An integrated decision analytic framework of machine learning with multi-criteria decision making for patient prioritization in elective surgeries

Objectif: De nombreux centres de santé à travers le monde utilisent des critères d'évaluation des préférences cliniques (CPAC) pour donner la priorité aux patients pour accéder aux chirurgies électives. Le processus de priorisation clinique du patient utilise à cette fin les caractéristiques du patient et se compose généralement de critères cliniques, d'expériences de patients précédemment hospitalisés et de commentaires sur les réseaux sociaux. Le but de la hiérarchisation des patients est de déterminer un ordre précis pour les patients et de déterminer combien chaque patient bénéficiera de la chirurgie. En d'autres termes, la hiérarchisation des patients est un type de problème de prise de décision qui détermine l'ordre de ceux qui ont le plus bénéficié de la chirurgie. Cette étude vise à développer une méthodologie hybride en intégrant des algorithmes d'apprentissage automatique et des techniques de prise de décision multicritères (MCDM) afin de développer un nouveau modèle de priorisation des patients. L'hypothèse principale est de valider le fait que l'intégration d'algorithmes d'apprentissage automatique et d'outils MCDM est capable de mieux prioriser les patients en chirurgie élective et pourrait conduire à une plus grande précision. Méthode: Cette étude vise à développer une méthodologie hybride en intégrant des algorithmes d'apprentissage automatique et des techniques de prise de décision multicritères (MCDM) afin de développer un modèle précis de priorisation des patients. Dans un premier temps, une revue de la littérature sera effectuée dans différentes bases de données pour identifier les méthodes récemment développées ainsi que les facteurs de risque / attributs les plus courants dans la hiérarchisation des patients. Ensuite, en utilisant différentes méthodes MCDM telles que la pondération additive simple (SAW), le processus de hiérarchie analytique (AHP) et VIKOR, l'étiquette appropriée pour chaque patient sera déterminée. Dans la troisième étape, plusieurs algorithmes d'apprentissage automatique seront appliqués pour deux raisons: d'abord la sélection des caractéristiques parmi les caractéristiques communes identifiées dans la littérature et ensuite pour prédire les classes de patients initialement déterminés. Enfin, les mesures détaillées des performances de prédiction des algorithmes pour chaque méthode seront déterminées. Résultats: Les résultats montrent que l'approche proposée a atteint une précision de priorisation assez élevée(~70 %). Cette précision a été obtenue sur la base des données de 300 patients et elle pourrait être considérablement améliorée si nous avions accès à plus de données réelles à l'avenir. À notre connaissance, cette étude présente la première et la plus importante du genre à combiner efficacement les méthodes MCDM avec des algorithmes d'apprentissage automatique dans le problème de priorisation des patients en chirurgie élective.Objective: Many healthcare centers worldwide use Clinical Preference Assessment criteria (CPAC) to prioritize patients for accessing elective surgeries [44]. The patient's clinical prioritization process uses patient characteristics for this purpose and usually consists of clinical criteria, experiences of patients who have been previously hospitalized, and comments on social media. The sense of patient prioritization is to determine an accurate ordering for patients and how much each patient will benefit from the surgery. This research intends to build a hybrid approach for creating a new patient prioritizing model by combining machine learning algorithms with multi-criteria decision-making (MCDM) methodologies. The central hypothesis is to validate that the integration of machine learning algorithms and MCDM tools can better prioritize elective surgery patients and lead to higher accuracy. Method: As a first step, a literature review was performed in different databases to identify the recently developed methods and the most common criteria in patient prioritization. Then, using various MCDM methods, including simple additive weighting (SAW), analytical hierarchy process (AHP), and VIKOR, the appropriate label for each patient was determined. As the third step, several machine learning algorithms were applied to predict each patient's classes. Finally, we established the algorithms' precise prediction performance metrics for each approach. Results: The results show that the proposed approach has achieved relatively high prioritization accuracy (~70%). This accuracy has been obtained based on the data from 300 patients, and it could be significantly improved if we have access to more accurate data in the future. To the best of our knowledge, this research is the first of its type to demonstrate the effectiveness of combining MCDM methodologies with machine learning algorithms in patient prioritization problems in elective surgery

Altered levels of immune-regulatory microRNAs in plasma samples of patients with lupus nephritis

Introduction: Lupus nephritis (LN) is a major cause of mortality and morbidity in the patients with lupus, a chronic autoimmune disease. The role of genetic and epigenetic factors is emphasized in the pathogenesis of LN. The aim of the present study was to evaluate the levels of immune-regulatory microRNAs (e.g., miR-31, miR-125a, miR-142-3p, miR-146a, and miR-155) in plasma samples of patients with LN. Methods: In this study, 26 patients with LN and 26 healthy individuals were included. The plasma levels of the microRNAs were evaluated by a quantitative real-time PCR. Moreover, the correlation of circulating plasma microRNAs with disease activity and pathological findings along with their ability to distinguish patients with LN were assessed. Results: Plasma levels of miR-125a (P = 0.048), miR-146a (P = 0.005), and miR-155 (P< 0.001) were significantly higher in comparison between the cases and controls. The plasma level of miR-146a significantly correlated with the level of anti-double strand-DNA antibody and proteinuria. Moreover, there was a significant correlation between miR-142-3p levels and disease chronicity and activity index (P <0.05). The multivariate ROC curve analysis indicated the plasma circulating miR-125a, miR-142-3p, miR-146, and miR-155 together could discriminate most of the patients with LN from controls with area an under curve (AUC) of 0.89 [95% CI, 0.80-0.98, P<0.001], 88% sensitivity, and 78% specificity. Conclusion: Based on the findings of the present study, the studied microRNAs may be involved in the pathogenesis and development of LN and have the potential to be used as diagnostic and therapeutic markers in LN

Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment

COVID-19 outbreak has put the whole world in an unprecedented difficult situation bringing life around the world to a frightening halt and claiming thousands of lives. Due to COVID-19’s spread in 212 countries and territories and increasing numbers of infected cases and death tolls mounting to 5,212,172 and 334,915 (as of May 22 2020), it remains a real threat to the public health system. This paper renders a response to combat the virus through Artificial Intelligence (AI). Some Deep Learning (DL) methods have been illustrated to reach this goal, including Generative Adversarial Networks (GANs), Extreme Learning Machine (ELM), and Long/Short Term Memory (LSTM). It delineates an integrated bioinformatics approach in which different aspects of information from a continuum of structured and unstructured data sources are put together to form the user-friendly platforms for physicians and researchers. The main advantage of these AI-based platforms is to accelerate the process of diagnosis and treatment of the COVID-19 disease. The most recent related publications and medical reports were investigated with the purpose of choosing inputs and targets of the network that could facilitate reaching a reliable Artificial Neural Network-based tool for challenges associated with COVID-19. Furthermore, there are some specific inputs for each platform, including various forms of the data, such as clinical data and medical imaging which can improve the performance of the introduced approaches toward the best responses in practical applications

National, sub-national, and risk-attributed burden of thyroid cancer in Iran from 1990 to 2019

An updated exploration of the burden of thyroid cancer across a country is always required for making correct decisions. The objective of this study is to present the thyroid cancer burden and attributed burden to the high Body Mass Index (BMI) in Iran at national and sub-national levels from 1990 to 2019. The data was obtained from the GBD 2019 study estimates. To explain the pattern of changes in incidence from 1990 to 2019, decomposition analysis was conducted. Besides, the attribution of high BMI in the thyroid cancer DALYs and deaths were obtained. The age-standardized incidence rate of thyroid cancer was 1.57 (95% UI: 1.33–1.86) in 1990 and increased 131% (53–191) until 2019. The age-standardized prevalence rate of thyroid cancer was 30.19 (18.75–34.55) in 2019 which increased 164% (77–246) from 11.44 (9.38–13.85) in 1990. In 2019, the death rate, and Disability-adjusted life years of thyroid cancer was 0.49 (0.36–0.53), and 13.16 (8.93–14.62), respectively. These numbers also increased since 1990. The DALYs and deaths attributable to high BMI was 1.91 (0.95–3.11) and 0.07 (0.04–0.11), respectively. The thyroid cancer burden and high BMI attributed burden has increased from 1990 to 2019 in Iran. This study and similar studies’ results can be used for accurate resource allocation for efficient management and all potential risks’ modification for thyroid cancer with a cost-conscious view

Global burden of chronic respiratory diseases and risk factors, 1990–2019: an update from the Global Burden of Disease Study 2019

Background: Updated data on chronic respiratory diseases (CRDs) are vital in their prevention, control, and treatment in the path to achieving the third UN Sustainable Development Goals (SDGs), a one-third reduction in premature mortality from non-communicable diseases by 2030. We provided global, regional, and national estimates of the burden of CRDs and their attributable risks from 1990 to 2019. Methods: Using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019, we estimated mortality, years lived with disability, years of life lost, disability-adjusted life years (DALYs), prevalence, and incidence of CRDs, i.e. chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease and pulmonary sarcoidosis, and other CRDs, from 1990 to 2019 by sex, age, region, and Socio-demographic Index (SDI) in 204 countries and territories. Deaths and DALYs from CRDs attributable to each risk factor were estimated according to relative risks, risk exposure, and the theoretical minimum risk exposure level input. Findings: In 2019, CRDs were the third leading cause of death responsible for 4.0 million deaths (95% uncertainty interval 3.6–4.3) with a prevalence of 454.6 million cases (417.4–499.1) globally. While the total deaths and prevalence of CRDs have increased by 28.5% and 39.8%, the age-standardised rates have dropped by 41.7% and 16.9% from 1990 to 2019, respectively. COPD, with 212.3 million (200.4–225.1) prevalent cases, was the primary cause of deaths from CRDs, accounting for 3.3 million (2.9–3.6) deaths. With 262.4 million (224.1–309.5) prevalent cases, asthma had the highest prevalence among CRDs. The age-standardised rates of all burden measures of COPD, asthma, and pneumoconiosis have reduced globally from 1990 to 2019. Nevertheless, the age-standardised rates of incidence and prevalence of interstitial lung disease and pulmonary sarcoidosis have increased throughout this period. Low- and low-middle SDI countries had the highest age-standardised death and DALYs rates while the high SDI quintile had the highest prevalence rate of CRDs. The highest deaths and DALYs from CRDs were attributed to smoking globally, followed by air pollution and occupational risks. Non-optimal temperature and high body-mass index were additional risk factors for COPD and asthma, respectively. Interpretation: Albeit the age-standardised prevalence, death, and DALYs rates of CRDs have decreased, they still cause a substantial burden and deaths worldwide. The high death and DALYs rates in low and low-middle SDI countries highlights the urgent need for improved preventive, diagnostic, and therapeutic measures. Global strategies for tobacco control, enhancing air quality, reducing occupational hazards, and fostering clean cooking fuels are crucial steps in reducing the burden of CRDs, especially in low- and lower-middle income countries

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Burden of tracheal, bronchus, and lung cancer in North Africa and Middle East countries, 1990 to 2019: Results from the GBD study 2019

ObjectiveTo provide estimates on the regional and national burden of tracheal, bronchus, and lung (TBL) cancer and its attributable risk factors from 1990 to 2019 in the North Africa and Middle East (NAME) region.Methods and materialsThe Global Burden of Disease (GBD) 2019 data were used. Disability-adjusted life years (DALYs), death, incidence, and prevalence rates were categorized by sex and age groups in the NAME region, in 21 countries, from 1990 to 2019. Decomposition analysis was performed to calculate the proportion of responsible factors in the emergence of new cases. Data are presented as point estimates with their 95% uncertainty intervals (UIs).ResultsIn the NAME region, TBL cancer caused 15,396 and 57,114 deaths in women and men, respectively, in 2019. The age-standardized incidence rate (ASIR) increased by 0.7% (95% UI -20.6 to 24.1) and reached 16.8 per 100,000 (14.9 to 19.0) in 2019. All the age-standardized indices had a decreasing trend in men and an increasing trend in women from 1990 to 2019. Turkey (34.9 per 100,000 [27.6 to 43.5]) and Sudan (8.0 per 100,000 [5.2 to 12.5]) had the highest and lowest age-standardized prevalence rates (ASPRs) in 2019, respectively. The highest and lowest absolute slopes of change in ASPR, from 1990 to 2019, were seen in Bahrain (-50.0% (-63.6 to -31.7)) and the United Arab Emirates (-1.2% (-34.1 to 53.8)), respectively. The number of deaths attributable to risk factors was 58,816 (51,709 to 67,323) in 2019 and increased by 136.5%. Decomposition analysis showed that population growth and age structure change positively contributed to new incident cases. More than 80% of DALYs could be decreased by controlling risk factors, particularly tobacco use.ConclusionThe incidence, prevalence, and DALY rates of TBL cancer increased, and the death rate remained unchanged from 1990 to 2019. All the indices and contribution of risk factors decreased in men but increased in women. Tobacco is still the leading risk factor. Early diagnosis and tobacco cessation policies should be improved

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

ارتز رباتيک مچ- کف پايي با حسگر لامسه جهت کنترل هوشمند راه رفتن افراد مبتلا به افتادگي مچ پا

چكيدهعارضه افتادگي پا را می‌توان بيماري عصبي، عضلاني و ناشي از نقص ژنتيکي، جراحات جنگي، تصادف، رشد غده‌ها و برخي سکته‌هاي مغزي ذکر کرد که در اثر آن عصب پا دچار آسيب ديدگي دایم يا برگشت‌پذير مي‌شود. در اثر اين عارضه شخص مجبور مي‌شود براي جبران اين نقص که موجب ناتواني در باز و بسته ‌کردن مچ پا مي‌شود، از ارتز استفاده کند. يا اين که مشکل لنگيدن پا براي وي به وجود خواهد آمد. دستگاه ارتز رباتيک هوشمند شامل صفحه حسگر فشار کف پا، نرم‌افزار محاسبه‌گر و بخش ربات متحرک است. با توجه به مدل ساخته شده بر اساس اطلاعات حرکتي افراد دچار افتادگي پا مشخص شد که نياز به سامانه رباتيکي است که بتواند ميزان گشتاور مورد نياز بيمار را براي راه رفتن بهبود بخشد و به همين دليل طراحي ارتز جديدي لازم بود که بر اساس سنجش فشار کف پا در شرايط گوناگون راه رفتن بتواند ميزان گشتاور جبراني مورد نياز را براي سامانه رباتيک تشخيص دهد و توسط نيروي الکتريکي تأمين نمايد. براي ايجاد ارتباط بين اين سامانه تأمين گشتاور و فشار سنجش شده از فشارسنج از مدل‌هاي هوشمند استفاده شده است. اين سامانه باعث بهبود عملکرد و رفع ناتواني فرد دچار افتادگي پا مي‌شود و توانايي شخص را در راه رفتن بهبود مي‌بخشد.کاهش فشار و آسيب به تاندون‌ها، کاهش ضربه به مفاصل، کمک به بهبود بيماري با استفاده از سامانه رباتيک و جبران ضعف عملکرد عضلات، از جمله اهدافي پيش‌بيني شده براي ساخت دستگاه بود.كليد واژه‌ها: اختلالات عصبي، افتادگي پا، مرکز فشار، راه رفت

Impact of one-way carsharing on car ownership and public transit : an empirical analysis from Metro Vancouver

While first conceived as cooperative station-based car ownership in the 1950s, smartphones have led to a rapid growth of free-floating carsharing in urban areas in the past 15 years. Their low-barrier to entry has had strong proponents and opponents. Proponents assert that it reduces car ownership and enhances the reach of public transit into less well-served neighbourhoods. Opponents worry that carsharing leads to more congestion and competes with public transit. Countless user surveys have explored the validity of these questions, indicating positive spillovers. In contrast to these studies, this thesis uses empirical data on vehicle booking and use from one-way carsharing providers in Vancouver to assess their impacts. This analysis reveals: • Actual shared vehicles are used 5-6 times per day on average. Past surveys indicate that each free-floating carsharing vehicle leads to 2-13 fewer vehicles owned. This study estimates 4.3 and shows that the majority of the users who change their vehicle ownership plans use carsharing with low frequency (1-4 trips per month). This could suggest that the major benefit from car-shedding due to free-floating carsharing is in relieving parking pressure and less so of traffic congestion. • Empirical data show that the most popular neighbourhoods for carsharing in which 75% of carsharing trips occur, host 59% of the population, retaining 37% of the carsharing service area and 63% of direct rapid transit routes. In other words, this group of neighbourhoods have 2.5 times higher population density than the rest of the neighbourhoods. The ratio of direct rapid transit routes available between these neighbourhoods to the rest of the direct rapid transit routes is 1.7, whereas the number of carsharing trips inside this group of neighbourhoods is 3 times higher than the rest of carsharing trips recorded. Thus, carsharing is primarily competing with public transit in denser neighbourhoods rather than complementing it in less dense areas. Finally, this study identified an understudied user misbehaviour. Roughly 30% of all vehicle reservations lapse; each time rendering these vehicles inaccessible to other users for 30 minutes. This leads to over-investment in the shared vehicle fleet and higher pressure on parking in popular neighbourhoods.Science, Faculty ofResources, Environment and Sustainability (IRES), Institute forGraduat