Predicting the burden of cancer in Switzerland up to 2025

Predicting the short-term evolution of the number of cancers is essential for planning investments and allocating health resources. The objective of this study was to predict the numbers of cancer cases and of the 12 most frequent cancer sites, and their age-standardized incidence rates, for the years 2019–2025 in Switzerland. Projections of the number of malignant cancer cases were obtained by combining data from two sources: forecasts of national age-standardized cancer incidence rates and population projections from the Swiss Federal Statistical Office. Age-standardized cancer incidence rates, approximating the individual cancer risk, were predicted by a low-order Autoregressive Integrated Moving Average (ARIMA) model. The contributions of changes in cancer risk (epidemiological component) and population aging and growth (demographic components) to the projected number of new cancer cases were each quantified. Between 2018 and 2025, age-standardized cancer incidence rates are predicted to stabilize for men and women at around 426 and 328/100,000, respectively (<1% change). These projected trends are expected for most cancer sites. The annual number of cancers is expected to increase from 45,676 to 52,552 (+15%), more so for men (+18%) than for women (+11%). These increases are almost entirely due to projected changes in population age structure (+12% for men and +6% for women) and population growth (+6% for both sexes). The rise in numbers of expected cancers for each site is forecast to range from 4.15% (thyroid in men) to 26% (bladder in men). While ranking of the three most frequent cancers will remain unchanged for men (1st prostate, 2nd lung, 3rd colon-rectum), colorectal cancer will overtake by 2025 lung cancer as the second most common female cancer in Switzerland, behind breast cancer. Effective and sustained prevention measures, as well as infrastructural interventions, are required to counter the increase in cancer cases and prevent any potential shortage of professionals in cancer care delivery

Development of a frailty score based on hospital discharge data linked to cohort data

Introduction Frailty is strongly associated with adverse health outcomes and health care costs in elders. However, we have almost no idea of the prevalence of frail older inpatients in Swiss hospitals. Hospital discharge data could contribute to predicting frailty in these patients, and eventually improving SwissDRGs system or casemix-adjustment. Objectives and Approach The HFrailty project aimed to develop a predictive model of Fried’s Frailty Phenotype (FFP) based on hospital discharge data. We linked Lausanne University Hospital (CHUV) discharge data to clinical data from the Lausanne cohort study (Lc65+) over the period 2004-2015. The Lc65+ is a longitudinal population-based cohort comprising three random samples of approximately 1500 Lausanne residents aged 65 to 70, born respectively before, during and after World War II. With stepwise and lasso penalized logistic regression, random forest and neural networks, we identified the best-performing model for predicting FFP using CHUV’s data recorded within 12 months prior to frailty assessments. Results Among Lc65+ participants, 1649 were assessed for frailty and hospitalized at least once during the follow-up period, resulting in 3499 FFP assessments of which 544 were preceded by at least one hospitalization within 12 months.  In total, 45.7% of the participants were men and 9.4% were frail (FFP score ≥ 3). As expected, prevalence of frailty increased with age from 4.1% in the 66-70 age group, to 5.3% and 10.5% in the 71-75 and 76-80 groups, respectively. Logistic regression with lasso penalty was finally the best model regarding both performance and complexity. It had an area under receiver operating curve of 0.67 to predict FFP based on detailed diagnosis and procedure codes. Conclusion/Implications Hospital discharge data may be used to identify frail and non-frail individuals and estimate their prevalence in the Swiss non-institutionalized population. Our predictive model showed limited performance and could be improved. We are currently testing groups of diagnosis and procedure codes, as predictors, instead of detailed ones

Development and validation of a knowledge-based score to predict Fried's frailty phenotype across multiple settings using one-year hospital discharge data: The electronic frailty score

Background: Most claims-based frailty instruments have been designed for group stratification of older populations according to the risk of adverse health outcomes and not frailty itself. We aimed to develop and validate a tool based on one-year hospital discharge data for stratification on Fried's frailty phenotype (FP). Methods: We used a three-stage development/validation approach. First, we created a clinical knowledge-driven electronic frailty score (eFS) calculated as the number of deficient organs/systems among 18 critical ones identified from the International Statistical Classification of Diseases and Related Problems, 10th Revision (ICD-10) diagnoses coded in the year before FP assessment. Second, for eFS development and internal validation, we linked individual records from the Lc65+ cohort database to inpatient discharge data from Lausanne University Hospital (CHUV) for the period 2004-2015. The development/internal validation sample included community-dwelling, non-institutionalised residents of Lausanne (Switzerland) recruited in the Lc65+ cohort in three waves (2004, 2009, and 2014), aged 65-70 years at enrolment, and hospitalised at the CHUV at least once in the year preceding the FP assessment. Using this sample, we selected the best performing model for predicting the dichotomised FP, with the eFS or ICD-10-based variables as predictors. Third, we conducted an external validation using 2016 Swiss nationwide hospital discharge data and compared the performance of the eFS model in predicting 13 adverse outcomes to three models relying on well-designed and validated claims-based scores (Claims-based Frailty Index, Hospital Frailty Risk Score, Dr Foster Global Frailty Score). Findings: In the development/internal validation sample (n = 469), 14·3% of participants (n = 67) were frail. Among 34 models tested, the best-subsets logistic regression model with four predictors (age and sex at FP assessment, time since last hospital discharge, eFS) performed best in predicting the dichotomised FP (area under the curve=0·71; F1 score=0·39) and one-year adverse health outcomes. On the external validation sample (n = 54,815; 153 acute care hospitals), the eFS model demonstrated a similar performance to the three other claims-based scoring models. According to the eFS model, the external validation sample showed an estimated prevalence of 56·8% (n = 31,135) of frail older inpatients at admission. Interpretation: The eFS model is an inexpensive, transportable and valid tool allowing reliable group stratification and individual prioritisation for comprehensive frailty assessment and may be applied to both hospitalised and community-dwelling older adults. Funding: The study received no external funding

Predicting cancer incidence in Switzerland

Projecting the future of cancer incidence in a country is an important task for planning future cancer interventions and research and for allocating economic resources. This is a complex exercise, however, as is any attempt to anticipate the future. Applying leave-future-out cross-validation to data from three Swiss cancer registries (Vaud, Geneva, and Neuchâtel) and the period 1982-2016, we compared the predictive performance of a large number of models used in the cancer prediction literature: widely used age-period-cohort (APC) models and their Bayesian counterparts (BAPC), classical generalized linear models (GLM), autoregressive integrated moving average (ARIMA) models, and linear models (LM). Perhaps surprisingly, we found that the simpler a model is, the better it performs in predicting future cancer incidence, in line with the famous Occam's razor principle, which recommends looking for explanations constructed with the smallest possible set of elements. Models simply extrapolating past tendencies (ARIMA, LM) outperformed models seeking to estimate and then project underlying effects (GLM, APC, BAPC). Among the first, models relying on few parameters (e.g. low-order ARIMA) outperformed more complex higher-order models that closely fit observed data, as well as methods based on the well-known AIC selection criterion. The best model in our comparative study, an ARIMA(2,1,1), was applied to predict cancer incidence in Switzerland until 2025, anticipating a substantial stabilization of the risk of developing cancer for the next few years. Combining this trend with the demographic projections of the Swiss Federal Statistical Office, however, we anticipated a substantial increase in the annual number of new cancer cases, entirely due to demographic changes. This increase was estimated at +18% for men and +11% for women, with increases ranging from 4.15% for thyroid in men to 26% for bladder in men. Estimating (and predicting) trends in cancer incidence over time can be confounded by changes in cancer detection, such as but not limited to: the introduction or modification of screening programs, the use of different screening tools, and incidental detection. In the third part of this thesis, we proposed a model capable of adjusting for these changes and thus estimating the true underlying trend in cancer incidence. -- La projection de l'avenir de l'incidence du cancer dans un pays est une tâche importante pour planifier les futures interventions et recherches sur le cancer et pour optimiser l’allocation des ressources. Il s'agit toutefois d'un exercice complexe, comme toute tentative d'anticiper l'avenir. En appliquant une validation croisée aux données de trois registres suisses du cancer (Vaud, Genève et Neuchâtel) pour la période 1982-2016, nous avons comparé la performance prédictive d'un grand nombre de modèles utilisés dans la littérature : les modèles âge-période-cohorte (APC) et leurs équivalents bayésiens (BAPC), les modèles linéaires généralisés (GLM), les modèles autorégressifs intégrés à moyenne mobile (ARIMA) et les modèles linéaires (LM). De manière peut-être surprenante, nous avons constaté que plus un modèle est simple, plus il est performant dans la prédiction, conformément au célèbre principe du rasoir d'Occam, qui veut que la solution la plus simple soit préférée. Les modèles qui se contentent d'extrapoler les tendances passées (ARIMA, LM) sont plus performants que ceux qui tentent d'estimer puis de projeter des effets sous-jacents (GLM, APC, BAPC). Le modèle le plus performant a été L’ARIMA (2,1,1). Ce dernier s’est notamment révélé meilleur que ceux qui sélectionnent la complexité du modèle avec un critère comme l’AIC. Ce meilleur modèle a été appliqué pour prédire l'incidence du cancer en Suisse jusqu'en 2025, anticipant une stabilisation du risque de développer un cancer dans les années à venir. En combinant cette tendance avec les projections démographiques de l'Office fédéral de la statistique, nous avons cependant anticipé une augmentation substantielle du nombre annuel de nouveaux cas de cancer, entièrement due aux changements démographiques. Cette augmentation a été estimée à +18% pour les hommes et +11% pour les femmes, avec des augmentations allant de 4,15% pour la thyroïde chez l'homme à 26% pour la vessie chez l'homme. L'estimation (et la prévision) des tendances de l'incidence du cancer au fil du temps peut être en partie faussée par les changements dans les processus de détection du cancer, tels que, mais sans s'y limiter : l'introduction ou la modification des programmes de dépistage, l'utilisation de différents outils de dépistage, et la détection opportuniste. Dans la troisième partie de cette thèse, nous avons proposé un modèle capable de s'ajuster à ces changements et donc d'estimer la véritable tendance sous-jacente de l'incidence du cancer

Evaluating the cost of simplicity in score building ::an example from alcohol research

Building a score from a questionnaire to predict a binary gold standard is a common research question in psychology and health sciences. When building this score, researchers may have to choose between statistical performance and simplicity. A practical question is to what extent it is worth sacrificing the former to improve the latter. We investigated this research question using real data, in which the aim was to predict an alcohol use disorder (AUD) diagnosis from 20 self-reported binary questions in young Swiss men (n = 233, mean age = 26). We compared the statistical performance using the area under the ROC curve (AUC) of (a) a “refined score” obtained by logistic regression and several simplified versions of it (“simple scores”): with (b) 3, (c) 2, and (d) 1 digit(s), and (e) a “sum score” that did not allow negative coefficients. We used four estimation methods: (a) maximum likelihood, (b) backward selection, (c) LASSO, and (d) ridge penalty. We also used bootstrap procedures to correct for optimism. Simple scores, especially sum scores, performed almost identically or even slightly better than the refined score (respective ranges of corrected AUCs for refined and sum scores: 0.828–0.848, 0.835–0.850), with the best performance been achieved by LASSO. Our example data demonstrated that simplifying a score to predict a binary outcome does not necessarily imply a major loss in statistical performance, while it may improve its implementation, interpretation, and acceptability. Our study thus provides further empirical evidence of the potential benefits of using sum scores in psychology and health sciences

Items for self-reported alcohol use disorders and alcohol-related consequences.

Items for self-reported alcohol use disorders and alcohol-related consequences.</p

Summary of the associations between the 20 questions and the gold standard.

Summary of the associations between the 20 questions and the gold standard.</p

Observed / corrected AUCs for refined and simple scores to predict the gold standard using various methods.

Observed / corrected AUCs for refined and simple scores to predict the gold standard using various methods.</p

STROBE statement—checklist of items that should be included in reports of observational studies.

STROBE statement—checklist of items that should be included in reports of observational studies.</p