Evaluating quality of life in pediatric palliative care: a cross-sectional analysis of children's and parents' perspectives.

The patient's perspective is an essential component of understanding the individual experience of suffering in children with palliative needs, but it is a perspective that is often overlooked. The aim of this study was to compare the perception of quality of life (QoL) of children with life-limiting and life-threatening conditions expressed by the children themselves and their parents. Through a cross-sectional study, the responses of 44 parent-child dyads were obtained and the analysis was performed with the statistics based on Student's t distribution and non-parametric tests. Children value QoL more positively (mean = 6.95, SD = 1.85) than their parents (mean = 5.39, SD = 2.43). This difference exists even if we consider sociodemographic and disease variables. The presence of exacerbated symptoms is the situation in which both parents (mean = 3.70; SD = 1.95) and children (mean = 5.60; SD = 1.17) evaluate QoL more negatively. CONCLUSIONS: Children have a more optimistic view than their parents. When the child is the one who reports a lower QoL score than their parent, the child should be carefully monitored. The voice of the child and that of the family members can be collected to create a "family voice" and can be complementary. WHAT IS KNOWN: • Children with life-limiting conditions experience multiple and changing symptoms that affect their QoL. • The child's perspective is often overlooked. WHAT IS NEW: • Children value QoL more positively than their parents do, even if we control for sociodemographic variables and the disease itself. • When the child is the one who reports a lower QoL score than their parent, the child should be carefully monitored.

A multidimensional review of the cash management problem.

In this paper, we summarize and analyze the relevant research on the cash management problem appearing in the literature. First, we identify the main dimensions of the cash management problem. Next, we review the most relevant contributions in this field and present a multidimensional analysis of these contributions, according to the dimensions of the problem. From this analysis, several open research questions are highlighted.

Assessing well-being in pediatric palliative care: A pilot study about views of children, parents and health professionals.

OBJECTIVES: Our research aims to compare the perception that children in the pediatric palliative care setting have of their emotional well-being, or that expressed by the parents, with the perception held by the professionals involved in their care. METHODS: In this cross-sectional study, the emotional well-being of 30 children with a mean age of 10.8 years (standard deviation [SD] = 6.1) is evaluated. Children, or parents where necessary, evaluate their situation with a question about emotional well-being on a 0-10 visual analog scale. For each child, a health professional also rates the child's emotional status using the same scale. RESULTS: The average child's emotional well-being score provided by children or parents was 7.1 (SD = 1.6), while the average score given by health professionals was 5.6 (SD = 1.2). Children or parents graded the children's emotional well-being significantly higher than professionals (t-test = 4.6, p-value < .001). Health professionals rated the children's emotional well-being significantly lower when the disease status was progressive than when the disease was not (t-test = 2.2, p-value = .037). SIGNIFICANCE OF RESULTS: Children themselves, or their parents, report more positive evaluations of emotional well-being than health professionals. Sociodemographic and disease variables do not seem to have a direct influence on this perception, rather it is more likely that children, parents, and professionals focus on different aspects and that children or parents need to hold on to a more optimistic vision. We must emphasize that when this difference is more pronounced, it can be a warning sign that further analysis is required of the situation.

Using contextual data to predict risky driving events: A novel methodology from explainable artificial intelligence.

Usage-based insurance has allowed insurers to dynamically tailor insurance premiums by understanding when and how safe policyholders drive. However, telematics information can also be used to understand the driving contexts experienced by the driver within each trip (e.g., road types, weather, traffic). Since different combinations of these conditions affect exposure to accidents, this understanding introduces predictive opportunities in driving risk assessment. This paper investigates the relationships between driving context combinations and risk using a naturalistic driving dataset of 77,859 km. In particular, XGBoost and Random Forests are used to determine the predictive significance of driving contexts for near-misses, speeding and distraction events. Moreover, the most important contextual factors in predicting these risky events are identified and ranked through Shapley Additive Explanations. The results show that the driving context has significant power in predicting driving risk. Speed limit, weather temperature, wind speed, traffic conditions and road slope appear in the top ten most relevant features for most risky events. Analysing contextual feature variations and their influence on risky events showed that low-speed limits increase the predicted frequency of speeding and phone unlocking events, whereas high-speed limits decrease harsh accelerations. Low temperatures decrease the expected frequency of harsh manoeuvres, and precipitations increase harsh acceleration, harsh braking, and distraction events. Furthermore, road slope, intersections and pavement quality are the most critical factors among road layout attributes. The methodology presented in this study aims to support road safety stakeholders and insurers by providing insights to study the contextual risk factors that influence road accident frequency and driving risk.

The Autonomous Capacity of the Elderly Population in Spain for Shopping and Preparing Meals.

A loss of the ability to buy and prepare meals, especially in people aged 65 and over, leads to a deterioration in their optimal level of nutrition. The Index of Autonomy in Food Acquisition (IAFA) was used to identify contributing factors. This is a composite indicator for shopping and meal preparation that can be used to assess the degree of autonomous capacity observed in a specific group. Data from the European Health Survey in Spain (7167 respondents aged 65 and over) show that capacity decreased with age and that women were less affected than men, with very little difference found in levels of autonomous capacity by territory. However, in relation to different income levels, after standardizing for age and sex, no evidence was found for differences in the ability to access and prepare meals in groups that were separated by income level. This result shows the importance of standardizing when analysing food acquisition autonomy in groups of people aged 65 years and over.

Acute respiratory infection rates in primary care anticipate ICU bed occupancy during COVID-19 waves.

BACKGROUND: Bed occupancy in the ICU is a major constraint to in-patient care during COVID-19 pandemic. Diagnoses of acute respiratory infection (ARI) by general practitioners have not previously been investigated as an early warning indicator of ICU occupancy. METHODS: A population-based central health care system registry in the autonomous community of Catalonia, Spain, was used to analyze all diagnoses of ARI related to COVID-19 established by general practitioners and the number of occupied ICU beds in all hospitals from Catalonia between March 26, 2020 and January 20, 2021. The primary outcome was the cross-correlation between the series of COVID-19-related ARI cases and ICU bed occupancy taking into account the effect of bank holidays and weekends. Recalculations were later implemented until March 27, 2022. FINDINGS: Weekly average incidence of ARI diagnoses increased from 252.7 per 100,000 in August, 2020 to 496.5 in October, 2020 (294.2 in November, 2020), while the average number of ICU beds occupied by COVID-19-infected patients rose from 1.7 per 100,000 to 3.5 in the same period (6.9 in November, 2020). The incidence of ARI detected in the primary care setting anticipated hospital occupancy of ICUs, with a maximum correlation of 17.3 days in advance (95% confidence interval 15.9 to 18.9). INTERPRETATION: COVID-19-related ARI cases may be a novel warning sign of ICU occupancy with a delay of over two weeks, a latency window period for establishing restrictions on social contacts and mobility to mitigate the propagation of COVID-19. Monitoring ARI cases would enable immediate adoption of measures to prevent ICU saturation in future waves.

Case study data for joint modeling of insurance claims and lapsation.

The dataset tracks 40,284 insurance clients over five years, between 2010 and 2015, who subscribed to both automobile and homeowners insurance. We have combined information on these customers. First, the characteristics including age, gender or driving experience, among others and dates of renewal for the two types of policies considered here. Note that we have only considered clients corresponding to persons and not commercial firms that can also underwrite home and motor insurance policies. Second, the policy data file for motor vehicle insurance consists of all vehicle insurance coverage including power, driving area or whether there is a second driver that drives the car occasionally. Third, the policy data file for homeowners insurance has information on the property such as value of the building (essentially the value of the home without any furniture, apparel and personal items), location and type of dwelling. Besides these three sources, we have access to data containing information on the number of claims and total cost of those claims per year and per policy type. So, for all policies that are in force, we finally have up to a five year record of the yearly cost of claims in the motor insurance and in the home coverage. If the customer does not renew one of those two policies or both, we do not have more information after this lapse occurs. After summarizing the data, we provide the usual marginal analysis, where we fit regression models using Tweedie distributions for claims and a logistic model for lapse. Data can be used for joint analysis of insurance policyholders with more than one product.

Driving Risk Assessment Using Near-Miss Events Based on Panel Poisson Regression and Panel Negative Binomial Regression.

This study proposes a method for identifying and evaluating driving risk as a first step towards calculating premiums in the newly emerging context of usage-based insurance. Telematics data gathered by the Internet of Vehicles (IoV) contain a large number of near-miss events which can be regarded as an alternative for modeling claims or accidents for estimating a driving risk score for a particular vehicle and its driver. Poisson regression and negative binomial regression are applied to a summary data set of 182 vehicles with one record per vehicle and to a panel data set of daily vehicle data containing four near-miss events, i.e., counts of excess speed, high speed brake, harsh acceleration or deceleration and additional driving behavior parameters that do not result in accidents. Negative binomial regression (AICoverspeed = 997.0, BICoverspeed = 1022.7) is seen to perform better than Poisson regression (AICoverspeed = 7051.8, BICoverspeed = 7074.3). Vehicles are separately classified to five driving risk levels with a driving risk score computed from individual effects of the corresponding panel model. This study provides a research basis for actuarial insurance premium calculations, even if no accident information is available, and enables a precise supervision of dangerous driving behaviors based on driving risk scores.

Differences in the risk profiles of drunk and drug drivers: Evidence from a mandatory roadside survey.

BACKGROUND: The study assesses the prevalence rates of alcohol- and drug-involved driving in Catalonia (Spain). METHOD: Drivers were randomly selected for roadside testing using a stratified random sampling procedure representative of all vehicles circulating on non-urban roads. Mandatory alcohol and drug tests were performed during autumn 2017. A sample of 6860 drivers were tested for alcohol use, of these 671 were also tested for drugs. Standard procedures were employed by traffic officers to detect alcohol and drug use. Alcohol breath tests were performed with breathalyser devices and on-site drug screening systems were used to test for drugs. RESULTS: The prevalence of alcohol use above the legal limit and drug use were 1.2 % (95 % CI: 0.9-1.5 %) and 8.3 % (95 % CI: 5.8-11.2 %), respectively. The most frequent drugs detected were THC (5.6 %, 95 % CI: 3.7-8.0 %), cocaine (3.5 %, 95 % CI: 2.0-5.5 %) and amphetamines (1.6 %, 95 % CI: 0.6-3.4 %). Alcohol use was detected more frequently on conventional roads, at weekends and during night-time hours. Drug use was detected more frequently in young males during daytime hours. CONCLUSIONS: Driver risk profiles associated with alcohol use and drug use differ. Positive alcohol use is not a predictor of drug use when controlling for all other factors.

Percentile charts for speeding based on telematics information.

Reference charts are widely used as a graphical tool for assessing and monitoring children's growth given gender and age. Here, we propose a similar approach to the assessment of driving risk. Based on telematics data, and using quantile regression models, our methodology estimates the percentiles of the distance driven at speeds above the legal limit depending on drivers' characteristics and the journeys made. We refer to the resulting graphs as percentile charts for speeding and illustrate their use for a sample of drivers with Pay-How-You-Drive insurance policies. We find that percentiles of distance driven at excessive speeds depend mainly on total distance driven, the percentage of driving in urban areas and the driver's gender. However, the impact on the estimated percentile for these covariates is not constant. We conclude that the heterogeneity in the risk of driving long distances above the speed limit can be easily represented using reference charts and that, conversely, individual drivers can be scored by calculating an estimated percentile for their specific case. The dynamics of this risk score can be assessed by recording drivers as they accumulate driving experience and cover more kilometres. Our methodology should be useful for accident prevention and, in the context of Manage-How-You-Drive insurance, reference charts can provide real-time alerts and enhance recommendations for ensuring safety.

A Bayesian joint model for zero-inflated integers and left-truncated event times with a time-varying association: Applications to senior health care.

Population aging in most industrialized societies has led to a dramatic increase in emergency medical demand among the elderly. In the context of private health care, an optimal allocation of the medical resources for seniors is commonly done by forecasting their life spans. Accounting for each subject's particularities is therefore indispensable, so the available data must be processed at an individual level. We use a large and unique dataset of insured parties aged 65 and older to appropriately relate the emergency care usage with mortality risk. Longitudinal and time-to-event processes are jointly modeled, and their underlying relationship can therefore be assessed. Such an application, however, requires some special features to also be considered. First, longitudinal demand for emergency services exhibits a nonnegative integer response with an excess of zeros due to the very nature of the data. These subject-specific responses are handled by a zero-inflated version of the hierarchical negative binomial model. Second, event times must account for the left truncation derived from the fact that policyholders must reach the age of 65 before they may begin to be observed. Consequently, a delayed entry bias arises for those individuals entering the study after this age threshold. Third, and as the main challenge of our analysis, the association parameter between both processes is expected to be age-dependent, with an unspecified association structure. This is well-approximated through a flexible functional specification provided by penalized B-splines. The parameter estimation of the joint model is derived under a Bayesian scheme.

Assessing Driving Risk Using Internet of Vehicles Data: An Analysis Based on Generalized Linear Models.

With the major advances made in internet of vehicles (IoV) technology in recent years, usage-based insurance (UBI) products have emerged to meet market needs. Such products, however, critically depend on driving risk identification and driver classification. Here, ordinary least square and binary logistic regressions are used to calculate a driving risk score on short-term IoV data without accidents and claims. Specifically, the regression results reveal a positive relationship between driving speed, braking times, revolutions per minute and the position of the accelerator pedal. Different classes of risk drivers can thus be identified. This study stresses both the importance and feasibility of using sensor data for driving risk analysis and discusses the implications for traffic safety and motor insurance.

Early Poverty and Future Life Expectancy with Disability among the Elderly in Argentina: Pobreza Temprana y Esperanza de Vida Futura con Discapacidad entre los Adultos Mayores de la Argentina.

Two aspects of the ageing process in Latin America should be specially taken into account in order to evaluate future perspectives of morbidity among the elderly in the region: 1) Cohorts who will compose the bulk of the elderly population in the 21st century in Latin America survived to old age largely because of improvements in medicine and to a much lesser extent to amelioration of living standards, as is the case in high income countries; 2) a high proportion of the Latin American population still live in poor economic conditions and even these vulnerable individuals continue to experience gains in (adult and older adult) survival. We aim to evaluate to what an extent recent levels of poverty and indigence among young children in Argentina could impact future levels of disability and demands for long-term care of older people. Our results show that given the levels of poverty and indigence in childhood observed between 1988 and 1994 and given the relationship between poor early conditions and the risk of being disabled among the elderly in Argentina, life expectancy with disability at age 60 old would increase substantially between 2000 and 2040 both in absolute and relative terms.

aPRIDIT Unsupervised Classification with Asymmetric Valuation of Variable Discriminatory Worth.

Sometimes one needs to classify individuals into groups, but there is no available grouping information due to social desirability bias in reporting behavior like unethical or dishonest intentions or unlawful actions. Assessing hard-to-detect behaviors is useful; however it is methodologically difficult because people are unlikely to self-disclose bad actions. This paper presents an unsupervised classification methodology utilizing ordinal categorical predictor variables. It allows for classification, individual respondent ranking, and grouping without access to a dependent group indicator variable. The methodology also measures predictor variable worth (for determining target behavior group membership) at a predictor variable category-by-category level, so different variable response categories can contain different amounts of information about classification. It is asymmetric in that a "0" on a binary predictor does not have a similar impact toward signaling "membership in the target group" as a "1" has for signaling "membership in the non-target group." The methodology is illustrated by identifying Spanish consumers filing fraudulent insurance claims. A second illustration classifies Portuguese high school student's propensity to alcohol abuse. Results show the methodology is useful when it is difficult to get dependent variable information, and is useful for deciding which predictor variables and categorical response options are most important.

Semi-autonomous vehicles: Usage-based data evidences of what could be expected from eliminating speed limit violations.

The use of advanced driver assistance systems and the transition towards semi-autonomous vehicles are expected to contribute to a lower frequency of motor accidents and to have a significant impact for the automobile insurance industry, as rating methods must be revised to ensure that risks are correctly measured. Telematics information and usage-based insurance research are analyzed to identify the effect of driving patterns on the risk of accident. This is used as a starting point for addressing risk quantification and safety for vehicles that can control speed. The effect of excess speed on the risk of accidents is estimated with a real telematics data set. Scenarios for a reduction of speed limit violations and the consequent decrease in the expected number of accident claims are shown. If excess speed could be eliminated, then the expected number of accident claims could be reduced to half of its initial value, applying the average conditions of the data used in this study. As a consequence, insurance premiums also diminish.

Molecular Features and mRNA Expression of the Receptor for Activated C Kinase 1 from Symbiodinium microadriaticum ssp. microadriaticum During Growth and the Light/Dark cycle.

Two genes of the RACK1 homolog from the photosynthetic dinoflagellate Symbiodinium microadriaticum ssp. microadriaticum (SmicRACK1), termed SmicRACK1A and SmicRACK1B, were found tandemly arrayed and displayed a single synonymous substitution (T/C) encoding threonine. They included two exons of 942 bp each, encoding 313 amino acids with seven WD-40 repeats and two PKC-binding motifs. The protein theoretical mass and pI were 34,200 Da and 5.9, respectively. SmicRACK1 showed maximum identities with RACK1 homologs at the amino acid and nucleotide level, respectively, of 92 and 84% with S. minutum, and phylogenetic analysis revealed clustered related RACK1 sequences from the marine dinoflagellates S. minutum, Heterocapsa triquetra, Karenia brevis, and Alexandrium tamarense. Interestingly, light-dependent regulatory elements were found both within the 282 bp SmicRACK1A promotor sequence, and within an intergenic sequence of 359 nucleotides that separated both genes, which strongly suggest light-related functions. This was further supported by mRNA accumulation analysis, which fluctuated along the light and dark phases of the growth cycle showing maximum specific peaks under either condition. Finally, qRT-PCR analysis revealed differential SmicRACK1 mRNA accumulation with maxima at 6 and 20 d of culture. Our SmicRACK1 characterization suggests roles in active growth and proliferation, as well as light/dark cycle regulation in S. microadriaticum.

The Use of Telematics Devices to Improve Automobile Insurance Rates.

Most automobile insurance databases contain a large number of policyholders with zero claims. This high frequency of zeros may reflect the fact that some insureds make little use of their vehicle, or that they do not wish to make a claim for small accidents in order to avoid an increase in their premium, but it might also be because of good driving. We analyze information on exposure to risk and driving habits using telematics data from a pay-as-you-drive sample of insureds. We include distance traveled per year as part of an offset in a zero-inflated Poisson model to predict the excess of zeros. We show the existence of a learning effect for large values of distance traveled, so that longer driving should result in higher premiums, but there should be a discount for drivers who accumulate longer distances over time due to the increased proportion of zero claims. We confirm that speed limit violations and driving in urban areas increase the expected number of accident claims. We discuss how telematics information can be used to design better insurance and to improve traffic safety.

Prevalence of drug use among drivers based on mandatory, random tests in a roadside survey.

BACKGROUND: In the context of road safety, this study aims to examine the prevalence of drug use in a random sample of drivers. METHODS: A stratified probabilistic sample was designed to represent vehicles circulating on non-urban roads. Random drug tests were performed during autumn 2014 on 521 drivers in Catalonia (Spain). Participation was mandatory. The prevalence of drug driving for cannabis, methamphetamines, amphetamines, cocaine, opiates and benzodiazepines was assessed. RESULTS: The overall prevalence of drug use is 16.4% (95% CI: 13.9; 18.9) and affects primarily younger male drivers. Drug use is similarly prevalent during weekdays and on weekends, but increases with the number of occupants. The likelihood of being positive for methamphetamines is significantly higher for drivers of vans and lorries. CONCLUSIONS: Different patterns of use are detected depending on the drug considered. Preventive drug tests should not only be conducted on weekends and at night-time, and need to be reinforced for drivers of commercial vehicles. Active educational campaigns should focus on the youngest age-group of male drivers.