Preferences for innovations in healthcare delivery models in the Swiss elderly population: a latent class, choice modelling study.

BACKGROUND: With the increasing number of people affected by multiple chronic conditions, it is essential for public-health professionals to promote strategies addressing patient needs for coordinated care. We aim to explore preference heterogeneity for better-coordinated care delivery models in Swiss older adults, and identify profiles of individuals more open to healthcare reforms. METHODS: A DCE (discrete choice experiment) survey was developed online and on paper for the Swiss adults aged 50+, following best practice. To elicit preferences, we estimated a latent class model allowing grouping individuals with similar preferences into distinct classes, and examined what background characteristics contributed to specific class membership. RESULTS: The optimal model identified three classes with different openness to reforms. Class 1 (49%) members were concerned with premium increases and were in favour of integrated care structures with care managed by interprofessional teams. Individuals in class 2 (19%) were younger, open to reforms, and expressed the needs for radical changes within the Swiss healthcare system. Class 3 respondents (32%) were strongly reluctant to changes. CONCLUSIONS: Our study goes beyond average preferences and identifies three distinct population profiles, a majority open to reforms on specific aspects of care delivery, a smallest group in favour radical changes, and a third strongly against changes. Therefore, tailored approaches around healthcare reforms are needed, e.g. explaining the role of interprofessional teams in coordinating care, electronic health records and insurance premium variation.

On children's motives to influence parents' long-term care insurance purchase: evidence from Switzerland.

Long-term care (LTC) is not only a concern for elderly individuals but also for their adult children, as the latter often provide financial support and informal care to their elderly dependents. Adult children may therefore have strong incentives to have their parents purchase LTC insurance. Using data from a 2019 Swiss survey, this article first identifies a set of variables, including self-reported interest about LTC insurance, whether elderly parents live with their children and if the latter have provided informal help with personal care, which help predict the interest of adult children in having their parents covered against LTC risk. Second, it investigates the main characteristics of children's motives for influencing their parents to purchase LTC insurance, which are classified as either altruistic, i.e. related to parental well-being, or self-interested, i.e. related to the child's well-being. The results offer valuable insights for both policymakers and insurers when designing public LTC policies and LTC insurance products.

How does regulating doctors' admissions affect health expenditures? Evidence from Switzerland.

BACKGROUND: Cost containment is a major issue for health policy, in many countries. Policymakers have used various measures to deal with this problem. In Switzerland, the national parliament and subnational (cantonal) governments have used moratoriums to limit the admission of specialist doctors and general practitioners. METHODS: We analyze the impact of these regulations on the number of doctors billing in free practice and on the health costs created by medical practice based on records from the data pool of Swiss health insurers (SASIS) from 2007 to 2018 using interrupted time series and difference-in-differences models. RESULTS: We demonstrate that the removal of the national moratorium in 2012 increased the number of doctors, but did not augment significantly the direct health costs produced by independent doctors. Furthermore, the reintroduction of regulations at the cantonal level in 2013 and 2014 decreased the number of doctors billing in free practice but, again, did not affect direct health costs. CONCLUSIONS: Our findings suggest that regulating healthcare supply through a moratorium on doctors' admissions does not directly contribute to limiting the increase in health expenditures.

Association between continuity of care (COC), healthcare use and costs: what can we learn from claims data? A rapid review.

OBJECTIVE: To describe how longitudinal continuity of care (COC) is measured using claims-based data and to review its association with healthcare use and costs. RESEARCH DESIGN: Rapid review of the literature. METHODS: We searched Medline (PubMed), EMBASE and Cochrane Central, manually checked the references of included studies, and hand-searched websites for potentially additional eligible studies. RESULTS: We included 46 studies conducted in North America, East Asia and Europe, which used 14 COC indicators. Most reported studies (39/46) showed that higher COC was associated with lower healthcare use and costs. Most studies (37/46) adjusted for possible time bias and discussed causality between the outcomes and COC, or at least acknowledged the lack of it as a limitation. CONCLUSIONS: Whereas a wide range of indicators is used to measure COC in claims-based data, associations between COC and healthcare use and costs were consistent, showing lower healthcare use and costs with higher COC. Results were observed in various population groups from multiple countries and settings. Further research is needed to make stronger causal claims.

The tandem duplicator phenotype as a distinct genomic configuration in cancer.

Next-generation sequencing studies have revealed genome-wide structural variation patterns in cancer, such as chromothripsis and chromoplexy, that do not engage a single discernable driver mutation, and whose clinical relevance is unclear. We devised a robust genomic metric able to identify cancers with a chromotype called tandem duplicator phenotype (TDP) characterized by frequent and distributed tandem duplications (TDs). Enriched only in triple-negative breast cancer (TNBC) and in ovarian, endometrial, and liver cancers, TDP tumors conjointly exhibit tumor protein p53 (TP53) mutations, disruption of breast cancer 1 (BRCA1), and increased expression of DNA replication genes pointing at rereplication in a defective checkpoint environment as a plausible causal mechanism. The resultant TDs in TDP augment global oncogene expression and disrupt tumor suppressor genes. Importantly, the TDP strongly correlates with cisplatin sensitivity in both TNBC cell lines and primary patient-derived xenografts. We conclude that the TDP is a common cancer chromotype that coordinately alters oncogene/tumor suppressor expression with potential as a marker for chemotherapeutic response.

Systems consequences of amplicon formation in human breast cancer.

Chromosomal structural variations play an important role in determining the transcriptional landscape of human breast cancers. To assess the nature of these structural variations, we analyzed eight breast tumor samples with a focus on regions of gene amplification using mate-pair sequencing of long-insert genomic DNA with matched transcriptome profiling. We found that tandem duplications appear to be early events in tumor evolution, especially in the genesis of amplicons. In a detailed reconstruction of events on chromosome 17, we found large unpaired inversions and deletions connect a tandemly duplicated ERBB2 with neighboring 17q21.3 amplicons while simultaneously deleting the intervening BRCA1 tumor suppressor locus. This series of events appeared to be unusually common when examined in larger genomic data sets of breast cancers albeit using approaches with lesser resolution. Using siRNAs in breast cancer cell lines, we showed that the 17q21.3 amplicon harbored a significant number of weak oncogenes that appeared consistently coamplified in primary tumors. Down-regulation of BRCA1 expression augmented the cell proliferation in ERBB2-transfected human normal mammary epithelial cells. Coamplification of other functionally tested oncogenic elements in other breast tumors examined, such as RIPK2 and MYC on chromosome 8, also parallel these findings. Our analyses suggest that structural variations efficiently orchestrate the gain and loss of cancer gene cassettes that engage many oncogenic pathways simultaneously and that such oncogenic cassettes are favored during the evolution of a cancer.

2,5-PRODAN derivatives as highly sensitive sensors of low solvent acidity.

Two 5-acyl-2-dimethylaminonaphthalene derivatives, one with a propionyl group and the other with a fused cyclohexanone ring, are investigated as sensors of H-bond-donating ability in protic solvents of low solvent acidity. Their fluorescence is highly quenched in protic solvents, and the quenching order of magnitude is linearly related to the H-bond-donating ability of the solvent as quantified by the solvent acidity (SA) scale. As the solvent acidity increases from 0.15 to 0.40, the fluorescence for both is quenched by more than a factor of ten; thus, they are extremely sensitive sensors of the hydrogen-bond-donating ability in this weakly acidic range. Preferential solvation studies suggest that quenching occurs from a doubly H-bonded excited state.

Systems analysis of EGF receptor signaling dynamics with microwestern arrays.

We describe microwestern arrays, which enable quantitative, sensitive and high-throughput assessment of protein abundance and modifications after electrophoretic separation of microarrayed cell lysates. This method allowed us to measure 91 phosphosites on 67 proteins at six time points after stimulation with five epidermal growth factor (EGF) concentrations in A431 human carcinoma cells. We inferred the connectivities among 15 phosphorylation sites in 10 receptor tyrosine kinases (RTKs) and two sites from Src kinase using Bayesian network modeling and two mutual information-based methods; the three inference methods yielded substantial agreement on the network topology. These results imply multiple distinct RTK coactivation mechanisms and support the notion that small amounts of experimental data collected from phenotypically diverse network states may enable network inference.

Signaling network state predicts twist-mediated effects on breast cell migration across diverse growth factor contexts.

Epithelial-mesenchymal transition (EMT), whether in developmental morphogenesis or malignant transformation, prominently involves modified cell motility behavior. Although major advances have transpired in understanding the molecular pathways regulating the process of EMT induction per se by certain environmental stimuli, an important outstanding question is how the activities of signaling pathways governing motility yield the diverse movement behaviors characteristic of pre-induction versus postinduction states across a broad landscape of growth factor contexts. For the particular case of EMT induction in human mammary cells by ectopic expression of the transcription factor Twist, we found the migration responses to a panel of growth factors (EGF, HRG, IGF, HGF) dramatically disparate between confluent pre-Twist epithelial cells and sparsely distributed post-Twist mesenchymal cells-but that a computational model quantitatively integrating multiple key signaling node activities could nonetheless account for this full range of behavior. Moreover, motility in both conditions was successfully predicted a priori for an additional growth factor (PDGF) treatment. Although this signaling network state model could comprehend motility behavior globally, modulation of the network interactions underlying the altered pathway activities was identified by ascertaining differences in quantitative topological influences among the nodes between the two conditions.

On the determinants and the role of the payers in the uptake of genetic testing and data sharing in personalized health.

Background: New health technologies and data offer tailored prevention and spot-on treatments, which can considerably reduce healthcare costs. In healthy individuals, insurers can participate in the creation of health capital through data and preventing the occurrence of a disease. In the onset of a disease, sequencing an individual's genome can provide information leading to the use of more efficient treatments. Both improvements are at the core of the "personalized health" paradigm. As a positive side effect, a reduction in healthcare costs is expected. However, the integration of personalized health in insurance schemes starts with a closer understanding of the demand drivers. Methods: Using novel data from a survey carried out in Switzerland, we determine the factors influencing the uptake and sharing of data from genetic tests. In our regression analyses, we use five sets of socioeconomic, lifestyle, health insurance, sentiment, and political beliefs variables. Furthermore, two framings assess the willingness to undertake a test and the readiness to share results with an insurer when the costs of the test are borne by the insurer or the individual. Results: We find that socioeconomic, lifestyle, or political belief variables have very little influence on the uptake of tests and the sharing of data. On the contrary, our results indicate that sentiment and insurance factors play a strong role. More precisely, if genetic tests are perceived as a mean to perform health prevention, this pushes individuals to take them. Furthermore, using the insurer's smartphone app leads to an increase of the likelihood to undergo a test and doubles the probability to share related data. Regarding insurance plans and deductible levels, there is no strong correlation neither with the willingness to take a test nor to share the data. Finally, individuals with complementary health insurance plans are less likely to share results. From the framings for the payment of genetic tests, our results indicate a positive effect of the insurer as a payer on the willingness to undertake tests as well as on data sharing. Conclusion: Our results lay the ground for a deeper understanding of the role of payers on health decisions and sharing of health-related data. In particular, we find that it is relevant for health insurers to engage with their clients.

A systematic literature review on sustainability issues along the value chain in insurance companies and pension funds.

Sustainability is now a priority issue that governments, businesses and society in general must address in the short term. In their role as major global institutional investors and risk managers, insurance companies and pension funds are strategic players in building socio-economic and sustainable development. To gain a comprehensive understanding of the current state of action and research on environmental, social and governance (ESG) factors in the insurance and pension sectors, we conduct a systematic literature review. We rely on the PRISMA protocol and analyze 1 731 academic publications available in the Web of Science database up to the year 2022 and refer to 23 studies outside of scientific research retrieved from the websites of key international and European organizations. To study the corpus of literature, we introduce a classification framework along the insurance value chain including external stakeholders. The main findings reveal that risk, underwriting and investment management are the most researched areas among the nine categories considered in our framework, while claims management and sales tend to be neglected. Regarding ESG factors, climate change, as part of the environmental factor, has received the most attention in the literature. After reviewing the literature, we summarize the main sustainability issues and potential related actions. Given the current nature of the sustainability challenges for the insurance sector, this literature review is relevant to academics and practitioners alike.

Is there a "pandemic effect" on individuals' willingness to take genetic tests?

In this cross-sectional, semi-longitudinal and quasi-experimental study, our goal was to determine the effect of data storage conditions on willingness to take a genetic test. We compared individuals' preferences regarding how they want to store health data collected from genetic tests through two survey experiments fielded in Switzerland in March 2020 and January 2022. We tested for differences whether genetic data are presented as private goods or public goods. Results confirm our initial research expectation: more control over storage increases willingness, so does framing genetic data as private good. However, they also show that the willingness to take a genetic test has noticeably increased between 2020 and 2022. Our results point toward a "pandemic effect" which would have increased willingness take a genetic test, nevertheless, more data are needed to understand this putative effect.

Preferences of older adults for healthcare models designed to improve care coordination: Evidence from Western Switzerland.

Implementing innovations in care delivery in Switzerland is challenging due to the fragmented nature of the system and the specificities of the political process (i.e., direct democracy, decentralized decision-making). In this context, it is particularly important to account for population preferences when designing policies. We designed a discrete choice experiment to study population preferences for coordination-improving care models. Specifically, we assessed the relative importance of model characteristics (i.e., insurance premium, presence of care coordinator, access to specialists, use of EMR, cost-sharing for chronic patients, incentives for informal care), and predicted uptake under different policy scenarios. We accounted for heterogeneity in preferences for the status quo option using an error component logit model. Respondents attached the highest importance to the price attribute (i.e. insurance premium) (0.31, CI: 0.27- 0.36) and to the presence of a care coordinator (0.27, CI: 0.23 - 0.31). Policy scenarios showed for instance that gatekeeping would be preferred to free access to specialists if the model includes a GP or an interprofessional team as a care coordinator. Although attachment to the status quo is high in the studied population, there are potential ways to improve acceptance of alternative care models by implementation of positively valued innovations.

Expressed Barcoding Enables High-Resolution Tracking of the Evolution of Drug Tolerance.

For a majority of patients with non-small cell lung cancer with EGFR mutations, treatment with EGFR inhibitors (EGFRi) induces a clinical response. Despite this initial reduction in tumor size, residual disease persists that leads to disease relapse. Elucidating the preexisting biological differences between sensitive cells and surviving drug-tolerant persister cells and deciphering how drug-tolerant cells evolve in response to treatment could help identify strategies to improve the efficacy of EGFRi. In this study, we tracked the origins and clonal evolution of drug-tolerant cells at a high resolution by using an expressed barcoding system coupled with single-cell RNA sequencing. This platform enabled longitudinal profiling of gene expression and drug sensitivity in response to EGFRi across a large number of clones. Drug-tolerant cells had higher expression of key survival pathways such as YAP and EMT at baseline and could also differentially adapt their gene expression following EGFRi treatment compared with sensitive cells. In addition, drug combinations targeting common downstream components (MAPK) or orthogonal factors (chemotherapy) showed greater efficacy than EGFRi alone, which is attributable to broader targeting of the heterogeneous EGFRi-tolerance mechanisms present in tumors. Overall, this approach facilitates thorough examination of clonal evolution in response to therapy that could inform the development of improved diagnostic approaches and treatment strategies for targeting drug-tolerant cells. SIGNIFICANCE: The evolution and heterogeneity of EGFR inhibitor tolerance are identified in a large number of clones at enhanced cellular and temporal resolution using an expressed barcode technology coupled with single-cell RNA sequencing.

PTMScout, a Web resource for analysis of high throughput post-translational proteomics studies.

The rate of discovery of post-translational modification (PTM) sites is increasing rapidly and is significantly outpacing our biological understanding of the function and regulation of those modifications. To help meet this challenge, we have created PTMScout, a web-based interface for viewing, manipulating, and analyzing high throughput experimental measurements of PTMs in an effort to facilitate biological understanding of protein modifications in signaling networks. PTMScout is constructed around a custom database of PTM experiments and contains information from external protein and post-translational resources, including gene ontology annotations, Pfam domains, and Scansite predictions of kinase and phosphopeptide binding domain interactions. PTMScout functionality comprises data set comparison tools, data set summary views, and tools for protein assignments of peptides identified by mass spectrometry. Analysis tools in PTMScout focus on informed subset selection via common criteria and on automated hypothesis generation through subset labeling derived from identification of statistically significant enrichment of other annotations in the experiment. Subset selection can be applied through the PTMScout flexible query interface available for quantitative data measurements and data annotations as well as an interface for importing data set groupings by external means, such as unsupervised learning. We exemplify the various functions of PTMScout in application to data sets that contain relative quantitative measurements as well as data sets lacking quantitative measurements, producing a set of interesting biological hypotheses. PTMScout is designed to be a widely accessible tool, enabling generation of multiple types of biological hypotheses from high throughput PTM experiments and advancing functional assignment of novel PTM sites. PTMScout is available at http://ptmscout.mit.edu.

Patient and Public Preferences for Coordinated Care in Switzerland: Development of a Discrete Choice Experiment.

OBJECTIVE: Our objective was to develop and test a discrete choice experiment (DCE) eliciting public and patient preferences for better-coordinated care in Switzerland. METHODS: We applied a multistage mixed-methods procedure using qualitative and quantitative approaches. First, to identify attributes, we performed a review of the DCE literature in healthcare with a focus on chronic care. Next, attribute selection involved stakeholders (N = 7) from various healthcare sectors to select the most relevant and actionable attributes, followed by three organized focus groups involving the general public and patients (N = 21) to verify the selection and the clarity of the DCE tasks and explanations. Finally, we conducted an online pilot in the target population to test the survey and obtain priors for a final six tested attributes to refine the final design of the experiment. RESULTS: After identifying an initial 33 attributes, a final list of six attributes was selected following stakeholder involvement and the three focus groups involving the target population. At the online pilot-testing stage with 301 participants, the majority of respondents found the DCE choice tasks socially relevant for Switzerland but challenging. The quality of the answers was relatively high. Most attributes had signs matching those in the literature and focus group discussions. CONCLUSION: This article will be useful to researchers designing DCEs from a broad health policy perspective. The multistage approach involving a range of stakeholders was essential for the development of a DCE that is relevant for policy makers and well-accepted by the general public and patients.

Continuity of care and multimorbidity in the 50+ Swiss population: An analysis of claims data.

Objective: To assess the relationship between continuity of care (COC) and multimorbidity in the older general population in Switzerland, accounting for relevant determinants of COC, and to apply various expressions of multimorbidity derived from claims data. Methods: We used data on 240'000 insured individuals aged 50+ for the period 2015-2018, received from one of the largest Swiss health insurance company. We calculated Bice-Boxerman index based on all doctor visits (overall COC) and visits to the general practitioners (COC GP). We analyzed the relationship between COC and multimorbidity using generalized linear and probit models. To express multimorbidity, we applied three approaches based on pharmacy-cost groups (PCGs) assigned to an individual. First, we used simple PCG counts. Second, we expressed multimorbidity via clinically relevant disease groups derived from PCGs. Finally, a data-driven approach allowed defining distinct clusters representing different patient complexities. Results: The association between overall COC and multimorbidity expressed in PCG counts was modest: COC among individuals with 3+ PCGs was 2 percentage points higher than COC among individuals with 0 PCGs. The approach of clinically relevant disease groups showed larger variation in COC and its association with multimorbidity. The data-driven approach showed that most complex ("high-cost high-need") individuals tended to have higher overall COC. Additionally, 70% of the sample visited exclusively one general practitioner (COC GP = 1.0). Other important factors associated with COC in the Swiss context were insurance model with gatekeeping, level of deductibles, and region of residence. Conclusions: Multimorbid patients require regular medical attention often involving multiple healthcare providers, which can lead to varying COC, depending on types of doctors seen and specific condition of the patient. Insurance models with gatekeeping may facilitate COC, prompting developments of better-designed models of care. This represents important implications for policymakers, health insurance representatives, medical professionals and hospital managers.

Continuity of care of Swiss residents aged 50+: a longitudinal study using claims data.

Background: Continuity of care (COC) should be measured for healthcare quality monitoring and evaluation and is a key process indicator for integrated care. Measurement of COC using routinely collected data is widespread, but there is no consensus on which indicator to use and the relevant time horizon to apply. Information about COC is especially warranted in highly fragmented healthcare systems, such as in Switzerland. Our study aimed to compare COC measures in Swiss residents aged 50+ obtained with various indices and time horizons. Methods: Using insurance claims data, we computed and compared several commonly used visit-based Continuity of Care Indices (COCIs): Bice-Boxerman Index, Usual Provider of Care, Herfindahl-Hirschman Index, Modified, Modified Continuity Index and Modified Continuity Index, based on all doctor visits and on primary care (PC) visits only. Indices were computed over short (1 year) and medium (4 years) terms. Results: The mean indices based on all visits varied between 0.51 and 0.77, while PC indices presented less variation with a median of 1.00 for all but one index. Indices focusing on a variety of individual providers decreased with time horizon, while indices focusing on the overall number of visits and providers showed the opposite trend. These findings suggest fundamental differences in the interpretation of COCIs. Conclusions: Broad COC appeared moderately low in Switzerland, although comparable to other countries, and PC COC was close to one. The choice of indices and time horizon influenced their interpretation. Understanding these differences is key to select the appropriate index for the monitoring of COC.

Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach.

BACKGROUND: Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. OBJECTIVE: This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data. METHODS: We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. RESULTS: Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. CONCLUSIONS: Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery.

Governing Personalized Health: A Scoping Review.

Genetic research is advancing rapidly. One important area for the application of the results from this work is personalized health. These are treatments and preventive interventions tailored to the genetic profile of specific groups or individuals. The inclusion of personalized health in existing health systems is a challenge for policymakers. In this article, we present the results of a thematic scoping review of the literature dealing with governance and policy of personalized health. Our analysis points to four governance challenges that decisionmakers face against the background of personalized health. First, researchers have highlighted the need to further extend and harmonize existing research infrastructures in order to combine different types of genetic data. Second, decisionmakers face the challenge to create trust in personalized health applications, such as genetic tests. Third, scholars have pointed to the importance of the regulation of data production and sharing to avoid discrimination of disadvantaged groups and to facilitate collaboration. Fourth, researchers have discussed the challenge to integrate personalized health into regulatory-, financing-, and service provision structures of existing health systems. Our findings summarize existing research and help to guide further policymaking and research in the field of personalized health governance.