Facility location models for electric vehicle charging infrastructure

This thesis deals with the study of current charging infrastructure availability in highways, as well as proposing optimal allocations for new stations. First, a Machine Learning model is trained in order to estimate the actual range of an electric vehicle. This model will be constructed using heterogeneous data sources and variables that influence the total autonomy, such as speed, temperature, degradation or elevation, among others. Second, this model is used in combination with geospatial data regarding French highway and charging infrastructure locations, in order to propose a methodology for analyzing the availability level of charging stations in highways for electric vehicles. Finally, an optimization framework is implemented to decide the opening of several charging stations inside a highway, providing as possible locations rest and service areas already built, and considering current highway operational charging points

Impact of the COVID-19 Pandemic on the Incidence of Suicidal Behaviors: A Retrospective Analysis of Integrated Electronic Health Records in a Population of 7.5 Million

The COVID-19 pandemic has caused remarkable psychological overwhelming and an increase in stressors that may trigger suicidal behaviors. However, its impact on the rate of suicidal behaviors has been poorly reported. We conducted a population-based retrospective analysis of all suicidal behaviors attended in healthcare centers of Catalonia (northeast Spain; 7.5 million inhabitants) between January 2017 and June 2022 (secondary use of data routinely reported to central suicide and diagnosis registries). We retrieved data from this period, including an assessment of suicide risk and individuals' socioeconomic as well as clinical characteristics. Data were summarized yearly and for the periods before and after the onset of the COVID-19 pandemic in Spain in March 2020. The analysis included 26,458 episodes of suicidal behavior (21,920 individuals); of these, 16,414 (62.0%) were suicide attempts. The monthly moving average ranged between 300 and 400 episodes until July 2020, and progressively increased to over 600 episodes monthly. In the postpandemic period, suicidal ideation increased at the expense of suicidal attempts. Cases showed a lower suicide risk; the percentage of females and younger individuals increased, whereas the prevalence of classical risk factors, such as living alone, lacking a family network, and a history of psychiatric diagnosis, decreased. In summary, suicidal behaviors have increased during the COVID-19 pandemic, with more episodes of suicidal ideation without attempts in addition to younger and lower risk profiles

The adjusted morbidity groups (GMA): an exhaustive and severity-balanced tool for risk assessment

Grups morbiditat ajustada; GMA; Eina d'estratificació; Avaluació de riscosGrupos morbilidad ajustada; GMA; Herramienta de estratificación; Evaluación de riesgosAdjusted morbidity groups; GMA; Stratification tool; Risk assessmentEls GMA consisteixen en una eina que permet avaluar el risc en salut a partir de les característiques demogràfiques dels pacients, les seves malalties cròniques i aquelles situacions o malalties agudes que puguin tenir-hi impacte. Aquesta eina proporciona un índex de risc que es pot utilitzar com a factor d’ajust en models específics d’una determinada malaltia i a la vegada actua com un agrupament per estratificar la població en diferents nivells de risc.Los GMA consisten en una herramienta que permite evaluar el riesgo en salud a partir de las características demográficas de los pacientes, sus enfermedades crónicas y aquellas situaciones o enfermedades agudas que puedan tener impacto. Esta herramienta proporciona un índice de riesgo que se puede utilizar como factor de ajuste en modelos específicos de una determinada enfermedad y al mismo tiempo actúa como un agrupamiento para estratificar la población en diferentes niveles de riesgo.GMAs are a tool that assesses health risk based on the demographic characteristics of patients, their chronic diseases and those situations or acute diseases that may have an impact. This tool provides a risk index that can be used as an adjustment factor in specific models of a given disease and at the same time acts as a grouping to stratify the population at different levels of risk

In the era of remote access to the healthcare system, the accurate measures of patient’s health complexity is a mainstay for a person-centred service allocation

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Reducing non-attendance in outpatient appointments: predictive model development, validation, and clinical assessment

Background Non-attendance to scheduled hospital outpatient appointments may compromise healthcare resource planning, which ultimately reduces the quality of healthcare provision by delaying assessments and increasing waiting lists. We developed a model for predicting non-attendance and assessed the effectiveness of an intervention for reducing non-attendance based on the model. Methods The study was conducted in three stages: (1) model development, (2) prospective validation of the model with new data, and (3) a clinical assessment with a pilot study that included the model as a stratification tool to select the patients in the intervention. Candidate models were built using retrospective data from appointments scheduled between January 1, 2015, and November 30, 2018, in the dermatology and pneumology outpatient services of the Hospital Municipal de Badalona (Spain). The predictive capacity of the selected model was then validated prospectively with appointments scheduled between January 7 and February 8, 2019. The effectiveness of selective phone call reminders to patients at high risk of non-attendance according to the model was assessed on all consecutive patients with at least one appointment scheduled between February 25 and April 19, 2019. We finally conducted a pilot study in which all patients identified by the model as high risk of non-attendance were randomly assigned to either a control (no intervention) or intervention group, the last receiving phone call reminders one week before the appointment. Results Decision trees were selected for model development. Models were trained and selected using 33,329 appointments in the dermatology service and 21,050 in the pneumology service. Specificity, sensitivity, and accuracy for the prediction of non-attendance were 79.90%, 67.09%, and 73.49% for dermatology, and 71.38%, 57.84%, and 64.61% for pneumology outpatient services. The prospective validation showed a specificity of 78.34% (95%CI 71.07, 84.51) and balanced accuracy of 70.45% for dermatology; and 69.83% (95%CI 60.61, 78.00) for pneumology, respectively. The effectiveness of the intervention was assessed on 1,311 individuals identified as high risk of non-attendance according to the selected model. Overall, the intervention resulted in a significant reduction in the non-attendance rate to both the dermatology and pneumology services, with a decrease of 50.61% (p<0.001) and 39.33% (p=0.048), respectively. Conclusions The risk of non-attendance can be adequately estimated using patient information stored in medical records. The patient stratification according to the non-attendance risk allows prioritizing interventions, such as phone call reminders, to effectively reduce non-attendance rates

Facility location models for electric vehicle charging infrastructure

This thesis deals with the study of current charging infrastructure availability in highways, as well as proposing optimal allocations for new stations. First, a Machine Learning model is trained in order to estimate the actual range of an electric vehicle. This model will be constructed using heterogeneous data sources and variables that influence the total autonomy, such as speed, temperature, degradation or elevation, among others. Second, this model is used in combination with geospatial data regarding French highway and charging infrastructure locations, in order to propose a methodology for analyzing the availability level of charging stations in highways for electric vehicles. Finally, an optimization framework is implemented to decide the opening of several charging stations inside a highway, providing as possible locations rest and service areas already built, and considering current highway operational charging points