Costs of minor bleeds in atrial fibrillation patients using a non-vitamin K antagonist oral anticoagulant

BACKGROUND: A very common side effect of non-vitamin K antagonist oral anticoagulant (NOAC) is (minor) bleeding. Data about impact and costs of minor bleeds in NOAC therapy is still limited or not present in current literature. In this patient orientated study, we aim to provide an estimate of the costs of minor bleeds in patients with atrial fibrillation (AF) treated with a NOAC. METHODS: A retrospective observational cohort study was conducted. Patients with AF and on NOAC therapy were included. Data was obtained by questionnaires and information from electronic patient records. Reference prices were used to calculate the costs per patient. Furthermore, cost of minor bleeds per patient is compared with literature-based costs of minor and major bleeding. RESULTS: 139 patients were included. A total of 94 minor bleed were reported by 71 patients. The sum of minor bleeding costs from societal perspective were €9,851.49, or on average €70,87 (95% CI €54,37 - €85,68) per patient with AF. The biggest cost drivers were rectal and vaginal bleeds, epistaxis was most commonly reported. CONCLUSION: Total costs of minor bleeds from a societal perspective, in AF patients using NOACs, are non trivial and exceed the costs presented in existing literature

Mapping Chronic Disease Prevalence based on Medication Use and Socio-demographic variables: an Application of LASSO in healthcare in the Netherlands

BACKGROUND: Policymakers generally lack sufficiently detailed health information to develop localized health policy plans. Chronic disease prevalence mapping is difficult as accurate direct sources are often lacking. Improvement is possible by adding extra information such as medication use and demographic information to identify disease. The aim of the current study was to obtain small geographic area prevalence estimates for four common chronic diseases by modelling based on medication use and socio-economic variables and next to investigate regional patterns of disease. METHODS: Administrative hospital records and general practitioner registry data were linked to medication use and socio-economic characteristics. The training set (n = 707,021) contained GP diagnosis and/or hospital admission diagnosis as the standard for disease prevalence. For the entire Dutch population (n = 16,777,888), all information except GP diagnosis and hospital admission was available. LASSO regression models for binary outcomes were used to select variables strongly associated with disease. Dutch municipality (non-)standardized prevalence estimates for stroke, CHD, COPD and diabetes were then based on averages of predicted probabilities for each individual inhabitant. RESULTS: Adding medication use data as a predictor substantially improved model performance. Estimates at the municipality level performed best for diabetes with a weighted percentage error (WPE) of 6.8%, and worst for COPD (WPE 14.5%)Disease prevalence showed clear regional patterns, also after standardization for age. CONCLUSION: Adding medication use as an indicator of disease prevalence next to socio-economic variables substantially improved estimates at the municipality level. The resulting individual disease probabilities could be aggregated into any desired regional level and provide a useful tool to identify regional patterns and inform local policy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-021-10754-4

Life years lost for users of specialized mental healthcare

Background: Mental disorders are burdensome and are associated with increased mortality. Mortality has been researched for various mental disorders, especially in countries with national registries, including the Nordic countries. Yet, knowledge gaps exist around national differences, while also relatively less studies compare mortality of those seeking help for mental disorders in specialized mental healthcare (SMH) by diagnosis. Additional insight into such mortality distributions for SMH users would be beneficial for both policy and research purposes. We aim to describe and compare the mortality in a population of SMH users with the mortality of the general population. Additionally, we aim to investigate mortality differences between sexes and major diagnosis categories: anxiety, depression, schizophrenia spectrum and other psychotic disorders, and bipolar disorder.Methods: Mortality and basic demographics were available for a population of N = 10,914 SMH users in the north of The Netherlands from 2010 until 2017. To estimate mortality over the adult lifespan, parametric Gompertz distributions were fitted on observed mortality using interval regression. Life years lost were computed by calculating the difference between integrals of the survival functions for the general population and the study sample, thus correcting for age. Survival for the general population was obtained from Statistics Netherlands (CBS).Results: SMH users were estimated to lose 9.5 life years (95% CI: 9.4–9.6). Every major diagnosis category was associated with a significant loss of life years, ranging from 7.2 (95% CI: 6.4–7.9) years for anxiety patients to 11.7 (95% CI: 11.0–12.5) years for bipolar disorder patients. Significant differences in mortality were observed between male SMH users and female SMH users, with men losing relatively more life years: 11.0 (95% CI: 10.9–11.2) versus 8.3 (95% CI: 8.2–8.4) respectively. This difference was also observed between sexes within every diagnosis, although the difference was insignificant for bipolar disorder. Conclusion: There were significant differences in mortality between SMH users and the general population. Substantial differences were observed between sexes and between diagnoses. Additional attention is required, and possibly specific interventions are needed to reduce the amount of life years lost by SMH users.</p

Dynamic effects of smoking cessation on disease incidence, mortality and quality of life: The role of time since cessation

<p>Abstract</p> <p>Background</p> <p>To support health policy makers in setting priorities, quantifying the potential effects of tobacco control on the burden of disease is useful. However, smoking is related to a variety of diseases and the dynamic effects of smoking cessation on the incidence of these diseases differ. Furthermore, many people who quit smoking relapse, most of them within a relatively short period.</p> <p>Methods</p> <p>In this paper, a method is presented for calculating the effects of smoking cessation interventions on disease incidence that allows to deal with relapse and the effect of time since quitting. A simulation model is described that links smoking to the incidence of 14 smoking related diseases. To demonstrate the model, health effects are estimated of two interventions in which part of current smokers in the Netherlands quits smoking.</p> <p>To illustrate the advantages of the model its results are compared with those of two simpler versions of the model. In one version we assumed no relapse after quitting and equal incidence rates for all former smokers. In the second version, incidence rates depend on time since cessation, but we assumed still no relapse after quitting.</p> <p>Results</p> <p>Not taking into account time since smoking cessation on disease incidence rates results in biased estimates of the effects of interventions. The immediate public health effects are overestimated, since the health risk of quitters immediately drops to the mean level of all former smokers. However, the long-term public health effects are underestimated since after longer periods of time the effects of past smoking disappear and so surviving quitters start to resemble never smokers. On balance, total health gains of smoking cessation are underestimated if one does not account for the effect of time since cessation on disease incidence rates. Not taking into account relapse of quitters overestimates health gains substantially.</p> <p>Conclusion</p> <p>The results show that simulation models are sensitive to assumptions made in specifying the model. The model should be specified carefully in accordance with the questions it is supposed to answer. If the aim of the model is to estimate effects of smoking cessation interventions on mortality and morbidity, one should include relapse of quitters and dependency on time since cessation of incidence rates of smoking-related chronic diseases. A drawback of such models is that data requirements are extensive.</p

Association between lung function and exacerbation frequency in patients with COPD

To quantify the relationship between severity of chronic obstructive pulmonary disease (COPD) as expressed by Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage and the annual exacerbation frequency in patients with COPD. We performed a systematic literature review to identify randomized controlled trials and cohort studies reporting the exacerbation frequency in COPD patients receiving usual care or placebo. Annual frequencies were determined for total exacerbations defined by an increased use of health care (event-based), total exacerbations defined by an increase of symptoms, and severe exacerbations defined by a hospitalization. The association between the mean forced expiratory volume in one second (FEV(1))% predicted of study populations and the exacerbation frequencies was estimated using weighted log linear regression with random effects. The regression equations were applied to the mean FEV(1)% predicted for each GOLD stage to estimate the frequency per stage. Thirty-seven relevant studies were found, with 43 reports of total exacerbation frequency (event-based, n = 19; symptom-based, n = 24) and 14 reports of frequency of severe exacerbations. Annual event-based exacerbation frequencies per GOLD stage were estimated at 0.82 (95% confidence interval 0.46-1.49) for mild, 1.17 (0.93-1.50) for moderate, 1.61 (1.51-1.74) for severe, and 2.10 (1.51-2.94) for very severe COPD. Annual symptom-based frequencies were 1.15 (95% confidence interval 0.67-2.07), 1.44 (1.14-1.87), 1.76 (1.70-1.88), and 2.09 (1.57-2.82), respectively. For severe exacerbations, annual frequencies were 0.11 (95% confidence interval 0.02-0.56), 0.16 (0.07-0.33), 0.22 (0.20-0.23), and 0.28 (0.14-0.63), respectively. Study duration or type of study (cohort versus trial) did not significantly affect the outcomes. This study provides an estimate of the exacerbation frequency per GOLD stage, which can be used for health economic and modeling purposes

Cost-effectiveness analysis of face-to-face smoking cessation interventions by professionals

Objectives: To estimate the cost-effectiveness of five face-to-face smoking cessation interventions: 1) Telephone Counseling (TC), 2) Minimal counseling by a general practitioner (H-MIS), 3) Minimal counseling by a general practitioner combined with Nicotine Replacement Therapy (H-MIS+NRT), 4) Intensive Counseling combined with Nicotine Replacement Therapy (IC+NRT) and 5) Intensive Counseling combined with Bupropion (IC+Bupr), in terms of costs per quitter, costs per life-year gained and costs per quality-adjusted life-year (QALY) gained. Methods: Scenarios on increased implementation of smoking cessation interventions were compared to current practice. Base-case scenarios assumed that one of the five interventions was implemented for a period of either 1 year, 10 years or 75 years and reached 25% of the smokers. A computer simulation model, the RIVM Chronic Disease Model, was used to project future gains in life-years and Quality Adjusted Life Years (QALYs), and savings of health care costs from a decrease in the incidence of smoking-related diseases. Regardless of the duration for which the intervention was implemented, our time horizon was 75 years, i.e. costs and effects were studied over a period of 75 years. Intervention costs were computed based on bottom up estimates of resource use and costs per unit of resource use. Cost calculations of smoking cessation interventions were carried out from a health care perspective, i.e. total direct medical costs were calculated based on estimates of real resource use. Effectiveness in terms of cessation rates was obtained from Cochrane meta-analyses. For the base-case scenarios, future costs and effects were discounted at an annual percentage of 4%. Incremental cost-effectiveness ratios were calculated as: (additional intervention costs minus the savings from a reduced incidence of smoking related diseases) / (gain in health outcomes). A series of one-way sensitivity analyses were performed to assess the robustness of the cost-effectiveness ratios with regard to variations in cessation rates, intervention costs, discount rates, time horizon, and the percentage of smokers reached by the intervention. Results: Base-case estimates for costs per quitter ranged from Euro 443 for H-MIS to Euro 2800 for IC+NRT. Compared to current practice H-MIS is a dominant intervention regardless of the duration of implementation. This means that H-MIS not onl

Cost-effectiveness of face-to-face smoking cessation interventions: A dynamic modeling study

Objectives: To estimate the cost-effectiveness of five face-to-face smoking cessation interventions (i.e., minimal counseling by a general practitioner (GP) with, or without nicotine replacement therapy (NRT), intensive counseling with NRT, or bupropion, and telephone counseling) in terms of costs per quitter, costs per life-year gained, and costs per quality-adjusted life-year (QALY) gained. Methods: Scenarios on increased implementation of smoking cessation interventions were compared with current practice in The Netherlands. One of the five interventions was implemented for a period of 1, 10, or 75 years reaching 25% of the smokers each year. A dynamic population model, the RIVM chronic disease model, was used to project future gains in life-years and QALYs, and savings of health-care costs from a decrease in the incidence of 11 smoking-related diseases over a time horizon of 75 years. This model allows the repetitive application of increased cessation rates to a population with a changing demographic and risk factor mix. Sensitivity analyses were performed for variations in costs, effects, time horizon, program size, and discount rates. Results: Compared with current practice, minimal GP counseling was a dominant intervention, generating both gains in life-years and QALYs and savings that were highe

Bounded-width polynomial-size branching programs recognize exactly those languages in NC1

AbstractWe show that any language recognized by an NC1 circuit (fan-in 2, depth O(log n)) can be recognized by a width-5 polynomial-size branching program. As any bounded-width polynomial-size branching program can be simulated by an NC1 circuit, we have that the class of languages recognized by such programs is exactly nonuniform NC1. Further, following Ruzzo (J. Comput. System Sci. 22 (1981), 365–383) and Cook (Inform. and Control 64 (1985) 2–22), if the branching programs are restricted to be ATIME(logn)-uniform, they recognize the same languages as do ATIME(log n)-uniform NC1 circuits, that is, those languages in ATIME(log n). We also extend the method of proof to investigate the complexity of the word problem for a fixed permutation group and show that polynomial size circuits of width 4 also recognize exactly nonuniform NC1