Auswirkungen wählbarer Selbstbehalte in der Krankenversicherung: Lehren aus der Schweiz?

In diesem Papier wird diskutiert, ob die Einführung wählbarer Selbstbehalte in der Gesetzlichen Krankenversicherung einen sinnvollen Weg zur Kosteneinsparung im Gesundheitswesen darstellt. Ausgehend von Befunden aus dem schweizerischen Gesundheitssystem, das solche wählbaren Selbstbehalte in der obligatorischen Grundversicherung seit 1996 kennt, wird argumentiert, dass die Wahlmöglichkeit des Selbstbehalts keinen starken Einfluss auf das Patientenverhalten zu haben scheint. Der Großteil der beobachteten Reduktion der Anzahl der Arztbesuche bei Versicherten mit höheren Selbstbehalten ist durch Selbstselektion zu erklären. Gesündere Versicherte oder Versicherte, die aufgrund ihrer Präferenzen weniger medizinische Leistungen in Anspruch nehmen, wählen höhere Selbstbehalte. Eine durch die gewählte höhere Selbstbeteiligung induzierte Verhaltensänderung in Richtung einer sparsameren Inanspruchnahme des Gesundheitswesens ist kaum festzustellen. Soll trotzdem eine Wahlmöglichkeit bei Selbstbehalten in Deutschland eingeführt werden, gilt es die Systemunterschiede zwischen der Individualversicherung (CH) und der Familienversicherung (D) zu beachten. Die Einführung von nicht wählbaren Kostenbeteiligungen der Patienten scheint leichter durchsetzbar, da die Selbstselektion von Gesunden und Kranken in verschiedene Versicherungskontrakte, die vielfach als unsolidarisch empfunden wird, nicht auftreten kann

Social isolation, physical inactivity and inadequate diet among European middle-aged and older adults

Social isolation is a growing public health concern for older adults, as it has been associated with poor health and premature mortality. On the other hand, physical inactivity and an inadequate diet are important health risk behaviours associated with physical and mental health problems. Considering that there is no research examining the possible relationship between social isolation and the above mentioned health risk behaviours of European middle-aged and older adults, this cross-sectional study aims to contribute to filling this gap.Fundação Calouste Gulbenkia

Mediation Analysis for Count and Zero-Inflated Count Data without Sequential Ignorability and Its Application in Dental Studies

Mediation analysis seeks to understand the mechanism by which a treatment affects an outcome. Count or zero‐inflated count outcomes are common in many studies in which mediation analysis is of interest. For example, in dental studies, outcomes such as the number of decayed, missing and filled teeth are typically zero inflated. Existing mediation analysis approaches for count data often assume sequential ignorability of the mediator. This is often not plausible because the mediator is not randomized so unmeasured confounders are associated with the mediator and the outcome. We develop causal methods based on instrumental variable approaches for mediation analysis for count data possibly with many 0s that do not require the assumption of sequential ignorability. We first define the direct and indirect effect ratios for those data, and then we propose estimating equations and use empirical likelihood to estimate the direct and indirect effects consistently. A sensitivity analysis is proposed for violations of the instrumental variables exclusion restriction assumption. Simulation studies demonstrate that our method works well for different types of outcome under various settings. Our method is applied to a randomized dental caries prevention trial and a study of the effect of a massive flood in Bangladesh on children\u27s diarrhoea

Evaluation of biases present in the cohort multiple randomised controlled trial design: a simulation study

Background The cohort multiple randomised controlled trial (cmRCT) design provides an opportunity to incorporate the benefits of randomisation within clinical practice; thus reducing costs, integrating electronic healthcare records, and improving external validity. This study aims to address a key concern of the cmRCT design: refusal to treatment is only present in the intervention arm, and this may lead to bias and reduce statistical power. Methods We used simulation studies to assess the effect of this refusal, both random and related to event risk, on bias of the effect estimator and statistical power. A series of simulations were undertaken that represent a cmRCT trial with time-to-event endpoint. Intention-to-treat (ITT), per protocol (PP), and instrumental variable (IV) analysis methods, two stage predictor substitution and two stage residual inclusion, were compared for various refusal scenarios. Results We found the IV methods provide a less biased estimator for the causal effect when refusal is present in the intervention arm, with the two stage residual inclusion method performing best with regards to minimum bias and sufficient power. We demonstrate that sample sizes should be adapted based on expected and actual refusal rates in order to be sufficiently powered for IV analysis. Conclusion We recommend running both an IV and ITT analyses in an individually randomised cmRCT as it is expected that the effect size of interest, or the effect we would observe in clinical practice, would lie somewhere between that estimated with ITT and IV analyses. The optimum (in terms of bias and power) instrumental variable method was the two stage residual inclusion method. We recommend using adaptive power calculations, updating them as refusal rates are collected in the trial recruitment phase in order to be sufficiently powered for IV analysis

Establishment and characterization of an hACE2/hTMPRSS2 knock-in mouse model to study SARS-CoV-2

Despite a substantial body of research, we lack fundamental understanding of the pathophysiology of COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) including pulmonary and cardiovascular outcomes, in part due to limitations of murine models. Most models use transgenic mice (K18) that express the human (h) angiotensin converting enzyme 2 (ACE2), ACE2 knock-in (KI) mice, or mouse-adapted strains of SARS-CoV-2. Further, many SARS-CoV-2 variants produce fatal neurologic disease in K18 mice and most murine studies focus only on acute disease in the first 14 days post inoculation (dpi). To better enable understanding of both acute (<14 dpi) and post-acute (>14 dpi) infection phases, we describe the development and characterization of a novel non-lethal KI mouse that expresses both the ACE2 and transmembrane serine protease 2 (TMPRSS2) genes (hACE2/hTMPRSS2). The human genes were engineered to replace the orthologous mouse gene loci but remain under control of their respective murine promoters, resulting in expression of ACE2 and TMPRSS2 instead of their murine counterparts. After intranasal inoculation with an omicron strain of SARS-CoV-2, hACE2/hTMPRSS2 KI mice transiently lost weight but recovered by 7 dpi. Infectious SARS-CoV-2 was detected in nasopharyngeal swabs 1-2 dpi and in lung tissues 2-6 dpi, peaking 4 dpi. These outcomes were similar to those in K18 mice that were inoculated in parallel. To determine the extent to which hACE2/hTMPRSS2 KI mice are suitable to model pulmonary and cardiovascular outcomes, physiological assessments measuring locomotion, behavior and reflexes, biomonitoring to measure cardiac activity and respiration, and micro computed tomography to assess lung function were conducted frequently to 6 months post inoculation. Male but not female SARS-CoV-2 inoculated hACE2/hTMPRSS2 KI mice showed a transient reduction in locomotion compared to control saline treated mice. No significant changes in respiration, oxygen saturation, heart rate variability, or conductivity were detected in SARS-CoV-2 inoculated mice of either sex. When re-inoculated 6 months after the first inoculation, hACE2/hTMPRSS2 KI became re-infected with disease signs similar to after the first inoculation. Together these data show that a newly generated hACE2/hTMPRSS2 KI mouse can be used to study mild COVID-19

Modelling the impact of women’s education on fertility in Malawi

Many studies have suggested that there is an inverse relationship between education and number of children among women from sub-Saharan Africa countries, including Malawi. However, a crucial limitation of these analyses is that they do not control for the potential endogeneity of education. The aim of our study is to estimate the role of women’s education on their number of children in Malawi, accounting for the possible presence of endogeneity and for nonlinear effects of continuous observed confounders. Our analysis is based on micro data from the 2010 Malawi Demographic Health Survey, and uses a flexible instrumental variable regression approach. The results suggest that the relationship of interest is affected by endogeneity and exhibits an inverted U-shape among women living in rural areas of Malawi, whereas it exhibits an inverse (nonlinear) relationship for women living in urban areas

Chemotherapy effectiveness in trial-underrepresented groups with early breast cancer:A retrospective cohort study

BACKGROUND: Adjuvant chemotherapy in early stage breast cancer has been shown to reduce mortality in a large meta-analysis of over 100 randomised trials. However, these trials largely excluded patients aged 70 years and over or with higher levels of comorbidity. There is therefore uncertainty about whether the effectiveness of adjuvant chemotherapy generalises to these groups, hindering patient and clinician decision-making. This study utilises administrative healthcare data-real world data (RWD)-and econometric methods for causal analysis to estimate treatment effectiveness in these trial-underrepresented groups. METHODS AND FINDINGS: Women with early breast cancer aged 70 years and over and those under 70 years with a high level of comorbidity were identified and their records extracted from Scottish Cancer Registry (2001-2015) data linked to other routine health records. A high level of comorbidity was defined as scoring 1 or more on the Charlson comorbidity index, being in the top decile of inpatient stays, and/or having 5 or more visits to specific outpatient clinics, all within the 5 years preceding breast cancer diagnosis. Propensity score matching (PSM) and instrumental variable (IV) analysis, previously identified as feasible and valid in this setting, were used in conjunction with Cox regression to estimate hazard ratios for death from breast cancer and death from all causes. The analysis adjusts for age, clinical prognostic factors, and socioeconomic deprivation; the IV method may also adjust for unmeasured confounding factors. Cohorts of 9,653 and 7,965 were identified for women aged 70 years and over and those with high comorbidity, respectively. In the ≥70/high comorbidity cohorts, median follow-up was 5.17/6.53 years and there were 1,935/740 deaths from breast cancer. For women aged 70 years and over, the PSM-estimated HR was 0.73 (95% CI 0.64-0.95), while for women with high comorbidity it was 0.67 (95% CI 0.51-0.86). This translates to a mean predicted benefit in terms of overall survival at 10 years of approximately3% (percentage points) and 4%, respectively. A limitation of this analysis is that use of observational data means uncertainty remains both from sampling uncertainty and from potential bias from residual confounding. CONCLUSIONS: The results of this study, as RWD, should be interpreted with caution and in the context of existing and emerging randomised data. The relative effectiveness of adjuvant chemotherapy in reducing mortality in patients with early stage breast cancer appears to be generalisable to the selected trial-underrepresented groups.</p

Tracking positive and negative effects of inequality on long-run growth

Despite extensive research controversy remains on the effects of income inequality on economic growth. The literature proposes several transmission channels through which these effects may occur and even the existence of two different forms of inequality. However, empirical studies have not generally distinguished between these channels, nor have they considered the two forms of inequality and their separate effects on growth. In this paper we review the theory and the evidence of the transmission channels through which inequality influences growth. We contribute to the literature by using a system of recursive equations, following a control function approach, to empirically assess the relevance of these channels and to differentiate between two forms of inequality. In a single model, we capture both a negative and a positive effect of inequality on long-run economic growt