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Background During the COVID-19 pandemic, provision of non-COVID healthcare was recurrently severely disrupted. The objective was to determine whether disruption of non-COVID hospital use, either due to cancelled, postponed, or forgone care, during the first pandemic year of COVID-19 impacted socioeconomic groups differently compared with pre-pandemic use. Methods National population registry data, individually linked with data of non-COVID hospital use in the Netherlands (2017-2020). in non-institutionalised population of 25-79 years, in standardised household income deciles (1 = low, 10 = high) as proxy for socioeconomic status. Generic outcome measures included patients who received hospital care (dichotomous): outpatient contact, day treatment, inpatient clinic, and surgery. Specific procedures were included as examples of frequently performed elective and acute procedures, e.g.: elective knee/hip replacement and cataract surgery, and acute percutaneous coronary interventions (PCI). Relative risks (RR) for hospital use were reported as outcomes from generalised linear regression models (binomial) with log-link. An interaction term was included to assess whether income differences in hospital use during the pandemic deviated from pre-pandemic use. Results Hospital use rates declined in 2020 across all income groups. With baseline (2019) higher hospital use rates among lower than higher income groups, relatively stronger declines were found for lower income groups. The lowest income groups experienced a 10% larger decline in surgery received than the highest income group (RR 0.90, 95% CI 0.87 - 0.93). Patterns were similar for inpatient clinic, elective knee/hip replacement and cataract surgery. We found small or no significant income differences for outpatient clinic, day treatment, and acute PCI. Conclusions Disruption of non-COVID hospital use in 2020 was substantial across all income groups during the acute phases of t

Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes.

The smallest worthwhile effect (SWE) is the smallest beneficial effect of an intervention that justifies the costs, risks, and inconveniences. The objective is to establish the SWE of spinal manipulative therapy (SMT) for the treatment of low back pain (LBP), and to gain insight into how different attributes of the treatment are traded among each other when choosing SMT. Part 1. A mixed-methods study will be conducted to establish and prioritize a list of attributes influencing choices for those who consider SMT for the treatment of LBP. Individual interviews and consensus groups with chiropractors, manual therapists, and osteopaths and their patients will be conducted. Interviews and consensus groups will be voice-recorded and transcribed verbatim. Part 2. A Discrete Choice Experiment (DCE) will be conducted among people with LBP who have limited to no experience with SMT. Participants will be recruited through an online independent panel company. The survey will consist of several choice sets with attributes and their levels established from Part 1. The DCE will be preceded by a short survey to understand the clinical aspects (i.e. presentation, history and previous treatment for LBP) as well as socio-demographic characteristics of the participants.

Introduction: Discrete choice experiments (DCE) are commonly used to elicit patient preferences and to determine the relative importance of attributes but can be complex and costly to administer. Simpler methods that measure relative importance exist, such as swing weighting with direct rating (SW-DR), but there is little empirical evidence comparing the two. This study aimed to directly compare attribute relative importance rankings and weights elicited using a DCE and SW-DR. Methods: A total of 307 patients with non–small-cell lung cancer in Italy and Belgium completed an online survey assessing preferences for cancer treatment using DCE and SW-DR. The relative importance of the attributes was determined using a random parameter logit model for the DCE and rank order centroid method (ROC) for SW-DR. Differences in relative importance ranking and weights between the methods were assessed using Cohen’s weighted kappa and Dirichlet regression. Feedback on ease of understanding and answering the 2 tasks was also collected. Results: Most respondents (>65%) found both tasks (very) easy to understand and answer. The same attribute, survival, was ranked most important irrespective of the methods applied. The overall ranking of the attributes on an aggregate level differed significantly between DCE and SW-ROC (P < 0.01). Greater differences in attribute weights between attributes were reported in DCE compared with SW-DR (P < 0.01). Agreement between the individual-level attribute ranking across methods was moderate (weighted Kappa 0.53–0.55). Conclusion: Significant differences in attribute importance between DCE and SW-DR were found. Respondents reported both methods being relatively easy to understand and answer. Further studies confirming these findings are warranted. Such studies will help to provide accurate guidance for methods selection when studying relative attribute importanc