Temporal and Spatial Trends of Adverse Pregnancy and Birth Outcomes in a Sample of Births from a Public Hospital in Chile.

Understanding temporal and spatial trends in pregnancy and birth outcomes within an urban area is important for the monitoring of health indicators of a population. We conducted a retrospective cohort study of all births in the public hospital of Temuco, a medium-sized city in Southern Chile between 2009 and 2016 (n = 17,237). Information on adverse pregnancy and birth outcomes, as well as spatial and maternal characteristics (insurance type, employment, smoking, age, and overweight/obesity), was collected from medical charts. Home addresses were geocoded and assigned to neighborhood. We tested whether births and prevalence of adverse pregnancy outcomes changed over time, whether birth events were spatially clustered (Moran's I statistic), and whether neighborhood deprivation was correlated to outcomes (Spearman's rho). We observed decreases in eclampsia, hypertensive disorders of pregnancy, and small for gestational age, while gestational diabetes, preterm birth, and low birth weight increased over the study period (all p < 0.01 for trend), with little changes after adjusting for maternal characteristics. We observed neighborhood clusters for birth rate, preterm birth, and low birth weight. Neighborhood deprivation was negatively correlated with low birth weight and preterm birth, but not correlated with eclampsia, preeclampsia, hypertensive disorders of pregnancy, small for gestational age, gestational diabetes, nor stillbirth. Several encouraging downward trends and some increases in adverse pregnancy and birth outcomes, which, overall, were not explained by changes in maternal characteristics were observed. Identified clusters of higher adverse birth outcomes may be used to evaluate preventive health coverage in this setting.

A novel methodology for determining low-cost fine particulate matter street sweeping routes.

This paper addresses the problem of low-cost PM10 (particulate matter with aerodynamic diameter < 10 microm) street sweeping route. In order to do so, only a subset of the streets of the urban area to be swept is selected for sweeping, based on their PM10 emission factor values. Subsequently, a low-cost route that visits each street in the set is computed. Unlike related problems of waste collection where streets must be visited once (Chinese or Rural Postman Problem, respectively), in this case, the sweeping vehicle route must visit each selected street exactly as many times as its number of street sides, since the vehicle can sweep only one street side at a time. Additionally, the route must comply with traffic flow and turn constraints. A novel transformation of the original arc routing problem into a node routing problem is proposed in this paper. This is accomplished by building a graph that represents the area to sweep in such a way that the problem can be solved by applying any known solution to the Traveling Salesman Problem (TSP). As a way of illustration, the proposed method was applied to the northeast area of the Municipality of Santiago (Chile). Results show that the proposed methodology achieved up to 37% savings in kilometers traveled by the sweeping vehicle when compared to the solution obtained by solving the TSP problem with Geographic Information Systems (GIS)--aware tools.

Investigating the Risk Factors Associated with Injury Severity in Pedestrian Crashes in Santiago, Chile.

Pedestrians are vulnerable road users that are directly exposed to road traffic crashes with high odds of resulting in serious injuries and fatalities. Therefore, there is a critical need to identify the risk factors associated with injury severity in pedestrian crashes to promote safe and friendly walking environments for pedestrians. This study investigates the risk factors related to pedestrian, crash, and built environment characteristics that contribute to different injury severity levels in pedestrian crashes in Santiago, Chile from a spatial and statistical perspective. First, a GIS kernel density technique was used to identify spatial clusters with high concentrations of pedestrian crash fatalities and severe injuries. Subsequently, partial proportional odds models were developed using the crash dataset for the whole city and the identified spatial clusters to examine and compare the risk factors that significantly affect pedestrian crash injury severity. The model results reveal higher increases in the fatality probability within the spatial clusters for statistically significant contributing factors related to drunk driving, traffic signage disobedience, and imprudence of the pedestrian. The findings may be utilized in the development and implementation of effective public policies and preventive measures to help improve pedestrian safety in Santiago.

Spatial autocorrelation analysis of cargo trucks on highway crashes in Chile.

The growing number of cargo trucks on highway crashes in recent years due to the increase in freight movement in Chile motivates this study to identify the formation of persistent crash clusters on highway Ruta 5 (R5). Two spatial statistical methods (Moran's I and Getis-Ord Gi*) were used to determine whether crashes on this highway showed spatial clustering over time from a global and local perspective. Globally, recurrent crash clusters are spatially correlated on vertical curves and straight highway sections on northern R5 with different truck types and with the tractor-trailer units during rainy days on southern R5. The local spatial autocorrelation results suggest that the contributing causes related to the loss of control of the vehicle, the fatigue and imprudence of the driver, and crashes involving tractor units with trailer tend to cause persistent rollover crash clusters throughout R5. Overall, clustering of crash attributes with high values (i.e., hot spots) occurring on highway locations with vertical curves and on cloudy days predominated in the northern R5, and the largest number of recurrent hot spots occurred on sunny days along southern R5. A hot spot spatial co-occurrence analysis was further performed to identify the strong relationships between the studied crash attributes, and the crash and injury types as outcomes. The indication of high risk for the clustering of cargo trucks on highways crashes provides a basis for improving highway safety and reduce the associated social and economic costs.

A spatial and temporal analysis of child pedestrian crashes in Santiago, Chile.

This paper presents a spatial and temporal analysis of child pedestrian crash data in Santiago, Chile during the period 2000-2008. First, this study identified seven critical areas with high child pedestrian crash risk employing kernel density estimation, and subsequently, statistically significant clusters of the main attributes associated to these crashes in each critical area were determined in a geographic information systems environment. Moran's I index test identified a positive spatial autocorrelation on crash contributing factors, time of day, straight road sections and intersections, and roads without traffic signs within the critical areas during the studied period, whereas a random spatial pattern was identified for crashes related to the age attribute. No statistical significance in the spatial relationship was obtained in child pedestrian crashes with respect to gender, weekday, and month of the year. The results from this research aid in determining the areas in which enhanced school-age child pedestrian safety is required by developing and implementing effective enforcement, educational, and engineering preventive measures.