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.

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.

Hospitalizations from covid-19: a health planning tool.

OBJECTIVE: Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD: Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS: Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS: Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease.

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.

Hospitalizations from covid-19: a health planning tool / Ingresos hospitalarios por covid-19: una herramienta de planificación sanitaria

ABSTRACT OBJECTIVE Estimate the future number of hospitalizations from Covid-19 based on the number of diagnosed positive cases. METHOD Using the covid-19 Panel data recorded in Spain at the Red Nacional de Vigilancia Epidemiológica, Renave (Epidemiological Surveillance Network), a regression model with multiplicative structure is adjusted to explain and predict the number of hospitalizations from the lagged series of positive cases diagnosed from May 11, 2020 to September 20, 2021. The effect of the time elapsed since the vaccination program starting on the number of hospitalizations is reviewed. RESULTS Nine days is the number of lags in the positive cases series with greatest explanatory power on the number of hospitalizations. The variability of the number of hospitalizations explained by the model is high (adjusted R2: 96.6%). Before the vaccination program starting, the expected number of hospitalizations on day t was 20.2% of the positive cases on day t-9 raised to 0.906. After the vaccination program started, this percentage was reduced by 0.3% a day. Using the same model, we find that in the first pandemic wave the number of positive cases was more than six times that reported on official records. CONCLUSIONS Starting from the covid-19 cases detected up to a given date, the proposed model allows estimating the number of hospitalizations nine days in advance. Thus, it is a useful tool for forecasting the hospital pressure that health systems shall bear as a consequence of the disease.

RESUMEN OBJETIVO Predecir el número futuro de hospitalizaciones por covid-19 a partir del número de casos positivos diagnosticados. MÉTODO Usando datos del Panel covid-19 registrados en España en la Red Nacional de Vigilancia Epidemiológica (Renave), se ajusta un modelo de regresión con estructura multiplicativa para explicar y predecir el número de hospitalizaciones a partir de la serie retardada de casos positivos diagnosticados durante el periodo entre el 11 de mayo de 2020 y el 20 de septiembre de 2021. Se analiza el efecto sobre el número de hospitalizaciones del tiempo transcurrido desde el inicio del programa de vacunación. RESULTADOS El número de retardos de la serie de casos positivos que mayor capacidad explicativa tiene sobre el número de hospitalizaciones es de nueve días. La variabilidad del número de hospitalizaciones explicada por el modelo es elevada (R2 ajustado: 96,6%). Antes del inicio del programa de vacunación, el número esperado de ingresos hospitalarios en el día t era igual al 20,2% de los casos positivos del día t-9 elevado a 0,906. Iniciado el programa de vacunación, este porcentaje se redujo un 0,3% diario. Con el mismo modelo se obtiene que en la primera ola de la pandemia el número de casos positivos fue más de seis veces el que figura en los registros oficiales. CONCLUSIONES Partiendo de los casos de covid-19 detectados hasta una fecha, el modelo propuesto permite estimar el número de hospitalizaciones con nueve días de antelación. Ello lo convierte en una herramienta útil para prever con cierta anticipación la presión hospitalaria que el sistema sanitario tendrá que soportar como consecuencia de la enfermedad.

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.

Community socioeconomic context and its influence on intermediary determinants of child health: evidence from Colombia.

Intermediary determinants are the most immediate mechanisms through which socioeconomic position shapes health inequities. This study examines the effect of community socioeconomic context on different indicators representing intermediary determinants of child health. In the context of Colombia, a developing country with a clear economic expansion, but one of the most unequal countries in the world, two categories of intermediary determinants, namely behavioural and psychosocial factors and the health system, are analysed. Using data from the 2010 Colombian Demographic and Health Survey (DHS), the results suggest that whilst the community context can exert a greater influence on factors linked directly to health, in the case of psychosocial factors and parent's behaviours, the family context can be more important. In addition, the results from multilevel analysis indicate that a significant percentage of the variability in the overall index of intermediary determinants of child health is explained by the community context, even after controlling for individual, family and community characteristics. These findings underline the importance of distinguishing between community and family intervention programmes in order to reduce place-based health inequities in Colombia.

Factors affecting hospital admission and recovery stay duration of in-patient motor victims in Spain.

Hospital expenses are a major cost driver of healthcare systems in Europe, with motor injuries being the leading mechanism of hospitalizations. This paper investigates the injury characteristics which explain the hospitalization of victims of traffic accidents that took place in Spain. Using a motor insurance database with 16,081 observations a generalized Tobit regression model is applied to analyse the factors that influence both the likelihood of being admitted to hospital after a motor collision and the length of hospital stay in the event of admission. The consistency of Tobit estimates relies on the normality of perturbation terms. Here a semi-parametric regression model was fitted to test the consistency of estimates, concluding that a normal distribution of errors cannot be rejected. Among other results, it was found that older men with fractures and injuries located in the head and lower torso are more likely to be hospitalized after the collision, and that they also have a longer expected length of hospital recovery stay.