Burden of disease of people with epilepsy during an optimized diagnostic trajectory: costs and quality of life

Neurological Health: Not Merely the Absence of Disease: Current Wellbeing Instruments Across the Spectrum of Neurology

OBJECTIVE

Well-being and quality of life can vary independently of disease. Instruments measuring well-being and quality of life are commonly used in neurology, but there has been little investigation into the extent in which they accurately measure wellbeing/quality of life or if they merely reflect a diseased state of an individual.

DESIGN

Systematic searches, thematic analysis and narrative synthesis were undertaken. Individual items from instruments represented in ≥ 5 publications were categorised independently, without prior training, by five neurologists and one well-being researcher, as relating to 'disease-effect' or 'Well-being' with a study-created instrument. Items were additionally categorised into well-being domains.

DATA SOURCES

MEDLINE, EMBASE, EMCARE and PsycINFO from 1990 to 2020 were performed, across the 13 most prevalent neurological diseases.

RESULTS

301 unique instruments were identified. Multiple sclerosis had most unique instruments at 92. SF-36 was used most, in 66 studies. 22 instruments appeared in ≥ 5 publications: 19/22 'well-being' outcome instruments predominantly measured disease effect (Fleiss kappa = .60). Only 1/22 instruments was categorised unanimously as relating to well-being. Instruments predominantly measured mental, physical and activity domains, over social or spiritual.

CONCLUSIONS

Most neurological well-being or quality-of-life instruments predominantly measure disease effect, rather than disease-independent well-being. Instruments differed widely in well-being domains examined.

Predicting in-hospital mortality for MIMIC-III patients: A nomogram combined with SOFA score

Predicting the mortality of patients provides a reference for doctors to judge their physical condition. This study aimed to construct a nomogram to improve the prediction accuracy of patients' mortality. Patients with severe diseases were screened from the Medical Information Mart for Intensive Care (MIMIC) III database; 70% of patients were randomly selected as the training set for the model establishment, while 30% were used as the test set. The least absolute shrinkage and selection operator (LASSO) regression method was used to filter variables and select predictors. A multivariable logistic regression fit was used to determine the association between in-hospital mortality and risk factors and to construct a nomogram. A total of 9276 patients were included. The area under the curve (AUC) for the clinical nomogram based on risk factors selected by LASSO and multivariable logistic regressions were 0.849 (95% confidence interval [CI]: 0.835-0.863) and 0.821 (95% CI: 0.795-0.846) in the training and test sets, respectively. Therefore, this nomogram might help predict the in-hospital mortality of patients admitted to the intensive care unit (ICU).

Association Between Cognition, Health Related Quality of Life, and Costs in a Population at Risk for Cognitive Decline

Background:

The association between health-related quality of life (HRQoL) and care costs in people at risk for cognitive decline is not well understood. Studying this association could reveal the potential benefits of increasing HRQoL and reducing care costs by improving cognition.

Objective:

In this exploratory data analysis we investigated the association between cognition, HRQoL utilities and costs in a well-functioning population at risk for cognitive decline.

Methods:

An exploratory data analysis was conducted using longitudinal 2-year data from the FINGER study (n = 1,120). A change score analysis was applied using HRQoL utilities and total medical care costs as outcome. HRQoL utilities were derived from the Short Form Health Survey-36 (SF-36). Total care costs comprised visits to a general practitioner, medical specialist, nurse, and days at hospital. Analyses were adjusted for activities of daily living (ADL) and depressive symptoms.

Results:

Although univariable analysis showed an association between cognition and HRQoL utilities, multivariable analysis showed no association between cognition, HRQoL utilities and total care costs. A one-unit increase in ADL limitations was associated with a -0.006 (p < 0.001) decrease in HRQoL utilities and a one-unit increase in depressive symptoms was associated with a -0.004 (p < 0.001) decrease in HRQoL utilities.

Conclusion:

The level of cognition in people at-risk for cognitive decline does not seem to be associated with HRQoL utilities. Future research should examine the level at which cognitive decline starts to affect HRQoL and care costs. Ideally, this would be done by means of cross-validation in populations with various stages of cognitive functioning and decline.

Trends in resource utilization and cost of illness in patients with active epilepsy in Germany from 2003 to 2020

OBJECTIVE

To calculate epilepsy-related direct, indirect, and total costs in adult patients with active epilepsy (ongoing unprovoked seizures) in Germany and to analyze cost components and dynamics compared to previous studies from 2003, 2008 and 2013. This analysis was part of the Epi2020 study.

METHODS

Direct and indirect costs related to epilepsy were calculated with a multicenter survey using an established and validated questionnaire with a bottom-up design and human capital approach over a 3-month period in late 2020. Epilepsy-specific costs in the German health care sector from 2003, 2008 and 2013 were corrected for inflation to allow for a valid comparison.

RESULTS

Data on the disease-specific costs for 253 patients in 2020 were analyzed. The mean total costs were calculated at € 5,551 (± € 5,805; median: € 2,611; range: € 274 to € 21,667) per three months, comprising mean direct costs of € 1,861 (± € 1,905; median: € 1,276; range: € 327 to € 13,158) and mean indirect costs of € 3,690 (± € 5,298; median: € 0; range: € 0 to € 11,925). The main direct costs components were hospitalization (42.4%), anti-seizure medication (42.2%) and outpatient care (6.2%). Productivity losses due to early retirement (53.6%), part-time work or unemployment (30.8%) and seizure-related off-days (15.6%) were the main reasons for indirect costs. However, compared to 2013, there was no significant increase of direct costs (-10.0%), and indirect costs significantly increased (p<0.028, +35.1%), resulting in a significant increase in total epilepsy-related costs (p<0.047, +20.2%). Compared to the 2013 study population, a significant increase of cost of illness could be observed (p=0.047).

SIGNIFICANCE

The present study shows that disease-related costs in adult patients with active epilepsy increased from 2013 to 2020. As direct costs have remained constant, this increase is attributable to an increase in indirect costs. These findings highlight the impact of productivity loss caused by early retirement, unemployment, working time reduction and seizure-related days off.

Economic and social cost of epilepsy in Poland: 5-year analysis

INTRODUCTION

Epilepsy affects nearly 50 million people around the world. As a common and chronic disease generates a high cost burden for healthcare system and patients.

AIM

We aimed to determine the most current direct and indirect costs of epilepsy in Poland from the social perspective for the years 2014-2018, to analyze the changes of expenditures over time, indicate trends and to determine key cost-drivers.

MATERIAL AND METHODS

Direct and indirect costs using a top-down approach were estimated based on the public institutions' data for the ICD-10 codes G40 and G41. Direct costs included pharmacotherapy, hospitalizations, outpatient specialist care and rehabilitation. A human capital approach was used to estimate loss of productivity due to sick leaves and long-term inability to work.

RESULTS

Annual total direct and indirect costs related to epilepsy accounted for EUR 410 million in 2014 and decreased in subsequent years to EUR 361 million in 2018. The indirect costs were dominant (76-83% of total costs) and in the majority related to the long-term absenteeism (87-92% of total indirect costs). In 2014-2018, patients with epilepsy generated EUR 341 million to EUR 282 million of indirect costs. Annual direct costs for patients with epilepsy were EUR 69 million in 2014 and increased to EUR 80 million in 2018. The biggest expenses were the costs of drugs (> 50%) and hospitalizations (~ 40%).

CONCLUSIONS

Epilepsy is an expensive disorder in terms of consumption of resources and social costs. Decision-makers should take it under special consideration.

Health Technology Assessment Report on Vagus Nerve Stimulation in Drug-Resistant Epilepsy

Background: Vagus nerve stimulation (VNS) is a palliative treatment for medical intractable epileptic syndromes not eligible for resective surgery. Health technology assessment (HTA) represents a modern approach to the analysis of technologies used for healthcare. The purpose of this study is to assess the clinical, organizational, financial, and economic impact of VNS therapy in drug-resistant epilepsies and to establish the congruity between costs incurred and health service reimbursement. Methods: The present study used an HTA approach. It is based on an extensive detailed bibliographic search on databases (Medline, Pubmed, Embase and Cochrane, sites of scientific societies and institutional sites). The HTA study includes the following issues: (a) social impact and costs of the disease; (b) VNS eligibility and clinical results; (c) quality of life (QoL) after VNS therapy; (d) economic impact and productivity regained after VNS; and (e) costs of VNS. Results: Literature data indicate VNS as an effective treatment with a potential positive impact on social aspects and on quality of life. The diagnosis-related group (DRG) financing, both on national and regional levels, does not cover the cost of the medical device. There was an evident insufficient coverage of the DRG compared to the full cost of implanting the device. Conclusions: VNS is a palliative treatment for reducing seizure frequency and intensity. Despite its economic cost, VNS should improve patients' quality of life and reduce care needs.

The health burden of non-communicable neurological disorders in the USA between 1990 and 2017

In this observational study, using the Global Burden of Disease and Risk Factors Study, we aimed to (i) report the magnitude of health loss due to non-communicable neurological disorders in the USA in 2017 by sex, age, years and States and (ii) to identify non-communicable neurological disorders attributable environmental, metabolic and behavioural risk factors. We provide estimates of the burden of non-communicable neurological disorders by reporting disability-adjusted life-years and their trends from 1990 to 2017 by age and sex in the USA. The non-communicable neurological disorders include migraines, tension-type headaches, multiple sclerosis, Alzheimer's disease and other dementias, Parkinson's disease, epilepsy, motor neuron diseases and other neurological disorders. In 2017, the global burdens of non-communicable neurological disorders were 1444.41 per 100 000, compared to the USA burden of 1574.0. Migraine was the leading age-standardized disability-adjusted life-years 704.7 per 100 000, with Alzheimer's disease and other dementias (41.8.7), and epilepsy (123.8) taking the second and third places, respectively. Between 1990 and 2017, the age-standardized disability-adjusted life-years rates for aggregate non-communicable neurological disorders relative to all cause increased by 3.42%. More specifically, this value for motor neuron diseases, Parkinson's disease and multiple sclerosis increase by 20.9%, 4.0%, 2.47%, 3.0% and 1.65%, respectively. In 2017, the age-standardized disability-adjusted life-years rates for the aggregate non-communicable neurological disorders was significantly higher in females than the males (1843.5 versus 1297.3 per 100 000), respectively. The age-standardized disability-adjusted life-years rates for migraine were the largest in both females (968.8) and males were (432.5) compared to other individual non-communicable neurological disorders. In the same year, the leading non-communicable neurological disorders age-standardized disability-adjusted life-years rates among children ≤9 was epilepsy (216.4 per 100 000). Among the adults aged 35-60 years, it was migraine (5792.0 per 100 000), and among the aged 65 and above was Alzheimer's disease and other dementias (78 800.1 per 100 000). High body mass index, smoking, high fasting plasma glaucous and alcohol use were the attributable age-standardized disability-adjusted life-years risks for aggregate and individual non-communicable neurological disorders. Despite efforts to decrease the burden of non-communicable neurological disorders in the USA, they continue to burden the health of the population. Children are most vulnerable to epilepsy-related health burden, adolescents and young adults to migraine, and elderly to Alzheimer's disease and other dementias and epilepsy. In all, the most vulnerable populations to non-communicable neurological disorders are females, young adults and the elderly.

Analysis for warning factors of type 2 diabetes mellitus complications with Markov blanket based on a Bayesian network model

BACKGROUND AND OBJECTIVE

Type 2 diabetes mellitus (T2DM) complications seriously affect the quality of life and could not be cured completely. Actions should be taken for prevention and self-management. Analysis of warning factors is beneficial for patients, on which some previous studies focused. They generally used the professional medical test factors or complete factors to predict and prevent, but it was inconvenient and impractical for patients to self-manage. With this in mind, this study built a Bayesian network (BN) model, from the perspective of diabetic patients' self-management and prevention, to predict six complications of T2DM using the selected warning factors which patients could have access from medical examination. Furthermore, the model was analyzed to explore the relationships between physiological variables and T2DM complications, as well as the complications themselves. The model aims to help patients with T2DM self-manage and prevent themselves from complications.

METHODS

The dataset was collected from a well-known data center called the National Health Clinical Center between 1st January 2009 and 31st December 2009. After preprocess and impute the data, a BN model merging expert knowledge was built with Bootstrap and Tabu search algorithm. Markov Blanket (MB) was used to select the warning factors and predict T2DM complications. Moreover, a Bayesian network without prior information (BN-wopi) model learned using 10-fold cross-validation both in structure and in parameters was added to compare with other classifiers learned using 10-fold cross-validation fairly. The warning factors were selected according the structure learned in each fold and were used to predict. Finally, the performance of two BN models using warning features were compared with Naïve Bayes model, Random Forest model, and C5.0 Decision Tree model, which used all features to predict. Besides, the validation parameters of the proposed model were also compared with those in existing studies using some other variables in clinical data or biomedical data to predict T2DM complications.

RESULTS

Experimental results indicated that the BN models using warning factors performed statistically better than their counterparts using all other variables in predicting T2DM complications. In addition, the proposed BN model were effective and significant in predicting diabetic nephropathy (DN) (AUC: 0.831), diabetic foot (DF) (AUC: 0.905), diabetic macrovascular complications (DMV) (AUC: 0.753) and diabetic ketoacidosis (DK) (AUC: 0.877) with the selected warning factors compared with other experiments.

CONCLUSIONS

The warning factors of DN, DF, DMV, and DK selected by MB in this research might be able to help predict certain T2DM complications effectively, and the proposed BN model might be used as a general tool for prevention, monitoring, and self-management.