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Background: Administrative data are used in the field of Alzheimer’s Disease and Related Syndromes (ADRS), however their performance to identify ADRS is unknown. Objective: i) To develop and validate a model to identify ADRS prevalent cases in French administrative data (SNDS), ii) to identify factors associated with false negatives. Methods: Retrospective cohort of subjects ≥ 65 years, living in South-Western France, who attended a memory clinic between April and December 2013. Gold standard for ADRS diagnosis was the memory clinic specialized diagnosis. Memory clinics’ data were matched to administrative data (drug reimbursements, diagnoses during hospitalizations, registration with costly chronic conditions). Prediction models were developed for 1-year and 3-year periods of administrative data using multivariable logistic regression models. Overall model performance, discrimination, and calibration were estimated and corrected for optimism by resampling. Youden index was used to define ADRS positivity and to estimate sensitivity, specificity, positive predictive and negative probabilities. Factors associated with false negatives were identified using multivariable logistic regressions. Results: 3360 subjects were studied, 52% diagnosed with ADRS by memory clinics. Prediction model based on age, all-cause hospitalization, registration with ADRS as a chronic condition, number of anti-dementia drugs, mention of ADRS during hospitalizations had good discriminative performance (c-statistic: 0.814, sensitivity: 76.0%, specificity: 74.2% for 2013 data). 419 false negatives (24.0%) were younger, had more often ADRS types other than Alzheimer’s disease, moderate forms of ADRS, recent diagnosis, and suffered from other comorbidities than true positives. Conclusion: Administrative data presented acceptable performance for detecting ADRS. External validation studies should be encouraged.

Fracture hospitalizations of people ≥ 65 years old living in France increased annually from 2015 until 2019 (average: 1.8%), until being reduced in 2020 (− 1.4%) with an abrupt decrease during the lockdown period. Decreased exposure to the risk of falling during COVID-19 year 2020 may have reflected in lower incidence of fractures.

Cost of illness (COI) studies estimate the overall economic burden of a specific disease, rather than simply treatment-related costs. While having been criticised for not allowing resource prioritisation, COI studies can provide useful guidance, so long as they adhere to accepted methodology. The aim of this review is to analyse the methods used to evaluate the cost of lung cancer. Because of the increasing incidence and high direct and indirect costs of lung cancer, it is an important disease in terms of economic implications, and therefore provides a relevant example with which to review COI study methodologies. First, the key points of the methodology relating to COI studies were identified. COI studies relating to lung cancer were then reviewed, focussing on an analysis of the different methods used and an identification of the strengths and weaknesses of each approach. The COI studies that were analysed confirmed that lung cancer is a costly illness, and that hospitalisation and treatments account for a large part of direct costs, while indirect costs represent a large part of the total costs. The review also showed that COI studies adopted significantly different approaches to estimate the costs of lung cancer, reflecting a lack of consensus on the methodology of COI studies in this area. Hence, to increase the credibility of COI studies, closer agreement among researchers on methodological principles would be desirable.

Background: Lymphomas are costly diseases that suffer from a lack of detailed economic information, notably in a real-world setting. Decision-makers are increasing the search for Real-World Evidence (RWE) to assess the impact, in real-life, of healthcare management and to support their public decisions. Thus, we aimed to assess the real-world net costs of the active treatment phases of adult Hodgkin Lymphoma (HL), Follicular Lymphoma (FL) and Diffuse Large B Cell Lymphoma (DLBCL). Methods: We performed a retrospective cohort study using population-based data from a national representative sample of the French population covered by the health insurance system. Cost analysis was performed from the French health insurance perspective and took into account direct and sick leave compensation costs (e2,018). Healthcare costs were studied over the active treatment phase. We used multivariate modeling to adjust cost differences between lymphoma subtypes. Results: Analyses were performed on 224 lymphoma patients and 896 controls. The mean additional monthly costs due to HL, FL and DLBCL patients were respectively e5,188, e3,242 and e7,659 for the active treatment phase. The main additional cost driver was principally inpatient stay (hospitalization costs and costly cancer-related drugs), followed by outpatient medication and productivity loss. When adjusted, DLBCL remains significantly the most costly lymphoma subtype. Conclusion:This study provides an accurate assessment of the main lymphoma subtypes related cost with high magnitude of details in a real-world setting. We underline where potential cost saving could be realized via the use of biosimilar medication, and where lymphoma management could be improved with the early management of adverse events. KEY POINTSThis is one of the first studies which assess the additional cost of lymphoma in Europe, according the main sub-types of lymphoma and with real-world database. The additional monthly cost due to HL, FL and DLBCL patients were respectively e5,188, e3,242 and e7,659 for the active treatment phase and the main additional cost driver was principally inpatient stay (i.e. hospitalization costs and additional inpatient medicines, notably rituximab), followed by outpatient medication and productivity loss. This study provides an accurate and detailed lymphoma subtype cost description and comparison which supply data for efficiency evaluations and will allow French health policy to improve lymphoma management.