of road traffic accident injuries and with less access to prehospital and hospital care services in Brazil.

Longitudinal analyses of the relationship between development density and the COVID-19 morbidity and mortality rates: Early evidence from 1,165 metropolitan counties in the United States

This longitudinal study aims to investigative the impacts of development density on the spread and mortality rates of COVID-19 in metropolitan counties in the United States. Multilevel Linear Modeling (MLM) is employed to model the infection rate and the mortality rate of COVID-19, accounting for the hierarchical (two-level) and longitudinal structure of the data. This study finds that large metropolitan size (measured in terms of population) leads to significantly higher COVID-19 infection rates and higher mortality rates. After controlling for metropolitan size and other confounding variables, county density leads to significantly lower infection rates and lower death rates. These findings recommend that urban planners and health professionals continue to advocate for compact development and continue to oppose urban sprawl for this and many other reasons documented in the literature, including the positive relationship between compact development and fitness and general health.

Mutual-Aid Mobile App for Emergency Care: Feasibility Study

BACKGROUND

Improving the quality of patient care through the use of mobile devices is one of the hot topics in the health care field. In unwanted situations like an accident, ambulances and rescuers often require a certain amount of time to arrive at the scene. Providing immediate cardiopulmonary resuscitation (CPR) to patients might improve survival.

OBJECTIVE

The primary objective of this study was to evaluate the feasibility of an emergency and mutual-aid app model in Taiwan and to provide a reference for government policy.

METHODS

A structured questionnaire was developed as a research tool. All questionnaires were designed according to the technology acceptance model, and a Likert scale was used to measure the degree of agreement or disagreement. Moreover, in-depth interviews were conducted with six experts from medical, legal, and mobile app departments. Each expert was interviewed once to discuss feasible countermeasures and suggestions. Statistical Package for the Social Sciences (SPSS version 19; IBM Corp, Armonk, New York) was used to perform all statistical analyses, including descriptive statistics, independent sample t-tests, variance analysis, and Pearson correlation analysis.

RESULTS

We conducted this study between October 20, 2017, and November 10, 2017, at the Taipei Medical University Hospital. Questionnaires were distributed to medical personnel, visiting guests, family members, and volunteers. A total of 113 valid questionnaires were finally obtained after the exclusion of incomplete questionnaires. Cronbach α values for self-efficacy (perceived ease of use), use attitude (perceived usefulness), and use willingness and frequency were above .85, meeting the criterion of greater than .70. We observed that the reliability of each subquestion was acceptable and the values for use attitude (perceive usefulness) and use willingness and frequency were more than .90.

CONCLUSIONS

The findings suggest that perceived ease of use and perceived usefulness of the app model affect use willingness. However, perceived usefulness had an intermediary influence on use willingness. Experts in law, medical, and technology fields consider that an emergency and mutual-aid model can be implemented in Taiwan. Along with the development of an emergency and mutual-aid app model, we recommend an increase in the number of automated external defibrillators per region and promotion of correct knowledge about CPR in order to decrease morbidity and mortality.

Predictors of transportation delay in patients with suspected ST-elevation-myocardial infarction in the VIENNA-STEMI network

OBJECTIVE

The emergency medical service (EMS) provides rapid pre-hospital diagnosis and transportation in ST-elevation myocardial infarction (STEMI) systems of care. Aim of the study was to assess temporal and regional characteristics of EMS-related delays in a metropolitan STEMI network.

METHODS

Patient call-to-arrival of EMS at site (call-to-site), transportation time from site to hospital (site-to-door), call-to-door, patient's location, month, weekday, and hour of EMS activation were recorded in 4751 patients referred to a tertiary center with suspicion of STEMI.

RESULTS

Median call-to-site, site-to-door, and call-to-door times were 9 (7-12), 39 (31-48), and 49 (41-59) minutes, respectively. The shortest transportation times were noted between 08:00 and 16:00 and in general on Sundays. They were significantly prolonged between midnight and 04:00, whereby the longest difference did not exceed 4 min in median. Patient's site of call had a major impact on transportation times, which were shorter in Central and Western districts as compared to Southern and Eastern districts of Vienna (p < 0.001 between-group difference for call-to-site, site-to-door, and call-to-door). After multivariable adjustment, patient's site of call was an independent predictor of call-to-site delay (p < 0.001). Moreover, age and hour of EMS activation were the strongest predictors of call-to-site, site-to-door, and call-to-door delays (p < 0.05).

CONCLUSION

In our Viennese STEMI network, the strongest determinants of pre-hospital EMS-related transportation delays were patient's site of call, patient's age, and hour of EMS activation. Due to the significant but small median time delays, which are within the guideline-recommended time intervals, no impact on clinical outcome can be expected.

Logistics of air medical transport: When and where does helicopter transport reduce prehospital time for trauma?

BACKGROUND

Trauma is a time sensitive disease. Helicopter emergency medical services (HEMS) have shown benefit over ground emergency medical services (GEMS), which may be related to reduced prehospital time. The distance at which this time benefit emerges depends on many factors that can vary across regions. Our objective was to determine the threshold distance at which HEMS has shorter prehospital time than GEMS under different conditions.

METHODS

Patients in the Pennsylvania trauma registry 2000 to 2013 were included. Distance between zip centroid and trauma center was calculated using straight-line distance for HEMS and driving distance from geographic information systems network analysis for GEMS. Contrast margins from linear regression identified the threshold distance at which HEMS had a significantly lower prehospital time than GEMS, indicated by nonoverlapping 95% confidence intervals. The effect of peak traffic times and adverse weather on the threshold distance was evaluated. Geographic effects across EMS regions were also evaluated.

RESULTS

A total of 144,741 patients were included with 19% transported by HEMS. Overall, HEMS became faster than GEMS at 7.7 miles from the trauma center (p = 0.043). Helicopter emergency medical services became faster at 6.5 miles during peak traffic (p = 0.025) compared with 7.9 miles during off-peak traffic (p = 0.048). Adverse weather increased the distance at which HEMS was faster to 17.1 miles (p = 0.046) from 7.3 miles in clear weather (p = 0.036). Significant variation occurred across EMS regions, with threshold distances ranging from 5.4 to 35.3 miles. There was an inverse but non-significant relationship between urban population and threshold distance across EMS regions (ρ, -0.351, p = 0.28).

CONCLUSION

This is the first study to demonstrate that traffic, weather, and geographic region significantly impact the threshold distance at which HEMS are faster than GEMS. Helicopter emergency medical services was faster at shorter distances during peak traffic while adverse weather increased this distance. The threshold distance varied widely across geographic region. These factors must be considered to guide appropriate HEMS triage protocols.

LEVEL OF EVIDENCE

Therapeutic, level IV.

From built environment to health inequalities: An explanatory framework based on evidence

OBJECTIVE

The Health in All Policies strategy aims to engage every policy domain in health promotion. The more socially disadvantaged groups are usually more affected by potential negative impacts of policies if they are not health oriented. The built environment represents an important policy domain and, apart from its housing component, its impact on health inequalities is seldom assessed.

METHODS

A scoping review of evidence on the built environment and its health equity impact was carried out, searching both urban and medical literature since 2000 analysing socio-economic inequalities in relation to different components of the built environment.

RESULTS

The proposed explanatory framework assumes that key features of built environment (identified as density, functional mix and public spaces and services), may influence individual health through their impact on both natural environment and social context, as well as behaviours, and that these effects may be unequally distributed according to the social position of individuals.

CONCLUSION

In general, the expected links proposed by the framework are well documented in the literature; however, evidence of their impact on health inequalities remains uncertain due to confounding factors, heterogeneity in study design, and difficulty to generalize evidence that is still very embedded to local contexts.

Effects of urban sprawl and vehicle miles traveled on traffic fatalities

OBJECTIVE

Previous research suggests that urban sprawl increases auto-dependency and that excessive auto use increases the risk of traffic fatalities. This indirect effect of urban sprawl on traffic fatalities is compared to non-vehicle miles traveled (VMT)-related direct effect of sprawl on fatalities.

METHODS

We conducted a path analysis to examine the causal linkages among urban sprawl, VMT, traffic fatalities, income, and fuel cost. The path diagram includes 2 major linkages: the direct relationship between urban sprawl and traffic fatalities and the indirect effect on fatalities through increased VMT in sprawling urban areas. To measure the relative strength of these causal linkages, path coefficients are estimated using data collected nationally from 147 urbanized areas in the United States.

RESULTS

Through both direct and indirect paths, urban sprawl is associated with greater numbers of traffic fatalities, but the direct effect of sprawl on fatalities is more influential than the indirect effect.

CONCLUSIONS

Enhancing traffic safety can be achieved by impeding urban sprawl and encouraging compact development. On the other hand, policy tools reducing VMT may be less effective than anticipated for traffic safety.

Jurisdictional spillover effects of sprawl on injuries and fatalities

There is a considerable literature on the relationship between sprawl and accidents. However, these studies do not account for the spatially correlated effects of sprawl on accidents. In our analysis of 122 jurisdictions in Southeast Michigan, we use a Bayesian spatial autoregressive model to estimate how injuries and fatalities in one jurisdiction are associated with sprawl in that jurisdiction and sprawl in neighboring jurisdictions; we also correct for heteroskedasticity in the data. Using principal component analysis, we create a sprawl index from five underlying land use characteristics. Our results show that the number of injuries and fatalities in a jurisdiction increases with the magnitude of sprawl in neighboring jurisdictions. We believe that this is because more drivers per capita in sprawled jurisdictions traverse similarly sprawled neighboring jurisdictions for daily activities. Furthermore, driving habits attuned to less defensive driving in sprawled jurisdiction are transferred to similarly designed neighboring jurisdictions, contributing to accidents in the latter.

A validation of ground ambulance pre-hospital times modeled using geographic information systems

Background

Evaluating geographic access to health services often requires determining the patient travel time to a specified service. For urgent care, many research studies have modeled patient pre-hospital time by ground emergency medical services (EMS) using geographic information systems (GIS). The purpose of this study was to determine if the modeling assumptions proposed through prior United States (US) studies are valid in a non-US context, and to use the resulting information to provide revised recommendations for modeling travel time using GIS in the absence of actual EMS trip data.

Methods

The study sample contained all emergency adult patient trips within the Calgary area for 2006. Each record included four components of pre-hospital time (activation, response, on-scene and transport interval). The actual activation and on-scene intervals were compared with those used in published models. The transport interval was calculated within GIS using the Network Analyst extension of Esri ArcGIS 10.0 and the response interval was derived using previously established methods. These GIS derived transport and response intervals were compared with the actual times using descriptive methods. We used the information acquired through the analysis of the EMS trip data to create an updated model that could be used to estimate travel time in the absence of actual EMS trip records.

Results

There were 29,765 complete EMS records for scene locations inside the city and 529 outside. The actual median on-scene intervals were longer than the average previously reported by 7–8 minutes. Actual EMS pre-hospital times across our study area were significantly higher than the estimated times modeled using GIS and the original travel time assumptions. Our updated model, although still underestimating the total pre-hospital time, more accurately represents the true pre-hospital time in our study area.

Conclusions

The widespread use of generalized EMS pre-hospital time assumptions based on US data may not be appropriate in a non-US context. The preference for researchers should be to use actual EMS trip records from the proposed research study area. In the absence of EMS trip data researchers should determine which modeling assumptions more accurately reflect the EMS protocols across their study area.